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CS Colloquium speaker Rowan Zellers: TODAY at 12:30pm
by Emily C. Lawrence 28 Mar '22

28 Mar '22

Speaker: Rowan Zellers, University of Washington Date: Monday, March 28, 2022 Time: 12:30pm EST Location: CS 105 Host: Danqi Chen Event page: https://www.cs.princeton.edu/events/26178 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Grounding Language by Seeing, Hearing, and Interacting Abstract: As humans, our understanding of language is grounded in a rich mental model about “how the world works” – that we learn through perception and interaction. We use this understanding to reason beyond what we literally observe or read, imagining how situations might unfold in the world. Machines today struggle at this kind of reasoning, which limits how they can communicate with humans. In my talk, I will discuss three lines of work to bridge this gap between machines and humans. I will first discuss how we might measure grounded understanding. I will introduce a suite of approaches for constructing benchmarks, using machines in the loop to filter out spurious biases. Next, I will introduce PIGLeT: a model that learns physical commonsense understanding by interacting with the world through simulation, using this knowledge to ground language. From an English-language description of an event, PIGLeT can anticipate how the world state might change – outperforming text-only models that are orders of magnitude larger. Finally, I will introduce MERLOT, which learns about situations in the world by watching millions of YouTube videos with transcribed speech. Through training objectives inspired by the developmental psychology idea of multimodal reentry, MERLOT learns to jointly reason over language, vision, and sound. Together, these directions suggest a path forward for building machines that learn language rooted in the world. Bio: Rowan Zellers is a final year PhD candidate at the University of Washington in Computer Science & Engineering, advised by Yejin Choi and Ali Farhadi. His research focuses on enabling machines to understand language, vision, sound, and the world beyond these modalities. He has been recognized through an NSF Graduate Fellowship and a NeurIPS 2021 outstanding paper award. His work has appeared in several media outlets, including Wired, the Washington Post, and the New York Times. In the past, he graduated from Harvey Mudd College with a B.S. in Computer Science & Mathematics, and has interned at the Allen Institute for AI.

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CS Colloquium Series: week of March 21-25
by Emily C. Lawrence 25 Mar '22

25 Mar '22

Here is the full list of CS Colloquium talks for next week. All talks will be recorded. ~~~~~ Speaker: Xinyun Chen, University of California, Berkeley Date: Monday, March 21, 2022 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: [ https://www.cs.princeton.edu/events/26175 | https://www.cs.princeton.edu/events/26175 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: Learning-Based Program Synthesis: Learning for Program Synthesis and Program Synthesis for Learning Abstract: With the advancement of modern technologies, programming becomes ubiquitous not only among professional software developers, but also for general computer users. However, gaining programming expertise is time-consuming and challenging. Therefore, program synthesis has many applications, where the computer automatically synthesizes programs from specifications such as natural language descriptions and input-output examples. In this talk, I will present my work on learning-based program synthesis, where I have developed deep learning techniques for various program synthesis problems. Despite the remarkable success of deep neural networks for many domains, including natural language processing and computer vision, existing deep neural networks are still insufficient for handling challenging symbolic reasoning and generalization problems. My learning-based program synthesis research lies in two folds: (1) learning to synthesize programs from potentially ambiguous and complex specifications; and (2) neural-symbolic learning for language understanding. I will first talk about program synthesis applications, where my work demonstrates the applicability of learning-based program synthesizers for production usage. I will then present my work on neural-symbolic frameworks that integrate symbolic components into neural networks, which achieve better reasoning and generalization capabilities. In closing, I will discuss the challenges and opportunities of further improving the complexity and generalizability of learning-based program synthesis for future work. Bio: Xinyun Chen is a Ph.D. candidate at UC Berkeley, working with Prof. Dawn Song. Her research lies at the intersection of deep learning, programming languages, and security. Her recent research focuses on learning-based program synthesis and adversarial machine learning. She received the Facebook Fellowship in 2020, and Rising Stars in Machine Learning in 2021. Her work SpreadsheetCoder for spreadsheet formula prediction was integrated into Google Sheets, and she was part of the AlphaCode team when she interned at DeepMind. ~~~~~ Speaker: Sherry Tongshuang Wu, University of Washington Date: Tuesday, March 22, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Andrés Monroy-Hernández & Adam Finkelstein Event page: [ https://www.cs.princeton.edu/events/26176 | https://www.cs.princeton.edu/events/26176 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: Interactive AI Model Debugging and Correction Abstract: Research in Artificial Intelligence (AI) has advanced at an incredible pace, to the point where it is making its way into our everyday lives, explicitly and behind the scenes. However, beneath their impressive progress, many AI models hide deficiencies that amplify social biases or even cause fatal accidents. How do we identify, improve, and cope with imperfect models, while still benefiting from their use? I will discuss my work empowering humans to interact with AI models in order to debug and correct them. I will describe both (1) how I help experts run scalable and testable analyses on models in development, and (2) how I help end users collaborate with deployed AI in a transparent and controllable way. In my final remarks, I will discuss my future research perspectives on building human-centered AI through data-centric approaches. Bio: Sherry Tongshuang Wu is a final year Ph.D. Candidate in Computer Science & Engineering at the University of Washington, advised by Jeffrey Heer and Dan Weld. She received her B.Eng in CSE from the Hong Kong University of Science and Technology. Her research lies at the intersection of Human-Computer Interaction (HCI) and Natural Language Processing (NLP), and aims to empower humans to debug and correct AI models interactively, both when the model is under active development, and after it is deployed for end users. Sherry has authored 19 papers in top-tier NLP, HCI and Visualization conferences and journals such as ACL, CHI, TOCHI, TVCG, etc, including a best paper award (top-1) and an honorable mention (top-3). You can find out more about her at https://homes.cs.washington.edu/~wtshuang/.

