12 CS Students Win Prestigious Fellowships

Graduate students from the department have been selected to receive scholarships. The diverse group is a mix of those new to Columbia and students who have received fellowships for the year. 

Google Fellowship

The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields.

Yiru Chen
Yiru Chen is a fourth-year Ph.D. student who works with Associate Professor Eugene Wu. Her research interests are database systems, human-computer interaction, and data exploration. Her work focuses on improving database usability by automatically generating database interfaces for interactive data analysis.

Chen graduated from Peking University with a B.S. in computer science summa cum laude and a B.A. in Economics in 2018. She enjoys cycling and playing the violin whenever she has free time.

NSF Graduate Research Fellowship Program (GRFP)

The GRFP is a five-year fellowship that recognizes and supports outstanding graduate students in NSF-supported STEM disciplines who are pursuing research-based master’s and doctoral degrees.

Philippe Chlenski
Philippe Chlenski is interested in developing and applying computational techniques to biological problems, particularly machine learning for microbial dynamics. He is a second-year PhD student in the Pe’er lab. Prior to Columbia, he worked for two years at the Fellowship for Interpretation of Genomes at the Argonne National Lab.

Chlenski graduated in 2018 from Yale University with a Bachelor’s degree in mathematics and philosophy. He also holds an Associate’s degree in liberal arts from Deep Springs College.


Sam Fereidooni
Sam Fereidooni is interested in investigating semantic representations through the lens of both cognitive neuroscience and natural language processing. He particularly hopes that the eventual findings from his work will lead to ameliorated treatments for those who suffer from language processing and production disorders. He is a first-year PhD student in the Theory group, and he is advised by Professor Christos Papadimitriou.

Fereidooni graduated in 2021 from Yale University with a B.S. in Cognitive Science, and a B.S. in Statistics and Data Science. Sam’s undergraduate studies were supported by the Questbridge Foundation National College Match scholarship, the Richter Undergraduate Research fellowship, and the Yale Club of New York City Charles S. Guggenheimer scholarship.


Shashaank N
Shashaank N is a first-year PhD student who will be advised by assistant professor David Knowles. His research interests are in computational genomics and neuroscience, with a focus on auditory processing disorders in the brain.

Shashaank recently graduated with an MS in Computer Science from Columbia University in 2021. He completed a BS in Interdisciplinary Studies from Western Kentucky University (WKU) in 2019 and received the Scholar of the College academic award.


Meghna Pancholi
Meghna Pancholi is a second-year PhD student advised by Associate Professor Simha Sethumadhavan. She is interested in cloud computing, systems security, and microservices. Before Columbia, Meghna was an undergraduate researcher at Cornell University where she worked on improving the performance of microservices applications with machine learning techniques.

Meghna graduated from Cornell University in 2020 with a BS in Computer Science.


Clayton Sanford
Clayton Sanford is a third-year PhD student working with Professors Rocco Servedio and Daniel Hsu on machine learning theory. The motivating goal of his research is to understand mathematically why deep learning performs so well in practice. Clayton’s work on the approximation capabilities of neural networks has been published at the COLT 2021 conference. He is a member of the CS Theory Group.

Clayton received an ScB in Applied Math and Computer Science with honors from Brown University in 2018.


Sky Wang
Sky Wang is an incoming first-year PhD student set to work with Assistant Professors Zhou Yu and Smaranda Muresan. His work focuses on natural language processing and he is interested in leveraging computational methods to understand social aspects of language and to use such insights in creating more effective and more equitable language technologies. He is particularly interested in the areas of situated dialogue systems, computational social science, and cultural analytics.

Wang graduated in 2020 from the University of Michigan with a B.S.E in Computer Science. He is a 2021 recipient of the University of Michigan’s EECS Undergraduate Outstanding Research Award and also received an honorable mention for the Computing Research Association Outstanding Undergraduate Research Award in 2021. He received a Best Poster award from the University of Michigan AI Symposium in 2018 and was recognized as a finalist in the NASA Goddard Space Flight Center Intern Research Fair in 2018.


Joseph Zuckerman
Joseph Zuckerman is a second-year PhD student in computer science at Columbia University, where he works in the System-Level Design group, advised by Professor Luca Carloni. His research interests include architectures, runtime management, and agile design methodologies for many-accelerator systems-on-chip.

Zuckerman contributes as one of the main developers to ESP, an open-source research platform for heterogeneous system-on-chip design. In 2019, he completed his S.B in electrical engineering at Harvard University, during which he completed internships at NVIDIA and the NASA Jet Propulsion Lab.


SEAS Fellowships

Columbia School of Engineering and Applied Sciences established the Presidential and SEAS fellowships to recruit outstanding students from around the world to pursue graduate studies at the school.

Blavatnik Fellow

Sebastian Salazar
Sebastian Salazar’s research interests include Machine Learning and Ethical AI. At Columbia, his work will be focused on counterfactual predictions and actionability of Machine Learning models. He is a first-year PhD student who will be working under the guidance of Ansaf Salleb-Aouissi.

