March 2020 - talks - lists.cs.princeton.edu

Colloquium Speaker Wenting Zheng TODAY at 12:30pm
by Emily C. Lawrence 10 Mar '20

10 Mar '20

***Due to the developing coronavirus situation, we want to reduce the number of attendees at invited talks this week. Attendance at the following talk will now be limited to "Princeton University faculty only". For other people who want to watch the talk, it will be available by live stream only, via the Princeton Broadcast Center (link: http://bc.princeton.edu/live/7/), for individual viewing; we will not be hosting a separate room for the live stream. For members of the CS department, a recording of the talk will be made available afterwards.*** CS Colloquium Speaker Wenting Zheng, University of California, Berkeley Tuesday, March 10 - 12:30pm Computer Science - Room 105 Host: Amit Levy [ https://www.cs.princeton.edu/events/25923 | https://www.cs.princeton.edu/events/25923 ] Title: Sharing without Showing: Building Secure Collaborative Systems In many domains such as finance and medicine, organizations have encountered obstacles in data acquisition because their target applications need sensitive data that reside across multiple parties. However, such data cannot be shared today due to data privacy concerns, policy regulation, and business competition. My graduate research focused on solving this problem by enabling organizations to run complex computations on the joint dataset without revealing their sensitive input to the other parties. My overall approach is to co-design systems with cryptography to build practical and functional systems that provide strong and provable security. In this talk, I will focus on two systems — Opaque and Helen — which secure SQL analytics and machine learning, respectively. My open source has been used by organizations such as IBM Research, Ericsson, Alibaba, and Microsoft. Bio: Wenting Zheng is a Ph.D. candidate at UC Berkeley, co-advised by Raluca Ada Popa and Ion Stoica . She completed her bachelor’s and master of engineering at MIT, where she was advised by Barbara Liskov . Her research interests are in computer systems, security, and applied cryptography. She is the recipient of a Berkeley Fellowship from 2014-2016, an IBM Research fellowship from 2017-2018, and was an invited participant at the 2019 EECS Rising Stars workshop.

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Colloquium Speaker Wenting Zheng tomorrow at 12:30pm - LIVESTREAM ONLY
by Emily Lawrence 09 Mar '20

09 Mar '20

***Due to the developing coronavirus situation, we want to reduce the number of attendees at invited talks this week. Attendance at the following talk will now be limited to "Princeton University faculty only". For other people who want to watch the talk, it will be available by live stream only, via the Princeton Broadcast Center (link: <http://bc.princeton.edu/live/7/> http://bc.princeton.edu/live/7/), for individual viewing; we will not be hosting a separate room for the live stream. For members of the CS department, a recording of the talk will be made available afterwards.*** Regards, Emily CS Colloquium Speaker Wenting Zheng, University of California, Berkeley Tuesday, March 10 - 12:30pm Computer Science - Room 105 Host: Amit Levy https://www.cs.princeton.edu/events/25923 Title: Sharing without Showing: Building Secure Collaborative Systems In many domains such as finance and medicine, organizations have encountered obstacles in data acquisition because their target applications need sensitive data that reside across multiple parties. However, such data cannot be shared today due to data privacy concerns, policy regulation, and business competition. My graduate research focused on solving this problem by enabling organizations to run complex computations on the joint dataset without revealing their sensitive input to the other parties. My overall approach is to co-design systems with cryptography to build practical and functional systems that provide strong and provable security. In this talk, I will focus on two systems — Opaque and Helen — which secure SQL analytics and machine learning, respectively. My open source has been used by organizations such as IBM Research, Ericsson, Alibaba, and Microsoft. Bio: Wenting Zheng is a Ph.D. candidate at UC Berkeley, co-advised by Raluca Ada Popa and Ion Stoica. She completed her bachelor’s and master of engineering at MIT, where she was advised by Barbara Liskov. Her research interests are in computer systems, security, and applied cryptography. She is the recipient of a Berkeley Fellowship from 2014-2016, an IBM Research fellowship from 2017-2018, and was an invited participant at the 2019 EECS Rising Stars workshop.