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Zoe Ashwood will present her Pre FPO, "Probabilistic models for characterizing animal learning and decision-making" on Friday, April 1, 2022 at 9am via Zoom.
by Nicki Mahler 24 Mar '22

24 Mar '22

Zoe Ashwood will present her Pre FPO, "Probabilistic models for characterizing animal learning and decision-making" on Friday, April 1, 2022 at 9am via Zoom. Zoom link: [ https://princeton.zoom.us/j/98847197070?pwd=U3ZiMFZDbnlYQ1IrYmZkRjQ0VHR6dz09 | https://princeton.zoom.us/j/98847197070?pwd=U3ZiMFZDbnlYQ1IrYmZkRjQ0VHR6dz09 ] The members of Zoe's committee are as follows: Readers: Alex Pouget (University of Geneva), Barbara Engelhardt Non-readers: Jonathan Pillow (advisor), Ryan Adams, Tom Griffiths Abstract: A central goal in neuroscience is to identify the strategies used by animals and humans as they make decisions, and to characterize the learning rules that shape these policies in the first place. In this talk, I will first discuss recent modeling work that revealed that — contrary to common wisdom — mice and humans use multiple strategies over the course of a single session to perform perceptual decision-making tasks. Our modeling framework, based on hidden Markov models with Bernoulli Generalized Linear Model observations, allows us to characterize the nature of these policies and to label the trials associated with each strategy. Next, I will discuss work that sought to uncover the learning rules used by mice and rats as they learned to perform this type of task. Our model tracked trial-to-trial changes in the animals’ choice policies, and separated these changes into components explainable by a reinforcement learning rule, and components that remained unexplained. Whereas the average contribution of the conventional REINFORCE learning rule to the policy update for mice learning a common task was just 30%, we found that adding baseline parameters allowed the learning rule to explain 92% of the animals' policy updates under our model. Finally, I will discuss work in progress that applies inverse reinforcement learning to the trajectories of mice exploring a maze environment. Unlike artificial agents, mice are very good at exploring novel environments, and in this work, we will seek to uncover the extrinsic and intrinsic reward functions that power such exploration.

