- talks - lists.cs.princeton.edu

Shai Caspin will present her General Exam "Enabling Privacy for Decentralized Applications with Noise" on Thursday, May 11, 2023 at 10:00 AM in Friend 125
by Louis W. Riehl 08 May '23

08 May '23

Shai Caspin will present her General Exam "Enabling Privacy for Decentralized Applications with Noise" on Thursday, May 11, 2023 at 10:00 AM in Friend 125. Committee Members: Amit Levy (advisor), Ravi Netravali, David Walker Abstract: Applications often use a centralized server architecture to store user data and mediate sharing. Unfortunately, this has resulted in repeated data leaks due to incorrect access control, cyber attacks, legal action, or malicious administrators. End-to-end encryption can largely avoid this problem by hiding user data from servers altogether. Leveraging existing end-to-end encryption frameworks is difficult, since they are highly specialized for their applications, enforce only simple data models, have rigid access control schemes, and provide weak consistency guarantees. I will present Noise, an application-agnostic platform that provides end-to-end encryption for many possible applications, across many devices, on top of a common core design. At its core, Noise establishes end-to-end encryption and strong consistency guarantees across all clients in the system, and maintains these guarantees in the face of Byzantine actors. I will discuss how to build data stores with varying consistency guarantees in this model. Additionally, I will discuss schemes to verify correct behavior in untrusted settings across distributed storage and replication. Reading List: [ https://docs.google.com/document/d/10maUgYVqeMQc0d5M1eU_qGLwQXMjWLUfWcgsI-B… | https://docs.google.com/document/d/10maUgYVqeMQc0d5M1eU_qGLwQXMjWLUfWcgsI-B… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Henri Schmidt will present his General Exam "Combinatorial models for reconstructing evolutionary histories" on Monday, May 8, 2023 at 11:00 AM in CS 301.
by Louis W. Riehl 03 May '23

03 May '23

Henri Schmidt will present his General Exam "Combinatorial models for reconstructing evolutionary histories" on Monday, May 8, 2023 at 11:00 AM in CS 301. Committee Members: Ben Raphael (advisor), Yuri Pritykin, Olga Troyanskya Abstract: Reconstructing evolutionary histories is a fundamental problem in biology, studied across life's organizational hierarchy. Often, this evolutionary reconstruction task, referred to as phylogenetic inference, is framed as a combinatorial optimization problem under an appropriate evolutionary model. In this work, we examine two such phylogenetic inference problems, lineage tracing and the copy number tree problem, through the lens of combinatorial optimization. CRISPR-Cas9 based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or lineage tracing, in tissues and whole organisms. While standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9 induced mutations. Here, we introduce the star homoplasy model, a novel evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the non-modifiability property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies by identifying a relationship between the star homoplasy model and the binary perfect phylogeny model. We use this characterization to develop an algorithm, Startle (Star tree lineage estimator), that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle outperforms other methods at lineage reconstruction on both real and simulated data. Low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. >From this data, one can infer the evolutionary history of the tumor, referred to as a copy number phylogeny, by modeling transformations of the genome via copy number aberrations. A widely used model to infer such copy number phylogenies is the copy number transformation (CNT) model. While the CNT model is useful, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model due to its difficult, combinatorial properties. Here, we introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of genomic loci with zero copies. We use our simplified model to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT model is a close approximation to the CNT model. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data. Reading List: [ https://docs.google.com/document/d/1pHhFrh2M6rcrGaRpMyDlAvmB5l50sPs8Y-PHtIc… | https://docs.google.com/document/d/1pHhFrh2M6rcrGaRpMyDlAvmB5l50sPs8Y-PHtIc… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Princeton Wireless Distinguished Seminar Series: Francesco Restucci, tomorrow, May 4th at 4:30pm
by Emily C. Lawrence 03 May '23

