- talks - lists.cs.princeton.edu

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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Reminder: Colloquium Speaker Omid Abari tomorrow at 12:30pm
by Emily C. Lawrence 04 Mar '20

04 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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CS and CITP Colloquium Speaker Rediet Abebe, Mon Mar 9 at 12:30pm
by Emily C. Lawrence 03 Mar '20

03 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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Distinguished Colloquium Speaker-Daniel Jackson, TODAY at 12:30pm
by Emily C. Lawrence 02 Mar '20

02 Mar '20

CS Distinguished Colloquium Speaker Daniel Jackson, CSAIL, Massachusetts Institute of Technology Monday, March 2 - 12:30pm Computer Science - Room 105 Host: Jennifer Rexford [ https://www.cs.princeton.edu/events/25913 | https://www.cs.princeton.edu/events/25913 ] Title: A New Abstraction for Software Design The internal design of software—how the code is structured—is powered by familiar abstractions (such as abstract types, classes and modules). But the external design—how the software actually behaves—is usually viewed informally, without the guidance of robust abstractions. For the last few years, I have been exploring a new abstraction that can shape how we think about software applications and systems, and that provides a way to organize behaviors, encapsulate reusable ideas, and evaluate designs. In this talk, I’ll define this abstraction and show how it can be used to explain a variety of flaws in familiar applications. I’ll also present some general principles that attempt to capture some key aspects of good software design. Bio: Daniel Jackson is a professor of computer science and MacVicar teaching fellow at MIT, and associate director of the Computer Science and Artificial Intelligence Laboratory. His research has focused primarily on software modeling and design, most notably the Alloy modeling language. He is also a photographer whose most recent project brings to light the experiences of those with depression and other mental health issues ( [ http://portraitsofresilience.com/ | http://portraitsofresilience.com ] ).

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Reminder: Distinguished Colloquium Speaker-Daniel Jackson tomorrow at 12:30pm
by Emily C. Lawrence 01 Mar '20

01 Mar '20

CS Distinguished Colloquium Speaker Daniel Jackson, CSAIL, Massachusetts Institute of Technology Monday, March 2 - 12:30pm Computer Science - Room 105 Host: Jennifer Rexford [ https://www.cs.princeton.edu/events/25913 | https://www.cs.princeton.edu/events/25913 ] Title: A New Abstraction for Software Design The internal design of software—how the code is structured—is powered by familiar abstractions (such as abstract types, classes and modules). But the external design—how the software actually behaves—is usually viewed informally, without the guidance of robust abstractions. For the last few years, I have been exploring a new abstraction that can shape how we think about software applications and systems, and that provides a way to organize behaviors, encapsulate reusable ideas, and evaluate designs. In this talk, I’ll define this abstraction and show how it can be used to explain a variety of flaws in familiar applications. I’ll also present some general principles that attempt to capture some key aspects of good software design. Bio: Daniel Jackson is a professor of computer science and MacVicar teaching fellow at MIT, and associate director of the Computer Science and Artificial Intelligence Laboratory. His research has focused primarily on software modeling and design, most notably the Alloy modeling language. He is also a photographer whose most recent project brings to light the experiences of those with depression and other mental health issues ( [ http://portraitsofresilience.com/ | http://portraitsofresilience.com ] ).

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