The Distinguished Lecture Series showcases pioneering thinkers whose work is driving the future of technology. From breakthroughs in theory to real-world applications that shape our daily lives, this year’s speakers will share insights at the forefront of computing. The series offers a unique opportunity to learn from leaders in the field, spark new ideas, and connect with the innovations transforming our world.
Abstract: In May 2013, Georgia Tech, together with its partners Udacity and AT&T, announced a new online master’s degree in computer science delivered through the platform popularized by massively open online courses (MOOCs). This new online MS in CS (OMSCS) costs less than $7,000 total, compared to a price tag of $40,000 for an MS CS at comparable public universities and upwards of $70,000 at private universities. The first-of-its-kind program was launched in January 2014 and has sparked a worldwide conversation about higher education in the 21st century. President Barack Obama has praised OMSCS by name twice, and hundreds of news stories have mentioned the program. It’s been described as a potential “game changer” and “ground zero of the revolution in higher education”. Harvard University researchers concluded that OMSCS is “the first rigorous evidence showing an online degree program can increase educational attainment” and predicted that OMSCS will single-handedly raise the number of annual MS CS graduates in the United States by at least 7 percent.
OMSCS started in 2014 with 5 courses and 380 students; in fall 2025 semester, it had 46 courses and almost 17,000 students. OMSCS is apparently the biggest academic program in the world in any subject, not necessarily online. So far, almost 14,500 students have graduated from OMSCS, over 7,000 in the last 3 years. The number of applications to OMSCS keeps rising. In the 2024-25 academic year, there were 9,860 applications, 31% higher than the record in the year before. The program has also paved the way for more than 50 similar programs in over 30 universities. In November 2023, a Forbes article described OMSCS as the best degree program ever. There has been a big shortage of computing professionals in the US. Therefore, OMSCS is satisfying a great national need. Starting in 2017, Georgia Tech expanded its online offerings to undergraduate computer science students. The talk will describe the OMSCS program, how it came about, its first twelve years, and what Georgia Tech has learned from the OMSCS experience. It will also discuss the speaker’s vision of the future of higher education with a much larger role for online learning.
November 5, 2025
How to Close the 100,000-Year “Data Gap” in Robotics Ken Goldberg
Abstract: Large models based on internet-scale data can now pass the Turing Test for intelligence. In this sense, data has “solved” language, and many analogously claim that data has solved speech recognition and computer vision. Will data also solve robotics and automation, allowing general-purpose humanoid robots to achieve human-level performance? Using commonly accepted metrics for converting word and image tokens into time, the amount of internet-scale data used to train contemporary large vision language models (VLMs) is on the order of 100,000 years. I’ll review 3 ways researchers are pursuing to close this gap, and a 4th approach, where data is collected as real robots operate in real commercial environments — which requires bootstrapping with AI and “good old-fashioned engineering” to create robots with real return on investment that will be adopted by industry. Such robots can create a “data flywheel” to increase performance and enable new functionality, accelerating the timeline to achieve reliable, general-purpose robots.
November 24, 2025
Quantum Networks: A Classical Perspective Don Towsley
Abstract: Quantum information processing is at the threshold of having significant impact on technology and society in the form of providing unbreakable security, ultra-high-precision distributed sensing, and polynomial/exponential speed-ups in computing. Many of these applications are enabled by high rate distributed shared entanglement between pairs and groups of users. A critical missing component that prevents crossing this threshold is a distributed infrastructure in the form of a world-wide “Quantum Internet”. This motivates the study of quantum networks, namely, to identify the right architecture and how should it operate, e.g., dynamic fair allocation of resources. Moreover, the architecture and network operation must account for operation in harsh, noisy environments.
This talk addresses the following question: what ideas can the design of a quantum network borrow from classical networks? At first glance the answer appears to be “very little”. The focus of this talk, however, is to argue that the opposite is true and that much can be borrowed from classical networks. We begin by reviewing two proposed quantum network architectures two-way and one-way architectures. A two-way network generates and distributes quantum entanglement to pairs or groups of users whereas a one-way network allows for direct transfer of quantum information from one user to another. We compare these architectures and conclude that a two-way architecture is superior. A two-way architecture appears very different from the classical Internet architecture. However, we will introduce a “connectionless” two-way quantum network architecture that allows one to easily adapt many ideas from classical networks (good and bad 🙂). We provide several examples of the adoption of good ideas and conclude with open research questions.
Henry Yuen, Srivani Family Associate Professor of Computer Science, explained quantum computing to ABC News Correspondent Elizabeth Schulze: the basics of what it is, how it might prove useful, and what the world should be wary of.
For 16 years, the Google PhD Fellowship Program has supported exceptional graduate students pioneering research in computer science and related fields, with the goal of supporting the next generation of scientists focused on critical foundational science.
A new paper from CS researchers introduces MINDFUL, a framework designed to guide the design of modern implantable brain-computer interfaces (BCIs)—devices that connect the human brain directly to the digital world. The work is a call to action for the computer architecture community to help build BCIs that are not only safe and practical, but also ready to run advanced artificial intelligence applications for real-world healthcare.
The paper will be presented at the MICRO 58 conference on October 22, one of the leading forums for computer architecture research. The MINDFUL paper represents a milestone for the field. Its inclusion signals that implantable BCIs are no longer a niche or futuristic topic; they are a serious and emerging area of system design that demands attention from the broader computing community.
Guy Eichler at MICRO 58
Rethinking How We Build Brain-Computer Interfaces
BCIs are rapidly evolving. Future systems are expected to wirelessly transmit vast amounts of brain data and use AI to translate thoughts into actions, such as moving a prosthetic limb or restoring speech. Yet, these implantable devices face stringent physical and power constraints that limit their complexity and functionality.
By analyzing a range of existing systems, the MINDFUL paper highlights a critical gap between today’s BCIs and the ambitious goal of building large-scale, AI-integrated devices. The framework provides a clear, quantitative approach to understanding these trade-offs, offering computer architects and hardware designers the tools to evaluate whether their designs are feasible, scalable, and safe.
“Until now, there hasn’t been a clear, accessible, and quantitative resource that captures the system-level challenges of implantable BCIs,” said Guy Eichler, the paper’s lead author. “MINDFUL changes that by offering a shared foundation for researchers and engineers who want to engage meaningfully with the field.”
Guy Eichler at MICRO 58
A Framework for Collaboration and Innovation
The framework is expected to attract not only computer engineers but also AI developers and neuroscience researchers eager to advance the connection between computing and the human brain. For the first time, it gives designers a structured way to model and evaluate how key system parameters—such as data acquisition, on-chip computation, and wireless communication—interact under real-world constraints of implantables in the brain.
The impact of such advancements extends far beyond the lab. For patients living with paralysis, speech loss, blindness, or neurological disorders like Parkinson’s disease, implantable BCIs hold the promise of restored function and independence. These devices could one day decode brain signals in real time to help patients move prosthetic limbs, communicate, or even see again.
Eichler began exploring this topic during PhD studies in the Systems-Level Design Group led by Professor Luca Carloni. Where hands-on work designing and testing wireless, implantable BCIs revealed significant inconsistencies between how computer architects modeled these systems and how they behaved in practice. That experience inspired the creation of a framework to bridge the gap, translating the complexities of neurotechnology into a language familiar to engineers.
From Idea to Implementation
Now a postdoctoral scientist in the Bioelectronic Systems Lab with Professor Ken Shepard, Eichler continues to advance these systems toward clinical trials and eventual FDA approval, an effort supported by new patents and collaborations across neuroscience and engineering. Looking ahead, he says the research points to three distinct stages in BCI development: the pre-BCI era, focused on safety and data quality; the intra-BCI era, where AI computation becomes integrated into the devices themselves; and the post-BCI era, when computational power within BCIs will need to scale dramatically to support next-generation applications.
Beyond the technology, the work also aims to reshape public perception of BCIs. While ethical and privacy concerns are valid, the researcher emphasizes that BCIs have the potential to transform lives and deepen our understanding of the human brain.
“People are understandably cautious about the idea of brain implants,” Eichler said. “But BCIs represent one of the most powerful tools we have to treat neurological disorders and restore lost function. They’re not just about connecting brains to machines—they’re about reconnecting people to the world.”
The award bestowed by the ACM Special Interest Group in Operating Systems chooses the recipient based on “contributions that are highly creative, innovative, and possibly high-risk, in keeping with the visionary spirit of Mark Weiser.”
On the a16z AI podcast, Professor Vishal Misra discusses whether LLMs can go beyond modeling human language to make new discoveries and move the needle on scientific progress.
Celebrated by ACL with a Lifetime Achievement Award, Kathleen McKeown continues to drive bold, cross-disciplinary research that redefines the field of natural language processing.
The Data, Agents, and Processes Lab (DAPLab) will present a slate of new research at the Symposium on Operating Systems Principles (SOSP 2025) workshops, spanning agentic infrastructure and self-tuning kernels. The research highlights the future of agentic infrastructure that will enable the safe, reliable, and efficient operation of large language model agents in real-world environments.