03 May '23

ECE Korhammer Seminar Series Princeton Wireless Distinguished Seminar Series Speaker : Francesco Restuccia, Northeastern University Day : May 4, 2023 Time : 4:30 pm Room : B205 Engineering Quadrangle Host : Yasaman Ghasempour & Pramod Viswanath Title: AI for Wireless and Wireless for AI: A Tale of Two AIs Abstract : The massive scale and strict performance requirements of next-generation wireless networks will require mobile devices to perform real-time fine-grained optimization of their spectrum usage. Yet, today's networking protocols and architectures are deeply rooted in inflexible designs, and utilize optimization models and strategies that are either too complex or too oversimplified to be fully effective in today's crowded spectrum environment. At the same time, next-generation networks will need to support the continuous execution of resource-expensive edge-assisted deep learning tasks. In other words, wireless systems and AI will need to tightly cooperate with each other in future networks. Specifically, network resources will need to be carefully optimized to satisfy heterogeneous application requirements while minimizing network usage, while the AI needs to be dynamically adapted to minimize wireless resource usage. In this talk, we are going to introduce and discuss our recent research toward the design of AI-driven self-adaptive spectrum-aware wireless systems, as well as our research on edge task offloading and split computing for effective and efficient mobile inference. Bio : Francesco Restuccia is an Assistant Professor in the Department of Electrical and Computer Engineering, and a member of the Institute for the Wireless Internet of Things and the Roux Institute at Northeastern University. His research interests lie in the design and experimental evaluation of next-generation edge-assisted data-driven wireless systems. Dr. Restuccia's research is funded by several grants from the National Science Foundation and the Department of Defense. He received the Office of Naval Research Young Investigator Award, the Air Force Office of Scientific Research Young Investigator Award and the Mario Gerla Award in Computer Science, as well as best paper awards at IEEE INFOCOM and IEEE WOWMOM. Dr. Restuccia has published over 60 papers in top-tier venues in computer networking, as well as co-authoring 16 U.S. patents and three book chapters. Dr. Restuccia serves as technical program committee member for several top-tier IEEE and ACM conferences. He is an Area Editor of Computer Networks and serves as Chair of the FutureG Working Group at the National Spectrum Consortium. This seminar is supported with funds from the Korhammer Lecture Series. Kevin Shaw Faculty Assistant Electrical and Computer Engineering B229 Engineering Quadrangle EM: [ mailto:kevinshaw@princeton.edu | kevinshaw(a)princeton.edu ] PH: 609-258-8090

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Art of Science 2023 Opening Reception: Tuesday, May 2 at 4:30pm
by Emily C. Lawrence 02 May '23

02 May '23

[ https://artofsci.princeton.edu/ | Art of Science 2023 Opening Reception ] Join us Tuesday to celebrate the opening of this year’s Art of Science exhibition at the Friend Center. Winners will be announced, food and drink will be served. Tuesday, May 2, 2023 4:30-6:00pm Main Atrium, Friend Center ( [ https://goo.gl/maps/pYaixxBipNBbD74h6 | Map It ] ) Princeton University Celebrating its 17th year, Art of Science explores the interplay between science and art. This year’s exhibition features still images, videos and animations from 50 creators—including students, alumni, staff and faculty from across the Princeton University community. Visit [ https://artofsci.princeton.edu/ | artofsci.princeton.edu ] for details. We look forward to seeing you at the event! The Exhibition Organizers

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Princeton Precision Health (PPH) workshop: tomorrow, May 2
by Emily C. Lawrence 01 May '23

