- talks - lists.cs.princeton.edu

CS Colloquium Speaker: Huacheng Yu, Thursday, March 4 at 12:30
by Emily C. Lawrence 25 Feb '21

25 Feb '21

CS Colloquium Speaker Huacheng Yu, Princeton University Host: Zeev Dvir Date: Thursday, March 4 Time: 12:30pm EST Event page: [ https://www.cs.princeton.edu/events/26051 | https://www.cs.princeton.edu/events/26051 ] Register for Zoom webinar here: [ https://princeton.zoom.us/webinar/register/WN_pNvnCKFHS_qoXLEEOSMOwA | https://princeton.zoom.us/webinar/register/WN_pNvnCKFHS_qoXLEEOSMOwA ] Title: Data Structures and Algorithms in Sublinear Computation Abstract: Sublinear algorithms are sustainable under the exponential increase of data volume and processing speed. Such algorithms use a sublinear amount of resources, e.g., spending time, space, or communication that is asymptotically smaller than the input data size. Typical examples include data structures, which compute query functions of the data in sublinear time, and streaming algorithms, which make one pass over massive data streams while maintaining a sublinear-sized memory. In this talk, I will give an overview of my work in sublinear computation, focusing on succinct data structures and distributed graph sketching algorithms. I will first discuss my work on a nearly optimal data structure for the dictionary problem, for which the textbook solution uses hash tables. Then, I will talk about detecting the connectivity of graphs using distributed sketching, and my recent work showing the optimality of a well-known sketching algorithm (the AGM sketch). I will conclude the talk with discussion on future directions and my other work in theoretical computer science. Bio: Huacheng Yu is an associate research scholar in the Department of Computer Science at Princeton University. His research interests include data structures and streaming algorithms, and other directions in theoretical computer science such as communication complexity and graph algorithms. Prior to Princeton, Huacheng was a postdoctoral researcher at Harvard University hosted by Jelani Nelson and Madhu Sudan. He received his Ph.D. from Stanford University (advised by Ryan Williams and Omer Reingold) and B.Eng from Tsinghua University, both in Computer Science.

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CS Colloquium Speaker: Sanmi Koyejo, Monday, Mar 1 at 12:30
by Emily C. Lawrence 23 Feb '21

23 Feb '21

CS Colloquium Speaker Sanmi Koyejo, University of Illinois at Urbana-Champaign Host: Barbara Engelhardt Date: Monday, March 1 Time: 12:30pm EST Event page: https://www.cs.princeton.edu/events/26040 Register for Zoom webinar here: https://princeton.zoom.us/webinar/register/WN_XMi3YQUVRn20UGAhEncHpQ Title: The Measurement and Mismeasurement of Trustworthy ML Abstract: Across healthcare, science, and engineering, we increasingly employ machine learning (ML) to automate decision-making that, in turn, affects our lives in profound ways. However, ML can fail, with significant and long-lasting consequences. Reliably measuring such failures is the first step towards building robust and trustworthy learning machines. Consider algorithmic fairness, where widely-deployed fairness metrics can exacerbate group disparities and result in discriminatory outcomes. Moreover, existing metrics are often incompatible. Hence, selecting fairness metrics is an open problem. Measurement is also crucial for robustness, particularly in federated learning with error-prone devices. Here, once again, models constructed using well-accepted robustness metrics can fail. Across ML applications, the dire consequences of mismeasurement are a recurring theme. This talk will outline emerging strategies for addressing the measurement gap in ML and how this impacts trustworthiness. Bio: Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in developing the principles and practice of trustworthy machine learning. Additionally, Koyejo focuses on applications to neuroscience and healthcare. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence (UAI), a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves on the board of the Black in AI organization.

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4:30pm Tue Mar 9 talk on "Building secure distributed systems atop the insecure Internet"
by Jennifer Rexford 22 Feb '21