In the very first week of the semester, a line of students wound through the Mudd lobby, the air buzzing with anticipation as they waited to enter Carleton Commons. The draw? The Fall Research Fair and a chance to join a research project. Over two days, hundreds of students showed up to explore more than 100 research projects from over 30 research groups. Their enthusiasm spoke volumes, highlighting not only their drive to learn beyond the classroom but also the vital role research plays in shaping the spirit of our department.
“The range of objectives that the students had was surprising,” said Elisavet Alvanaki, a PhD student in the Software Systems Laboratory who was looking for students to join her AI for electronic design automation project. She shared that some first-year students wanted to gain exposure to different research areas, and there were many students whose primary major wasn’t CS but who wanted some exposure to CS. “Several promising students reached out afterward, and we’re excited to collaborate with them.”
When students dive into hands-on projects, they bring fresh ideas and curiosity that enrich the entire community. These experiences not only help students grow as thinkers and problem-solvers but also encourage collaboration and spark creativity across the department.
One of the department’s new assistant professors,Silvia Sellan, was at the event searching for both graduate and undergraduate students to work on real-world problems that involve digital, three-dimensional geometry. “A professor once gave me a chance when I was an undergrad,” she explained. “I know firsthand how important that opportunity can be.”
While faculty see working with students as a way to mentor and build the future of the field, the students themselves bring the real energy to these projects. From lab work to field studies to creative problem-solving, they each have their own story to tell about what research has meant to them. Below are a few of their experiences in their own words.
Smriti Vaidyanathan MS student, SEAS Faculty Mentor: David Knowles
I joined Professor David’s lab when I started my master’s program last August. His research on RNA splicing immediately caught my interest, and after meeting with a PhD student in the group, I was excited to jump in. My project focuses on building a deep generative model that integrates signals from both gene expression and splicing to better represent individual cells. I’ve been working on the design and testing of the model’s architecture, designing and implementing evaluation techniques, and fine-tuning parameters to optimize its performance. It’s challenging work—sometimes things don’t go as planned—but I enjoy the process of problem-solving and continuous learning. Research has become a fulfilling part of my academic journey, and it’s inspired me to apply to PhD programs so I can keep contributing to new discoveries.
Tianle Zhou Undergraduate, School of General Studies Faculty Mentor: Eugene Wu
This summer, I had the chance to work with Professors Eugene Wu and Kostis Kaffes on a project exploring an operating system approach to solving AI agent problems. Our goal was to test the feasibility in theory and build a prototype, and with their guidance, we were able to make real progress. Research is never something I feel completely “ready” for—I’m always learning along the way—but that’s what makes it so challenging and rewarding. Balancing coursework and research wasn’t easy, but the experience was invaluable. I know my research journey won’t end with graduation; it’s something I want to continue long into the future.
Alex Xu MS student, SEAS Faculty Mentor: Kostis Kaffes
As an undergrad, I joined Professor Kostis’s Operating Systems course as a TA before working on an RL-based GPU scheduling project, which gave me exposure to the intersection of systems and machine learning. But I was most interested in system-level architecture and design, which led me to my current project with Professors Kostis Kaffes and Eugene Wu: a system-forking approach to supporting AI agents. While my teammate focuses on the agent application layer, I work on the systems foundation to make the agent practical. Having a teammate with complementary perspectives has made the project especially rewarding.
Balancing coursework and research isn’t always easy, but when you care about a project, it stops feeling like a burden and becomes a lifestyle. For me, the challenge has always been worthwhile, and the experience has been incredibly fulfilling.
The Data Management Group will be at the 51st International Conference on Very Large Data Bases (VLDB 2025), presenting three research papers and contributing to two panels.
The department had a strong showing at the 2025 Annual Meeting of the Association for Computational Linguistics (ACL 2025). Kathleen McKeown won the ACL Lifetime Achievement Award, and Julia Hirschberg received the Dragomir Radev Distinguished Service Award, a testament to their impact on the field and the dedication of their research teams.
Several papers authored by faculty, students, and collaborators were accepted to this year’s conference, reflecting the depth and innovation of our ongoing research in natural language processing.
Narutatsu Ri Columbia University, Nicholas Deas Columbia University, and Kathleen McKeown Columbia University
Abstract Generating unbiased summaries in real-world settings such as political perspective summarization remains a crucial application of Large Language Models (LLMs). Yet, existing evaluation frameworks rely on traditional metrics for measuring key attributes such as coverage and faithfulness without verifying their applicability, and efforts to develop improved summarizers are still nascent. We address these gaps by (1) identifying reliable metrics for measuring perspective summary quality, and (2) investigating the efficacy of LLM-based methods beyond zero-shot inference. Namely, we build a test set for benchmarking metric reliability using human annotations and show that traditional metrics underperform compared to language model–based metrics, which prove to be strong evaluators. Using these metrics, we show that reranking-based methods yield strong results, and preference tuning with synthetically generated and reranking-labeled data further boosts performance. Our findings aim to contribute to the reliable evaluation and development of perspective summarization methods.
Nicholas Deas Columbia University, Blake Vente Columbia University, Amith Ananthram Columbia University, Jessica A. Grieser University of Michigan, Desmond Patton University of Pennsylvania, Shana Kleiner University of Pennsylvania, James Shepard University of Tennessee, Kathleen McKeown Columbia University
Abstract With a combination of quantitative experiments, human judgments, and qualitative analyses, we evaluate the quantity and quality of African American Language (AAL) representation in 12 predominantly English, open-source pretraining corpora. We specifically focus on the sources, variation, and naturalness of included AAL texts representing the AALspeaking community. We find that AAL is underrepresented in all evaluated pretraining corpora compared to US demographics, constituting as few as 0.007% and at most 0.18% of documents. We also find that more than 25% of AAL texts in C4 may be perceived as inappropriate for LLMs to generate and to reinforce harmful stereotypes. Finally, we find that most automated filters are more likely to conserve White Mainstream English (WME) texts over AAL in pretraining corpora.
David Sasu IT University of Copenhagen, Zehui Wu Columbia University, Ziwei Gong Columbia University, Run Chen Columbia University, Pengyuan Shi Columbia University, Lin Ai Columbia University, Julia Hirschberg Columbia University, Natalie Schluter IT University of Copenhagen
Abstract In this paper, we introduce the Akan Conversation Emotion (AkaCE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of resources for low-resource languages in emotion recognition research. AkaCE, developed for the Akan language, contains 385 emotion-labeled dialogues and 6,162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations. The presence of prosodic labels in this dataset also makes it the first prosodically annotated African language dataset. We demonstrate the quality and utility of AkaCE through experiments using state-of-the-art emotion recognition methods, establishing solid baselines for future research. We hope AkaCE inspires further work on inclusive, linguistically and culturally diverse NLP resources.
Xiao Yu Columbia University, Ruize Xu Columbia University, Chengyuan Xue University of Toronto, Jinzhong Zhang Intellipro Group Inc., Xu Ma Intellipro Group Inc., Zhou Yu Columbia University
Abstract A reliable resume-job matching system helps a company recommend suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a list of job posts. However, since job seekers apply only to a few jobs, interaction labels in resume-job datasets are sparse. We introduce CONFIT V2, an improvement over CONFIT to tackle this sparsity problem. We propose two techniques to enhance the encoder’s contrastive training process: augmenting job data with hypothetical reference resume generated by a large language model; and creating high-quality hard negatives from unlabeled resume/job pairs using a novel hardnegative mining strategy. We evaluate CONFIT V2 on two real-world datasets and demonstrate that it outperforms CONFIT and prior methods (including BM25 and OpenAI text-embedding003), achieving an average absolute improvement of 13.8% in recall and 17.5% in nDCG across job-ranking and resume-ranking tasks.
Sky CH-Wang Columbia University, Darshan Deshpande Patronus AI, Smaranda Muresan Columbia University,Anand Kannappan Patronus AI, Rebecca Qian Patronus AI
Abstract We introduce BROWSING LOST UNFORMED RECOLLECTIONS, a tip-of-the-tongue knowni tem search and reasoning benchmark for general AI assistants. BLUR introduces a set of 573 real-world validated questions that demand searching and reasoning across multimodal and multilingual inputs, as well as proficient tool use, in order to excel on. Humans easily ace these questions (scoring on average 98%), while the best-performing system scores around 56%. To facilitate progress toward addressing this challenging and aspirational use case for general AI assistants, we release 350 questions through a public leaderboard, retain the answers to 250 of them, and have the rest as a private test set.
