- talks - lists.cs.princeton.edu

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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CS Colloquium speaker Gilbert Bernstein: TODAY at 12:30pm
by Emily C. Lawrence 15 Mar '22

15 Mar '22

Speaker: Gilbert Bernstein, University of California, Berkeley and MIT Date: Tuesday, March 15, 2022 Time: 12:30pm EST Location: CS 105 Host: Felix Heide Event page: [ https://www.cs.princeton.edu/events/26172 | https://www.cs.princeton.edu/events/26172 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: High-Performance Languages for Visual Computing Abstract: Computer Graphics and Visual Computing problems challenge us with a near inexhaustible demand for more resolution and scale in order to simulate the climate, reconstruct 3d environments, train neural networks, and produce games & movies. Building such applications requires integrating disciplinary expertise (e.g. physics, numerical methods, geometry and custom hardware) into a single system. However, abstraction barriers are regularly discarded in the name of higher-performance, leading to code that must be written and maintained by super-experts—programmers who simultaneously possess deep knowledge of all relevant disciplines. Programming languages, especially Domain Specific Languages (DSLs) are perhaps the most promising approach to recovering a separation of concerns in such high-performance systems. In this talk, I will first describe my work on DSLs to enable parallel portability of physical simulation and optimization programs, including the use of relational algebra and automatic differentiation to structure these problem domains. Then I will discuss more recent work on “horizontal DSLs” designed to dig into specific sub-problems: maximizing utilization of novel hardware accelerators, formally verifying optimizations of tensor programs, and extending automatic differentiation to correctly handle discontinuities in inverse rendering and simulation problems. Bio: Gilbert Bernstein is a Postdoctoral Scholar at the University of California, Berkeley and MIT CSAIL, working with Professor Jonathan Ragan-Kelley. His research lies in Computer Graphics and Programming Languages, especially the design of high-performance domain specific languages for numeric computing applications such as physical simulation, optimization and inverse problems. His work spans the gamut from user interfaces, to differentiable programming, parallel-portability, and new hardware design languages. His work has been published at SIGGRAPH, POPL, PLDI, & OOPSLA, as well as being incorporated into products at Adobe, Autodesk, and Disney. He holds a Ph.D. in Computer Science from Stanford University, where he was advised by Pat Hanrahan.

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CS Colloquium speaker: Kostis Kaffes TODAY at 12:30pm
by Emily C. Lawrence 14 Mar '22

14 Mar '22

Speaker: Kostis Kaffes, Stanford University Date: Monday, March 14, 2022 Time: 12:30pm EST Location: Zoom Webinar Host: Ravi Netravali Event page: [ https://www.cs.princeton.edu/events/26171 | https://www.cs.princeton.edu/events/26171 ] Please register here: [ https://princeton.zoom.us/webinar/register/WN_Nn4iOb-mRqSEJbGsbzg5uQ | https://princeton.zoom.us/webinar/register/WN_Nn4iOb-mRqSEJbGsbzg5uQ ] Title: Solving the Cloud Efficiency Crisis with Fast and Accessible Scheduling Abstract: Operating systems (OS) specialization is necessary as the one-size-fits-all approach of fundamental OS operations such as scheduling is incompatible with today's diverse application landscape. Such specialization can improve application performance and cloud platform efficiency by an order of magnitude or more. Towards this goal, I will first discuss Shinjuku, a specialized OS that supports an order of magnitude higher load and lower tail latency than state-of-the-art systems by enabling better scheduling. Shinjuku leverages hardware support for virtualization to preempt as often as every 5 microseconds and disproves the conventional wisdom that interrupts are incompatible with microsecond timescales. Then, I will present Syrup, a framework that enables everyday application developers to specify custom scheduling policies easily and safely deploy them across different layers of the stack over existing operating systems like Linux, bringing the benefits of specialized scheduling to everyone. For example, Syrup allowed us to implement policies that previously required specialized dataplanes in less than 20 lines of code and improve the performance of an in-memory database by 8x without needing any application modification. Bio: Kostis Kaffes is a final-year Ph.D. candidate in Electrical Engineering at Stanford University, advised by Christos Kozyrakis. He is broadly interested in computer systems, cloud computing, and scheduling. His thesis focuses on end-host, rack-scale, and cluster-scale scheduling for microsecond-scale tail latency with the goal of improving efficiency in the cloud. Recently, he has been looking for ways to make it easier to implement and deploy custom scheduling policies across different layers of the stack. Kostis's research has been supported by a Facebook Research Award and various scholarships and fellowships from Stanford, A.G. Leventis Foundation, and Gerondelis Foundation. Prior to Stanford, he received his undergraduate degree in Electrical and Computer Engineering from the National Technical University of Athens in Greece.