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ECE SEMINAR: Maria Bauza Villalonga, MIT, TODAY at 4:30pm
by Emily C. Lawrence 23 Mar '22

23 Mar '22

ECE SEMINAR Speaker: Maria Bauza Villalonga, MIT Title: Learning to interact with the world: when generality meets precision Day: March 23, 2022 Time: 4:30 pm Location: B205 Engineering Quadrangle Host: Jaime Fernández Fisac Abstract: Robotics stands as one of the most impactful and promising endeavors of our times. Learning to interact with the world is fundamental for solving some of our most pressing societal challenges: from taking care of our aging population and aiding with labor-intensive jobs to assisting in climate-related disasters and rescue emergencies. In this talk, I will argue that such a level of autonomy and performance requires robots that can excel across diverse settings while remaining accurate and reliable. My talk will focus on how we can develop learning algorithms that foster robotic generalization while ensuring the desired task performance. First, I will present a learning approach to pose estimation for novel objects based on visuo-tactile sensing that doesn't rely on real data and results in accurate pose distributions. Then, I will demonstrate how this approach enables precise robotic pick-and-place using task-aware grasping. The robotic system reasons over the models for grasping, planning, and perception in order to optimize its actions based only on simulated data. In real experiments, we demonstrate that our approach learned purely in simulation, allows robots to successfully manipulate new objects and perform highly accurate placements. Bio: Maria Bauza Villalonga is a PhD student in Robotics at the Massachusetts Institute of Technology, working with Professor Alberto Rodriguez. Before that, she received Bachelor's degrees in Mathematics and Physics from CFIS, an excellence center at the Polytechnic University of Catalonia. Her research focuses on achieving precise robotic generalization by learning probabilistic models of the world that allow robots to reuse their skills across multiple tasks and environments. Maria has received several fellowships, including Facebook, NVIDIA, and LaCaixa fellowships. Her research has obtained awards such as Best Paper Finalist in Service Robotics at ICRA 2021, Best Cognitive Paper award at IROS 2018, and Best Paper award finalist at IROS 2016. She was also part of the MIT-Princeton Team participating in the Amazon Robotics Challenge, winning the stowing task in 2017 and receiving the 2018 Amazon Best Systems Paper Award in Manipulation.

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Fwd: James Baldwin Lecture (3/28) [POSTPONED]
by Elizabeth Wang 22 Mar '22

22 Mar '22

Greetings, Please see below that the James Baldwin Lecture will be postponed. It has not yet been rescheduled. Best, Elizabeth From: "Anthony K Gibbons Jr." <akg2(a)princeton.edu> To: "Elizabeth Wang" <ew0001(a)cs.princeton.edu> Sent: Tuesday, March 22, 2022 10:17:18 AM Subject: James Baldwin Lecture (3/28) [POSTPONED] Hello Elizabeth , It is with the deepest regret that we announce this year’s [ https://aas.princeton.edu/events/2022/james-baldwin-lecture-series-limits-q… | James Baldwin Lecture ] will be postponed for AY23. We extend our sincere apologies for any inconvenience this may cause, and will update you as soon as we can reschedule. Thank you for your continued support of African American Studies. Thank You, Anthony Gibbons Jr. Communications & Media Specialist Department of African American Studies Princeton University 003 Morrison Hall, Princeton, NJ 08544 [ https://aas.princeton.edu/ | aas.princteon.edu ]

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CS Colloquium speaker Sherry Wu: TODAY at 12:30pm
by Emily C. Lawrence 22 Mar '22

