People

World leader in probabilistic ML. 10-year test of time awards at top ML conferences, including NeurIPS and ICML. Numerous awards including the AAAI John McCarthy award, Allen Newell Award, ACM Prize in Computing, Guggenheim Fellow, and Sloan Fellowship.

PAMI Young Researcher Awardee, NSF CAREER. Senior program chair for ICLR 2025, the top ML and AI conference along with NeurIPS and ICML.

World leader in developing embodied AI agents for real-world robotics. Best Papers at ICRA, CoRL. Sony Faculty Innovation Award.

Proved first optimality bounds of Thompson's sampling (core algorithm in RL). NSF CAREER. Co-chair of AIStats 2025, one of the top theoretical AI conferences.

World leader in theoretical foundations of machine learning and AI. Sloan fellow and AI's 10 to Watch in 2015. Co-chair of ICML 2025, the top ML conference.

Built foundations of fundamental algorithms in pricing and decision-focused learning. NSF CAREER, IBM faculty award, Forbes 30 under 30, INFORMS Early Career Gaver Award.

Pioneer of CodeLLMs and AI for Software Engineering. NSF CAREER, VMWare, and IBM faculty awards, IEEE CS TCSE Rising Star Award.

World leader in robustness and security of AI. Winner of the Sloan fellow, Air Force YIP awardee, NSF CAREER, research awards from Google, Meta, Amazon, best paper awards at top systems and ML conferences.

Pioneered accurate simulations of human-AI agent strategic behavior. Winner of Facebook, Adobe, and Amazon awards. Recipient of the Sloan Research Fellowship.

Winner of the Amazon Alexa Prize, developed the best-rated chatbot system on Amazon Alexa, reaching tens of millions of customers. Forbes 30 under 30.

Pioneered microsecond-scale decision making in cloud computing. Meta Research Award, former Google Visiting Faculty.

Winner of the NSF CAREER, VLDB 2018 Test-of-time, Google, Adobe, Amazon awards.

Pioneered the building of digital twins and the orchestration of experimentation and AI-agent platforms. Recipient of multiple awards in the field of Operations, including the Daniel H. Wagner Prize, APS, and RMP student paper awards.

Assistant Professor at Columbia DBMI and Visiting Faculty at Google. Leads the SEA Lab focusing on human-centered AI and health applications. Distinguished Paper Award at UbiComp 2023. Pioneered generalizable behavior modeling and intelligent intervention techniques for health and well-being.

Developed software security techniques that strengthen program assurance, enable precise auditing, and improve vulnerability discovery. Recipient of the ACM SIGSAC Doctoral Dissertation Award and a DEF CON CTF champion.

Specializes in computer graphics and geometry processing; in particular, on implicit shape representations and surface reconstruction.

Expert in datacenter system infrastructure and security. Founded Sookasa (acquired by Barracuda Networks). Best papers in top systems, database and security conferences.

NVIDIA Pioneer of AI Award, ICML 10-year test of time award, Canadian AI Lifetime Achievement Award. Past chair of NeurIPS, FAccT conferences. Co-Founder & Research Director Vector Institute for AI. Director NSF AI Institute for Artificial and Natural Intelligence.

Founder of Berkeley's AMPLab.

Lily Xu is Sun-Wu Assistant Professor at Columbia and directs the caPP Lab which advances computation and algorithms for People and the Planet. Her group develops new methods across machine learning and optimization to enable practitioners to make effective decisions in the face of limited data, taking actions that are robust to uncertainty, effective at scale, and future-looking.

Develop efficient and reliable systems, architectures, and chips for AI, and use AI for computing system design. Recipient of the ACM SIGDA Outstanding PhD Dissertation Award, best paper awards at DAC and IEEE CAL, IEEE Micro Top Picks, and ML and Systems Rising Star

Yaniv Nemcovsky is an Associate Research Scientist in the Department of Computer Science at Columbia University, working with Professor Junfeng Yang. His research focuses on trustworthy AI, including system robustness and the mitigation of threats such as physical adversarial attacks, jailbreaks, unsafe behaviors of large language models, data poisoning, and backdoors. He received his PhD in Computer Science from the Technion, Israel Institute of Technology, where his thesis addressed adversarial attacks in the real world. His work on realistic physical attacks against visual navigation systems, one of the first credible demonstrations of real-world adversarial threats, received the Most Promising Paper Award at the ECCV 2022 workshop on Adversarial Robustness in the Real World. He previously held a short postdoc position at Ben-Gurion University of the Negev and taught a graduate course on adversarial threats at the Technion. He aims to pursue a faculty position and to lead a research group developing methods that keep AI systems reliable and safe in real-world deployment.

Nikos Pagonas is a third-year Computer Science PhD student at Columbia University advised by Prof. Kostis Kaffes, where he conducts research in the DAPLab on systems for agentic AI. His work builds systems that leverage workflow structure and runtime characteristics to optimize model routing, scheduling, and resource allocation for efficient agentic serving.

Post-training, RL, & Evals

MS Data Science and Research. Research: Latent Communication, Reasoning and Monitoring.

Multi-agent latent communication and deterministic guard railing

Multi-agent systems and agent benchmarks

Multi-agent collaboration, Agent Memory