Colloquium

Despite significant advancements in high-throughput data acquisition in genomics and cell biology, our understanding of the diverse cell types within the human body remains limited. The principles governing intracellular molecular spatial organization and interactions, as well as cellular spatial organization within complex tissues, are still largely unclear. A major challenge lies in developing computational methods capable of integrating heterogeneous, multiscale molecular, cellular, and tissue information. In this talk, I will discuss our work on developing machine learning approaches to advance regulatory genomics through single-cell 3D epigenomics. Additionally, I will introduce our recent efforts in creating interpretable, self-supervised models for the multiscale delineation of cellular interactions in tissues. These methods hold the potential to reveal new insights into fundamental genome structure, gene regulation, and cellular function across a wide range of biological contexts in both health and disease.

Bio: Jian Ma is the Ray and Stephanie Lane Professor of Computational Biology in the School of Computer Science at Carnegie Mellon University. He leads a research group dedicated to developing advanced AI/ML methods for exploring the structural and functional complexity of the human genome and cellular organization. He recently founded the Center for AI-Driven Biomedical Research (AI4BIO) at CMU, which aims to advance AI/ML development for decoding the molecular language governing cellular behavior. He serves as the Contact PI for a Center grant in the NIH 4D Nucleome Program and as Co-Chair of its Steering Committee. He is also a member of the Scientific Advisory Board of the Chan Zuckerberg Biohub Chicago and the RECOMB Steering Committee. His contributions have earned him several honors, including an NSF CAREER Award, a Guggenheim Fellowship, and election as a Fellow of the American Association for the Advancement of Science (AAAS).

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If you need an accommodation for a disability please contact Emily Lawrence at emilyl@cs.princeton.edu at least one week before the event.

Contributions to and/or sponsorship of any event does not constitute departmental or institutional endorsement of the specific program, speakers or views presented.

The recent arrival of impressive large language models and coding assistants has led to speculation that the way we interact with computers would dramatically (and quickly!) change. That hasn’t really happened… yet, but we are at an inflection point where we can influence interaction for both better and, potentially, worse. In this talk, I’ll use examples from our research to highlight four coming challenges and opportunities in how we interact with computers in (i) maintaining user agency, (ii) designing user interfaces that encourage responsibility, (iii) making computer systems accessible, and (iv) designing, generating, and navigating user interfaces automatically.

The future of human-computer interaction will be both more familiar and less familiar than we think; this talk is intended to help develop your sense of what is likely to be and which futures you want to build.

Bio: Jeffrey P. Bigham is an Associate Professor in the Human-Computer Interaction and Language Technologies Institutes in the School of Computer Science at Carnegie Mellon University, and the Director of Human-Centered Machine Learning within AIML at Apple. He builds systems that advance how people can responsibly work with machine learning to do interesting and useful things. This has taken on a variety of focuses throughout his career – he has worked on applications in accessibility for disabilities, systems that used crowdsourcing to power a wide variety of real-time experiences, and most recently on how we can design responsible and useful experiences using generative AI. Much of his work has focused on accessibility because he sees the field as a window into the future, given that people with disabilities are often the earliest adopters of AI. Bigham received his B.S.E degree in Computer Science from Princeton University in 2003, and his Ph.D. in Computer Science and Engineering from the University of Washington in 2009.

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If you need an accommodation for a disability please contact Emily Lawrence at emilyl@cs.princeton.edu at least one week before the event.

Contributions to and/or sponsorship of any event does not constitute departmental or institutional endorsement of the specific program, speakers or views presented.

In many applications of autonomy in robotics, guarantees that constraints are satisfied throughout the learning process are paramount. We present a controller synthesis technique based on the computation of reachable sets, using optimal control and game theory. Then, we present methods for combining reachability with learning-based methods, to enable performance improvement while maintaining safety, and to move towards safe robot control with learned models of the dynamics and the environment. We will discuss different interaction models with other agents, and some implications of model vs. learning-based predictions.

Bio: Claire Tomlin is the James and Katherine Lau Professor and Chair of the Department of Electrical Engineering and Computer Sciences at UC  Berkeley. She was an Assistant, Associate, and Full Professor at Stanford from 1998-2007, and in 2005 she joined Berkeley. Claire works in hybrid systems and control and integrates machine learning methods with control theoretic methods in the field of safe learning.  She works in the applications of air traffic and unmanned air vehicle systems.  She is a MacArthur Fellow, and a member of the National Academy of Engineering and the American Academy of Arts and Sciences.