22 Mar '22

CS Colloquium Speaker Speaker: Sherry Tongshuang Wu, University of Washington Date: Tuesday, March 22, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Andrés Monroy-Hernández & Adam Finkelstein Event page: [ https://www.cs.princeton.edu/events/26176 | https://www.cs.princeton.edu/events/26176 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: Interactive AI Model Debugging and Correction Abstract: Research in Artificial Intelligence (AI) has advanced at an incredible pace, to the point where it is making its way into our everyday lives, explicitly and behind the scenes. However, beneath their impressive progress, many AI models hide deficiencies that amplify social biases or even cause fatal accidents. How do we identify, improve, and cope with imperfect models, while still benefiting from their use? I will discuss my work empowering humans to interact with AI models in order to debug and correct them. I will describe both (1) how I help experts run scalable and testable analyses on models in development, and (2) how I help end users collaborate with deployed AI in a transparent and controllable way. In my final remarks, I will discuss my future research perspectives on building human-centered AI through data-centric approaches. Bio: Sherry Tongshuang Wu is a final year Ph.D. Candidate in Computer Science & Engineering at the University of Washington, advised by Jeffrey Heer and Dan Weld. She received her B.Eng in CSE from the Hong Kong University of Science and Technology. Her research lies at the intersection of Human-Computer Interaction (HCI) and Natural Language Processing (NLP), and aims to empower humans to debug and correct AI models interactively, both when the model is under active development, and after it is deployed for end users. Sherry has authored 19 papers in top-tier NLP, HCI and Visualization conferences and journals such as ACL, CHI, TOCHI, TVCG, etc, including a best paper award (top-1) and an honorable mention (top-3). You can find out more about her at [ https://homes.cs.washington.edu/~wtshuang/ | https://homes.cs.washington.edu/~wtshuang/ ] .

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ECE SEMINAR: Shuwen Deng, Yale University TODAY at 4:30pm
by Emily C. Lawrence 21 Mar '22

21 Mar '22

ECE SEMINAR Speaker: Shuwen Deng, Yale University Title: Processor Microarchitecture Security Day: March 21, 2022 Time: 4:30pm Location: B205 Engineering Quadrangle Host: David Wentzlaff Abstract: As computer systems grow more and more complicated, various performance optimizations can unintentionally introduce security vulnerabilities in these systems. The vulnerabilities can lead to user information and data being compromised or stolen. Many of the computer processor optimizations often focus on sharing or re-using the processor hardware between different users or programs. This can lead to different types of timing-based security attacks where the sharing or re-using of hardware components influences the timing of the operations performed on the processor. Different timing can be exploited by malicious users to gain information about operations or data of the co-located victim program or user. To address the security threats, this talk will present research on developing a theoretical model for cache and TLB timing attacks to help evaluate the susceptibility of different architectures and microarchitectures to timing attacks on caches and TLBs. Further, it will present a benchmark suite built upon the model to test if processor caches, or secure cache designs, are vulnerable to the theoretical vulnerabilities found using the theoretical model. The talk will also present defenses for the TLBs which were developed to address the newly found vulnerabilities. Going beyond caches and TLBs, the presentation will also discuss research on, and evaluation of, timing and power-based attacks in the processor frontend. Bio: Shuwen Deng is a final-year Ph.D. candidate at Yale University, advised by Prof. Jakub Szefer. Her research interests span computer architecture and security. Specifically, she focuses her research on developing secure processor microarchitectures by proposing side-channel vulnerability checking schemes and combining the theoretical modeling with practical benchmarking analysis. She also researches new microarchitectural vulnerabilities, including those of value predictors and processor frontend. She is the recipient of the 2020 Google PhD Fellowship in Privacy and Security, and an Elihu Elias Dickerman Fellowship. She has been named a Rising Star in EECS by the University of California Berkeley. Her work has been selected as Top Picks 2021 in Hardware and Embedded Security.

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CS Colloquium speaker Xinyun Chen: TODAY at 12:30pm
by Emily C. Lawrence 21 Mar '22