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CS Colloquium Series: week of March 14-18
by Emily C. Lawrence 11 Mar '22

11 Mar '22

Here is the full list of CS Colloquium talks for next week. All talks will be recorded. ~~~~~ Speaker: Kostis Kaffes, Stanford University Date: Monday, March 14, 2022 Time: 12:30pm EST Location: Zoom Webinar Host: Ravi Netravali Event page: [ https://www.cs.princeton.edu/events/26171 | https://www.cs.princeton.edu/events/26171 ] Please register here: [ https://princeton.zoom.us/webinar/register/WN_Nn4iOb-mRqSEJbGsbzg5uQ | https://princeton.zoom.us/webinar/register/WN_Nn4iOb-mRqSEJbGsbzg5uQ ] Title: Solving the Cloud Efficiency Crisis with Fast and Accessible Scheduling Abstract: Operating systems (OS) specialization is necessary as the one-size-fits-all approach of fundamental OS operations such as scheduling is incompatible with today's diverse application landscape. Such specialization can improve application performance and cloud platform efficiency by an order of magnitude or more. Towards this goal, I will first discuss Shinjuku, a specialized OS that supports an order of magnitude higher load and lower tail latency than state-of-the-art systems by enabling better scheduling. Shinjuku leverages hardware support for virtualization to preempt as often as every 5 microseconds and disproves the conventional wisdom that interrupts are incompatible with microsecond timescales. Then, I will present Syrup, a framework that enables everyday application developers to specify custom scheduling policies easily and safely deploy them across different layers of the stack over existing operating systems like Linux, bringing the benefits of specialized scheduling to everyone. For example, Syrup allowed us to implement policies that previously required specialized dataplanes in less than 20 lines of code and improve the performance of an in-memory database by 8x without needing any application modification. Bio: Kostis Kaffes is a final-year Ph.D. candidate in Electrical Engineering at Stanford University, advised by Christos Kozyrakis. He is broadly interested in computer systems, cloud computing, and scheduling. His thesis focuses on end-host, rack-scale, and cluster-scale scheduling for microsecond-scale tail latency with the goal of improving efficiency in the cloud. Recently, he has been looking for ways to make it easier to implement and deploy custom scheduling policies across different layers of the stack. Kostis's research has been supported by a Facebook Research Award and various scholarships and fellowships from Stanford, A.G. Leventis Foundation, and Gerondelis Foundation. Prior to Stanford, he received his undergraduate degree in Electrical and Computer Engineering from the National Technical University of Athens in Greece. ~~~~~ Speaker: Gilbert Bernstein, University of California, Berkeley and MIT Date: Tuesday, March 15, 2022 Time: 12:30pm EST Location: CS 105 Host: Felix Heide Event page: [ https://www.cs.princeton.edu/events/26172 | https://www.cs.princeton.edu/events/26172 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: High-Performance Languages for Visual Computing Abstract: Computer Graphics and Visual Computing problems challenge us with a near inexhaustible demand for more resolution and scale in order to simulate the climate, reconstruct 3d environments, train neural networks, and produce games & movies. Building such applications requires integrating disciplinary expertise (e.g. physics, numerical methods, geometry and custom hardware) into a single system. However, abstraction barriers are regularly discarded in the name of higher-performance, leading to code that must be written and maintained by super-experts—programmers who simultaneously possess deep knowledge of all relevant disciplines. Programming languages, especially Domain Specific Languages (DSLs) are perhaps the most promising approach to recovering a separation of concerns in such high-performance systems. In this talk, I will first describe my work on DSLs to enable parallel portability of physical simulation and optimization programs, including the use of relational algebra and automatic differentiation to structure these problem domains. Then I will discuss more recent work on “horizontal DSLs” designed to dig into specific sub-problems: maximizing utilization of novel hardware accelerators, formally verifying optimizations of tensor programs, and extending automatic differentiation to correctly handle discontinuities in inverse rendering and simulation problems. Bio: Gilbert Bernstein is a Postdoctoral Scholar at the University of California, Berkeley and MIT CSAIL, working with Professor Jonathan Ragan-Kelley. His research lies in Computer Graphics and Programming Languages, especially the design of high-performance domain specific languages for numeric computing applications such as physical simulation, optimization and inverse problems. His work spans the gamut from user interfaces, to differentiable programming, parallel-portability, and new hardware design languages. His work has been published at SIGGRAPH, POPL, PLDI, & OOPSLA, as well as being incorporated into products at Adobe, Autodesk, and Disney. He holds a Ph.D. in Computer Science from Stanford University, where he was advised by Pat Hanrahan. ~~~~~ Speaker: Adam Yala, Massachusetts Institute of Technology Date: Wednesday, March 16, 2022 Time: 12:30pm EST Location: CS 105 Host: Karthik Narasimhan Event page: [ https://www.cs.princeton.edu/events/26173 | https://www.cs.princeton.edu/events/26173 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: Seeing into the future: Machine learning methods for personalized screening Abstract: While AI has the potential to transform patient care: improving outcomes, reducing costs and eliminating health disparities. However, the development of equitable clinical AI models and their translation to hospitals remains difficult. From a computational perspective, these tools must deliver consistent performance across diverse populations while learning from biased and scarce data. In this talk, I will discuss approaches addressing the above challenges in three areas: 1) cancer risk assessment from imaging, 2) personalized screening policy design and 3) private data sharing through neural obfuscation. I’ve demonstrated that these clinical models offer significant improvements over the current standard of care across globally diverse patient populations. Bio: Adam Yala is a PhD student in Electrical Engineering and Computer Science at MIT. He is a member of MIT Jameel Clinic for AI and Health and a member of CSAIL. His research focuses on developing machine learning methods for personalized medicine and translating them to clinical care. Adam's tools have been deployed at multiple health systems around the world and his research has been featured in the Washington Post, New York Times, Boston Globe and Wired. ~~~~~ 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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Ryan Amos will present his FPO "Consumer Protection on the Web with Longitudinal Web Crawls and Analysis" on March 17, 2022 at 3pm in CS 402.
by Nicki Mahler 10 Mar '22

