- talks - lists.cs.princeton.edu

Andrew Jones will present his FPO "Probabilistic models for structured biomedical data" on Friday, December 16, 2022 at 9:30 AM in COS 402 and Zoom.
by gradinfo＠cs.princeton.edu 23 Nov '22

23 Nov '22

Andrew Jones will present his FPO "Probabilistic models for structured biomedical data" on Friday, December 16, 2022 at 9:30 AM in COS 402 and Zoom. Location: Zoom link: https://princeton.zoom.us/j/95479201507 <https://www.google.com/url?q=https://princeton.zoom.us/j/95479201507&sa=D&s ource=calendar&ust=1669648765658473&usg=AOvVaw0W0kAo0lJDADvaWIY_Kr4D> The members of Andrew's committee are as follows: Examiners: Barbara Engelhardt (Adviser), Ben Raphael, Adji Bousso Dieng Readers: Jonathan Pillow, Olga Russakovsky A copy of his thesis will be available, upon request, two weeks before the FPO. Please email gradinfo(a)cs.princeton.edu <mailto:gradinfo@cs.princeton.edu> if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Modern biomedical datasets-from molecular measurements of gene expression to pathology images-hold promise for discovering new therapeutics and probing basic questions about the behavior of cells. Thoughtful statistical modeling of these complex, high-dimensional data is crucial to elucidate robust scientific findings. A common assumption in data analysis that the data samples are independent and identically distributed. However, this assumption is nearly always violated in practice. This is especially true in the setting of biomedical data, which often exhibit some amount of structure, such as subgroups of patients, cells, or tissue types or other correlation structure among the samples. In this body of work, I propose data analysis and experimental design frameworks to account for several types of highly-structured biomedical data. These approaches, which take the form of Bayesian models and associated inference algorithms, are specifically tailored for datasets with group structure, multiple data modalities, and spatial organization of samples. In the first line of work, I propose a model for contrastive dimension reduction that decomposes the sources of variation in samples that belong to case and control conditions. Second, I propose a computational framework for aligning spatially-resolved genomics data into a common coordinate system that accounts for spatial correlation among the samples and models multiple data modalities. Finally, I propose a family of methods for optimally designing spatially-resolved genomics experiments that is tailored to the highly-structured data collection process of these studies. Together, this body of work advances the field of biomedical data analysis by developing models that directly exploit common types of structure within these data and demonstrating the advantage of these modeling approaches across an array of data types.

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CS Colloquium: Shuran Song, Monday, Nov 7 at 12:30pm
by Emily C. Lawrence 21 Nov '22

21 Nov '22

CS Colloquium Series Speaker: Shuran Song, Columbia University Date: Monday, November 7 Time: 12:30pm Location: CS 105 Host: Szymon Rusinkiewicz Event page: [ https://www.cs.princeton.edu/events/26279 | https://www.cs.princeton.edu/events/26279 ] Title: Unpatched Vulnerabilities in Cellular Standards Abstract: Despite the incredible capabilities (speed, repeatability) of our hardware, most robot manipulators today are deliberately programmed to avoid dynamics – moving slow enough so they can adhere to quasi-static assumptions about the world. In contrast, people frequently (and subconsciously) make use of dynamic phenomena to manipulate everyday objects – from unfurling blankets to tossing trash, to improve efficiency and physical reach range. These abilities are made possible by an intuition of physics, a cornerstone of intelligence. How do we impart the same on robots? In this talk, I will discuss how we might enable robots to leverage dynamics for manipulation in unstructured environments. Modeling the complex dynamics of unseen objects from pixels is challenging. However, by tightly integrating perception and action, we show it is possible to relax the need for accurate dynamical models. Thereby, allowing robots to (i) learn dynamic skills for complex objects, (ii) adapt to new scenarios using visual feedback, and (iii) use their dynamic interactions to improve their understanding of the world. By changing the way we think about dynamics – from avoiding it to embracing it – we can simplify a number of classically challenging problems, leading to new robot capabilities. Bio: Shuran Song is an Assistant Professor in the Department of Computer Science at Columbia University. Before that, she received her Ph.D. in Computer Science at Princeton University, BEng. at HKUST. Her research interests lie at the intersection of computer vision and robotics. Song’s research has been recognized through several awards including the Best Paper Awards at RSS’22 and T-RO’20, Best System Paper Awards at CoRL’21, RSS’19, and Amazon Robotics’18, and finalist at RSS’22, ICRA'20, CVPR'19, RSS’19, and IROS'18. She is also a recipient of the NSF Career Award, as well as research awards from Microsoft, Toyota Research, Google, Amazon, JP Morgan, and Sloan Foundation. To learn more about Shuran’s work please visit: [ https://www.cs.columbia.edu/~shurans/ | https://www.cs.columbia.edu/~shurans/ ]