22 Feb '21

> > > > ECE SEMINAR > > Speaker: > Maria Apostolaki, ETH > Title: > Building secure distributed systems atop the insecure Internet > Day: > Tuesday, March 9, 2021 > Time: > 4:30 pm > Room: > Please register here <https://princeton.zoom.us/meeting/register/tJUlc-2oqTwiHdZVqHbVqzMkeIX1y6v7…> > Host: > Prateek Mittal > > Abstract: > Distributed systems are increasingly important for our everyday life, allowing for high performance, fault tolerance, and flexibility. Many of these systems nowadays rely on the inherently insecure Internet infrastructure. Surely though, they should have been designed to take this into account...? > In this talk, I will answer negatively to this question using a concrete example: public blockchain systems such as Bitcoin. These are novel distributed systems that are designed according to stringent failure models. In this context, I will explain how an adversary controlling pieces of Internet infrastructure can practically compromise: (i) Bitcoin's consensus protocol (by partitioning the network); (ii) Bitcoin's anonymity guarantees (by mapping pseudonyms to real-world identities); and (iii) Bitcoin's availability (by eclipsing clients). > While these attacks are worrying, I will also introduce practical and effective defenses to counter them both at the network and the application layer. Beyond Bitcoin, this work teaches essential lessons for distributed-system design. > > > > Bio: > Maria Apostolaki is a PhD Student at ETH Zurich advised by Laurent Vanbever. During her studies, she has been a visiting student at MIT (2019) and a research intern at Microsoft Research (2018) and Google (2017). Before joining ETH, she earned her diploma in Electrical and Computer Engineering at the National Technical University of Athens, Greece. > Her research focuses on building secure, performant, and deployable networked systems using both hardware and software. Her research led to discovering significant vulnerabilities in the Bitcoin system and spearheaded changes to the Bitcoin codebase. Maria’s work received widespread media coverage and was awarded an Applied Networking Research Prize by IETF.

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March 1, 2021 - ECE Circuits Seminar - Carmel Majidi, Carnegie Mellon University
by Emily C. Lawrence 22 Feb '21

22 Feb '21

ECE CIRCUITS SEMINAR Speaker: Carmel Majidi , Carnegie Mellon University Title: Soft-Matter Engineering for Robotics and Wearables Day: Monday, March 1, 2021 Time: 4:30 pm Zoom Link: Meeting link below Host: Prof. Kaushik Sengupta Abstract: Progress in soft lithography and soft materials integration have led to extraordinary new classes of soft-matter sensors, circuits, and transducers. These material technologies are composed almost entirely out of soft matter – elastomers, gels, and conductive fluids like eutectic gallium-indium (EGaIn) – and represent the building blocks for machines and electronics that are soft, flexible, and stretchable. Because of their intrinsic compliance and elasticity, such devices can be incorporated into soft, biologically-inspired robots or be worn on the body and operate continuously without impairing natural body motion. In this talk, I will review recent contributions from my research group in creating soft multifunctional materials for wearable electronics and soft robotics using these emerging practices in “soft-matter engineering.” In particular, I will focus on elastomer composites and microfluidic EGaIn architectures for highly stretchable digital electronics, wearable energy harvesting, and electrically-responsive actuation. When possible, I will relate the design and operation of these soft-matter technologies to underlying principles of soft matter physics and practices in controls and machine. In addition to presenting my own research in the field, I will also briefly review broader efforts and emerging challenges in utilizing soft electronic materials for applications in wearable electronics and soft robotics . Bio: Carmel Majidi is the Clarence H. Adamson Professor of Mechanical Engineering at Carnegie Mellon University, where he leads the Soft Machines Lab. His lab is dedicated to the discovery of novel material architectures that allow machines and electronics to be soft, elastically deformable, and biomechanically compatible. Currently, his research is focused on fluid-filled elastomers that exhibit unique combinations of mechanical, electrical, and thermal properties and can function as “artificial” skin, nervous tissue, and muscle for soft robotics and wearables. Carmel has received grants from industry and federal agencies along with early career awards from DARPA, ONR, AFOSR, and NASA to explore challenges in soft-matter engineering and robotics. Prior to arriving at CMU, Prof. Majidi had postdoctoral appointments at Harvard and Princeton Universities and received his PhD in Electrical Engineering at UC Berkeley. Join Zoom Meeting : [ https://princeton.zoom.us/j/99499632211?pwd=Qm4wWFFWSUFyMjJVQ3lseFh1TGNyUT09 | https://princeton.zoom.us/j/99499632211?pwd=Qm4wWFFWSUFyMjJVQ3lseFh1TGNyUT09 ]

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CS Colloquium Speaker: Ellen Vitercik today at 12:30pm EST
by Emily C. Lawrence 22 Feb '21