10 Mar '22

Ryan Amos will present his FPO "Consumer Protection on the Web with Longitudinal Web Crawls and Analysis" on March 17, 2022 at 3pm in CS 402. The members of his committee are as follows: Examiners: Edward Felten (adviser), Prateek Mittal (adviser), and Arvind Narayanan; Readers: Jonathan Mayer and Joe Calandrino (Federal Trade Commission) A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract: The world wide web has brought with it new consumer protection hazards, such as deceptive reviews and online tracking. While many academics have studied consumer protection on the web at specific points in time, we approach this problem from a longitudinal perspective, exploring how consumers' rights to privacy and to be informed have been impacted by the web. Our work highlights the key role in study of consumer protection issues played by longitudinal analyses and longitudinal data collection -- data collected over repeated, time-spaced passes. We investigate consumer protection issues on the web through longitudinal studies in two landscapes: website privacy policies and reviews on Yelp. We approach both problems by collecting data with automated, repeated visits to the websites of interest to collect large scale datasets. In our study of privacy policies, we aggregate Internet Archive's crawls to perform longitudinal collection, and in our online reviews study, we crawl the data ourselves. We collected 1M privacy policies spanning 22 years and 12.5M reviews over 11 months. We used our data to study the evolution of privacy policies raising concerns with rights to privacy and information. We find gaps in disclosure of privacy-related practices. We show declining readability over the long term, doubling in length and becoming more complex. We show disparities in website-reported and independently-observed tracking. In our study of online reviews we raise concerns with the right to be informed. We present the first study of "reclassification," wherein a platform changes its filtering decision for a review. We find that reviews routinely move between Yelp's two main classifier classes ("Recommended" and "Not Recommended"), up to five reclassifications on a single review. We identify demographic disparities in review prevalence and filtering decisions. By showing phenomena that cannot be studied without longitudinal data collection and analysis, we emphasize the importance of longitudinal study for consumer protection issues online. Our work helps lay the groundwork for future work on these issues through our software and data releases, easing the pathway for future researchers.