Bolei Ma LMU Munich & Munich Center for Machine Learning, Yuting Li University of Cologne, Wei Zhou University of Augsburg, Ziwei Gong Columbia University, Yang Janet Liu LMU Munich & Munich Center for Machine Learning, Katja Jasinskaja University of Cologne, Annemarie Friedrich University of Augsburg, Julia Hirschberg Columbia University, Frauke Kreuter LMU Munich & Munich Center for Machine Learning, Barbara Plank LMU Munich & Munich Center for Machine Learning
Abstract Understanding pragmatics—the use of language in context—is crucial for developing NLP systems capable of interpreting nuanced language use. Despite recent advances in language technologies, including large language models, evaluating their ability to handle pragmatic phenomena such as implicatures and references remains challenging. To advance pragmatic abilities in models, it is essential to understand current evaluation trends and identify existing limitations. In this survey, we provide a comprehensive review of resources designed for evaluating pragmatic capabilities in NLP, categorizing datasets by the pragmatic phenomena they address. We analyze task designs, data collection methods, evaluation approaches, and their relevance to real-world applications. By examining these resources in the context of modern language models, we highlight emerging trends, challenges, and gaps in existing benchmarks. Our survey aims to clarify the landscape of pragmatic evaluation and guide the development of more comprehensive and targeted benchmarks, ultimately contributing to more nuanced and context-aware NLP models.
Yuji Zhang University of Illinois Urbana-Champaign, Sha Li University of Illinois Urbana-Champaign, Cheng Qian University of Illinois Urbana-Champaign, Jiateng Liu University of Illinois Urbana-Champaign, Pengfei Yu University of Illinois Urbana-Champaign, Chi Han University of Illinois Urbana-Champaign, Yi R. Fung University of Illinois Urbana-Champaign, Kathleen McKeown Columbia University, Chengxiang Zhai University of Illinois Urbana-Champaign, Manling Li Northwestern University, Heng Ji University of Illinois Urbana-Champaign
Abstract Hallucination is a persistent challenge in large language models (LLMs), where even with rigorous quality control, models often generate distorted facts. This paradox, in which error generation continues despite high-quality training data, calls for a deeper understanding of the underlying LLM mechanisms. To address it, we propose a novel concept: knowledge overshadowing, where model’s dominant knowledge can obscure less prominent knowledge during text generation, causing the model to fabricate inaccurate details. Building on this idea, we introduce a novel framework to quantify factual hallucinations by modeling knowledge overshadowing. Central to our approach is the log-linear law, which predicts that the rate of factual hallucination increases linearly with the logarithmic scale of (1) Knowledge Popularity, (2) Knowledge Length, and (3) Model Size. The law provides a means to preemptively quantify hallucinations, offering foresight into their occurrence even before model training or inference. Built on the overshadowing effect, we propose a new decoding strategy CoDA, to mitigate hallucinations, which notably enhance model factuality on Overshadow (27.9%), MemoTrap (13.1%) and NQ-Swap (18.3%). Our findings not only deepen understandings of the underlying mechanisms behind hallucinations but also provide actionable insights for developing more predictable and controllable language models.
Every year, leading conferences across computer science honor research that has stood the test of time—work that continues to shape the field years, or even decades, after its original publication. In 2025, several professors have earned this distinguished recognition, highlighting the lasting influence of their contributions across data management, hardware design, and theoretical computer science.
And at the 57th Annual ACM Symposium on Theory of Computing (STOC ’25), Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth received the 10-Year Test of Time Award for their 2015 paper Preserving Statistical Validity in Adaptive Data Analysis, a breakthrough that reshaped our understanding of privacy and validity in machine learning.
The department is extremely proud of all of our students!
The Columbia Engineering Class of 2025 gathered at Morningside Heights to celebrate Class Day on May 19th.
The department honored this year’s graduates at a graduation celebration on May 21st. A number of students received awards from the department for their service and academic excellence. The list of CS awardees is in this year’s graduation handout.
CS@CU by the Numbers Fall 2024 – Spring 2025
1,651 CS majors 49% of CS majors who are women 14,344 Class enrollments
Class of 2025 645 undergrad 419 MS 41 PhDs
Claudia Cortell and Luca Carloni
Guillermo Garcia Cobo and Luca Carloni
Noa Shmueli and Luca Carloni
Suwei Ma and Luca Carloni
Abenezer Amanuel and Luca Carloni
Alex Chen and Luca Carloni
Jennifer Oettinger and Luca Carloni
Maximo Librandi and Luca Carloni
Rodolfo Costa Raimundo and Luca Carloni
Faustina Cheng and Luca Carloni
Geoffrey Wu and Luca Carloni
Kareem DaCosta and Luca Carloni
Aryaman Kejriwal and Luca Carloni
Brennan McManus and Luca Carloni
Darien Moment and Luca Carloni
Denzel Farmer and Luca Carloni
Angel Cui and Luca Carloni
Liana Goldstein and Luca Carloni
Madeline Skeel and Luca Carloni
Martha Barker and Luca Carloni
Elisabeth Cohen and Luca Carloni
Paul Seham and Luca Carloni
Sophie Tsanang and Luca Carloni
Shreya Somayajula and Luca Carloni
Zachary Thayer and Luca Carloni
Arnold Asiimwe and Luca Carloni
Anna Reis and Luca Carloni
Kashvi Gupta and Luca Carloni
Kylie Berg and Luca Carloni
William Das and Luca Carloni
Edward Ri and Luca Carloni
Andreas Kellas and Luca Carloni
Judah Goldfeder and Luca Carloni
Joseph Zuckerman and Luca Carloni
Siyan Li and Luca Carloni
Xiao Yu and Luca Carloni
Yangruibo Ding and Luca Carlonia
Andreas Kellas and Luca Carloni
2025 CS Award Winners
Jonathan L. Gross Award for Academic Excellence This award was established in 2017 in honor of the much-loved Professor Emeritus Jonathan Gross. Each year, a cash gift is awarded to one graduating masters student and to one graduating senior from each of the four undergraduate schools served by the Department of Computer Science.
MS: Guillermo Garcia Cobo
SEAS: Michael Straus
BC: Claudia Cortell
CC: Suwei Ma
GS: Noa Shumeli
Computer Science Scholarship Award A cash prize awarded to two B.A. and two B.S. degree candidates for outstanding academic achievement in computer science.
SEAS: Alex Chen
SEAS: Jennifer Marie Oettinger
CC: Abenezer Amanuel
GS: Seung Joon Rhee
Russell C. Mills Award This annual award, established by the computer science department in 1992 in memory of Russell C. Mills, is a cash prize given to a computer science major who has exhibited excellence in the area of computer science.
SEAS: Jessica Zhang
SEAS: Geoffrey Wu
SEAS: Kareem DaCosta
GS: Faustina Cheng
Theodore R. Bashkow Award Presented to a computer science senior who has excelled in independent projects. This is awarded in honour of Professor Theodore R. Bashkow, whose contributions as a researcher, teacher, and consultant have significantly advanced the state of the art of computer science.
SEAS: Kashvi Gupta
SEAS: Edward Ri
CC: Kylie Noelani Berg
CC: Anna Reis
CC: Arnold Asiimwe
CC: William Hirasawa Das
Andrew P. Kosoresow Memorial Award for Excellence in Teaching and Service Awarded for outstanding contributions to teaching in the Department of Computer Science and exemplary service to the Department and its mission.
CC: Liana Goldstein
CC: Preach Apintanapong
GS: Palash Sharma
SEAS: Denzel Farmer
SEAS: Chelsea Soemitro
SEAS: Madeline Skeel
SEAS: Zachary Thayer
SEAS: Darien Moment
SEAS: Sophie Tsanang
SEAS: Aryaman Kejriwal
SEAS: Paul Seham
SEAS: Shreya Somayajula
SEAS: Brennan McManus
SEAS: Orli Elisabeth Cohen
SEAS: Angel Cui
PhD: Roland Maio
PhD: Martha Barker
Behring Foundation Award Awarded to undergraduate and/or graduate students enrolled at SEAS who are pursuing a degree in Computer Science who best exhibit academic excellence, have entrepreneurial interests, and possess leadership skills. Preference is given to students who have lived, worked, or studied in Brazil or other Latin American countries.
SEAS: Rodolfo Costa Raimundo
SEAS: Maximo Libtandi
Davide Giri Memorial Prize This award is given to a graduate student (PhD) in Computer Science who has combined excellence in research results with continued outstanding efforts to promote research collaboration
PhD: Joseph Zuckerman
Paul Charles Michelman Memorial Award for Exemplary Departmental Service This award is given in memory of Dr. Paul Michelman, 1993, who devoted himself to improving the Computer Science Department through service while excelling as a researcher
PhD: Andreas Kellas
PhD Service Award This award is given to PhD students who demonstrate superior contributions to the community life of the Department of Computer Science
Charlie Carver (PhD ‘24) received an Honorable Mention from the ACM SIGMOBILE Dissertation Award 2025 for research that introduced new laser-based communication and sensing systems for aquatic, terrestrial, and aerial environments.
Carver’s work redefines the role of laser-based technologies in mobile and networked environments through a series of innovative systems. Throughout his doctoral studies, he led the development of several key projects, beginning with AmphiLight, which demonstrated direct laser communication across the air-water boundary. This foundational work expanded into subsequent systems such as Sunflower, Lasertag, and Phaser, each extending the capabilities of laser light into new application spaces. Beyond technical innovation, the thesis highlights a thoughtful progression of research that integrates physics, engineering, and system design.
