- talks - lists.cs.princeton.edu

Colloquium Speaker Sarah Chasins, TODAY at 12:30pm
by Emily C. Lawrence 11 Apr '19

11 Apr '19

Colloquium Speaker Sarah Chasins from University of California, Berkeley Thursday, April 11 - 12:30pm Computer Science - Room 105 Host: Andrew Appel Democratizing Web Automation: Programming for Social Scientists and Other Domain Experts [ https://www.cs.princeton.edu/events/25842 | https://www.cs.princeton.edu/events/25842 ] We have promised social scientists a data revolution, but it hasn’t arrived. What stands between practitioners and the data-driven insights they want? Acquiring the data. In particular, acquiring the social media, online forum, and other web data that was supposed to help them produce big, rich, ecologically valid datasets. Web automation programming is resistant to high-level abstractions, so end-user programmers end up stymied by the need to reverse engineer website internals—DOM, JavaScript, AJAX. Programming by Demonstration (PBD) offered one promising avenue towards democratizing web automation. Unfortunately, as the web matured, the programs became too complex for PBD tools to synthesize, and web PBD progress stalled. In this talk, I’ll describe how I reformulated traditional web PBD around the insight that demonstrations are not always the easiest way for non-programmers to communicate their intent. By shifting from a purely Programming-By-Demonstration view to a Programming-By-X view that accepts a variety of user-friendly inputs, we can dramatically broaden the class of programs that come in reach for end-user programmers. My Helena ecosystem combines (i) usable PBD-based program drafting tools, (ii) learnable programing languages, and (iii) novel programming environment interactions. The end result: non-coders write Helena programs in 10 minutes that can handle the complexity of modern webpages, while coders attempt the same task and time out in an hour. I’ll conclude with predictions about the abstraction-resistant domains that will fall next—robotics, analysis of unstructured texts, image processing—and how hybrid PL-HCI breakthroughs will vastly expand access to programming. Bio: Sarah Chasins is a Ph.D. candidate at UC Berkeley, advised by Ras Bodik . Her research interests lie at the intersection of programming languages and human-computer interaction. Much of her work is shaped by ongoing collaborations with social scientists, data scientists, and other non-traditional programmers. She has been awarded an NSF graduate research fellowship and a first place award in the ACM Student Research Competition.

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Colloquium Speaker Amy Zhang, Tuesday, April 16 at 12:30pm
by Emily C. Lawrence 10 Apr '19

10 Apr '19

Colloquium Speaker Amy Zhang from Massachusetts Institute of Technology Tuesday, April 16 - 12:30pm Computer Science - Room 105 Host: Adam Finkelstein [ https://www.cs.princeton.edu/events/25825 | https://www.cs.princeton.edu/events/25825 ] Systems to Improve Online Discussion Discussions online are integral to everyday life, affecting how we learn, work, socialize, and participate in public society. Yet the systems that we use to conduct online discourse, whether they be email, chat, or forums, have changed little since their inception many decades ago. As more people participate and more venues for discourse migrate online, new problems have arisen, and old problems have intensified. People are still drowning in information and must now juggle dozens of disparate discussion silos in addition. Finally, an unfortunately significant proportion of this online interaction is unwanted or unpleasant, with clashing norms leading to people bickering or getting harassed into silence. My research in human-computer interaction is on reimagining outdated designs towards designing novel online discussion systems that fix what's broken about online discussion. To solve these problems, I develop tools that empower users and communities to have direct control over their experiences and information. These include: 1) summarization tools to make sense of large discussions, 2) annotation tools to situate conversations in the context of what is being discussed, as well as 3) moderation tools to give users more fine-grained control over content delivery. Bio: Amy X. Zhang is a graduate student at MIT’s Computer Science and Artificial Intelligence Laboratory, focusing on human-computer interaction and social computing, and a 2018-19 Fellow at the Harvard Berkman Klein Center. She has interned at Microsoft Research and Google Research, received awards at ACM CHI and CSCW, and featured in stories by ABC News, BBC, CBC, and more. She has an M.Phil. in CS at University of Cambridge on a Gates Fellowship and a B.S. in CS at Rutgers, where she captained the Division I Women’s tennis team. Her research is supported by a Google PhD Fellowship and an NSF Graduate Research Fellowship.