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Claudia Roberts will present her FPO "Human-machine Collaboration in Real-World Machine-Learning Applications” on Thursday, December 15, 2022 at 2:00pm in CS 105 (correction)
by Nicki Mahler 18 Nov '22

18 Nov '22

Claudia Roberts will present her FPO "Human-machine Collaboration in Real-World Machine-Learning Applications” on Thursday, December 15, 2022 at 2:00pm in CS 105. The members of her committee are as follows: Arvind Narayanan (Adviser); Adji Dieng and Barbara Engelhardt (Readers); Andrés Monroy-Hernández and Matt Salganik (Examiners) All are welcome to attend. Abstract: Automation tools like machine learning are a necessity in our big data world. Thanks to the Internet and advancements in all facets of computer and storage technology, almost everyone has a voice in the Internet connected world. However, there are still very real physical limits in our physical world. This dichotomy—the seemingly limitless nature of technology enabled data colliding with the physical limits of the real world—has made automation tools a necessity, and predictive models powered by machine learning algorithms are one such tool. The promise of machine learning to accurately predict future human behavior and human preferences has lead practitioners and researchers alike to apply machine learning automation tools to tasks such as product recommendations and speculatory activities such as long term job applicant success. However, due to the mercurial nature of humans, developing mathematical intermediaries to attempt to model and predict human behavior is challenging and not a straight-forward task. One way of harnessing the power of machine-learning backed automation to help reduce the scale of many real-world applications in more challenging domain settings is by having humans and machines collaborating in non-trivial ways. In this dissertation, we delineate the various ways in which humans and machines collaborate in challenging real-world applications. Moreover, we highlight three specific ways in which we can use human-machine collaboration to keep or increase utility and reduce real-world harm when using these systems in the wild: (i) humans enabling computers with domain specific knowledge, (ii) computers providing humans with algorithmic explanations, (iii) humans and computers working together in decision making. [ https://www.cs.princeton.edu/events/26295 | https://www.cs.princeton.edu/events/26295 ]

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Claudia Roberts will present her FPO "Human-machine Cooperation in Machine-Learning Resistant Real-World Applications" on Thursday, December 15, 2022 at 2:00pm in CS 105.
by Nicki Mahler 17 Nov '22

17 Nov '22

Claudia Roberts will present her FPO "Human-machine Cooperation in Machine-Learning Resistant Real-World Applications" on Thursday, December 15, 2022 at 2:00pm in CS 105. The members of her committee are as follows: Arvind Narayanan (Adviser); Adji Dieng and Barbara Engerhardt (Readers); Andrés Monroy-Hernández and Matt Salganik (Examiners) All are welcome to attend. Abstract: Automation tools like machine learning are a necessity in our big data world. Thanks to the Internet and advancements in all facets of computer and storage technology, almost everyone has a voice in the Internet connected world. However, there are still very real physical limits in our physical world. This dichotomy—the seemingly limitless nature of technology enabled data colliding with the physical limits of the real world—has made automation tools a necessity, and predictive models powered by machine learning algorithms are one such tool. The promise of machine learning to accurately predict future human behavior and human preferences has lead practitioners and researchers alike to apply machine learning automation tools to tasks such as product recommendations and speculatory activities such as long term job applicant success. However, due to the mercurial nature of humans, developing mathematical intermediaries to attempt to model and predict human behavior is challenging and not a straight-forward task. One way of harnessing the power of machine-learning backed automation to help reduce the scale of many real-world applications in more challenging domain settings is by having humans and machines collaborating in non-trivial ways. In this dissertation, we delineate the various ways in which humans and machines collaborate in challenging real-world applications. Moreover, we highlight three specific ways in which we can use human-machine collaboration to keep or increase utility and reduce real-world harm when using these systems in the wild: (i) humans enabling computers with domain specific knowledge, (ii) computers providing humans with algorithmic explanations, (iii) humans and computers working together in decision making. [ https://www.cs.princeton.edu/events/26295 | https://www.cs.princeton.edu/events/26295 ]

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David Liu will present his FPO "A Serverless Architecture for Application-level Orchestration" on Wednesday, December 14, 2022 at 2pm in CS 402
by Nicki Mahler 17 Nov '22