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Santiago Cuellar will present his FPO "Concurrent Permission Machine for modular proofs of optimizing compilers with shared memory concurrency" on Friday, March 13, 2020 in CS 402 at 10:30am
by Nicki Mahler 09 Mar '20

09 Mar '20

Santiago Cuellar will present his FPO "Concurrent Permission Machine for modular proofs of optimizing compilers with shared memory concurrency" on Friday, March 13, 2020 in CS 402 at 10:30am. The members of his committee are Andrew Appel (adviser); Readers: Zachary Kincaid and Gil Hur (Seoul National University); Examiners: Lennart Beringer, Aarti Gupta, and Andrew Appel. A copy of his thesis, is available upon request. Please email ngotsis(a)cs.princeton if you would like a copy of the thesis. Everyone is invited to attend his talk. The talk abstract follows below: Optimizing compilers change a program based on a formal analysis of its code, and modern processors further rearrange the program order. It is hard to reason about such transformations, which makes them a source of bugs, particularly for concurrent shared-memory programs where the order of execution is critical. On the other hand, programmers should reason about their program in the source language, which abstracts such low level details. We present the Concurrent Permission Machine (CPM), a semantic model for shared-memory concurrent programs, which is: (1) sound for higerh-order Concurrent Separation Logic, (2) convenient to reason about compiler correctness, and (3) useful for proving reduction theorems on weak memory models. The key feature of the CPM is that it exploits the fact that correct shared-memory programs are permission coherent: threads have (at any given time) noncompeting permission to access memory, and their load/store operations respect those permissions. Compilers are often written with sequential code in mind, and proving the correctness of those compilers is hard enough without concurrency. Indeed, the machinechecked proof of correctness for the CompCert C compiler was a major advance in the field. Using the CPM to conveniently distinguish sequential execution from concurrent interactions, I show how to reuse the (sequential) CompCert proof, without major changes, to guarantee a stronger concurrent-permission-aware notion of correctness

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Before the colloquium, wash your hands
by Emily Lawrence 09 Mar '20

09 Mar '20

For the duration of the coronavirus emergency, before any CS department event at which lunch is served, wash your hands before getting in line for lunch. In the serving line, you touch utensils used by many others. Washing your hands is one of the most effective ways of limiting the spread of the virus. Here's how <https://www.cdc.gov/handwashing/when-how-handwashing.html> : Follow these five steps every time. 1. Wet your hands with clean, running water (warm or cold), turn off the tap, and apply soap. 2. Lather your hands by rubbing them together with the soap. Lather the backs of your hands, between your fingers, and under your nails. 3. Scrub your hands for at least 20 seconds. Need a timer? Hum the “Happy Birthday” song from beginning to end twice. 4. Rinse your hands well under clean, running water. 5. Dry your hands using a clean towel or air dry them. (And then, between washing your hands and filling your plate, don't touch your face...) This starts with today's colloquium.

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Reminder: Colloquium Speaker Wenting Zheng tomorrow at 12:30pm
by Emily C. Lawrence 09 Mar '20

09 Mar '20

CS Colloquium Speaker Wenting Zheng, University of California, Berkeley Tuesday, March 10 - 12:30pm Computer Science - Room 105 Host: Amit Levy [ https://www.cs.princeton.edu/events/25923 | https://www.cs.princeton.edu/events/25923 ] Title: Sharing without Showing: Building Secure Collaborative Systems In many domains such as finance and medicine, organizations have encountered obstacles in data acquisition because their target applications need sensitive data that reside across multiple parties. However, such data cannot be shared today due to data privacy concerns, policy regulation, and business competition. My graduate research focused on solving this problem by enabling organizations to run complex computations on the joint dataset without revealing their sensitive input to the other parties. My overall approach is to co-design systems with cryptography to build practical and functional systems that provide strong and provable security. In this talk, I will focus on two systems — Opaque and Helen — which secure SQL analytics and machine learning, respectively. My open source has been used by organizations such as IBM Research, Ericsson, Alibaba, and Microsoft. Bio: Wenting Zheng is a Ph.D. candidate at UC Berkeley, co-advised by Raluca Ada Popa and Ion Stoica . She completed her bachelor’s and master of engineering at MIT, where she was advised by Barbara Liskov . Her research interests are in computer systems, security, and applied cryptography. She is the recipient of a Berkeley Fellowship from 2014-2016, an IBM Research fellowship from 2017-2018, and was an invited participant at the 2019 EECS Rising Stars workshop.

