CS Welcomes Six New Faculty

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.

CS Welcomes New Faculty

Xia Zhou, Brian Borowski, and Kostis Kaffes join the department. They will facilitate research and learning in mobile computing, software systems, and networks.

Xia Zhou
Associate Professor
PhD Computer Science, University of California Santa Barbara
MS Computer Science, Peking University
BS Computer Science and Technology, Wuhan University

Xia Zhou is an expert in mobile computing and networks whose research is focused on wireless systems and mobile health. Zhou joins Columbia after nine years at Dartmouth where she was the co-director of the Dartmouth Networking and Ubiquitous Systems and the Dartmouth Reality and Robotics Lab. At Columbia, she will direct the Mobile X Laboratory.

Brian Borowski
Lecturer in Discipline
PhD Computer Science, Stevens Institute of Technology
MS Computer Science, Stevens Institute of Technology
BS Computer Science, Seton Hall University

Brian Borowski is an expert in software systems who aims to present a blend of theoretical and practical instruction so his students can be successful after graduation, regardless of the path they choose. He studied underwater acoustic communication and won several awards for his teaching at Stevens Institute of Technology.

Kostis Kaffes
Assistant Professor
PhD Electrical Engineering, Stanford University
MS Electrical Engineering, Stanford University
BS Electrical and Computer Engineering, National Technical University of Athens

Kostis Kaffes is interested in computer systems, cloud computing, and scheduling across the stack. He arrives in Fall 2023 and will spend the next year doing research at Google Cloud with the Systems Research Group.

CS Welcomes Three New Faculty Members

Elias Bareinboim, Brian Smith, and Shuran Song join the department.

Elias Bareinboim
Associate Professor, Computer Science
Director, Causal Artificial Intelligence Lab
Member, Data Science Institute
PhD, Computer Science, University of California, Los Angeles (UCLA), 2014
BS & MS, Computer Science, Federal University of Rio de Janeiro (UFRJ), 2007

Elias Bareinboim’s research focuses on causal and counterfactual inference and its application to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. His work was the first to propose a general solution to the problem of “causal data fusion,” providing practical methods for combining datasets generated under heterogeneous experimental conditions and plagued with various biases. This theory and methods constitute an integral part of the discipline called “causal data science,” which is a principled and systematic way of performing data analysis with the goal of inferring cause and effect relationships.

More recently, Bareinboim has been investigating how causal inference can help to improve decision-making in complex systems (including classic reinforcement learning settings), and also how to construct human-friendly explanations for large-scale societal problems, including fairness analysis in automated systems.

Bareinboim is the recipient of the NSF Faculty Early Career Development (CAREER) Award, IEEE AI’s 10 to Watch, and a number of best paper awards. Later this year, he will be teaching a causal inference class intended to train the next generation of causal inference researchers and data scientists. Bareinboim directs the Causal Artificial Intelligence Lab, which currently has open positions for Ph.D. students and Postdoctoral scholars.

Brian Smith
Assistant Professor
PhD, Computer Science, Columbia University, 2018
MPhil, Computer Science, Columbia University, 2015
MS, Computer Science, Columbia University, 2011
BS, Computer Science, Columbia University, 2009

Brian Smith’s interests lie in human-computer interaction (HCI) and creating computers that can help people better experience the world. His past research on video games for the visually impaired was featured in Quartz, TechCrunch, the Huffington Post, among others.

Smith has spent the last year at Snap Research (Snap is best known for Snapchat) developing new concepts in human–computer interaction (HCI), games, social computing, and augmented reality. He will continue to work on projects with Snap while at Columbia.

He comes back to the department as an assistant professor and is set to teach a class on user interface design this fall. That class had a waitlist of 235 students hoping to be part of the class. Smith shared that back when he was a student, there were only 35 students in the class he was enrolled in. “There is definitely more interest in computer science now compared to even five years ago,” he said.

Smith hopes to start a HCI group and is looking for PhD students. He encourages students from underrepresented groups to apply.

Shuran Song
Assistant Professor
PhD, Computer Science, Princeton University, 2018
MS, Computer Science, Princeton University, 2015
BEng, Computer Engineering, Hong Kong University of Science and Technology, 2013

Shuran Song is interested in artificial intelligence with an emphasis on computer vision and robotics. The goal of her research is to enable machines to perceive and understand their environment in a way that allows them to intelligently operate and assist people in the physical world.

Previously, Song worked at Google Brain Robotics as a researcher and developed TossingBot, a robot that learns to how to accurately throw arbitrary objects through self-supervised learning.

This fall, she is teaching a seminar class on robot learning. Song currently has one PhD student who is working on active perception — enabling robots to learn from their interactions with the physical world, and autonomously acquire the perception and manipulation skills necessary to execute complex tasks. She is looking for more students who are interested in machine learning for vision and robotics.

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