17 Nov '22

David Liu will present his FPO "A Serverless Architecture for Application-level Orchestration" on Wednesday, December 14, 2022 at 2pm in CS 402. The members of his committee is as follows: Amit Levy and Mike Freedman (Advisers); Wyatt Lloyd and Ravi Netravali (Readers); Mike Freedman, Jennifer Rexford (Examiners) All are welcome to attend. Abstract: This thesis examines the problem of building large-scale applications using the serverless computing model and proposes decentralized, application-level orchestration for serverless workloads. Comparing with standalone orchestrators, the state-of-the-art solution to building large-scale serverless applications, we demonstrate that application-level orchestration is possible and practical using just the basic APIs of existing serverless infrastructures, and that it benefits both cloud users and cloud providers. It empowers cloud users with the flexibility of application-specific optimizations. It frees cloud providers from hosting and maintaining yet another performance-critical service. Furthermore, the performance and efficiency of application-level orchestration improve as the underlying systems develop. Thus, cloud providers can direct freed-up resources to core services in their serverless infrastructure and automatically reaps the benefits of a better orchestrator. This thesis describes mechanisms and implementations that help realize the goal of applicationlevel orchestration. In particular, we explain the necessity and challenges of decentralizing orchestration, and present a system for decentralized orchestration named Unum. Unum introduces an intermediate representation (IR) language to express execution graphs using only node-local information to decentralize the orchestration logic of applications. Unum implements orchestration as a library that runs in-situ with user-defined FaaS functions, rather than as a standalone service. The library relies on a minimal set of existing serverless APIs—function invocation and a few basic datastore operations—that are common across cloud platforms. Unum ensures workflow correctness despite multiple executions of non-deterministic functions by using checkpoints to commit to exactly one output for a function invocation. Our results show that a representative set of applications scale better, run faster, and cost significantly less with Unum than a state-of-the-art centralized orchestrator. We also show that Unum’s IR allows hand-tuned applications to run faster by using application-specific optimizations and supporting a richer set of application patterns. We hope the results in this thesis inspire cloud practitioners to reconsider the approach of supporting new functionalities by simply adding more services to the cloud infrastructure. And we hope to encourage the building of other application-level orchestration systems from the serverless community. [ https://www.cs.princeton.edu/events/26287 | https://www.cs.princeton.edu/events/26287 ]

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ECE Seminar: Jakub Szefer, Friday, Nov 18 at 12:30 pm
by Emily C. Lawrence 16 Nov '22

16 Nov '22

ECE Seminar Speaker: Jakub Szefer, Associate Professor, Dept. of Electrical Engineering, Yale University Day: November 18, 2022 Time: 12:30 pm EST Location: EQuad B205 Host: David Wentzlaff Title: Quantum Computer Cybersecurity Abstract: Quantum computers are becoming larger every year, and are already today easily accessible thanks to different cloud-based services from companies such as IBM Quantum, Microsoft Azure, or Amazon Braket. Increasing quantum computer sizes and computational abilities bring closer the potential for these computers to be able to generate novel solutions and results in chemistry, medicine, or material science, for example. However, as these computers promise to generate novel results and data, they will become targets of cyber attackers, who may want to steal information, influence the computation, or cause disruption to the operation of the quantum computers. The security threat-level is heightened by the fact that these computers are network-connected and are open to untrusted cloud users running malicious code on them. In this talk, we will present some of the first research on security evaluation of real, cloud-based quantum computers. We will first discuss potential “software” threats to quantum computers, by demonstrating insecurity of some of the basic gates, or operations, that are today available on the quantum computers. We will also demonstrate potential security threats considering future, multi-tenant use of the quantum computers where multiple users share a single quantum computer. The potential threats were found and evaluated by collecting data and running quantum programs on real, cloud-based quantum computers. The talk will also touch upon defenses we have proposed. In addition, the talk will aim to motivate the need to study cybersecurity of quantum computers and include security protections in their design. Today, large amounts of money and effort are put into development of bigger and more capable quantum computers, quantum software frameworks, and cloud infrastructures for these computers. Meanwhile, their security analysis and protections are lagging much behind. Bio: Prof. Jakub Szefer is currently an Associate Professor of Electrical Engineering at Yale University, where he leads the Computer Architecture and Security Laboratory (CASLAB). Jakub’s research encompasses secure processor architectures, cloud and device security, fast implementation of post-quantum cryptographic algorithms, as well as quantum computer cybersecurity. Among others, Jakub is the author of first book focusing on processor architecture security: “Principles of Secure Processor Architecture Design”, published in 2018. Most recently, Jakub is a recipient of the 2021 Ackerman Award for Teaching and Mentoring from Yale University.