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Students: To meet with Claire Tomlin, please email Julia L. Brav no later than 5:00PM on Wednesday, April 3, and indicate your department.

Hardware heterogeneity is everywhere, from the high-performance server chips that comprise our data centers to the milliwatt-scale chips on board our biomedical devices. The central thesis of my talk is that hardware heterogeneity breaks through traditional computing abstractions to enable orders of magnitude performance improvements, but that these performance improvements are useful to software developers only when hardware continues to remain easy to program. I will discuss ongoing research in my group on balancing hardware heterogeneity with abstractions/interfaces to enable programmability/flexibility. As exemplars of this question, I will focus on the benefits and challenges of building shared address spaces between general-purpose CPUs and domain-specific hardware accelerators. I will also discuss my work on building flexible neural interfaces driven by a collection of programmable ASICs. My talk will highlight cross-cutting lessons learned and their implications on future accelerator-rich computer systems.

Bio: Abhishek Bhattacharjee is a Professor of Computer Science at Yale University, and is also affiliated with Yale's Wu Tsai Institute for the Brain Sciences as well as Yale's Center for Brain & Mind Health. He is interested in the hardware/software interface. Abhishek's research on address translation has shipped in over one billion AMD Zen CPU cores, over tens of millions of NVIDIA GPUs, over two billion Linux kernel downloads, and has also helped the group tasked with deciding the RISC-V page table format. For these contributions, Abhishek was the recipient of the 2023 ACM SIGARCH Maurice Wilkes Award. Abhishek teaches courses on computer architecture, operating systems, and compilers. In recognition of his teaching and mentoring of undergraduate and graduate students, Abhishek was the recipient of the 2022 Yale Engineering Ackerman Award.

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To request accommodations for a disability, please contact Emily Lawrence at emilyl@cs.princeton.edu at least one week prior to the event.
Live stream only available to Princeton University students, faculty, and staff.  Webinar registration here.

Talk details here

To request disability-related accommodations, please contact Jessica Varela at jv2026@princeton.edu no later than three working days prior to the event.

Over the past decade, the ability to sequence genomes from both present-day and archaic humans (including our closest evolutionary relatives, the Neanderthals) has transformed our understanding of human history. Analyzing these genome sequences paints a picture of human history in which present-day humans migrated out of Africa but exchanged genes with multiple archaic human populations.

I will describe statistical methods that identify segments of DNA inherited from archaic humans that are surviving in our genomes today and how these maps of introgressed archaic DNA are providing insights into human migration and biology.  Despite this progress, our understanding of the contribution of archaic introgression to populations in Africa remains limited, in part due to the challenges in obtaining ancient DNA in Africa. Leveraging recently developed approaches that enable inferences about archaic populations without access to their genome sequences, we show that west African populations today inherit substantial genetic ancestry from an as-yet-unidentified archaic ghost population that diverged prior to the split of modern humans and Neanderthals. Finally, we combine maps of introgressed Neanderthal DNA with phenotypic datasets collected in hundreds of thousands of individuals to assess the contribution of introgressed Neanderthal DNA to complex traits.

I will discuss the implications of these results for our understanding of human evolution as well as the statistical challenges that need to be solved in this endeavor.

Bio: Sriram Sankararaman is a professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA. His research interests lie at the interface of computer science, statistics and biology. His lab develops machine learning algorithms to analyze genomic data and biomedical data with the broad goal of understanding the interplay between evolution, genomes and traits. 

He received a B.Tech. in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a postdoctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of a NSF Career Award, NIH Pathway to Independence Award, and fellowships from Microsoft Research, the Sloan Foundation, the Okawa Foundation and the Simons Institute.

