March 2024 - talks - lists.cs.princeton.edu

Princeton Robotics Seminar: Ye Zhao, Friday March 22
by Emily C. Lawrence 19 Mar '24

19 Mar '24

Speaker: [ https://lab-idar.gatech.edu/ | Ye Zhao ] , GA Tech Date: Friday, March 22nd, 2024 Time: 11am ET Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] PhD students and postdocs can [ https://docs.google.com/forms/d/e/1FAIpQLSdJT4YAF7or7kl5Ht7Op-a0syql4iRy_e1… | signup to join Ye for lunch here ] . There will also be a Robotics Social at 4:00 PM in the F-Wing Cafe Area - all are welcome to attend! Title: Unlocking Agility, Safety, and Resilience for Legged Navigation: Addressing Real-world Challenges in Uncertain Environments Abstract: While legged robots have made remarkable progress in dynamic balancing and locomotion, there remains substantial room for improvement in terms of safe navigation and decision-making capabilities. One major challenge stems from the difficulty of designing safe, resilient, and real-time planning and decision-making frameworks for these complex legged machines navigating unstructured environments. Symbolic planning and distributed trajectory optimization offer promising yet underexplored solutions. This talk will introduce three perspectives on enhancing safety and resilience in task and motion planning (TAMP) for agile legged navigation. First, we'll discuss hierarchically integrated TAMP for dynamic locomotion in environments susceptible to perturbations, focusing on robust recovery behaviors. Next, we'll cover our recent work on safe and socially acceptable legged navigation planning in environments that are partially observable and crowded with humans. Lastly, we'll delve into distributed contact-aware trajectory optimization methods achieving dynamic consensus for agile locomotion behaviors. Bio: Ye Zhao is an Assistant Professor at The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology. He received his Ph.D. degree in Mechanical Engineering from The University of Texas at Austin in 2016. After that, he was a Postdoctoral Fellow at Agile Robotics Lab, Harvard University. At Georgia Tech, he leads the Laboratory for Intelligent Decision and Autonomous Robots. His research interest focuses on planning and decision-making algorithms of highly dynamic and contact-rich robots. He received the George W. Woodruff School Faculty Research Award at Georgia Tech in 2023, NSF CAREER Award in 2022, and ONR YIP Award in 2023. He serves as an Associate Editor of T-RO, TMECH, RA-L, and L-CSS. His co-authored work has received multiple paper awards, including the 2021 ICRA Best Automation Paper Award Finalist, the 2023 Best Paper Award at the NeurIPS Workshop on Touch Processing, and the 2016 IEEE-RAS Whole-Body Control Best Paper Award Finalist.

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TODAY 3/18/2024 - ECE SEMINAR Zhijian Liu, MIT
by Emily C. Lawrence 18 Mar '24

18 Mar '24

ECE SEMINAR Speaker: Zhijian Liu, MIT Title: Efficient Deep Learning with Sparsity: Algorithms, Systems, and Applications Date: Monday, March 18, 2024 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Mengdi Wang Abstract: Machine learning is widely used across a broad spectrum of applications. However, behind its remarkable performance lies an increasing gap between the demand for and supply of computation. On the demand side, the computational costs of machine learning models have surged dramatically, driven by ever-larger input and model sizes. On the supply side, as Moore's Law slows down, hardware no longer delivers increasing performance within the same power budget. In this talk, I will discuss my research efforts to bridge this demand-supply gap through the lens of sparsity. I will begin by discussing my research on input sparsity. First, I will introduce algorithms that systematically eliminate the least important patches/tokens from dense input data, such as images, enabling up to 60% sparsity without any loss in accuracy. Then, I will present the system library that we have developed to effectively translate the theoretical savings from sparsity to practical speedups on hardware. Our system is up to 3 times faster than the leading industry solution from NVIDIA. Following this, I will touch on my research on model sparsity, highlighting a family of automated, hardware-aware model compression frameworks that surpass manual solutions in accuracy and reduce the design process from weeks of human efforts to mere hours of GPU computation. Finally, I will present several examples demonstrating the use of sparsity to accelerate computation-intensive AI applications, such as autonomous driving, language modeling, and high-energy physics. I will conclude this talk with an overview of my ongoing work and my vision towards building more efficient and accessible AI. Bio: Zhijian Liu is a Ph.D. candidate at MIT, advised by Song Han. His research focuses on efficient machine learning. He has developed efficient ML algorithms and provided them with effective system/algorithm support. He has also contributed to accelerating computation-intensive AI applications in computer vision, natural language processing, and scientific discovery. His work has been featured as oral and spotlight presentations at conferences such as NeurIPS, ICLR, and CVPR. He was selected as the recipient of the Qualcomm Innovation Fellowship and the NVIDIA Graduate Fellowship. He was also recognized as a Rising Star in ML and Systems by MLCommons and a Rising Star in Data Science by UChicago and UCSD. Previously, he was the founding research scientist at OmniML, which was acquired by NVIDIA.

