March 2022 - talks - lists.cs.princeton.edu

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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Ankit Goyal will present his Pre FPO on Wednesday, March 16 at 11am on Zoom.
by Louis W. Riehl 09 Mar '22

09 Mar '22

Ankit Goyal will present his Pre FPO on Wednesday, March 16 at 11am on Zoom. Zoom link: [ https://princeton.zoom.us/j/95476811864 | 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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Alejandro Newell will present his FPO "Learning to Solve Structured Vision Problems" on Tuesday, March 15, 2022 at 10 AM at Friend 125 and on Zoom (https://princeton.zoom.us/j/7228079201)
by Grad Info 08 Mar '22

08 Mar '22

Alejandro Newell will present his FPO "Learning to Solve Structured Vision Problems" on Tuesday, March 15, 2022 at 10AM at Friend 125 and on Zoom (https://princeton.zoom.us/j/7228079201). Location: Friend 125 & https://princeton.zoom.us/j/7228079201 The members of Alejandro's committee are as follows: Examiners: Jia Deng (advisor), Adam Finkelstein, Felix Heide Readers: Olga Russakovsky, Szymon Rusinkiewicz A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu <mailto:gradinfo@cs.princeton.edu> if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: We want computer vision models to understand the rich world captured in images and video. This requires not just recognizing objects, but identifying their relationships and interactions. Combining contributions in both neural architecture and loss design, we expand the capacity of convolutional networks to express such interactions and solve a broad range of structured computer vision tasks. We first introduce a convolutional network architecture for dense per-pixel prediction. We show how intermediate supervision and repeated processing across feature scales lead to better network performance, referring to the architecture as a “stacked hourglass” network based on the successive steps of pooling and up sampling during inference. We benchmark on the task of human pose estimation achieving state-of-the-art performance. Next, we introduce associative embedding, a method for supervising networks to solve detection and grouping tasks. A number of problems can be framed in this manner including multi-person pose estimation, instance segmentation, and multi-object tracking. Usually these problems are addressed with multi-stage pipelines, instead we train a model to simultaneously output detections and group assignments. We can then extend the use of associative embeddings to define arbitrary graphs. We demonstrate how to supervise embeddings such that a model both detects the objects in a scene and defines semantic relationships between pairs of objects. Finally, we perform an investigation of self-supervision methods. Recent self-supervised losses rely on a similar learning signal to the loss we leverage in our as-sociative embedding work. But it is unclear how useful these losses are for general purpose visual feature pretraining. We investigate what factors play a role in the utility of such pretraining by evaluating self-supervised algorithms across a comprehensive array of synthetic datasets and downstream tasks. Our experiments highlight how self-supervision can be more or less useful depending on the amount of labeled data, the complexity of the data, and the target downstream task. Together the work in this thesis shows how to build and train better models while providing insights into what steps lead to the best performance across a wide variety of computer vision tasks.

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ECE SEMINAR Christopher Torng, Stanford University 3/7/2022
by Emily C. Lawrence 03 Mar '22

03 Mar '22

ECE SEMINAR Speaker: Christopher Torng, Stanford University Title: Accelerating Chip-Building Design Cycles for Future Generations of Computing Day: March 7, 2022 Time: 4:30 pm Zoom Link: Please sign up at [ https://princeton.zoom.us/meeting/register/tJUtcuuqrTwvHNWG3rFwtpcYlfsC8LtS… | https://princeton.zoom.us/meeting/register/tJUtcuuqrTwvHNWG3rFwtpcYlfsC8LtS… ] Host: David Wentzlaff Abstract: The chip building industry is a major cornerstone of the global economy. As a result, addressing the causes behind a multi-year global chip shortage is important for both near and long term futures. Unfortunately, one major challenge is that it is difficult to produce high-quality designs quickly and at low cost using traditional hardware design flows. This means that the industry wastes valuable fabrication slots learning painful design lessons rather than meeting economic demands. My research focuses on building new architectures, systems, and design tools to accelerate chip building design cycles for future generations of computing systems. To support this goal, my research spans across the computing stack, ranging from applications, compilers, architectures, and down to chip implementation. In this talk, I will first present a set of vertically integrated techniques (compiler, architecture, and VLSI) that significantly reduces the design effort for extremely fine-grain power control in spatial architectures. Next, I will introduce my work on a new generation of open and agile hardware flow tools that leverage modern programming language features to increase code reuse in physical design. Finally, I will discuss recent work on Amber SoC, a coarse-grained reconfigurable array designed with an end-to-end agile accelerator-compiler co-designed flow. I will conclude with my future directions in supporting chip building for the next generation of computing. Bio: Christopher Torng is a postdoctoral researcher at Stanford University. He received his Ph.D. degree, M.S. degree, and B.S degree (2019, 2016, 2012) in Electrical and Computer Engineering from Cornell University. His projects target the development of architectures and tools to accelerate building chips and complex hardware systems. His tools have achieved use across multiple universities to support over ten academic tapeouts in technologies ranging from 180nm to 16nm. His activities have resulted in a selection as a Rising Star in Computer Architecture (2018) by Georgia Tech and an IEEE MICRO Top Pick from Hot Chips (2018).

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