We spoke with him about the inspiration, process, and challenges that shaped his research. From laser testbeds on kitchen tables during COVID to multi-year collaborations, his reflections offer a deeper look into how this remarkable thesis came together—and what’s next.
Q: What makes your dissertation unique within the field of mobile and ubiquitous systems?
My thesis leverages the versatility of laser light to create micro- and macro-scale exploration and monitoring systems for aquatic, terrestrial, and aerial environments. Fundamentally, each work uses laser light in an unconventional or overlooked way and attempts to challenge the status quo of wireless communication and sensing.
Q: Your thesis spans several distinct projects. How did you approach the progression of these works—was there an overarching research strategy, or did one idea naturally lead to the next?
At the start of my PhD, our lab was working with the Dartmouth Robotics Lab to investigate underwater applications of light. This collaboration aligned nicely with my first project, AmphiLight, which demonstrated that direct, wireless communication through the air-water interface was possible using laser light. We continued this line of underwater research with Sunflower, which used laser light to enable bidirectional communication and sensing between flying drones and moving underwater robots. At this point, we realized many of the systems I was designing for underwater applications would work nicely on land (such as for high-speed, wireless VR), so we published Lasertag to support high-mobility, short-range laser applications in the air. The final thread we were curious about was beaming power to mobile targets via laser light, and we eventually demonstrated combined sensing, communication, and power delivery for battery-free robots in Phaser. In parallel with all of these works, I was also designing a method of passive laser polarization sensing over optical fiber with Google, and wrapped up the 3-year collaboration to include the work in my thesis.
Q: What initially drew you to work with laser-based systems, and how did your background influence the direction of your research?
When I began my PhD, my advisor wanted to expand her visible light communication/sensing research to include laser light. Since my undergraduate degree was in Physics, this line of work sounded really cool to me. Through a combination of planning and a good amount of luck, we repeatedly demonstrated that laser light could be robustly used in mobile scenarios for wireless communication, sensing, and power delivery.
Q: Each system you developed required specialized knowledge and tools. What skills or learning curves did you encounter as you moved from project to project?
Each project took about a year to two years to complete, usually starting in parallel with finishing the previous one. Some projects, like Sunflower, were prolonged by COVID due to the difficulty of system building from home—I used to keep an oscilloscope, laser testbed, and fish tank on my kitchen table for experiments! At the beginning of my PhD, I relied on some very helpful senior labmates to guide me through major system-building concepts, such as circuits, soldering, and optics. After publishing my first paper, I would iteratively gain new skills by solving problems with the earlier systems and improving on them for the latest work.
Q: In your view, what was the most rewarding or surprising aspect of conducting this research?
Research is a fun mix of asking (and answering, hopefully!) interesting questions, and then sharing what you find with others. I particularly like studying light because of how ubiquitous it is, which makes new applications all the more exciting. I also enjoy research projects when they involve things I can physically build and hold in my hands. And finally, I think it’s fun to write about new findings and to tell a story around the results and future implications.
Q: Now that you’ve completed your PhD, what are you doing?
I work at MIT, where I research space-related systems at their Lincoln Laboratory. For the most up-to-date information on my work, feel free to check out my website: https://www.mit.edu/~carver/. And for anyone looking to get in touch or discuss future collaborations, please email me directly at: carver@mit.edu.
The MS Bridge program was created to open doors for talented individuals from non-traditional backgrounds to thrive in computer science.
Few stories capture the spirit of the MS Bridge Program better than those of Ryan Soeyadi and Peter Ma. Soeyadi, a classically trained pianist and Juilliard graduate, and Ma, a former medical student, each took bold steps to pivot into tech. Through the Bridge program, they gained the skills, confidence, and community needed to launch successful careers as software engineers, while continuing to draw on the unique perspectives their previous experiences bring to the field.
Now working at The New York Times and Bloomberg, they are putting their technical skills to use in high-impact roles while continuing to embrace what makes them unique. Whether it’s designing user experiences for millions of daily puzzle solvers or building systems that support global financial markets, both exemplify the Bridge program’s mission: empowering driven individuals to reimagine their futures and make meaningful contributions to the world of technology.
Ryan Soeyadi
Coming from a background in classical music from The Juilliard School, transitioning into the world of computer science was both a bold and deeply personal journey for me. The MS Bridge program offered the ideal path—it was rigorous yet welcoming, and it gave me the foundational tools I needed to think like a computer scientist. My favorite class, Advanced Programming with Senior Lecturer in Discipline Jae Woo Lee, was a turning point. For the first time, I felt like I wasn’t just writing code but truly understanding the underlying concepts. That clarity and confidence fundamentally shifted how I approach technical problem-solving, and it’s something I now carry into every project.
Today, I split my time between working at The New York Times and continuing my musical work at Juilliard. At NYT, I’m a software engineer on the Games team, where I build user-facing features and contribute to the growth and engagement of a product that brings joy to millions. We use modern tools like TypeScript, React, Node, and GraphQL, and I’ve grown tremendously from building everything from paywalls to retention strategies. Meanwhile, at Juilliard, I serve as a staff accompanist, collaborating mostly with violists in lessons and recitals. Maintaining both careers is deeply important to me—tech fulfills my love of problem-solving and creation, while music keeps me grounded artistically. It’s important for me to have a balance between work and artistic fulfillment, and I’m quite happy where Columbia has helped me land.
Peter Ma
Participating in the Bridge program has been a transformative step in my career journey. With a background that began in medicine, I made the difficult but ultimately fulfilling decision to pivot toward computer science. The Bridge program not only provided me with a rigorous academic foundation—covering everything from data structures and algorithms to operating systems and compilers—but also immersed me in a vibrant, supportive community of peers who share the same drive and curiosity. This environment allowed me to grow both technically and personally, and offered access to invaluable networking opportunities through the department’s strong connections in the tech industry.
Throughout the program, I discovered a particular interest in software systems, taking advanced coursework in operating systems, distributed systems, and infrastructure at scale. These experiences sharpened my technical skills and solidified my desire to solve complex engineering challenges. In parallel, I found joy in teaching and mentoring as a TA and academic support advisor, which deepened my appreciation for collaborative learning. I’m now excited to begin my career as a software engineer at Bloomberg, where I look forward to building impactful systems, continuing to learn from industry leaders, and contributing meaningfully to solving real-world problems at scale.
A driving force in the field of natural language processing, Kathleen McKeown will receive this year’s Faculty Mentoring Award bestowed at University Commencement.
At the BRITE ’25 conference, Henry Yuen broke down and shared his insights on the pace of advancement of quantum computing and what it will likely impact in the future, and what it won’t.
The role of a teaching assistant (TA) is more than just answering questions or helping debug code—it’s about building community and creating pathways for others to succeed. For two graduating seniors, Kylie Berg and Anna Reis, serving as TAs became a defining part of their college journey. Inspired by the support they received when they were new to CS, they each stepped into the role hoping to offer that same encouragement to students who, like them, were just starting out.
“Teaching assistants are often the first point of contact for students learning how to code,” said Adam Cannon, a Senior Lecturer in Discipline. Berg and Reis are Head TAs for his class, where they have mentored hundreds of students. “When those TAs are not only technically strong but also approachable and encouraging, the entire learning experience changes—for the better.”
Adam Cannon, Kylie Berg, and Anna Reis
Through teaching, both students deepened their understanding of course material, built meaningful connections with faculty and peers, and gained confidence as leaders. Their experiences show how the department’s commitment to undergraduate involvement, through teaching, research, and mentorship, benefits everyone in the classroom.
Below, Berg and Reis reflect on their experiences as teaching assistants and how those roles helped them grow while giving back to the CS community.
Kylie Berg
During my time at Columbia, the friendships I’ve formed have been the most meaningful part of my experience. While the academics were rigorous and the opportunities were plentiful, it was the connections I built with classmates, mentors, and peers that truly defined my journey.
One of the most impactful decisions I made was becoming a TA for Intro to Computer Science (1004). Coming into the program with no prior CS experience, I remember vividly how overwhelming and isolating it felt. But the TAs I met as a freshman showed incredible patience and encouragement, and they helped me believe that I could succeed. That experience inspired me to become a TA myself, and I’m proud to have played a small part in students’ journeys—and in doing so, I’ve grown immensely myself. Having debugged hundreds of student programs over the years, I have become better at quickly understanding unfamiliar code and much better at debugging, both of which are invaluable skills.
The past four years have been an interesting and complicated slice of Columbia’s history. The opportunities I have had here, from the chance to do research in quantum computing and publish a paper, to attending hackathons, to experiencing amazing courses taught by dedicated professors and beyond, have been incredible. I have personally witnessed the department and faculty’s dedication to the quality and equity of our curriculum, and I am grateful to have been a part of it. I’m glad that I’m staying in New York City, and I’ll be working as a software engineer at SeatGeek after graduation.