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Reminder: Colloquium Speaker Sarah Chasins, tomorrow at 12:30pm
by Emily C. Lawrence 10 Apr '19

10 Apr '19

Colloquium Speaker Sarah Chasins from University of California, Berkeley Thursday, April 11 - 12:30pm Computer Science - Room 105 Host: Andrew Appel Democratizing Web Automation: Programming for Social Scientists and Other Domain Experts [ https://www.cs.princeton.edu/events/25842 | https://www.cs.princeton.edu/events/25842 ] We have promised social scientists a data revolution, but it hasn’t arrived. What stands between practitioners and the data-driven insights they want? Acquiring the data. In particular, acquiring the social media, online forum, and other web data that was supposed to help them produce big, rich, ecologically valid datasets. Web automation programming is resistant to high-level abstractions, so end-user programmers end up stymied by the need to reverse engineer website internals—DOM, JavaScript, AJAX. Programming by Demonstration (PBD) offered one promising avenue towards democratizing web automation. Unfortunately, as the web matured, the programs became too complex for PBD tools to synthesize, and web PBD progress stalled. In this talk, I’ll describe how I reformulated traditional web PBD around the insight that demonstrations are not always the easiest way for non-programmers to communicate their intent. By shifting from a purely Programming-By-Demonstration view to a Programming-By-X view that accepts a variety of user-friendly inputs, we can dramatically broaden the class of programs that come in reach for end-user programmers. My Helena ecosystem combines (i) usable PBD-based program drafting tools, (ii) learnable programing languages, and (iii) novel programming environment interactions. The end result: non-coders write Helena programs in 10 minutes that can handle the complexity of modern webpages, while coders attempt the same task and time out in an hour. I’ll conclude with predictions about the abstraction-resistant domains that will fall next—robotics, analysis of unstructured texts, image processing—and how hybrid PL-HCI breakthroughs will vastly expand access to programming. Bio: Sarah Chasins is a Ph.D. candidate at UC Berkeley, advised by Ras Bodik . Her research interests lie at the intersection of programming languages and human-computer interaction. Much of her work is shaped by ongoing collaborations with social scientists, data scientists, and other non-traditional programmers. She has been awarded an NSF graduate research fellowship and a first place award in the ACM Student Research Competition.

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Jonathan Lu will present his MSE thesis talk on Friday, April 12, 2019 at 11am in CS 302.
by Nicki Mahler 09 Apr '19

09 Apr '19

Jonathan Lu will present his MSE thesis talk on Friday, April 12, 2019 at 11am in CS 302. The members of his committee are Barbara Engelhardt (advisor) and Ben Raphael (reader). Everyone is invited to attend his talk. His talk title and abstract follows below. Title: Regularized Causal Network Inference from Gaussian Process Time Series Abstract: Many methods have been developed to infer causal networks from time series data, but are often insufficient for modern challenges, such as unevenly spaced time series and hundreds to thousands of possible causal variables. For example, single-cell genomics data consists of thousands of protein-coding genes, whose "pseudotime" trajectories are indexed continuously at varying intervals. Here we develop EncoreGP, a causal network inference method that uses Elastic net regression to discover causal relations between continuous time series trajectories (Gaussian Processes). EncoreGP uses impulse functions as causal kernels to bypass previous assumptions of equal timepoint spacing and discrete effects. It uses elastic net regularization to discover sparse, interpretable networks from high-dimensional data. We validate on multiple benchmark networks for bulk and single-cell genomics. While genomics is our focus, EncoreGP can be applied for causal network inference from any large set of time series with uneven timepoints.

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Colloquium Speaker-Chris Maddison TODAY at 12:30pm
by Emily C. Lawrence 09 Apr '19

09 Apr '19

Colloquium Speaker Chris Maddison from University of Oxford Tuesday, April 9 - 12:30pm Computer Science - Room 105 Host: Ryan Adams AlphaGo and the Computational Challenges of Machine Learning [ http://www.cs.princeton.edu/events/25841 | http://www.cs.princeton.edu/events/25841 ] Many computational challenges in machine learning involve the three problems of optimization, integration, and fixed-point computation. These three can often be reduced to each other, so they may also provide distinct vantages on a single problem. In this talk, I present a small part of this picture through a discussion of my work on AlphaGo and two vignettes on my work on the interplay between optimization and Monte Carlo. AlphaGo is the first computer program to defeat a world-champion player, Lee Sedol , in the board game of Go. My work laid the groundwork of the neural net components of AlphaGo , and culminated in our Nature publication describing AlphaGo's algorithm, at whose core hide these three problems. In the first vignette, I present the Hamiltonian descent methods we introduced for first-order optimization. These methods are inspired by the Monte Carlo literature and can achieve fast linear convergence without strong convexity by using a non-standard kinetic energy to condition the optimization. In the second vignette I cover our A* Sampling method, which reduces the problem of Monte Carlo simulation to an optimization problem, and an application to gradient estimation in stochastic computation graphs. Bio: Chris Maddison is a PhD candidate in the Statistical Machine Learning Group in the Department of Statistics at the University of Oxford. He is an Open Philanthropy AI Fellow and spends two days a week as a Research Scientist at DeepMind. His research is broadly focused on the development of numerical methods for deep learning and machine learning. He has worked on methods for variational inference, numerical optimization, and Monte Carlo estimation with a specific focus on those that might work at scale with few assumptions. Chris received his MSc. from the University of Toronto. He received a NeurIPS Best Paper Award in 2014, and was one of the founding members of the AlphaGo project.