Sebastian graduated magna cum laude from Columbia University in 2021 with a B.S. in Applied Physics.


Dean’s Fellows

Huy Ha
Huy Ha is an incoming first-year PhD student interested in computer vision, natural language processing, and robot learning. His research studies how embodied intelligence could combine information from different modalities (vision, language, interaction) to understand its environment, solve tasks, and assist people. He is advised by Assistant Professor Shuran Song and is a member of the Columbia Artificial Intelligence and Robotics (CAIR) lab.

Ha graduated in 2021with a BS in Computer Science from Columbia University. He was a Dean’s Fellow and received the Theodore Bashkow Award. He did research during the summer as a Bonomi Summer Scholar. During his free time, Ha likes to take photos, rock climb, bike, and train his two border collies for frisbee.


Yun-Yun Tsai
A first-year PhD student, Yun-Yun Tsai works with Professor Junfeng Yang. Her research interests are in security and artificial intelligence. In particular, she is interested in improving robustness over neural networks and machine learning (ML) algorithms so that they make fewer mistakes on malicious samples. She will work on research related to making AI applications less fragile against unusual inputs.

Tsai received a B.Sc. and M.Sc. degrees in computer science at National Tsing Hua University (NTHU) Taiwan in 2014 and 2018, respectively. Previously, she was advised by Professor Tsung-Yi Ho and Dr. Pin-Yu Chen from Trusted AI group, IBM Thomas J. Watson Research Center, NY USA.


Mudd Fellow

Anjali Das
Anjali Das is a first-year PhD student who works with Professors Itsik Pe’er and David Knowles. Her research interest is in developing and applying machine learning methods to problems in genomics. Specifically, she is interested in the genetics of neurological diseases.

Das graduated from the University of Chicago in June of 2020 with a BS in statistics and a minor in computer science. After graduating, she worked as a data scientist at UChicago’s Research Computing Center before joining Columbia.


Privacy-preserving Contact Tracing

Moti Yung talks about his work on the Google Apple Exposure Notification API contact tracing project at the Responsible Data Summit.

Fadi Biadsy (PhD ’11) Develops Tool to Help People with Atypical Speech Patterns

Most people take for granted that when they speak, they will be heard and understood. But for the millions who live with speech impairments caused by physical or neurological conditions, trying to communicate with others can be difficult and lead to frustration. While there have been a great number of recent advances in automatic speech recognition (ASR; a.k.a. speech-to-text) technologies, these interfaces can be inaccessible for those with speech impairments. Further, applications that rely on speech recognition as input for text-to-speech synthesis (TTS) can exhibit word substitution, deletion, and insertion errors. Critically, in today’s technological environment, limited access to speech interfaces, such as digital assistants that depend on directly understanding one’s speech, means being excluded from state-of-the-art tools and experiences, widening the gap between what those with and without speech impairments can access. 

Rachel Wu (SEAS ’19) Chosen To Present At Google’s Annual Women Engineers Summit

Wu gave a lightning talk, “Life of a Machine Learning Dataset”, at the conference last July. She was selected out of 60 applicants to deliver a 15-minute talk to 464 attendees from over 18 North American office locations.


My internship at Google came as a result of my independent research during my five month study abroad program. Two days after I returned to the United States on December 14, 2017, I delivered a 40 minute talk on bias in AI and hiring algorithms at Google headquarters for 453 attendees.

In the talk, I performed neural network experiments demonstrating bias in predictive image/video machine learning programs. A Google employee came up to me afterwards and told me to send him my resume. Taking a chance, I sent it in on the last day summer internship applications were open. Two weeks later, I had an offer.

At Google, I had three major responsibilities. First, I identified key issues across machine learning data curation process, leveraging consulting skills to scope, prioritize and identify technical solutions to major pain points. Second, I collaborated with Google’s machine learning engineers and Google’s 700+ member Operations team in Gurgaon, India to eliminate inefficiencies in training by building a training platform. Third, I created presentations with compelling messages tailored to different audience levels, including directors.

Katie Girskis, the organizer of the Google Women Engineers conference, wanted to highlight intern projects from across Google and selected me to be one of the lightning talk speakers. On the first day of the conference, I presented “Life of a Machine Learning Dataset” at Google’s annual women engineers summit, focusing on how we’re putting a price on units of human judgements/expertise more directly than ever. For the talk, I collaborated with Google’s Business and Strategy Operations team to calculate data curation costs, resulting in new metrics to evaluate ML training costs.

Overall by the end fo the internship, my coworkers awarded me 1 kudos and 2 peer bonuses, as well as a return offer. At Google, I learned to write code using their internal technology stack, collaborate with departments at a larger scale, and communicate more effectively. Apart from working with highly skilled people, I enjoyed making friends with the other interns in Mountain View, and having lunch with Jeff Dean. Most importantly, I loved the fact that my code went into production and that my work is being continued even after my summer internship ended.