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Princeton AI Alignment and Safety Seminar (PASS): Tuesday, March 19 at 2pm
by Emily C. Lawrence 18 Mar '24

18 Mar '24

Princeton AI Alignment and Safety Seminar (PASS) Catastrophic misalignment of large language models [ https://paulfchristiano.com/ | Paul Christiano, Alignment Research Center ] Tuesday, March 19 2:00 - 3:00 pm [ https://bit.ly/3IHlz4h | View the livestream ] Abstract: I’ll discuss two possible paths by which AI systems could be so misaligned that they attempt to deceive and disempower their human operators. I’ll review the current state of evidence about these risks, what we might hope to learn over the next few years, and how we could become confident that the risk is adequately managed. Bio: I run the [ http://alignmentresearchcenter.org/ | Alignment Research Center ] . I previously ran the language model alignment team at [ https://openai.com/ | OpenAI ] , and before that received my PhD from the [ http://theory.cs.berkeley.edu/ | theory group ] at UC Berkeley. You may be interested in my [ https://ai-alignment.com/ | writing about alignment ] , my [ http://sideways-view.com/ | blog ] , my [ https://scholar.google.com/citations?hl=en&user=B7oP0bIAAAAJ&view_op=list_w… | academic publications ] , or [ https://paulfchristiano.com/fun-and-games/ | fun and games ] . I am an advisor and board member at [ https://metr.org/ | METR ] , an external advisor to the [ https://www.gov.uk/government/publications/ai-safety-institute-overview/int… | UK AI Safety Institute ] , and a trustee of [ https://www.anthropic.com/index/anthropics-responsible-scaling-policy | Anthropic’s Long-Term Benefit Trust ] . Stay informed and receive seminar reminders by joining our mailing list: [ https://tinyurl.com/pass-mailing | https://tinyurl.com/pass-mailing ] Organized by: [ https://pli.princeton.edu/ | Princeton Language and Intelligence ]

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CITP Lecture: Emily Tseng, Wednesday, March 20 at 12:30pm
by Emily C. Lawrence 15 Mar '24