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CS and CITP Colloquium Speaker Rediet Abebe TODAY at 12:30pm
by Emily C. Lawrence 09 Mar '20

09 Mar '20

CS and CITP Colloquium Speaker Rediet Abebe, Harvard Society of Fellows Monday, March 9 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan [ https://www.cs.princeton.edu/events/25922 | https://www.cs.princeton.edu/events/25922 ] Title: Designing Algorithms for Social Good Algorithmic and artificial intelligence techniques show immense potential to deepen our understanding of socioeconomic inequality and inform interventions designed to improve access to opportunity. Interventions aimed at historically under-served communities are made particularly challenging by the fact that disadvantage and inequality are multifaceted, notoriously difficult to measure, and reinforced by feedback loops in underlying structures. In this talk, we develop and analyze algorithmic and computational techniques to address these issues through two types of interventions: one in the form of allocating scarce societal resources and another in the form of improving access to information. We examine the ways in which techniques from algorithms, discrete optimization, and network and computational science can combat different forms of disadvantage, including susceptibility to income shocks, disparities in access to health information, and social segregation. We discuss current policy and practice informed by this work and close with a discussion of an emerging research area -- Mechanism Design for Social Good (MD4SG, http://md4sg.com/) -- around the use of algorithms, optimization, and mechanism design to address this category of problems. Bio: Rediet Abebe is a Junior Fellow at the Harvard Society of Fellows. She holds a Ph.D. in computer science from Cornell University, where she was advised by Jon Kleinberg, as well as an M.S. in applied mathematics from Harvard University, an M.A. in mathematics from the University of Cambridge, and a B.A. in mathematics from Harvard College. Her research is in the fields of algorithms and AI, with a focus on discrete algorithms, optimization, network and computational science, and their applications to equity and social good concerns. As part of this research agenda, Abebe co-founded Mechanism Design for Social Good (MD4SG), a multi-institutional, interdisciplinary initiative working to improve access to opportunity. This initiative has participants from over 100 institutions in 20 countries and has been supported by Schmidt Futures, the MacArthur Foundation, and the Institute for New Economic Thinking. Abebe's work has informed policy and practice at various organizations, including the Ethiopian Ministry of Education and the National Institutes of Health. In 2019, she served on the NIH Advisory Committee to the Director Working Group on AI (https://acd.od.nih.gov/working-groups/ai.html), whose recommendations were unanimously approved by the General Director's advisory committee. Abebe was recently recognized by the 2019 MIT Technology Review’s 35 Innovators Under 35 award and honored as a one to watch by the 2018 Bloomberg 50 list. She has presented her research in venues such as the National Academy of Sciences, the United Nations, and the Museum of Modern Art. Her work has been covered by outlets including Forbes, the Boston Globe, and the Washington Post. In 2017 Abebe co-founded Black in AI (https://blackinai.github.io/), a non-profit organization tackling diversity and inclusion issues in the field. Her research is deeply influenced by her upbringing in her hometown of Addis Ababa, Ethiopia. This talk is being co-sponsored by CITP and the Department of Computer Science.

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Reminder: Colloquium Speaker Rediet Abebe tomorrow at 12:30pm
by Emily C. Lawrence 08 Mar '20