Anna Reis
One of the most rewarding parts of my time at Columbia has been my involvement with the department, both as a teaching assistant and through research. These experiences not only helped me grow academically but also shaped me as a person. From debugging code in crowded TA hours to collaborating on projects that pushed me intellectually, CS has been more than a major—it’s been a community that has challenged and inspired me in equal measure.
I became a TA for the introductory CS courses because of the incredible impact my own TAs had on me when I first entered the field. I came in uncertain, unsure if I belonged, but the encouragement I received from those early mentors made all the difference. Stepping into that role myself gave me the opportunity to offer the same support, especially to women and other underrepresented students who might be questioning their place in tech. It’s a privilege to help build confidence in students just starting out, and I’ve found the role deeply fulfilling. I absolutely recommend that students become a TA; the benefits are multifaceted, including learning more deeply about the topic that you are teaching, developing a stronger relationship with faculty and students, and getting compensated for your work.
Beyond the classroom, I’m also proud of studying abroad in Spain during my junior year. Balancing a CS course load while immersing myself in another culture was a unique and invaluable experience. I’m so grateful Columbia made that possible—and now, after graduation, I’m excited to continue my journey as a software engineer at Bloomberg in NYC.
The CS major talks about becoming a leader focused on building community and driving change, and how she found a strong sense of community within the department.
Diakonikolas, who received his PhD from Columbia under Mihalis Yannakakis, received the Association for Computing Machinery’s Grace Murray Hopper Award, given annually to an “outstanding young computer professional of the year, selected on the basis of a single recent major technical or service contribution.”
Andrew, a double major in applied physics and applied mathematics, is this year’s Valedictorian and Illig Award recipient. This year’s Salutatorian, Jennifer, is a computer science major with a minor in applied math and economics. Both graduating seniors will be recognized at the May 19 Columbia Engineering Class Day ceremony.
Raman Odgers, a master’s student in computer science, will address his fellow students on the South Lawn at Columbia’s Morningside campus. Graduate Student Life Leadership Award winners Xiaolong Wu, a PhD candidate in earth and environmental engineering, and Kimberly Kroupa, a PhD student in mechanical engineering, will also be honored at Class Day.
The top academic honors for the Columbia College Class of 2025 have been awarded, with Eilidh MacLeod (CC’25) being named valedictorian and CS major Suwei Ma (CC’25) as salutatorian.
The NSF-GRFP supports outstanding graduate students in NSF-supported scientific disciplines or in STEM education who are pursuing full-time, research-based master’s and doctoral degrees at accredited American institutions.
Adam Lin didn’t set out to become a computer scientist. In fact, his academic journey began in finance—driven by family expectations and a clear, conventional career path. But a chance encounter working with Excel macros during an internship lit a spark. What started as a curiosity in programming soon evolved into a deep commitment to using AI to solve real-world problems, especially in healthcare.
That commitment became personal the moment Adam stepped into a neonatal intensive care unit (NICU) to support a research project predicting Necrotizing Enterocolitis (NEC), a gastrointestinal problem in preterm infants. Surrounded by fragile newborns and determined clinicians, he realized that his work could make a real impact on lives. Now a PhD student working on cutting-edge machine learning techniques—like using privileged information for better prediction in medical settings—Lin is focused on applying AI to improve maternal and neonatal health outcomes.
His story is one of curiosity, compassion, and the drive to turn complex algorithms into tools for real human benefit.
Q: Your academic journey started in finance. What led you to pivot to computer science and machine learning? My plan was to build a career in finance, but during the last year of my undergraduate study, I interned at Fund of Funds, where I worked on maintaining their MATLAB-based fund analyzer. I found myself enjoying the programming aspect of the work more than the finance itself.
That led me to explore computer science, and while taking an Introduction to AI course with Professor Ansaf Salleb-Aouissi, I became fascinated with the potential of AI to solve real-world problems. At the end of the semester, I asked Professor Salleb-Aouissi if there were any projects I could pursue. A few months later, she invited me to work on a research project that utilizes machine learning to predict Necrotizing Enterocolitis (NEC) in preterm infants.
Q: What was the turning point that made you realize you wanted to focus on AI for healthcare? Initially, I was excited about the technical challenges of the NEC prediction project. But when I visited the neonatal intensive care unit (NICU) and saw those tiny, vulnerable babies, everything changed. It was no longer just an academic exercise—this research had real-life stakes. That moment solidified my motivation to use AI for something meaningful, and it ultimately guided my decision to pursue graduate studies in computer science with a focus on healthcare applications.
Q: When did you decide to pursue a PhD? What motivated you to continue beyond your master’s? Even while completing my master’s, I knew I wanted to pursue a PhD at some point. I’ve always been someone who enjoys deep exploration of problems and truly understanding a field. Since I was already conducting research with Professor Ansaf Salleb-Aouissi and working closely with clinicians, the transition felt natural. When she invited me to officially join the PhD program, it just clicked—I wanted to keep working on meaningful AI research, particularly in healthcare.
Q: Your research covers a range of healthcare challenges. How did your projects evolve over time? My first major project was predicting NEC in preterm infants, which introduced me to multiple instance learning—where we had stool microbiome samples from different time points but no exact onset for the disease. We used an attention mechanism to weigh each sample’s importance dynamically, improving prediction accuracy. This project ignited my interest in exploring how advanced machine learning techniques could be applied in healthcare.
That research expanded into predicting preterm birth and preeclampsia, using data from the Nulliparous Pregnancy Outcomes Study (nuMoM2b). We found that while we could build robust models for predicting indicated preterm birth (clinician-initiated due to complications), spontaneous preterm birth remained elusive with standard clinical data. This highlighted the need for additional predictive factors, such as microbiome analysis.
As we analyzed the dataset further, we realized many features were underutilized, especially information available only after delivery or adverse pregnancy outcomes (APOs). By leveraging privileged information, we can effectively incorporate this information in training our model, where it serves as essentially a “teacher” that guides the “learner” to build better models. During this phase, we found that XGBoost was particularly robust against overfitting, and we built upon it to incorporate privileged information, introducing XGBoost+.
This work became even more personal when my baby was born during my PhD. Experiencing the healthcare system firsthand and seeing how much uncertainty surrounds maternal and neonatal health deepened my commitment to this research. It reinforced the importance of developing AI-driven solutions to provide better clinical insights and improve outcomes for mothers and babies.
My thesis brings together all these experiences, focusing on combining transfer learning and privileged information to enhance the prediction model in maternal and neonatal health. We are actively exploring large datasets such as CDC records, clinical studies like the Maternal-Fetal Medicine Units (MFMU) Network preterm dataset, and alternative data modalities such as the vaginal microbiome to improve predictions for preeclampsia and indicated preterm birth. Also, we hope that by incorporating different modalities of datasets and external data sources, we can develop a reliable predictive model for spontaneous preterm birth.
Q: Can you explain your research on predicting Proximal Junctional Kyphosis (PJK) and why it matters? My paper, “A LUPI Distillation-Based Approach: Application to Predicting Proximal Junctional Kyphosis (PJK),” focuses on predicting PJK. PJK is a post-operative complication in adult spinal deformity patients, occurring in about 17–46% of cases. It leads to abnormal spinal curvature and significant patient morbidity. Predicting PJK early is crucial for better prevention strategies and patient outcomes.
A limited amount of patient data and only a few features are available, making predicting PJK a challenging problem. In our work, we propose XGBoost+, a novel extension of the widely used XGBoost algorithm, to incorporate privileged information – data available at training time but not at inference time. In the case of PJK, privileged information is the post-operative data. By leveraging Learning Using Privileged Information (LUPI) in a distillation framework, our model improves predictive performance over traditional methods like standard XGBoost and Support Vector Machines (SVM). Our results demonstrate that XGBoost+ significantly enhances predictive accuracy, especially in healthcare applications with common data limitations at inference time.
Our study introduced a LUPI distillation-based approach, using XGBoost+ to incorporate privileged information (post-operative data) during training. This method outperformed traditional models like standard XGBoost and Support Vector Machines (SVM), offering a practical way to improve predictions even with limited available data. The research is essential because it introduces an effective way to handle prediction tasks with limited data, a significant challenge in machine learning in healthcare. In the case of PJK, we incorporated a third of the features that would likely be unused in traditional models.
Q: Your research focuses heavily on privileged information. What is it, and why is it important in healthcare AI? In healthcare, many prediction models face a fundamental limitation—certain crucial information is available only after an outcome occurs. For example, in maternal health, some of the most predictive data comes after delivery or complications, making it unavailable for real-time decision-making.
Traditional approaches in healthcare machine learning often assume that if a condition appears unpredictable from known risk factors, the solution is to collect more data or search for entirely new attributes. However, in many clinical settings, data is limited, and certain features may only become predictive when considered in combination with others. This means we can’t outright dismiss a feature because it doesn’t appear predictive.
Q: How has your personal life influenced your research direction? During my PhD, my own child was born, and experiencing the healthcare system firsthand gave me a deeper appreciation for the challenges faced by clinicians and patients alike. Seeing the uncertainty surrounding maternal and neonatal health reinforced my commitment to developing AI-driven solutions that can provide better clinical insights.