22 Feb '21

CS Colloquium Speaker Ellen Vitercik, Carnegie Mellon University Host: Sanjeev Arora Date: Monday, February 22 Time: 12:30pm EST Event page: [ https://www.cs.princeton.edu/events/26042 | https://www.cs.princeton.edu/events/26042 ] Register for the Zoom webinar here: [ https://princeton.zoom.us/webinar/register/WN_gbMN0OUJQRainEiRTS4VYQ | https://princeton.zoom.us/webinar/register/WN_gbMN0OUJQRainEiRTS4VYQ ] Title: Integrating Machine Learning into Algorithm Design Abstract: An important property of those algorithms that are typically used in practice is broad applicability—the ability to solve problems across diverse domains. However, the default, out-of-the-box performance of these algorithms can be unsatisfactory, with slow runtime, poor solution quality, and even negative long-term social ramifications. In practice, there is often ample data available about the types of problems that an algorithm will be run on, data that can potentially be harnessed to fine-tune the algorithm’s performance. We therefore need principled approaches for using this data to obtain strong application-specific performance guarantees. In this talk, I will give an overview of my research that provides practical methods built on firm theoretical foundations for incorporating machine learning and optimization into the process of algorithm design, selection, and configuration. I will describe my contributions across several diverse domains, including integer programming, clustering, mechanism design, and computational biology. As I will demonstrate, these seemingly disparate areas are connected by overarching structure which implies broadly-applicable guarantees. Bio: Ellen Vitercik is a PhD student at Carnegie Mellon University where she is co-advised by Maria-Florina Balcan and Tuomas Sandholm. Her research revolves around artificial intelligence, algorithm design, and the interface between economics and computation, with a particular focus on machine learning theory. Among other honors, she is a recipient of the Exemplary Artificial Intelligence Track Paper Award at EC'19, the Best Presentation by a Student or Postdoctoral Researcher Award at EC'19, the NSF Graduate Research Fellowship, the IBM PhD Fellowship, the Fellowship in Digital Health from CMU's Center for Machine Learning and Health, and the Teaching Assistant of the Year Award from CMU's Machine Learning Department. This talk will be recorded.

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Reminder: Ellen Vitercik tomorrow at 12:30pm EST
by Emily C. Lawrence 21 Feb '21

21 Feb '21

CS Colloquium Speaker Ellen Vitercik, Carnegie Mellon University Host: Sanjeev Arora Date: Monday, February 22 Time: 12:30pm EST Event page: [ https://www.cs.princeton.edu/events/26042 | https://www.cs.princeton.edu/events/26042 ] Register for the Zoom webinar here: [ https://princeton.zoom.us/webinar/register/WN_gbMN0OUJQRainEiRTS4VYQ | https://princeton.zoom.us/webinar/register/WN_gbMN0OUJQRainEiRTS4VYQ ] Title: Integrating Machine Learning into Algorithm Design Abstract: An important property of those algorithms that are typically used in practice is broad applicability—the ability to solve problems across diverse domains. However, the default, out-of-the-box performance of these algorithms can be unsatisfactory, with slow runtime, poor solution quality, and even negative long-term social ramifications. In practice, there is often ample data available about the types of problems that an algorithm will be run on, data that can potentially be harnessed to fine-tune the algorithm’s performance. We therefore need principled approaches for using this data to obtain strong application-specific performance guarantees. In this talk, I will give an overview of my research that provides practical methods built on firm theoretical foundations for incorporating machine learning and optimization into the process of algorithm design, selection, and configuration. I will describe my contributions across several diverse domains, including integer programming, clustering, mechanism design, and computational biology. As I will demonstrate, these seemingly disparate areas are connected by overarching structure which implies broadly-applicable guarantees. Bio: Ellen Vitercik is a PhD student at Carnegie Mellon University where she is co-advised by Maria-Florina Balcan and Tuomas Sandholm. Her research revolves around artificial intelligence, algorithm design, and the interface between economics and computation, with a particular focus on machine learning theory. Among other honors, she is a recipient of the Exemplary Artificial Intelligence Track Paper Award at EC'19, the Best Presentation by a Student or Postdoctoral Researcher Award at EC'19, the NSF Graduate Research Fellowship, the IBM PhD Fellowship, the Fellowship in Digital Health from CMU's Center for Machine Learning and Health, and the Teaching Assistant of the Year Award from CMU's Machine Learning Department. This talk will be recorded.