08 Mar '20

CS and CITP Colloquium Speaker Rediet Abebe, Harvard Society of Fellows Monday, March 9 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan [ https://www.cs.princeton.edu/events/25922 | https://www.cs.princeton.edu/events/25922 ] Title: Designing Algorithms for Social Good Algorithmic and artificial intelligence techniques show immense potential to deepen our understanding of socioeconomic inequality and inform interventions designed to improve access to opportunity. Interventions aimed at historically under-served communities are made particularly challenging by the fact that disadvantage and inequality are multifaceted, notoriously difficult to measure, and reinforced by feedback loops in underlying structures. In this talk, we develop and analyze algorithmic and computational techniques to address these issues through two types of interventions: one in the form of allocating scarce societal resources and another in the form of improving access to information. We examine the ways in which techniques from algorithms, discrete optimization, and network and computational science can combat different forms of disadvantage, including susceptibility to income shocks, disparities in access to health information, and social segregation. We discuss current policy and practice informed by this work and close with a discussion of an emerging research area -- Mechanism Design for Social Good (MD4SG, http://md4sg.com/) -- around the use of algorithms, optimization, and mechanism design to address this category of problems. Bio: Rediet Abebe is a Junior Fellow at the Harvard Society of Fellows. She holds a Ph.D. in computer science from Cornell University, where she was advised by Jon Kleinberg, as well as an M.S. in applied mathematics from Harvard University, an M.A. in mathematics from the University of Cambridge, and a B.A. in mathematics from Harvard College. Her research is in the fields of algorithms and AI, with a focus on discrete algorithms, optimization, network and computational science, and their applications to equity and social good concerns. As part of this research agenda, Abebe co-founded Mechanism Design for Social Good (MD4SG), a multi-institutional, interdisciplinary initiative working to improve access to opportunity. This initiative has participants from over 100 institutions in 20 countries and has been supported by Schmidt Futures, the MacArthur Foundation, and the Institute for New Economic Thinking. Abebe's work has informed policy and practice at various organizations, including the Ethiopian Ministry of Education and the National Institutes of Health. In 2019, she served on the NIH Advisory Committee to the Director Working Group on AI (https://acd.od.nih.gov/working-groups/ai.html), whose recommendations were unanimously approved by the General Director's advisory committee. Abebe was recently recognized by the 2019 MIT Technology Review’s 35 Innovators Under 35 award and honored as a one to watch by the 2018 Bloomberg 50 list. She has presented her research in venues such as the National Academy of Sciences, the United Nations, and the Museum of Modern Art. Her work has been covered by outlets including Forbes, the Boston Globe, and the Washington Post. In 2017 Abebe co-founded Black in AI (https://blackinai.github.io/), a non-profit organization tackling diversity and inclusion issues in the field. Her research is deeply influenced by her upbringing in her hometown of Addis Ababa, Ethiopia. This talk is being co-sponsored by CITP and the Department of Computer Science.

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Colloquium Speaker Adji Bousso Dieng, Thurs Mar 12 at 12:30pm
by Emily C. Lawrence 06 Mar '20

06 Mar '20

CS Colloquium Speaker Adji Bousso Dieng, Columbia University Thursday, March 12 - 12:30pm Computer Science - Room 105 Host: Ryan Adams [ https://www.cs.princeton.edu/events/25924 | https://www.cs.princeton.edu/events/25924 ] Title: Deep Probabilistic Graphical Modeling Deep learning (DL) is a powerful approach to modeling complex and large scale data. However, DL models lack interpretable quantities and calibrated uncertainty. In contrast, probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and a way to express uncertainty about what we do not know. How can we develop machine learning methods that bring together the expressivity of DL with the interpretability and calibration of PGM? I call this line of research deep probabilistic graphical modeling (DPGM). In this talk, I will discuss my work on developing DPGM for text data. In particular, I will show how DPGM enables flexible and interpretable topic modeling at large scale, unlocking several known challenges. Furthermore, I will describe how we can account for both local and long-range context, under the DPGM framework, to build a flexible sequential document model that leads to state-of-the-art performance on a downstream document classification task. Bio: Adji Bousso Dieng is a PhD Candidate at Columbia University where she is jointly advised by David Blei and John Paisley. Her research is in Artificial Intelligence and Statistics, bridging probabilistic graphical models and deep learning. Dieng is supported by a Dean Fellowship from Columbia University. She won a Microsoft Azure Research Award and a Google PhD Fellowship in Machine Learning. She was recognized as a rising star in machine learning by the University of Maryland. Prior to Columbia, Dieng worked as a Junior Professional Associate at the World Bank. She did her undergraduate studies in France where she attended Lycee Henri IV and Telecom ParisTech --France's Grandes Ecoles system. She spent the third year of Telecom ParisTech's curriculum at Cornell University where she earned a Master in Statistics.