Q: What’s next after your PhD? Do you plan to continue in this research field? First, I plan to take a well-earned break—hopefully traveling to China with my son before he starts 3-K!
Research-wise, I’m particularly excited about the potential of large language models (LLMs) for risk factor selection and transfer learning in healthcare AI. LLMs could enhance clinical prediction models, making them both more accurate and interpretable. I don’t have a specific job lined up yet, but I’d love to join an industry healthcare research lab where I can continue working on AI for maternal and neonatal health, clinical decision-making, and diagnostics.
Q: Any advice for students who are considering a career in AI for healthcare? Be curious and open to exploring different disciplines. My journey started in finance, but a single exposure to programming changed everything. The intersection of AI and healthcare is incredibly rewarding, but it requires collaboration with domain experts, patience in dealing with complex medical data, and a deep commitment to solving real-world problems. Most importantly, find work that feels meaningful to you—because that’s what will keep you going when challenges arise.
CS students Danielle Maydan (SEAS ’26) and Steven Yu (SEAS ’26) are among the students awarded the 2025 Goldwater Scholarships, the preeminent undergraduate award in the fields of mathematics, the natural sciences, and engineering.
When pigeons outnumber pigeonholes, some birds must double up. This obvious statement — and its inverse — has deep connections to many areas of math and computer science.
The Columbia Engineering AI Demo Session held Mar. 4 in Carleton Commons showcased innovations in computer vision, robotics, sensing, and sustainability. As part of the Columbia AI Summit—a University-wide event highlighting Columbia’s deep expertise in artificial intelligence—the session provided attendees with a firsthand look at AI research and technology.
For many students, transitioning into a new academic field is like stepping into uncharted territory. The unfamiliar concepts and coursework can be a challenge that can make even the most determined students question if they belong. But for one teaching assistant (TA), that very experience became the foundation for mentorship.
Karla Zuniga is forging a path at the intersection of science and computer science. With an undergraduate degree in the sciences, she spent two consecutive years as a research intern in a Bioengineering and Applied Physics Laboratory at Harvard University, where she discovered her passion for technology. Driven by curiosity, she embraced the challenge of transitioning into computer science for her master’s degree, diving into foundational courses and expanding her technical skill set. Along the way, she deepened her engagement in the field as a teaching assistant for courses led by Senior Lecturer Paul Blaer and Adjunct Professor Donald Ferguson.
Now, as a Head TA, she’s paying it forward—helping students who feel just as lost as she once did. With long hours spent grading, holding office hours, and guiding students through their academic journeys, she sees TA-ing as more than just a role—it’s an opportunity to build a supportive learning environment, develop leadership skills, and give back to a community that shaped her.
We dive into her journey: why she became a TA, the challenges and rewards of the role, and how she balances everything with her young family. If you’ve ever considered becoming a TA—or simply wondered what it’s like to mentor and inspire others—her story offers valuable insight into the impact of teaching beyond the classroom.
Q: Why did you decide to become a TA, even before officially joining the MS program? I wanted to give back to the community and pay it forward. Coming from a science background, I took all the introductory CS courses and often experienced imposter syndrome while transitioning to engineering.
Adapting to a new way of thinking was challenging, and I remember how overwhelming courses like Advanced Programming felt at times. I became a TA to support students who might be going through a similar experience—helping them navigate the learning curve, build confidence, and realize that they belong in the field.
Q: How many hours do you spend as a TA, and how do you balance it with everything else? On average, I dedicate about 20 hours per week to TA responsibilities, including grading, holding office hours, preparing materials, and assisting in class discussions. Balancing this with the things I love, like having a family of my own (my husband and children), has been an incredibly fulfilling experience and their support has meant the world to me. It has taught me to be more intentional with my time, prioritize effectively, and find joy in both teaching and personal growth. Every challenge is an opportunity to improve, and every moment spent helping students reminds me why I love what I do. I approach this role with immense gratitude, knowing that I have the privilege of making a meaningful impact while continuing to grow myself.
Q: What do you gain from being a TA, and how does it help you build mentorship relationships with professors? TA-ing offers more than just an academic experience—it’s an opportunity to gain leadership skills, build confidence, and develop the ability to explain complex topics in a way that makes sense to others. It allows me to foster a supportive learning environment, help students overcome obstacles, and celebrate their progress. Most importantly, it’s deeply fulfilling to know that my guidance can make a real difference in someone’s academic journey.
As a TA, you collaborate with faculty and fellow TAs, creating valuable opportunities for mentorship and professional growth. Faculty can offer career advice, research opportunities, and industry insights, which can be instrumental in shaping your academic and professional path.
Q: How has being a TA helped you? TA-ing has been instrumental in my academic and professional growth. Teaching concepts to students has reinforced my own understanding and strengthened my ability to think critically. Professionally, it has helped me develop leadership, communication, and problem-solving skills—qualities that are essential in any career. Additionally, it has made me a stronger candidate for job opportunities, helped me network with faculty and peers, and given me the confidence to mentor and support others.
Q: How challenging is it to become a TA? Securing a TA position in the computer science department can be a very competitive process. Students must be in strong academic standing and typically need to have earned an A in the course they wish to TA for. However, excelling academically is just the baseline—TA selection goes far beyond grades. Many computer science courses enroll upwards of 600 students, yet only eight to nine TA positions depending on the course, making the selection process highly selective.
Applicants must formally apply through MICE. It’s important to demonstrate strong communication skills, and showing a genuine passion for teaching can significantly boost your chances. Additionally, expressing interest early, and engaging with professors and other TAs. Perseverance and preparation can make all the difference.
Students who enjoy mentoring, are passionate about the course and want to develop leadership skills are great candidates for a TA position. It’s ideal for those who are patient, responsible, and eager to support others while reinforcing their own knowledge. If you’re looking for a rewarding experience that helps you grow both academically and professionally, TA-ing is a great opportunity.
Q: Is there anything else people should know about TA-ing? As a current Head TA, I highly encourage students to apply for TA positions in courses they genuinely enjoy. Teaching a subject you’re passionate about makes the experience even more rewarding. Beyond academics, being a TA provides leadership experience, professional connections, and opportunities that can benefit your career. I highly recommend applying—you’ll gain so much more than just a title it’s an extremely rewarding experience.
Lastly, I would like to extend my sincere gratitude to Professor Paul Blaer and Professor Don Ferguson for believing in my TA abilities and giving me the opportunity to TA their courses.
The department is thrilled to introduce a cadre of new researchers poised to join the charge in artificial intelligence (AI) and quantum computing.
“This is an incredible year for hiring, and we are fortunate that so many of our young colleagues have chosen to come to Columbia,” said Vishal Misra, professor and faculty recruiting chair. “They saw our vision and the dynamic and vibrant atmosphere in the department. They are investing their future in us, and we couldn’t be happier. Onwards and upwards!”
The new faculty members bring a wealth of experience and expertise, with research interests spanning a broad spectrum of AI and quantum computing applications. Their arrival marks a pivotal moment in the university’s trajectory, signaling a renewed focus on tackling some of humanity’s most pressing challenges.
“We are thrilled to welcome this new group of outstanding talents joining the Department of Computer Science and Columbia Engineering. They will add to our tremendous momentum in furthering our academic excellence and fulfilling our Engineering for Humanity vision,” added Shih-Fu Chang, Dean of the School of Engineering and Applied Science of Columbia University.
The addition of these exceptional scholars also underscores Columbia’s commitment to fostering an environment of academic excellence and innovation. Through collaboration, curiosity, and a relentless pursuit of knowledge, the computer science department is poised to chart new frontiers in AI and quantum computing, paving the way for a future defined by possibility and progress.
“Recruiting these six young talents underscores our dedication to remaining at the forefront of all areas of computer science,” remarked Luca Carloni, professor and chair of the department. “The addition of their expertise aligns with our strategic vision. It also reflects our commitment to sustaining the demand for innovative courses and interdisciplinary research collaborations from students and researchers across the entire university.”
About the new faculty:
James Bartusek PhD Computer Science, University of California, Berkeley Research Area: Quantum/Cryptography
Bartusek was a member of the theory group at UC Berkeley, where he was advised by Sanjam Garg. His research interests are in cryptography and quantum information.
He completed a BSE in computer science in 2016 and an MSE in computer science in 2019 at Princeton University.
Adam Block PhD Mathematics and Statistics, Massachusetts Institute of Technology Research Area: Theory/Machine Learning
Block was part of the math department at MIT, where he was advised by Alexander (Sasha) Rakhlin. He was affiliated with the Laboratory for Information & Decisions Systems and the Statistics and Data Science Center. His research interest lies in machine learning, to bridge theory and practice by designing algorithms with provable guarantees.
An NSF Graduate Research Fellowship supported his graduate studies. Block graduated from Columbia University with a BA in Mathematics (summa cum laude) in 2019.