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REMINDER: Colloquium Speaker-Chris Maddison tomorrow at 12:30pm
by Emily C. Lawrence 08 Apr '19

08 Apr '19

Colloquium Speaker Chris Maddison from University of Oxford Tuesday, April 9 - 12:30pm Computer Science - Room 105 Host: Ryan Adams AlphaGo and the Computational Challenges of Machine Learning [ http://www.cs.princeton.edu/events/25841 | http://www.cs.princeton.edu/events/25841 ] Many computational challenges in machine learning involve the three problems of optimization, integration, and fixed-point computation. These three can often be reduced to each other, so they may also provide distinct vantages on a single problem. In this talk, I present a small part of this picture through a discussion of my work on AlphaGo and two vignettes on my work on the interplay between optimization and Monte Carlo. AlphaGo is the first computer program to defeat a world-champion player, Lee Sedol , in the board game of Go. My work laid the groundwork of the neural net components of AlphaGo , and culminated in our Nature publication describing AlphaGo's algorithm, at whose core hide these three problems. In the first vignette, I present the Hamiltonian descent methods we introduced for first-order optimization. These methods are inspired by the Monte Carlo literature and can achieve fast linear convergence without strong convexity by using a non-standard kinetic energy to condition the optimization. In the second vignette I cover our A* Sampling method, which reduces the problem of Monte Carlo simulation to an optimization problem, and an application to gradient estimation in stochastic computation graphs. Bio: Chris Maddison is a PhD candidate in the Statistical Machine Learning Group in the Department of Statistics at the University of Oxford. He is an Open Philanthropy AI Fellow and spends two days a week as a Research Scientist at DeepMind. His research is broadly focused on the development of numerical methods for deep learning and machine learning. He has worked on methods for variational inference, numerical optimization, and Monte Carlo estimation with a specific focus on those that might work at scale with few assumptions. Chris received his MSc. from the University of Toronto. He received a NeurIPS Best Paper Award in 2014, and was one of the founding members of the AlphaGo project.

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Colloquium Speaker-David van Dijk, TODAY at 12:30pm
by Emily C. Lawrence 05 Apr '19

05 Apr '19

David van Dijk from Yale University Friday, April 5 - 12:30pm Computer Science - Room 105 [ https://www.cs.princeton.edu/events/25845 | https://www.cs.princeton.edu/events/25845 ] Manifold learning uncovers hidden structure in complex cellular state space In the era of big biological data, there is a pressing need for methods that visualize, integrate and interpret high-throughput high-dimensional data to enable biological discovery. There are several major challenges in analyzing high-throughput biological data. These include the curse of (high) dimensionality, noise, sparsity, missing values, bias, and collection artifacts. In my work, I try to solve these problems using computational methods that are based on manifold learning. A manifold is a smoothly varying low-dimensional structure embedded within high-dimensional ambient measurement space. In my talk, I will present a number of my recently completed and ongoing projects that utilize the manifold, implemented using graph signal processing and deep learning, to understand large biomedical datasets. These include MAGIC, a data denoising and imputation method designed to ‘fix’ single-cell RNA-sequencing data, PHATE, a dimensionality reduction and visualization method specifically designed to reveal continuous progression structure, and two deep learning methods that use specially designed constraints to allow for deep interpretable representations of heterogeneous systems. I will demonstrate that these methods can give insight into diverse biological systems such as breast cancer epithelial-to-mesenchymal transition, human embryonic stem cell development, the gut microbiome, and tumor infiltrating lymphocytes.