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CSML Lunch & Learn Seminar, Nov 21 at 12:30
by Emily C. Lawrence 15 Nov '22

15 Nov '22

CSML Lunch and Learn SEMINAR Speaker: Mert Gurbuzbalaban, Rutgers University Title: Robust and Risk-Averse Accelerated Gradient Methods Day: November 21, 2022 Time: 12:30 PM, Lunch from 12:15 RSVP to [ mailto:csml@princeton.edu | csml(a)princeton.edu ] Location: CSML, Classroom 103 Host: Peter Ramadge, Director CSML Abstract: In the context of first-order algorithms subject to random gradient noise, we study the trade-offs between the convergence rate (which quantifies how fast the initial conditions are forgotten) and the "risk" of suboptimality, i.e., deviations from the expected suboptimality. We focus on a general class of momentum methods (GMM) that recover popular methods such as gradient descent (GD), accelerated gradient descent (AGD), and the heavy-ball (HB) method as special cases depending on the choice of GMM parameters. We use well-known risk measures "entropic risk" and "entropic value at risk" to quantify the risk of suboptimality. For strongly convex smooth minimization, we first obtain new convergence rate results for GMM with a unified theory that applies to both AGD and HB, improving some of the existing results for HB. We then provide explicit bounds on the entropic risk and entropic value at risk of suboptimality at a given iterate which also provides explicit bounds on the probability that the suboptimality exceeds a given threshold based on Chernoff's inequality. Our results unveil fundamental trade-offs between the convergence rate and the risk of suboptimality and result in Heisenberg-style uncertainty principles. We then plug the entropic risk and convergence rate estimates we obtained in a computationally tractable optimization framework and propose entropic risk-averse GMM (RA-GMM) and entropic risk-averse AGD (RA-AGD) methods that can select the GMM parameters to systematically trade-off the entropic value at risk with the convergence rate. We show that RA-AGD and RA-GMM improve performance on quadratic optimization and logistic regression problems compared to the standard choice of parameters. To our knowledge, our work is the first to resort to coherent measures to design the parameters of momentum methods systematically. We will then discuss how these ideas can be leveraged to develop efficient algorithms for non-convex optimization and min-max optimization problems, including those arising in distributionally robust stochastic optimization. Bio: Mert Gurbuzbalaban is an associate professor at Rutgers University. Previously, he was an assistant professor at Rutgers University and a postdoctoral associate at the Laboratory for Information and Decision Systems (LIDS) at MIT. He is broadly interested in optimization and computational science, driven by applications in network science and data science. He received his B.Sc. degrees in Electrical Engineering and Mathematics as a valedictorian from Bogazici University, Istanbul, Turkey, the “Diplôme d’ingénieur” degree from École Polytechnique, France, and the M.S. and Ph.D. degrees in Mathematics from the Courant Institute of Mathematical Sciences, New York University. Dr. Gurbuzbalaban is a recipient of the Dean's Research Award, Dean's Young Researcher Award, and Dean's Summer Fellowship Award at the Rutgers Business School in 2021 and a co-recipient of the Honorable Mention for the Best Paper Award at the International Conference in Machine Learning (ICML 2019). He also received the Kurt Friedrichs Prize (given by the Courant Institute of New York University for an outstanding thesis) in 2013, the Bronze Medal in the École Polytechnique Scientific Project Competition in 2006, the Nadir Orhan Bengisu Award (given by the electrical-electronics engineering department of Bogazici University to the best graduating undergraduate student) in 2005.

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ECE/CS/CITP Seminar: Yongdae Kim, KAIST - Nov 11 at 4:30pm
by Emily C. Lawrence 11 Nov '22

11 Nov '22

ECE/CS/CITP Seminar Speaker: Yongdae Kim, KAIST Title: Unpatched Vulnerabilities in Cellular Standards Day: Friday, November 11 Time: 4:30pm Location: B205 Engineering Quadrangle Host: Maria Apostolaki and Prateek Mittal Abstract: In a couple of years, "study items" for the 6G security standard will be set. Security issues not included in these study items are unlikely to be standardized and patched even in 6G. Therefore, before these study items are set, the security research community needs to put in effort to find security vulnerabilities in cellular standards up to 5G. Furthermore, as a community, we need to find solutions to these vulnerabilities that are practical enough to be accepted by the standard bodies. In this talk, I will introduce unpatched design vulnerabilities and attacks in cellular standards up to 5G. I will also talk about potential defense mechanisms and reasons why they have not been accepted in 3GPP so far. Bio: Yongdae Kim is a Professor in the Department of Electrical Engineering and the Graduate School of Information Security and a head of Police Science and Technology Research Center at KAIST. He received a PhD degree from the computer science department at the University of Southern California in 2002. Before joining KAIST in 2012, he was a professor in the Department of Computer Science and Engineering at the University of Minnesota - Twin Cities for 10 years. He served as a KAIST Chair Professor between 2013 and 2016 and a director of Cyber Security Research Center between 2018 and 2020. He is currently serving as a steering committee member of ACM WISEC and served as a general chair for ACM CCS 2021, a program committee chair for ACM WISEC 2022, an associate editor for ACM TOPS and a steering committee member of NDSS. His main research interest is finding and fixing novel vulnerabilities for emerging technologies such as drones, self-driving cars, and cellular networks. This seminar is partially supported with funds from the Korhammer Lecture Series Sponsored by: Electrical and Computer Engineering, Computer Science, and the Center for Information Technology Policy