01 May '23

Hi everyone, Just a reminder that tomorrow , May 2, we will be holding the first Princeton Precision Health (PPH) workshop on interdisciplinary, date-driven approaches for precision medicine. We will bring together biomedical researchers, clinicians, computer scientists, ethicists, and social scientists to discuss a wide range of topics, working toward our goal of enabling more responsive, equitable, and precisely targeted health care. Registration is free but required for an accurate head count for breakfast and lunch: [ https://forms.gle/XFa4RSNhcrDTo5gp7 | https://forms.gle/XFa4RSNhcrDTo5gp7 ] The schedule is below. Hope to see you there! Olga *************************************************************** Olga Troyanskaya Director, Princeton Precision Health Professor of Computer Science and the Lewis-Sigler Institute for Integrative Genomics Princeton University 9:00 am - coffee and light breakfast, Icahn Lab Oval Lounge 9:45-10:00 am: Welcome and Overview. Olga Troyanskaya, PhD , Director, PPH, Professor of Computer Science and the Lewis-Sigler Institute for Integrative Genomics, Princeton University. Icahn Lab 101 10:00-11:00 am: Integrating data across disciplines and continents for targeted therapies in kidney diseases. Matthias Kretzler , MD , Warner-Lambert/Parke-Davis Professor, Internal Medicine, University of Michigan. 11:00-11:30 am: Integrating ethics into precision health: developing a framework . Zoë Fritz, MBBS, Wellcome fellow in Society and Ethics, University of Cambridge, Consultant Physician in Acute medicine at Addenbrooke’s Hospital. 11:30-12:00 pm: The case for equitable computational method development for precision oncology. Mona Singh , PhD , Wang Family Professor in Computer Science. Professor of Computer Science and the Lewis-Sigler Institute for Integrative Genomics, Princeton University. 12:00-1:00 pm lunch, Oval Lounge 1:00-1:30 pm: Defining and Slowing Reproductive Aging. Coleen Murphy, PhD , James A. Elkins Jr. Professor in the Life Sciences. Professor of Molecular Biology and the Lewis-Sigler Institute for Integrative Genomics, Director of the Paul Glenn Laboratories for Aging Research at Princeton University. 1:30-2:30 pm: Ethical Considerations in Predicting and Tracking Health Outcomes. Presentations and Panel Discussion: Ian Peebles, Harold T. Shapiro Postdoctoral Research Associate in Bioethics at the Center for Human Values at Princeton University; David Kinney, Postdoctoral Research Associate in Cognitive Science of Values, at the Center for Human Values at Princeton University; Emily Foster-Hanson , Postdoctoral Research Associate in Cognitive Science of Values at Princeton University (Current); Assistant Professor of Psychology at Swarthmore College (Fall 2023). 2:30-3:00 pm: Break, Oval Lounge 3:00-4:00 pm: Going beyond bioethics to doing justice in precision health. Sridhar Venkatapuram, PhD , Deputy Director, Global Health, Director of Global Health Education and Training, King’s College London. 4:00-4:30 pm: From genomics to clinical phenotypes in neuropsychiatric disease. Olga Troyanskaya, PhD Closing remarks, light reception to follow.

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DeCenter Seminar: Ari Juels, Thursday, May 4 at 12:30pm
by Emily C. Lawrence 01 May '23

01 May '23

DeCenter Seminar: Non-Fungible Tokens (NFTs): Two Market Challenges Speaker: Ari Juels, Cornell Tech [ https://decenter.princeton.edu/events/decenter-seminar-non-fungible-tokens-… | https://decenter.princeton.edu/events/decenter-seminar-non-fungible-tokens-… ] Thursday, May 4, 2023 - 12:30pm-1:20pm Location: Computer Science Room 105. Lunch will be available beginning at noon. A recording will be available on the DeCenter website after the seminar. Abstract: Non-fungible tokens (NFTs) have leapt to prominence as a technical and social phenomenon. Collections such as the Bored Ape Yacht Club (BAYC) remain popular and have active communities even in the midst of the current “crypto winter.” The dynamics around NFT sales, however, leave much to be desired. I’ll discuss two problems in NFT markets. The first relates to “drops,” i.e., bulk sales, of popular NFTs today. It’s hard to achieve fairness in drops: Bots often snap up new NFT offerings and corner the market. The second problem relates to royalty payments for creators, one of the most attractive features of NFTs. Royalties have become a battleground among NFT markets, which often leads to creators being deprived of royalties or market access. I’ll discuss candidate solutions to these two problems using privacy-preserving oracles systems and a new cryptographic concept called Complete Knowledge. Bio : Ari Juels is the Weill Family Foundation and Joan and Sanford I. Weill Professor in the [ http://tech.cornell.edu/experience/jacobs-institute/ | Jacobs Technion-Cornell Institute ] at [ http://tech.cornell.edu/ | Cornell Tech ] and the Technion and a Computer Science faculty member at [ http://www.cornell.edu/ | Cornell University ] . He is a Co-Director of the [ http://www.initc3.org/ | Initiative for CryptoCurrencies and Contracts (IC3) ] . He is also Chief Scientist at [ https://chainlinklabs.com/ | Chainlink Labs ] .