15 Mar '24

CITP Lecture Emily Tseng '14, Cornell University Wednesday, March 20 12:30 p.m. 105 Computer Science [ https://citp.princeton.edu/event/tseng/ | https://citp.princeton.edu/event/tseng/ ] Title: Digital Safety and Security for Survivors of Technology-Mediated Harms Abstract: Platforms, devices, and algorithms are increasingly weaponized to control and harass the most vulnerable among us. Some of these harms occur at the individual and interpersonal level: for example, abusers in intimate partner violence (IPV) use smartphones and social media to surveil and stalk their victims. Others are more subtle, at the level of social structure: for example, in organizations, workplace technologies can inadvertently scaffold exploitative labor practices. This talk will discuss research (1) investigating these harms via online measurement studies, (2) building interventions to directly assist survivors with their security and privacy; and (3) instrumenting these interventions as observatories, to enable scientific research into new types of harms as attackers and technologies evolve. The talk will close by sharing a vision for centering inclusion and equity in digital safety, security and privacy, towards brighter technological futures for us all. Bio: Emily Tseng is a Ph.D. candidate in information science at Cornell University. Her research develops the systems, interventions, and design principles we need to make digital technology safe and affirming for everyone. Tseng’s work has been published at top-tier venues in human-computer interaction (ACM CHI, CSCW) and computer security and privacy (USENIX Security, IEEE Oakland). For five years, she has worked as a researcher-practitioner with the Clinic to End Tech Abuse, where her work has enabled specialized security services for over 500 survivors of intimate partner violence (IPV). She is the recipient of a Microsoft Research Ph.D. Fellowship, Rising Stars in EECS, Best Paper Awards at CHI, CSCW, and USENIX Security, and third place in the Internet Defense Prize. She previously interned with the Social Media Collective at Microsoft Research, and holds a bachelor’s degree from Princeton University. This talk is open to the public. If you need an accommodation for a disability please contact Jean Butcher at butcher(a)princeton.edu at least one week before the event.

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CS and ECE SEMINAR 3/20/2024 - Max Simchowitz, MIT
by Emily C. Lawrence 14 Mar '24

14 Mar '24

ECE/CS Colloquium Speaker Speaker: Max Simchowitz, Massachusetts Institute of Technology Date: Wednesday, March 20 Time: 12:30pm EST Location: B205 E-Quad Host: Elad Hazan, Chi Jin Event page: https://ece.princeton.edu/events/mathematical-foundations-physical-agents Title: Mathematical Foundations for Physical Agents Abstract: From robotics to autonomous vehicles, machine learning agents deployed in the physical world (“physical agents”) promise to revolutionize endeavors ranging from manufacturing to agriculture to domestic labor. In this talk, we will develop mathematical foundations, from the ground up, for how to carry out this vision. We will begin our investigation by examining linear dynamical systems, a simple and fundamental model of the interaction between a physical agent and its environment. We prove mathematically that simple exploration attains optimal performance for some of both the simplest and the most complex learning problems in this class. The above finding, while powerful, strongly motivates moving past linear dynamics as a mathematical testbed for understanding learning with physical agents. Hence, we turn to providing mathematical guarantees for a setting of real-world importance that does not fit the linear mold: behavior cloning. Behavior cloning — teaching a robot to imitate from example demonstrations — lies at the heart of many of today’s most promising robot learning endeavors due to its intuitive data collection and simplicity. Though it can work incredibly well, we still do not have a clear understanding of what circumstances ensure its success. Bringing together the flexibility of generative models with key intuitions arising from the study of linear control, we introduce a framework for behavior cloning that enables an agent to imitate nearly arbitrary behavior with provable guarantees, even when the dynamics governing the agent and environments interaction are nonlinear. We conclude by outlining ongoing work and future steps towards building out the mathematical and conceptual tooling for understanding the next steps towards general, capable and flexible physical agents. Bio: Max Simchowitz is a postdoctoral researcher in the Robot Locomotion Group at MIT CSAIL. He studies the theoretical foundations of machine learning problems with a sequential or dynamical component; he currently focuses on robotics and out-of-distribution learning, and with past work ranging broadly across control, reinforcement learning, optimization and algorithmic fairness. He received his PhD from University of California, Berkeley in 2021 under Ben Recht and Michael I. Jordan, and his work has been recognized with an ICML 2018 Best Paper Award, ICML 2022 Outstanding Paper Award, and RSS 2023 Best Paper Finalist designation.