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CS Distinguished Colloquium Speaker Christos Papadimitriou, today at 12:30
by Emily C. Lawrence 19 Feb '21

19 Feb '21

CS Distinguished Colloquium Speaker Christos Papadimitriou, Columbia University Host: Sanjeev Arora Date: Friday, February 19 Time: 12:30pm EST Event page: https://www.cs.princeton.edu/events/26038 Register for Zoom webinar here: https://princeton.zoom.us/webinar/register/WN_oQAMcljNQbWYTJcaDmTKDQ Title: Language, Brain, and Computation Abstract: How does the brain beget the mind? How do molecules, cells and synapses effect reasoning, intelligence, language? Despite dazzling progress in experimental neuroscience, as well as in cognitive science at the other extreme of scale, we do not seem to be making progress in the overarching question -- the gap is huge and a completely new approach seems to be required. As Richard Axel recently put it: "We don't have a logic for the transformation of neural activity into thought [...]." What kind of formal system would qualify as this "logic"? I will introduce the Assembly Calculus, a computational system whose basic data structure is the assembly -- assemblies are large populations of neurons representing concepts, words, ideas, episodes, etc., The Assembly Calculus is biologically plausible in the following two orthogonal senses: Its primitives are properties of assemblies observed in experiments or useful for explaining experiments, and can be provably (through both mathematical proof and simulations) "compiled down" to the activity of neurons and synapses. I will also explain why the Assembly Calculus is especially powerful in exploring how language happens in the brain. Bio: Christos Harilaos Papadimitriou is the Donovan Family Professor of Computer Science at Columbia University. Before joining Columbia in 2017, he was a professor at UC Berkeley for the previous 22 years, and before that he taught at Harvard, MIT, NTU Athens, Stanford, and UCSD. He has written five textbooks and many articles on algorithms and complexity, and their applications to optimization, databases, control, AI, robotics, economics and game theory, the Internet, evolution, and the brain. He holds a PhD from Princeton (1976), and eight honorary doctorates, including from ETH, University of Athens, EPFL, and Univ. de Paris Dauphine. He is a member of the National Academy of Sciences of the US, the American Academy of Arts and Sciences, and the National Academy of Engineering, and he has received the Knuth prize, the Go"del prize, the von Neumann medal, as well as the 2018 Harvey Prize by Technion. In 2015 the president of the Hellenic republic named him commander of the order of the Phoenix. He has also written three novels: “Turing ,” “Logicomix” and his latest “Independence.”

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Reminder: Christos Papadimitriou, tomorrow at 12:30
by Emily C. Lawrence 18 Feb '21

18 Feb '21

CS Distinguished Colloquium Speaker Christos Papadimitriou, Columbia University Host: Sanjeev Arora Date: Friday, February 19 Time: 12:30pm EST Event page: https://www.cs.princeton.edu/events/26038 Register for Zoom webinar here: https://princeton.zoom.us/webinar/register/WN_oQAMcljNQbWYTJcaDmTKDQ Title: Language, Brain, and Computation Abstract: How does the brain beget the mind? How do molecules, cells and synapses effect reasoning, intelligence, language? Despite dazzling progress in experimental neuroscience, as well as in cognitive science at the other extreme of scale, we do not seem to be making progress in the overarching question -- the gap is huge and a completely new approach seems to be required. As Richard Axel recently put it: "We don't have a logic for the transformation of neural activity into thought [...]." What kind of formal system would qualify as this "logic"? I will introduce the Assembly Calculus, a computational system whose basic data structure is the assembly -- assemblies are large populations of neurons representing concepts, words, ideas, episodes, etc., The Assembly Calculus is biologically plausible in the following two orthogonal senses: Its primitives are properties of assemblies observed in experiments or useful for explaining experiments, and can be provably (through both mathematical proof and simulations) "compiled down" to the activity of neurons and synapses. I will also explain why the Assembly Calculus is especially powerful in exploring how language happens in the brain. Bio: Christos Harilaos Papadimitriou is the Donovan Family Professor of Computer Science at Columbia University. Before joining Columbia in 2017, he was a professor at UC Berkeley for the previous 22 years, and before that he taught at Harvard, MIT, NTU Athens, Stanford, and UCSD. He has written five textbooks and many articles on algorithms and complexity, and their applications to optimization, databases, control, AI, robotics, economics and game theory, the Internet, evolution, and the brain. He holds a PhD from Princeton (1976), and eight honorary doctorates, including from ETH, University of Athens, EPFL, and Univ. de Paris Dauphine. He is a member of the National Academy of Sciences of the US, the American Academy of Arts and Sciences, and the National Academy of Engineering, and he has received the Knuth prize, the Go"del prize, the von Neumann medal, as well as the 2018 Harvey Prize by Technion. In 2015 the president of the Hellenic republic named him commander of the order of the Phoenix. He has also written three novels: “Turing ,” “Logicomix” and his latest “Independence.”