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CS Colloquium Speaker Lydia Liu TODAY at 12:30pm
by Emily C. Lawrence 01 May '23

01 May '23

Speaker: Lydia Liu, Cornell University Date: Wednesday, April 5 Time: 12:30pm EST Location: CS 105 Hosts: Olga Russakovsky and Peter Ramadge Event page: https://www.cs.princeton.edu/events/26353 Title: Towards Responsible Machine Learning in Societal Systems Abstract: Machine learning systems are deployed in consequential domains such as education, employment, and credit, where decisions have profound effects on socioeconomic opportunity and life outcomes. High stakes decision settings present new statistical, algorithmic, and ethical challenges. In this talk, we examine the distributive impact of machine learning algorithms in societal contexts, and investigate the algorithmic and sociotechnical interventions that bring machine learning systems into alignment with societal values---equity and long-term welfare. First, we study the dynamic interactions between machine learning algorithms and populations, for the purpose of mitigating disparate impact in applications such as algorithmic lending and hiring. Next, we consider data-driven decision systems in competitive environments such as markets, and devise learning algorithms to ensure efficiency and allocative fairness. We end by outlining future directions for responsible machine learning in societal systems that bridge the gap between the optimization of predictive models and the evaluation of downstream decisions and impact. Bio: Lydia T. Liu is a postdoctoral researcher in Computer Science at Cornell University, working with Jon Kleinberg, Karen Levy, and Solon Barocas. Her research examines the theoretical foundations of machine learning and algorithmic decision-making, with a focus on societal impact and human welfare. She obtained her PhD in Electrical Engineering and Computer Sciences from UC Berkeley, advised by Moritz Hardt and Michael Jordan, and has received a Microsoft Ada Lovelace Fellowship, an Open Philanthropy AI Fellowship, an NUS Development Grant, and a Best Paper Award at the International Conference on Machine Learning. This talk is co-sponsored with Electrical and Computer Engineering and the Center for Information Technology Policy.

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Mike Wong will present his General Exam "MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations" on Monday, May 15, 2023 at 1pm in CS 302.
by Nicki Mahler 28 Apr '23

28 Apr '23

Mike Wong will present his General Exam "MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations" on Monday, May 15, 2023 at 1pm in CS 302. The members of his committee are as follows: Ravi Netravali (adviser), Amit Levy, and Wyatt Lloyd Abstract: Camera orientations (i.e., rotation and zoom) govern the content that a camera captures in a given scene, which in turn heavily influences the accuracy of live video analytics pipelines. However, existing analytics approaches leave this crucial adaptation knob untouched, instead opting to only alter the way that captured images from fixed orientations are encoded, streamed, and analyzed. We present MadEye, a camera-server system that automatically and continually adapts orientations to maximize accuracy for the workload and resource constraints at hand. To realize this using commodity pan-tilt-zoom (PTZ) cameras, MadEye embeds (1) a search algorithm that rapidly explores the massive space of orientations to identify a fruitful subset at each time, and (2) a novel knowledge distillation strategy to efficiently (with only camera resources) select the ones that maximize workload accuracy. Experiments on diverse workloads show that MadEye boosts accuracy by 2.9-25.7% for the same resource usage, or achieves the same accuracy with 2-3.7× lower resource costs. Link to abstract and reading list can be found here: [ https://docs.google.com/document/d/1bQBds_E_Iiq0D8pIM0MYTV4gakVTCBWa1bUh4my… | https://docs.google.com/document/d/1bQBds_E_Iiq0D8pIM0MYTV4gakVTCBWa1bUh4my… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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CITP Special Event: Thursday, May 4 at 4:30pm
by Emily C. Lawrence 28 Apr '23