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Colloquium Speaker Omid Abari TODAY at 12:30pm
by Emily C. Lawrence 05 Mar '20

05 Mar '20

CS Colloquium Speaker Omid Abari, University of Waterloo Thursday, March 5 - 12:30pm Computer Science - Room 105 Host: Kyle Jamieson [ https://www.cs.princeton.edu/events/25921 | https://www.cs.princeton.edu/events/25921 ] Title: Software-Hardware Systems for the Internet of Things Recently, there has been a huge interest in Internet of Things ( IoT ) systems, which bring the digital world into the physical world around us. However, barriers still remain to realizing the dream applications of IoT . One of the biggest challenges in building IoT systems is the huge diversity of their demands and constraints (size, energy, latency, throughput, etc.). For example, virtual reality and gaming applications require multiple gigabits-per-second throughput and millisecond latency. Tiny sensors spread around a greenhouse or smart home must be low-cost and batteryless to be sustainable in the long run. Today's networking technologies fall short in supporting these IoT applications with a hugely diverse set of constraints and demands. As such, they require distinct innovative solutions. In this talk, I will describe how we can design a new class of networking technologies for IoT by designing software and hardware jointly, with an understanding of the intended application. In particular, I will present two examples of our solutions. The first solution tackles the throughput limitations of existing IoT networks by developing new millimeter wave devices and protocols, enabling many new IoT applications, such as untethered high-quality virtual reality. The second solution tackles the energy imitations of IoT networks by introducing new wireless devices that can sense and communicate without requiring any batteries. I demonstrate how our solution is applicable in multiple, diverse domains such as HCI, medical, and agriculture. I will conclude the talk with future directions in IoT research, both in terms of technologies and applications. Bio: Omid Abari is an Assistant Professor at the University of Waterloo, School of Computer Science. He received his Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in 2018. His research interests are in the area of computer networks and mobile systems, with applications to the Internet of Things ( IoT ). He is currently leading the Intelligent Connectivity (ICON) Lab, where his team focuses on the design and implementation of novel softwarehardware systems that deliver ubiquitous sensing, communication and computing at scale. His work has been selected for GetMobile research highlights (2018, 2019), and been featured by several media outlets, including Wired, TechCrunch, Engadget , IEEE Spectrum, and ACM Tech News.

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Colloquium Speaker Wenting Zheng, Tues Mar 10 at 12:30pm
by Emily C. Lawrence 04 Mar '20

04 Mar '20

CS Colloquium Speaker Wenting Zheng, University of California, Berkeley Tuesday, March 10 - 12:30pm Computer Science - Room 105 Host: Amit Levy [ https://www.cs.princeton.edu/events/25923 | https://www.cs.princeton.edu/events/25923 ] Title: Sharing without Showing: Building Secure Collaborative Systems In many domains such as finance and medicine, organizations have encountered obstacles in data acquisition because their target applications need sensitive data that reside across multiple parties. However, such data cannot be shared today due to data privacy concerns, policy regulation, and business competition. My graduate research focused on solving this problem by enabling organizations to run complex computations on the joint dataset without revealing their sensitive input to the other parties. My overall approach is to co-design systems with cryptography to build practical and functional systems that provide strong and provable security. In this talk, I will focus on two systems — Opaque and Helen — which secure SQL analytics and machine learning, respectively. My open source has been used by organizations such as IBM Research, Ericsson, Alibaba, and Microsoft. Bio: Wenting Zheng is a Ph.D. candidate at UC Berkeley, co-advised by Raluca Ada Popa and Ion Stoica . She completed her bachelor’s and master of engineering at MIT, where she was advised by Barbara Liskov . Her research interests are in computer systems, security, and applied cryptography. She is the recipient of a Berkeley Fellowship from 2014-2016, an IBM Research fellowship from 2017-2018, and was an invited participant at the 2019 EECS Rising Stars workshop.

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