21 Mar '22

CS Colloquium Speaker Speaker: Xinyun Chen, University of California, Berkeley Date: Monday, March 21, 2022 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: https://www.cs.princeton.edu/events/26175 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Learning-Based Program Synthesis: Learning for Program Synthesis and Program Synthesis for Learning Abstract: With the advancement of modern technologies, programming becomes ubiquitous not only among professional software developers, but also for general computer users. However, gaining programming expertise is time-consuming and challenging. Therefore, program synthesis has many applications, where the computer automatically synthesizes programs from specifications such as natural language descriptions and input-output examples. In this talk, I will present my work on learning-based program synthesis, where I have developed deep learning techniques for various program synthesis problems. Despite the remarkable success of deep neural networks for many domains, including natural language processing and computer vision, existing deep neural networks are still insufficient for handling challenging symbolic reasoning and generalization problems. My learning-based program synthesis research lies in two folds: (1) learning to synthesize programs from potentially ambiguous and complex specifications; and (2) neural-symbolic learning for language understanding. I will first talk about program synthesis applications, where my work demonstrates the applicability of learning-based program synthesizers for production usage. I will then present my work on neural-symbolic frameworks that integrate symbolic components into neural networks, which achieve better reasoning and generalization capabilities. In closing, I will discuss the challenges and opportunities of further improving the complexity and generalizability of learning-based program synthesis for future work. Bio: Xinyun Chen is a Ph.D. candidate at UC Berkeley, working with Prof. Dawn Song. Her research lies at the intersection of deep learning, programming languages, and security. Her recent research focuses on learning-based program synthesis and adversarial machine learning. She received the Facebook Fellowship in 2020, and Rising Stars in Machine Learning in 2021. Her work SpreadsheetCoder for spreadsheet formula prediction was integrated into Google Sheets, and she was part of the AlphaCode team when she interned at DeepMind.

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CS Colloquium speaker Pang Wei Koh: TODAY at 12:30pm
by Emily C. Lawrence 17 Mar '22

17 Mar '22

Speaker: Pang Wei Koh, Stanford University Date: Thursday, March 17, 2022 Time: 12:30pm EST Location: CS 105 Host: Sanjeev Arora Event page: [ https://www.cs.princeton.edu/events/26174 | https://www.cs.princeton.edu/events/26174 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: Reliable machine learning in the wild Abstract: Machine learning systems are widely deployed today, but they are unreliable. They can fail – and with catastrophic consequences – on subpopulations of the data, such as particular demographic groups, or when deployed in different environments from what they were trained on. In this talk, I will describe our work towards building reliable machine learning systems that are robust to these failures. First, I will show how we can use influence functions to understand the predictions and failures of existing models through the lens of their training data. Second, I will discuss the use of distributionally robust optimization to train models that perform well across all subpopulations. Third, I will describe WILDS – a benchmark of in-the-wild distribution shifts spanning applications such as pathology, conservation, remote sensing, and drug discovery – and show how current state-of-the-art methods, which perform well on synthetic distribution shifts, still fail to be robust on these real-world shifts. Finally, I will describe our work on building more reliable COVID-19 models, using anonymized cellphone mobility data, to inform public health policy; this is a challenging application as the underlying environment is often changing and there is substantial heterogeneity across demographic subpopulations. Bio: Pang Wei Koh is a PhD student at Stanford, advised by Percy Liang. He studies the theory and practice of building reliable machine learning systems. His research has been published in Nature and Cell, featured in media outlets such as The New York Times and The Washington Post, and recognized by best paper awards at ICML and KDD, a Meta Research PhD fellowship, and the Kennedy Prize for best honors thesis at Stanford. Prior to his PhD, he was the 3rd employee and Director of Partnerships at Coursera.

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Danqi Liao will present her MSE talk "Selective Feature Aggregation for Semi-supervised Temporal Action Localization" on March 22, 2022 at 11:30am in Friend 125 and Zoom.
by Nicki Mahler 16 Mar '22

16 Mar '22

Danqi Liao will present her MSE talk "Selective Feature Aggregation for Semi-supervised Temporal Action Localization" on March 22, 2022 at 11:30am in Friend 125 and Zoom. Zoom link: [ https://princeton.zoom.us/j/3846231080 | https://princeton.zoom.us/j/3846231080 ] The members of her committee are as follows: Olga Russakovsky (adviser) and Jia Deng (reader) All are welcome to attend. Abstract: Action detection in untrimmed video has been a long standing goal in computer vision. Recently, single-frame annotation has emerged as a promising direction that bridges the gap between the video-level weak-supervision and costly full supervision. In this talk, I will be discussing Selective Feature Aggregation module that selectively aggregates frame features. The module utilizes temporal contextual information, leading to performance boosts in single frame-supervised temporal action localization without complex heuristic-based system designs.

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