28 Apr '23

CITP Special Event: Confused by All the Chatter? Journalists, Researchers & Policymakers Talk Chatbots and Other Large Language Models Date: Thursday, May 4, 2023 Time: 4:30 pm - 6:00 pm Location: Lewis Auditorium, Robertson Hall Princeton, 08540 United States [ https://www.google.com/maps/search/?api=1&query=40.3815302%2C-74.651754 | + Google Map ] As part of the CITP Digital Investigators Conference the public is invited to join us in person or virtually for this event. Please register [ https://docs.google.com/forms/d/1VgdnePbpgNWi-xdBNMdXxSrwS3pdiDxtz_twsv_jgV… | here ] to attend in person. The livestream will be available [ https://mediacentrallive.princeton.edu/ | here. ] Powerful new technologies like [ https://openai.com/blog/chatgpt | OpenAI’s “ChatGPT” ] or [ https://blog.google/technology/ai/bard-google-ai-search-updates/ | Google’s “Bard” ] have sparked excitement over the potential they have to transform how we work, learn and communicate for the better. But their potential harms also trigger fears and unease. As a result, the public discourse around such large language models (LLMs) can be noisy or chaotic. CITP has convened a panel of experts from the journalism, tech research and public policy sectors to discuss their experiences with – and approaches to – engaging with these emerging technologies in their respective professions. We will also talk about the responsibilities journalists and academics may have in shaping the public conversation around digital technologies, and how they can support each other’s work for the benefit of the public. Panel Speakers: [ https://juliaangwin.com/ | Julia Angwin ] is an award-winning investigative journalist and contributing opinion writer at The New York Times. She founded The Markup, a nonprofit newsroom that investigates the impacts of technology on society, and is Entrepreneur in Residence at Columbia Journalism School’s Brown Institute. Angwin was previously a senior reporter at the independent news organization ProPublica, where she led an investigative team that was a finalist for a Pulitzer Prize in Explanatory Reporting in 2017 and won a Gerald Loeb Award in 2018. From 2000 to 2013, she was a reporter at The Wall Street Journal, where she led a privacy investigative team that was a finalist for a Pulitzer Prize in Explanatory Reporting in 2011 and won a Gerald Loeb Award in 2010. In 2003, she was on a team of reporters at The Wall Street Journal that was awarded the Pulitzer Prize in Explanatory Reporting for coverage of corporate corruption. She is also the author of “Dragnet Nation: A Quest for Privacy, Security and Freedom in a World of Relentless Surveillance” (Times Books, 2014) and “Stealing MySpace: The Battle to Control the Most Popular Website in America” (Random House, March 2009). She earned a B.A. in mathematics from the University of Chicago, and an M.B.A. from the Graduate School of Business at Columbia University. [ http://sorelle.friedler.net/ | Sorelle Friedler ] is the Shibulal Family Associate Professor of Computer Science at Haverford College. She served as the assistant director for Data and Democracy in the White House Office of Science and Technology Policy under the Biden-Harris Administration where her work included the Blueprint for an AI Bill of Rights. Her research focuses on the fairness and interpretability of machine learning algorithms, with applications from criminal justice to materials discovery. She holds a Ph.D. in computer science from the University of Maryland, College Park. [ https://www.cs.princeton.edu/~arvindn/ | Arvind Narayanan ] is a professor of computer science at Princeton University. He co-authored a textbook on fairness and machine learning and is currently co-authoring a book on AI snake oil. He led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information. His work was among the first to show how machine learning reflects cultural stereotypes, and his doctoral research showed the fundamental limits of de-identification. Narayanan is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), twice a recipient of the Privacy Enhancing Technologies Award, and thrice a recipient of the Privacy Papers for Policy Makers Award. To request accommodations for a disability please contact Jean Butcher, butcher(a)princeton.edu , at least one week prior to the event. This seminar will be recorded and posted to the CITP website, YouTube channel and Princeton University’s Media Central channel.

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Alex Guerra will present his General Exam "Neuro-Symbolic Function Representations" on Wednesday, May 3, 2023 at 9:00 AM in CS 402 and via zoom.
by Louis W. Riehl 28 Apr '23

28 Apr '23

Alex Guerra will present his General Exam "Neuro-Symbolic Function Representations" on Wednesday, May 3, 2023 at 9:00 AM in CS 402 and via zoom. Zoom link: https://princeton.zoom.us/j/9379621328 Committee Members: Ryan Adams (advisor), Jia Deng, Tom Griffiths Abstract: Science is driven by the quest to find patterns in the universe. Patterns can be expressed as functions, with inputs that give rise to some output. In machine learning, we typically aim to find useful representations of data. However, because the universe is most naturally described by the functions giving rise to this data, we argue that being able to represent functions themselves is a more general, and hence powerful capability. By creating representations of functions themselves, we can learn operators on top of these functions that give us a powerful set of tools to solve problems in new ways. In this work, we introduce a novel method of representing functions, along with a way of learning operators on top of these representations such as composition, differentiation, and integration. We demonstrate how these tools can help us solve a wide array of scientific problems in new ways. The results shown here are merely a small sample of the capabilities of these representations can afford us, and we hope that this work serves to inspire the reader to think about their own problems from a new perspective. At its core, this method is a highly general way of modelling interaction, and we hope that it inspires a new way of thinking about representing symbolic structures. Reading List: https://docs.google.com/document/d/1Y67d2G0fRF-jxLLYB3pop0LQV9V285LE-ErNl6B… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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