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Gregory Gundersen will present his Pre FPO "Tractable Inference for Latent Variable Models with Applications to Science and Engineering" on Tuesday, February 23, 2021 at 3:30pm via Zoom.
by Nicki Mahler 16 Feb '21

16 Feb '21

Gregory Gundersen will present his Pre FPO " Tractable Inference for Latent Variable Models with Applications to Science and Engineering" on Tuesday, February 23, 2021 at 3:30pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/96395535605 | https://princeton.zoom.us/j/96395535605 ] Committee: Barbara Engelhardt, Ryan Adams, and Jonathan Pillow (examiners); and Tom Griffiths and Brandon Stewart (readers) Title: Tractable Inference for Latent Variable Models with Applications to Science and Engineering Abstract: Latent variables allow practitioners to encode assumptions into their statistical models. A latent variable might, for example, represent an unobserved covariate, measurement error, or a missing class label. Inference is challenging because one must account for the conditional dependence structure induced by these variables, and marginalization is often intractable. In this talk, I present several practical algorithms for inferring latent structure in models used in neuroscience, computational biology, and time-series analysis. First, I present a family of nonlinear dimension-reduction models that use random features to support non-Gaussian data likelihoods. Approximating a nonlinear relationship between the latent variables and observations with a function that is linear with respect to random features induces closed-form gradients of the posterior distribution with respect to the latent variables. This allows for gradient-based nonlinear dimension-reduction models for a variety of data likelihoods. I discuss results on text and image datasets, as well as neural spike trains. Next, I present a multi-view framework that combines neural networks and probabilistic CCA to estimate shared and view-specific latent structure of paired samples of histological images and gene expression levels. The model is trained end-to-end to estimate all parameters simultaneously, and I show that the latent variables capture interpretable hidden structure, such as tissue-specific and morphological variation. Finally, I discuss lowering the computational cost of online Bayesian filtering of time series with abrupt changes in structure, called changepoints. In time series with multiple data sources and associated costs, one can trade the cost of collecting an observation against the quality or "fidelity" of the measurement and how this fidelity affects the estimation of changepoints. Our framework makes cost-sensitive decisions about which data fidelity to use based on maximizing information gain with respect to the posterior distribution over changepoints. I present results on streaming video and audio data. I conclude by discussing several lines of work extending these projects.

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CS Colloquium Speaker: Ellen Vitercik on Monday, Feb 22
by Emily C. Lawrence 16 Feb '21

16 Feb '21

CS Colloquium Speaker Ellen Vitercik, Carnegie Mellon University Host: Sanjeev Arora Date: Monday, February 22 Time: 12:30pm EST Event page: [ https://www.cs.princeton.edu/events/26042 | https://www.cs.princeton.edu/events/26042 ] Register for the Zoom webinar here: [ https://princeton.zoom.us/webinar/register/WN_gbMN0OUJQRainEiRTS4VYQ | https://princeton.zoom.us/webinar/register/WN_gbMN0OUJQRainEiRTS4VYQ ] Title: Integrating Machine Learning into Algorithm Design Abstract: An important property of those algorithms that are typically used in practice is broad applicability—the ability to solve problems across diverse domains. However, the default, out-of-the-box performance of these algorithms can be unsatisfactory, with slow runtime, poor solution quality, and even negative long-term social ramifications. In practice, there is often ample data available about the types of problems that an algorithm will be run on, data that can potentially be harnessed to fine-tune the algorithm’s performance. We therefore need principled approaches for using this data to obtain strong application-specific performance guarantees. In this talk, I will give an overview of my research that provides practical methods built on firm theoretical foundations for incorporating machine learning and optimization into the process of algorithm design, selection, and configuration. I will describe my contributions across several diverse domains, including integer programming, clustering, mechanism design, and computational biology. As I will demonstrate, these seemingly disparate areas are connected by overarching structure which implies broadly-applicable guarantees. Bio: Ellen Vitercik is a PhD student at Carnegie Mellon University where she is co-advised by Maria-Florina Balcan and Tuomas Sandholm. Her research revolves around artificial intelligence, algorithm design, and the interface between economics and computation, with a particular focus on machine learning theory. Among other honors, she is a recipient of the Exemplary Artificial Intelligence Track Paper Award at EC'19, the Best Presentation by a Student or Postdoctoral Researcher Award at EC'19, the NSF Graduate Research Fellowship, the IBM PhD Fellowship, the Fellowship in Digital Health from CMU's Center for Machine Learning and Health, and the Teaching Assistant of the Year Award from CMU's Machine Learning Department. This talk will be recorded.

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