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Princeton Robotics Seminar: Marin Kobilarov, Friday Mar 8 at 11am
by Emily C. Lawrence 05 Mar '24

05 Mar '24

Speaker: [ https://asco.lcsr.jhu.edu/ | Marin Kobilarov ] , JHU Date: Friday, March 8th, 2024 Time: 11am ET Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: High-confidence Robot Motion Planning under Uncertainty Abstract: This talk will provide an overview of research activities at the ASCO lab, currently including robot-assisted surgical micro-manipulation and navigation of autonomous vehicles (aerial, underwater, or ground) under state and perception uncertainty. We will specifically focus on motion planning with built-in robustness guarantees, i.e. by aiming to certify expected performance before actual deployment. The core idea is to employ probably-approximately-correct (PAC) bounds on performance which are used as an objective function in control policy optimization. Such robust policies could then provide high-confidence performance guarantees, such as “with 99% chance the robot will reach its goal, while avoiding collisions with 99.9% chance”, and result in improved safety and reliability. Bio: Marin Kobilarov is an Associate Professor at the Johns Hopkins University and a Principal Engineer at Zoox/Amazon. At JHU he leads the Autonomous Systems, Control and Optimization (ASCO) lab which develops algorithms and software for planning, learning, and control of autonomous robotic systems. Their focus is on computational theory at the intersection of planning and learning, and on the system integration and deployment of robots that can operate safely and efficiently in challenging environments. PhD students & faculty: If you are interested in meeting with Marin on 3/8, please reach out to [ mailto:nsimon@princeton.edu | nsimon(a)princeton.edu ] . PhD students: If you are interested in joining the speaker for lunch on 3/8 from 12:15 - 1:30 PM, please [ https://forms.gle/r6F5hMMUdEHawor87 | fill out this form ] .

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Ksenia Sokolova will present her FPO "Deep Learning for Sequence-Based Gene Expression Prediction" on Monday, March 18, 2024 at 10:30 AM in the 252 Nassau Street Conference room.
by gradinfo＠cs.princeton.edu 04 Mar '24

04 Mar '24

Ksenia Sokolova will present her FPO "Deep Learning for Sequence-Based Gene Expression Prediction" on Monday, March 18, 2024 at 10:30 AM in the 252 Nassau Street Conference room. Location: 252 Nassau Street Conference room The members of Ksenia's committee are as follows: Examiners: Olga Troyanskaya (Adviser), Kai Li, Ellen Zhong Readers: Mona Singh, Yuri Pritykin Everyone is invited to attend her talk. Abstract follows below: Human biology is rooted in highly specialized cell types programmed by a common genome, 98% of which is outside of genes. While genetic variation in the enormous noncoding space is linked to the majority of disease risk, the impact of this variation is poorly understood. The recent advances in sequencing technology made it possible to perform whole genome sequencing of the large cohorts, uncovering many variants per individual. A crucial challenge is to understand the collective impact of these variants on gene expression across varied human cell types and their subsequent roles in disease progression. This dissertation begins by tackling the challenge of associating noncoding genetic variants with changes in gene expression in primary human cell types. We introduce ExPectoSC, an atlas of modular deep-learning-based models for predicting cell-type-specific gene expression directly from sequence. With models spanning 105 primary human cell types across seven organ systems, it offers a detailed insight into the effect of variation. The resulting atlas of sequence-based gene expression and variant effects is publicly available in a user-friendly interface and readily extensible to any primary cell types. We follow this work with an example application of the ExpectoSC to the study of glomerular diseases, a major cause of end stage renal disease in the US. Despite having similar clinical presentations, these diseases are known for their heterogeneity and variable patient outcomes. By integrating whole-genome sequencing data with ExPectoSC's predictions, we construct comprehensive gene expression disruption profiles for patients. 4 Finally, we developed a new method for genomic-centered contrastive pre-training, called cGen, to improve training of the models from sequence alone in limited-data contexts. Utilizing sequence augmentations, after pre-training cGen generates unsupervised embeddings that highlight functional clusters and are informative of gene expression in the absence of any labeled information. Together, these contributions highlight the power of computational approaches to decode the noncoding genome, offering new avenues for the diagnosis, prognosis, and treatment of human diseases.

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