John Hewitt PhD Computer Science, Stanford University Research Area: Natural Language Processing
Hewitt worked with Chris Manning and Percy Liang in the Stanford Natural Language Processing Group. His interests are in neural representations of language, language models, and interpretability. His long-term goals are to design systems that learn many of the world’s languages and provide interfaces for controlling and understanding their behavior.
He was an NSF Graduate Fellow and received an Outstanding Paper Award at ACL 2023, a Best Paper Runner-Up at EMNLP 2019, an Honorable Mention for Best Paper at the Robustness of Few-Shot Learning in Foundation Models Workshop (R0-FoMo) at NeurIPS 2023, and an Outstanding Paper Award at the Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackBoxNLP) at EMNLP 2020.
Hewitt completed a BSE in Computer and Information Science from the University of Pennsylvania in 2018.
Aleksander Hołyński Berkeley/DeepMind/U Washington PhD Computer Science and Engineering, University of Washington Research Area: Vision/Generative AI
Hołyński is a research scientist at Google DeepMind and a postdoctoral scholar at Berkeley AI Research, working with Alyosha Efros and Angjoo Kanazawa.
He completed his PhD studies at the University of Washington, where he was advised by Steve Seitz, Brian Curless, and Rick Szeliski. He received a BS in Computer Science with High Honors from the University of Illinois at Urbana-Champaign in 2014. His co-authored work has received a best student paper award at ICCV 2023.
Yunzhu Li Assistant Professor, University of Illinois at Urbana-Champaign PhD Computer Science, Massachusetts Institute of Technology Research Area: Robotics
Li’s work stands at the intersection of robotics, computer vision, and machine learning, with the goal of helping robots perceive and interact with the physical world as dexterously and effectively as humans do. He received the Adobe Research Fellowship and was selected as the First Place Recipient of the Ernst A. Guillemin Master’s Thesis Award in Artificial Intelligence and Decision-Making at MIT. His research has been published in top journals and conferences, including Nature, NeurIPS, CVPR, and RSS, and featured by major media outlets, including CNN, BBC, The Wall Street Journal, Forbes, The Economist, and MIT Technology Review.
Li received an MS in Electrical Engineering and Computer Science from MIT in 2020 and a BS in Computer Science from Peking University in 2017.
Silvia Sellán PhD Computer Science, University of Toronto Research Area: Graphics
Sellán is a postdoctoral associate working with Justin Solomon at MIT EECS.
Sellán completed a PhD in computer science at the University of Toronto, working in computer graphics and geometry processing. She was a Vanier Doctoral Scholar, an Adobe Research Fellow, and the 2021 University of Toronto Arts & Science Dean’s Doctoral Excellence Scholarship winner. She has interned twice at Adobe Research and the Fields Institute of Mathematics. She is also a founder and organizer of the Toronto Geometry Colloquium and a member of WiGRAPH.
Sellán graduated from the University of Oviedo with a BSc in Mathematics and a BSc in Physics in 2019.
Columbia University is the first academic institution to partner with Richtech Robotics Inc. in its Richtech Accelerator Program, an initiative to advance localized AI and robotics research at U.S. universities.
Zhou Yu
Columbia Engineering’s Zhou Yu, associate professor of computer science, will lead research on Natural Language Processing (NLP) and using localized NLP models within robotic systems, empowering robots to comprehend and perform tasks through NLP instead of relying on engineers to program each function. This integration allows robots to use AI tailored to their specific settings, improving their ability to understand and respond to human interactions or environmental factors more effectively.
The program’s primary objective is to enable manufacturing, healthcare, and the service sector industries to leverage AI-driven robotic solutions, enhancing efficiency and mitigating labor shortages. Learn more about the accelerator program here.
Three computer science students have been honored with the prestigious 2025 CRA Outstanding Undergraduate Researcher Award by the Computing Research Association (CRA). This recognition celebrates Emre Adabag, Jiaqian Li, and Kechen Liu for their dedication to research and academic excellence. Their nominations, submitted by faculty members, highlight not only their academic achievements but also their impactful contributions beyond the classroom.
Emre Adabag – Finalist Emre Adabag is a senior in SEAS and does research in Barnard College’s Accessible and Accelerated Robotics (A2R) Lab. He worked with Assistant Professor Brian Plancher to develop which uses the power of graphics processing units (GPUs) to accelerate robot motion planning and control. This computational breakthrough enables robots to move more agilely and dexterously. The research was published in the Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2024) and also won a Best Poster Award at the annual poster session of the IEEE RAS Technical Committee on Model-Based Optimization for Robotics. Furthermore, this work serves as the foundation for the A2R Lab’s new NSF CSSI grant, aimed at creating open-source, GPU-accelerated optimal control tools to benefit the broader robotics community.
Jiaqian Li – Honorable Mention Jiaqian Li works in the Crypto Lab under the guidance of Professor Tal Malkin. He has been working on a cryptography research project that studies the relationship between oblivious black-box reductions and Total Function Nondeterministic Polynomial (TFNP) hardness. Total problems are ones that are guaranteed to have a solution, even if it may be hard to find. Examples include factoring or finding a Nash equilibrium. Li’s research explores the possibility of establishing hardness of total problems from a cryptographic tool called one-way functions (OWFs).
Li’s previous research on game theory was published at the AAAI Conference on Artificial Intelligence (AAAI 2024). He worked with Professor Minming Li of the City University of Hong Kong, and they explored how to fairly locate undesirable facilities, like waste treatment plants, among different groups of people, aiming to design systems that encourage honesty about location preferences and minimize the negative impact on all groups. The study proposed and analyzed several mechanisms, including both deterministic and randomized approaches, with the goal of finding locations that are as fair as possible and close to the ideal solution, while also establishing limitations on what is achievable.
Kechen Liu – Honorable Mention Kechen Liu earned an honorable mention for her research in wireless power delivery systems for micro-robotic applications. Under the guidance of Associate Professor Xia Zhou at the MobileX lab, Liu contributed to the development of a laser-based power delivery system that enabled sustainable power for microrobots. She worked on designing and implementing an event camera-based tracking system that achieved millimeter-level tracking accuracy and continuous laser alignment with microrobots in motion. Aside from that, she contributed to multiple projects at the MobileX group including developing a palm-vein biometric authentication system. Liu is also interested in wireless sensing and communication systems, and she has worked with Professor Gil Zussman on a sub-Terahertz sensing project.
The recognition highlights the department’s dynamic research community, where students are empowered to innovate, explore, and excel in their respective fields. Professors often offer research opportunities for undergraduate students. Those interested should proactively reach out to faculty members to inquire about available positions.
Professor Tal Malkin typically has one or two undergraduate students who work on cryptography research. Students need to have mathematical maturity; ideally, they should have taken Malkin’s graduate-level Introduction to Cryptography class.
Associate Professor Xia Zhou mentors two to three undergraduate students per semester, guiding them through hands-on research in the lab. Students must be highly motivated and demonstrate strong analytical and system-building skills. Since many projects involve developing physical prototypes, proficiency with hardware is essential.
Assistant Professor Brian Plancher hosts a select group of undergraduate researchers each semester. While prior experience in robotics, parallel programming, or machine learning is not required, a strong foundation in computer science, electrical engineering, or mathematics is expected, as the research involves mathematical rigor and programming expertise.
In a world fixated on ever-higher resolutions and increasingly detailed images, a revolutionary new camera takes a daring step in the opposite direction. The Minimalist Camera is an innovation designed to prioritize efficiency and privacy over unnecessary detail. By capturing only the minimal data needed for a specific task, this groundbreaking technology challenges conventional thinking about imaging—and redefines what cameras don’t need to do.
Jeremy Klotz
Developed by Jeremy Klotz, a third-year PhD student in the CAVE Lab, in collaboration with Professor Shree Nayar, the minimalist camera forgoes traditional images. Instead, it relies on a handful of custom-shaped “freeform” pixels, carefully tailored to the task at hand. The result? A device that preserves privacy by avoiding the capture of identifiable details while consuming so little power that it’s entirely self-sustaining. Whether monitoring traffic flow or analyzing crowd movements, this camera captures only the essential data—empowering practical applications without compromising individual privacy.
Jeremy Klotz and Shree Nayar at ECCV 2024
The innovation has not gone unnoticed, earning a Best Paper Award at the European Conference on Computer Vision (ECCV 2024). More than just an accolade, the minimalist camera signals a paradigm shift in how cameras can function in our increasingly interconnected world.
We caught up with Klotz to explore the story behind the minimalist camera, the ups and downs of PhD life, and what it means to push the boundaries of imaging technology in the name of privacy and efficiency.
Q: How did you develop the idea for the minimalist camera? When I started my PhD, my advisor and I began brainstorming research directions. After discussing different ideas, we landed on the high-level concept of creating a camera that captures the least information necessary to perform a vision task. In contrast to a traditional camera that uses millions of tiny square pixels, our idea was to let each pixel take on an arbitrary shape (which we call a freeform pixel). Once we evaluated this idea in simulation, we found that freeform pixels can solve vision tasks with significantly fewer pixels than traditional cameras.