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ECE SEMINAR Heng Yang, MIT 3/16/2022
by Emily C. Lawrence 10 Mar '22

10 Mar '22

ECE SEMINAR Speaker: Heng Yang, MIT Title: Certifiable Outlier-Robust Geometric Perception: Robots that See through the Clutter with Confidence Day: March 16, 2022 Time: 4:30pm Location: B205 Engineering Quadrangle Host: Jaime Fernández Fisac Abstract: Geometric perception is the task of estimating geometric models (e.g., object pose and 3D structure) from sensor measurements and priors (e.g., point clouds and neural network detections). Geometric perception is a fundamental building block for robotics applications ranging from intelligent transportation to space autonomy. The ubiquitous existence of outliers -measurements that tell no or little information about the models to be estimated- makes it theoretically intractable to perform estimation with guaranteed optimality. Despite this theoretical intractability, safety-critical robotics applications still demand trustworthiness and performance guarantees on perception algorithms. In this talk, I present certifiable outlier-robust geometric perception, a new paradigm to design tractable algorithms that enjoy rigorous performance guarantees, i.e., they return an optimal estimate with a certificate of optimality for a majority of problem instances, but declare failure and provide a measure of suboptimality for worst-case instances. Particularly, I present two general-purpose algorithms in the certifiable perception toolbox: (i) an estimator that uses graph theory to prune gross outliers and leverages graduated non-convexity to compute the optimal model estimate with high probability of success, and (ii) a certifier that employs sparse semidefinite programming (SDP) relaxation and a novel SDP solver to endow the estimator with an optimality certificate or escape local minima otherwise. The estimator is fast and robust against up to 99% random outliers in practical perception applications, and the certifier can compute high-accuracy optimality certificates for large-scale problems beyond the reach of existing SDP solvers. I showcase certifiable outlier-robust perception on robotics applications such as scan matching, satellite pose estimation, and vehicle pose and shape estimation. I conclude by remarking three opportunities arising from certifiable perception: to speedup online global optimization by offline learning from data; to enable safe learning-based perception by bridging certifiable estimation with deep representation learning; and to couple and unify perception with action towards trustworthy autonomy. Bio: Heng Yang is a final-year Ph.D. candidate in the Laboratory for Information & Decision Systems and the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), working with Prof. Luca Carlone. He holds a B.S. degree from Tsinghua University and an S.M. degree from MIT, both in Mechanical Engineering. His research interests include large-scale convex optimization, semidefinite relaxation, robust estimation, and machine learning, applied to robotics and trustworthy autonomy. His work includes developing certifiable outlier-robust machine perception algorithms, large-scale semidefinite programming solvers, and self-supervised geometric perception frameworks. Heng Yang is a recipient of the Best Paper Award in Robot Vision at the 2020 IEEE International Conference on Robotics and Automation (ICRA), a Best Paper Award Honorable Mention from the 2020 IEEE Robotics and Automation Letters (RA-L), and a Best Paper Award Finalist at the 2021 Robotics: Science and Systems (RSS) conference. He is a Class of 2021 RSS Pioneer.

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UPDATE: Ankit Goyal will present his Pre FPO on Wednesday, March 16 at 11am on Zoom & CS 302
by Louis Riehl 09 Mar '22

09 Mar '22

UPDATE: Ankit Goyal will present his Pre FPO on Wednesday, March 16 at 11am on Zoom and in CS 302 Zoom link: https://princeton.zoom.us/j/95476811864 Committee members: Jia Deng (examiner, adviser), Szymon Rusinkiewicz (examiner), Danqi Chen (examiner), Karthik Narasimhan (reader), Vladlen Koltun (reader, Apple) Abstract: Embodied Intelligent Agents are systems that have physical embodiment and can change the physical world to accomplish various goals. To act successfully, agents must have three capabilities. First, they must be able to perceive the 3D environment. Second, they must be able to reason about the 3D space. And third, they must be able to talk to humans about the 3D world. In this talk, I will present our efforts in building systems and benchmarks to improve embodied agents across all three capabilities

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