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Colloquium Speaker Sarah Chasins, Thursday, April 11 at 12:30pm
by Emily C. Lawrence 05 Apr '19

05 Apr '19

Colloquium Speaker Sarah Chasins from University of California, Berkeley Thursday, April 11 - 12:30pm Computer Science - Room 105 Host: Andrew Appel Democratizing Web Automation: Programming for Social Scientists and Other Domain Experts [ https://www.cs.princeton.edu/events/25842 | https://www.cs.princeton.edu/events/25842 ] We have promised social scientists a data revolution, but it hasn’t arrived. What stands between practitioners and the data-driven insights they want? Acquiring the data. In particular, acquiring the social media, online forum, and other web data that was supposed to help them produce big, rich, ecologically valid datasets. Web automation programming is resistant to high-level abstractions, so end-user programmers end up stymied by the need to reverse engineer website internals—DOM, JavaScript, AJAX. Programming by Demonstration (PBD) offered one promising avenue towards democratizing web automation. Unfortunately, as the web matured, the programs became too complex for PBD tools to synthesize, and web PBD progress stalled. In this talk, I’ll describe how I reformulated traditional web PBD around the insight that demonstrations are not always the easiest way for non-programmers to communicate their intent. By shifting from a purely Programming-By-Demonstration view to a Programming-By-X view that accepts a variety of user-friendly inputs, we can dramatically broaden the class of programs that come in reach for end-user programmers. My Helena ecosystem combines (i) usable PBD-based program drafting tools, (ii) learnable programing languages, and (iii) novel programming environment interactions. The end result: non-coders write Helena programs in 10 minutes that can handle the complexity of modern webpages, while coders attempt the same task and time out in an hour. I’ll conclude with predictions about the abstraction-resistant domains that will fall next—robotics, analysis of unstructured texts, image processing—and how hybrid PL-HCI breakthroughs will vastly expand access to programming. Bio: Sarah Chasins is a Ph.D. candidate at UC Berkeley, advised by Ras Bodik . Her research interests lie at the intersection of programming languages and human-computer interaction. Much of her work is shaped by ongoing collaborations with social scientists, data scientists, and other non-traditional programmers. She has been awarded an NSF graduate research fellowship and a first place award in the ACM Student Research Competition.

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Colloquium Speaker-Henry Corrigan-Gibbs, TODAY at 12:30pm
by Emily C. Lawrence 04 Apr '19

04 Apr '19

Colloquium Speaker Henry Corrigan-Gibbs from Stanford University Thursday, April 4 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan [ https://www.cs.princeton.edu/events/25830 | https://www.cs.princeton.edu/events/25830 ] Protecting Privacy by Splitting Trust When the maker of my phone, smart-watch, or web browser collects data about how I use it, must I trust the manufacturer to protect that sensitive information from theft? When I use the cryptographic hardware module in my laptop, need I trust that it will keep my secrets safe? When I use a messaging app to chat with friends, must I trust the app vendor not to sell the details of my messaging activity for profit? This talk will show that we can get the functionality we want from our systems without having to put blind faith in the correct behavior of the companies collecting our data, building our hardware, or designing our apps. The principle is to split our trust -- among organizations, or devices, or users. I will introduce new cryptographic techniques and systems-level optimizations that make it practical to split trust in a variety of settings. Then, I will present three built systems that employ these ideas, including one that now ships with the Firefox browser. Bio: Henry Corrigan-Gibbs is a Ph.D. candidate at Stanford, advised by Dan Boneh . His research interests are in computer security, applied cryptography, and online privacy. Henry and his collaborators have received the Best Young Researcher Paper Award at Eurocrypt 2018, the 2016 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies, and the 2015 IEEE Security and Privacy Distinguished Paper Award, and Henry's work has been cited by IETF and NIST.

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Reminder: Colloquium Speaker-David van Dijk, tomorrow at 12:30pm
by Emily C. Lawrence 04 Apr '19

04 Apr '19

David van Dijk from Yale University Friday, April 5 - 12:30pm Computer Science - Room 105 [ https://www.cs.princeton.edu/events/25845 | https://www.cs.princeton.edu/events/25845 ] Manifold learning uncovers hidden structure in complex cellular state space In the era of big biological data, there is a pressing need for methods that visualize, integrate and interpret high-throughput high-dimensional data to enable biological discovery. There are several major challenges in analyzing high-throughput biological data. These include the curse of (high) dimensionality, noise, sparsity, missing values, bias, and collection artifacts. In my work, I try to solve these problems using computational methods that are based on manifold learning. A manifold is a smoothly varying low-dimensional structure embedded within high-dimensional ambient measurement space. In my talk, I will present a number of my recently completed and ongoing projects that utilize the manifold, implemented using graph signal processing and deep learning, to understand large biomedical datasets. These include MAGIC, a data denoising and imputation method designed to ‘fix’ single-cell RNA-sequencing data, PHATE, a dimensionality reduction and visualization method specifically designed to reveal continuous progression structure, and two deep learning methods that use specially designed constraints to allow for deep interpretable representations of heterogeneous systems. I will demonstrate that these methods can give insight into diverse biological systems such as breast cancer epithelial-to-mesenchymal transition, human embryonic stem cell development, the gut microbiome, and tumor infiltrating lymphocytes.

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