I worked with my advisor on every aspect of the project, from refining the high-level idea to building a prototype camera. This involved careful thinking about how to design freeform pixels, simulating them in software, and then building a camera that uses a very small number of freeform pixels to solve real-world vision tasks. This project took about one and a half years from start to finish.
Shree Nayar and Jeremy Klotz
Q: Can you describe your research focus and what motivates your work? My research is in computational imaging, where we design new cameras using novel hardware and software. This area is particularly exciting since it merges research in computer vision (typically all software) with imaging hardware. In particular, I love building prototypes to demonstrate our research ideas. Working with hardware is definitely challenging, but seeing a prototype work at the end of the day makes it even more rewarding.
I’m interested in asking questions like, “What are the fewest measurements needed to solve a vision task?” and “How can we build a camera that captures the fewest measurements?” These questions are particularly relevant right now. Most cameras produce exceptionally high-quality images, but this comes at a cost: high-resolution images often reveal too much information about the world, and the cameras consume so much power that they can only be deployed on buildings (with a tether for power) or with a battery that needs to be recharged.
Q: Why did you decide to pursue a PhD? Before coming to Columbia, I studied electrical and computer engineering at Carnegie Mellon. While I was an undergrad, I was introduced to research in computational imaging. I didn’t plan to pursue a PhD at the time, but after this foray into research, I found that I really enjoyed the open-ended problems and decided to pursue a PhD.
My undergraduate research was the most important experience that prepared me for my PhD. Although it’s hard to completely understand what a PhD entails until you start, my undergrad research introduced me to how it feels to do research full-time and what it’s like to work with a professor rather than for a professor.
Now as a PhD student, my work’s direction is completely up to me. If I believe that an idea is worth pursuing, then I can commit all of my time to working on it. This freedom is incredible, and it allows me to choose the most interesting problems to work on.
Q: What standout moments or experiences have shaped your journey at Columbia so far? I’ve really enjoyed going to conferences—presenting research and meeting others in the field is a blast. I’ve also enjoyed attending department seminars on research outside my area. It’s helped me to ask thoughtful questions about work in other fields.
With my research, we’ve had quite a few ideas that simply don’t work out. My strategy is to try to determine if a new idea is viable as early as possible, and quickly pivot if it isn’t.
Q: What is your advice to students on how to navigate their time at Columbia? If you want to do research, keep an open mind to explore areas you may not be familiar with. A lot of research can appear intimidating at first, but the students and faculty working in the area are extremely passionate and excited to chat if you ask.
In this conversation, Dean Shih-Fu Chang and Vice Dean Vishal Misra discuss how the School is driving and responding to this exciting moment in the development of artificial intelligence.
Joon Baek (CC’21 & MS SEAS’25) and Zino Haro (SEAS’20) launched Youth for Privacy, a nonprofit that raises awareness about online privacy rights and cybersecurity.
Researchers from the department showcased their work at the 16th Innovations in Theoretical Computer Science (ITCS) conference, a premier forum for advancing the field through groundbreaking research, innovative methodologies, and interdisciplinary exploration that drives progress and inspires the theoretical computer science community.
Below are the abstracts of their accepted papers.
Data-Driven Solution Portfolios Marina Drygala EPFL, Silvio Lattanzi Google, Andreas Maggiori Columbia University, Miltiadis Stouras EPFL, Ola Svensson EPFL, Sergei Vassilvitskii Google Research
Abstract: In this paper, we consider a new problem of portfolio optimization using stochastic information. In a setting where there is some uncertainty, we ask how to best select k potential solutions, with the goal of optimizing the value of the best solution. More formally, given a combinatorial problem Π, a set of value functions V over the solutions of Π, and a distribution D over V, our goal is to select k solutions of Π that maximize or minimize the expected value of the best of those solutions. For a simple example, consider the classic knapsack problem: given a universe of elements each with unit weight and a positive value, the task is to select r elements maximizing the total value. Now suppose that each element’s weight comes from a (known) distribution. How should we select k different solutions so that one of them is likely to yield a high value? In this work, we tackle this basic problem, and generalize it to the setting where the underlying set system forms a matroid. On the technical side, it is clear that the candidate solutions we select must be diverse and anti-correlated; however, it is not clear how to do so efficiently. Our main result is a polynomial-time algorithm that constructs a portfolio within a constant factor of the optimal.
Abstract: We study the risks of validator reuse across multiple services in a restaking protocol. We characterize the robust security of a restaking network as a function of the buffer between the costs and profits from attacks. For example, our results imply that if attack costs always exceed attack profits by 10%, then a sudden loss of .1% of the overall stake (e.g., due to a software error) cannot result in the ultimate loss of more than 1.1% of the overall stake. We also provide local analogs of these overcollateralization conditions and robust security guarantees that apply specifically for a target service or coalition of services. All of our bounds on worst-case stake loss are the best possible. Finally, we bound the maximum-possible length of a cascade of attacks. Our results suggest measures of robustness that could be exposed to the participants in a restaking protocol. We also suggest polynomial-time computable sufficient conditions that can proxy for these measures.
A Lower Bound on the Trace Norm of Boolean Matrices and Its Applications Tsun-Ming Cheung McGill University, Hamed Hatami McGill University, Kaave Hosseini University of Rochester, Aleksandar Nikolov University of Toronto, Toniann Pitassi Columbia University, Morgan Shirley University of Toronto
Abstract: We present a simple method based on a variant of Hölder’s inequality to lower-bound the trace norm of Boolean matrices. As the main result, we obtain an exponential separation between the randomized decision tree depth and the spectral norm (i.e. the Fourier L1-norm) of a Boolean function. This answers an open question of Cheung, Hatami, Hosseini and Shirley (CCC 2023). As immediate consequences, we obtain the following results.
We give an exponential separation between the logarithm of the randomized and the deterministic parity decision tree size. This is in sharp contrast with the standard binary decision tree setting where the logarithms of randomized and deterministic decision tree size are essentially polynomially related, as shown recently by Chattopadhyay, Dahiya, Mande, Radhakrishnan, and Sanyal (STOC 2023).
We give an exponential separation between the approximate and the exact spectral norm for Boolean functions.
We give an exponential separation for XOR functions between the deterministic communication complexity with oracle access to Equality function (DEQ) and randomized communication complexity. Previously, such a separation was known for general Boolean matrices by Chattopadhyay, Lovett, and Vinyals (CCC 2019) using the Integer Inner Product (IIP) function.
Finally, our method gives an elementary and short proof for the mentioned exponential DEQ lower bound of Chattopadhyay, Lovett, and Vinyals for Integer Inner Product (IIP).
Abstract: Unclonable cryptography is concerned with leveraging the no-cloning principle to build cryptographic primitives that are otherwise impossible to achieve classically. Understanding the feasibility of unclonable encryption, one of the key unclonable primitives, satisfying indistinguishability security in the plain model has been a major open question in the area. So far, the existing constructions of unclonable encryption are either in the quantum random oracle model or are based on new conjectures. We present a new approach to unclonable encryption via a reduction to a novel question about nonlocal quantum state discrimination: how well can non-communicating – but entangled – players distinguish between different distributions over quantum states? We call this task simultaneous state indistinguishability. Our main technical result is showing that the players cannot distinguish between each player receiving independently-chosen Haar random states versus all players receiving the same Haar random state. We leverage this result to present the first construction of unclonable encryption satisfying indistinguishability security, with quantum decryption keys, in the plain model. We also show other implications to single-decryptor encryption and leakage-resilient secret sharing.
Sparsity Lower Bounds for Probabilistic Polynomials Josh Alman Columbia University, Arkadev Chattopadhyay TIFR, Mumbai, Ryan Williams MIT
Dean Boyce's statement on amicus brief filed by President Bollinger
President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”
This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.
I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.
Sincerely,
Mary C. Boyce
Dean of Engineering
Morris A. and Alma Schapiro Professor
OMSCS – The Best Degree Program Ever?
How to Close the 100,000-Year “Data Gap” in Robotics
November 24, 2025
Smriti Vaidyanathan
Tianle Zhou
Alex Xu
Ryan Soeyadi
Peter Ma
Kylie Berg
Anna Reis
Q: How has your personal life influenced your research direction?
Karla Zuniga is forging a path at the intersection of science and computer science. With an undergraduate degree in the sciences, she spent two consecutive years as a research intern in a Bioengineering and Applied Physics Laboratory at Harvard University, where she discovered her passion for technology. Driven by curiosity, she embraced the challenge of transitioning into computer science for her master’s degree, diving into foundational courses and expanding her technical skill set. Along the way, she deepened her engagement in the field as a teaching assistant for courses led by Senior Lecturer Paul Blaer and Adjunct Professor Donald Ferguson.
James Bartusek 
Adam Block
John Hewitt 
Aleksander Hołyński
Yunzhu Li 
Silvia Sellán 
Emre Adabag is a senior in SEAS and does research in Barnard College’s 
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