Developing robots that can accept instructions from and collaborate with human users is greatly enhanced by an ability to engage in natural language dialog. Unlike most other dialog scenarios, this requires grounding the semantic analysis of language in perception and action in the world. Although deep-learning has greatly enhanced methods for such grounded language understanding, it is difficult to ensure that the data used to train such models covers all of the concepts that a robot might encounter in practice. Therefore, we have developed methods that can continue to learn from dialog with users during ordinary use by acquiring additional targeted training data from the responses to intentionally designed clarification and active learning queries. These methods use reinforcement learning to automatically acquire dialog strategies that support both effective immediate task completion as well as learning that improves future performance. Using both experiments in simulation and with real robots, we have demonstrated that these methods exhibit life-long learning that improves long-term performance.

Bio: Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin. He received his Ph.D. in 1988 from the University of Illinois at Urbana/Champaign. He is an author of over 180 published research papers, primarily in the areas of machine learning and natural language processing. He was the President of the International Machine Learning Society from 2008-2011, program co-chair for AAAI 2006, general chair for HLT-EMNLP 2005, and co-chair for ICML 1990. He is a Fellow of AAAI, ACM, and ACL and the recipient of the Classic Paper award from AAAI-19 and best paper awards from AAAI-96, KDD-04, ICML-05 and ACL-07. 

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This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/

One of the graduate certificate requirements is for students to give a seminar on their dissertation research before graduation, typically in the last year once significant results can be reported. This research seminar occurs as part of a colloquium with other program participants and is organized by PICSciE.

Each research seminar is approximately 20 minutes in length with additional time for questions from the audience and is accessible to the broader University community with an interest in computational science and engineering. 

The University community is invited to participate as audience members in the colloquium. Students enrolled in the program are highly encouraged to attend.

Recording available here.

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Efficient verification of computation is fundamental to computer science, and is at the heart of the P vs. NP question. Recently it has had growing practical significance, especially with the increasing popularity of blockchain technologies and cloud computing.  In this talk, I will present schemes for verifying the correctness of a computation. I will discuss both their practical aspects and their impact on quantum complexity, hardness of approximation, and the complexity of Nash equilibrium.

Bio: Yael Tauman Kalai received her BA (1997) from the Hebrew University in Jerusalem, MA (2001) under the supervision of Adi Shamir at the Weizmann Institute, and PhD (2006) under the supervision of Shafi Goldwasser at MIT. After postdoctoral positions at Microsoft Research and the Weizmann Institute, she is now a Principal Senior Researcher at Microsoft Research New England and an adjunct professor at MIT.  Her research focuses on cryptography.

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To request accommodations for a disability please contact Emily Lawrence, emilyl@cs.princeton.edu, at least one week prior to the event.

This talk will be recorded.

Please register here

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Abstract: After more than a decade of advanced R&D and ecosystem trials, commercial 5G mmWave service is now available in more than 55 U.S. cities and 160 areas in Japan. Looking forward, we expect 5G mmWave to expand into new geographic regions across the globe, and new device types and tiers will emerge to take full advantage of mmWave’s virtually unlimited capacity. On the research front, Qualcomm continues to push the technology boundaries of mmWave for 5G/6G by bringing new capabilities and enhancements. Join this seminar to:

• Review the key technical achievements and milestones at Qualcomm that enabled the commercialization of 5G mmWave systems.
• See our vision for 5G mmWave and the new opportunities it poises to bring for the broader ecosystem.
• Learn about the mmWave capabilities and enhancements coming in 3GPP Release -17 and beyond (e.g. Integrated Access and Backhaul, 60GHz and beyond, IIOT, etc.).
• Track the latest update on the global commercial rollout of 5G mmWave networks and devices.

Bio: Ozge Koymen is a Senior Director of Technology at Qualcomm Technologies, Inc. where he has been since 2006. He has led the 5G millimeter-wave program within Qualcomm R&D since early 2015, from early conceptual evaluation to commercial deployment. His previous areas as a technical contributor includes Wireless Backhaul, Small Cells, LTE-D, LTE and UMB. Prior to Qualcomm, he was a member of Flarion Technologies developing a pioneering OFDMA cellular system, Flash-OFDM, during 2003-2006. His earlier work experience includes full-time and consulting work for Impinj, Inc. (2000-2003) and TRW (1996-2000). He received the B.S. in Electrical and Computer Engineering from Carnegie Mellon University in 1996 and the M.S. and Ph.D. in Electrical Engineering from Stanford University in 1997 and 2003, respectively.