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Princeton Robotics Seminar - Andy Zeng, Fri. Nov 11
by Emily C. Lawrence 08 Nov '22

08 Nov '22

Princeton Robotics Seminar Speaker: Andy Zeng, Senior Research Scientist, Google Brain Date: Friday Nov 11, 2022 Time: 11am EST Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Language as Robot Middleware Abstract: We'd like to build robots that can help us with just about anything. Central to this is getting robots to build a general-purpose representation of the world from perception, and then use it to inform actions. Should this representation be 2D? or 3D? how do we "anchor" it onto a desired latent space? should it be an implicit representation? object-centric? can it be self-supervised? While many options exist out there, I'll talk about one in particular that's becoming my favorite – natural language. Partly motivated by the advent of large language models, but also motivated by recent work in multi-task learning. In the context of robots, I'll talk about: (i) why we're starting to think that it might actually be a good idea to revisit "language" as a symbolic representation to glue our systems together to do cool things, and (ii) in the process of building these systems, discovering various "gaps" in grounding language to control that I think we could really use your help in figuring out. Biography: Andy Zeng is a Senior Research Scientist at Google Brain working on vision and language for robotics. He received his Bachelors in Computer Science and Mathematics at UC Berkeley '15, and his PhD in Computer Science at Princeton University '19. Andy is a recipient of several awards including the Best Paper Award at T-RO '20, Best Systems Paper Awards at RSS '19 and Amazon Robotics '18, 1st Place (Stow) at the Amazon Picking Challenge '17, and has been finalist for Best Paper Awards at conferences CoRL '21, CoRL '20, ICRA '20, RSS '19, IROS '18. His research has been recognized through the Princeton SEAS Award for Excellence '18, NVIDIA Fellowship '18, and Gordon Y.S. Wu Fellowship in Engineering and Wu Prize '16, and his work has been featured in many popular press outlets, including the New York Times, BBC, and Wired. To learn more about Andy's work please visit [ https://andyzeng.github.io/ | https://andyzeng.github.io ]

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Ludwig Princeton Branch Seminar - Michael Skinnider, Monday, Nov 7
by Emily C. Lawrence 07 Nov '22

07 Nov '22

Ludwig Princeton Branch Seminar Speaker: Michael Skinnider, The University of British Columbia Date: Monday, November 7, 2022 Time: 12:30pm Location: 101 Icahn Title: “Machine learning approaches to human disease” Abstract: Advances in genomic, transcriptomic, and proteomic technologies now enable the routine measurement of thousands of biomolecules within any biological sample. With the advent of single-cell techniques that multiplex these measurements over millions of cells, the scale of biological data generation is outpacing our human ability to comprehend this data. Approaches based on machine intelligence offer the possibility of augmenting our human capacity for understanding in order to enable biological discovery. In this seminar, I will explore applications of machine learning to enable biological discovery from genomic, proteomic, transcriptomic, and metabolomic datasets, and discuss implications for the diagnosis and treatment of human disease. Areas of focus will include the discovery of novel antibiotics from nature using genomic and metabolomic datasets; charting protein-protein interactions within mammalian tissues and across the tree of life using proteomic datasets; mapping neural circuits in high-throughput using single-cell and spatial transcriptomics datasets; and the identification of novel designer drugs of abuse from metabolomic datasets. Looking forward, I will explore how machine learning might bridge the most significant remaining gap in our ability to measure biological systems: namely, our ability to measure all of the small molecules within any biological sample. I will discuss the use of chemical artificial intelligence to identify unknown molecules within mass spectrometry data, and the implications of this technology for our understanding of cancer metabolism and germline cancer risk. Bio: Michael is an MD/PhD who has carried out groundbreaking work using artificial intelligence to infer the structures of small molecules, from metabolites to designer drugs, impacting human health.

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