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DTSTART;TZID=America/Los_Angeles:20251112T110000
DTEND;TZID=America/Los_Angeles:20251112T120000
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SUMMARY:TILOS-HDSI Seminar: Adam Oberman - AI Safety Theory: The Missing Middle Ground
DESCRIPTION:The next TILOS-HDSI seminar will be Wednesday\, November 12 at 11am PST with Adam Oberman (McGill University). The title is AI Safety Theory: The Missing Middle Ground. \nTalk Information \nSpeaker: Adam Oberman (McGill University) \nDate & Time: Wednesday\, November 12 @ 11am PST \nVenue: HDSI 123 \nAbstract: Over the past few years\, the capabilities of generative artificial intelligence (AI) systems have advanced rapidly. Along with the benefits of AI\, there is also a risk of harm. In order to benefit from AI while mitigating the risks\, we need a grounded theoretical framework. \nThe current AI safety theory\, which predates generative AI\, is insufficient. Most theoretical AI safety results tend to reason absolutely: a system is a system is “aligned” or “mis-aligned”\, “honest” or “dishonest”. But in practice safety is probabilistic\, not absolute. The missing middle ground is a quantitative or relative theory of safety — a way to reason formally about degrees of safety. Such a theory is required for defining safety and harms\, and is essential for technical solutions as well as for making good policy decisions. \nIn this talk I will: \n\nReview current AI risks (from misuse\, from lack of reliability\, and systemic risks to the economy) as well as important future risks (lack of control).\nReview theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.\nExplain why technical and theoretical safety solutions are valuable\, even by contributors outside of the major labs.\nDiscuss some gaps in the theory and present some open problems which could address the gaps.\n\nBio: Adam Oberman is a Full Professor of Mathematics and Statistics at McGill University\, a Canada CIFAR AI Chair\, and an Associate Member of Mila. He is a research collaborator at LawZero\, Yoshua Bengio’s AI Safety Institute. He has been researching AI safety since 2024. His research spans generative models\, reinforcement learning\, optimization\, calibration\, and robustness. Earlier in his career\, he made significant contributions to optimal transport and nonlinear partial differential equations. He earned degrees from the University of Toronto and the University of Chicago\, and previously held faculty and postdoctoral positions at Simon Fraser University and the University of Texas at Austin.
URL:https://datascience.ucsd.edu/event/tilos-hdsi-seminar-adam-oberman-ai-safety-theory-the-missing-middle-ground/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:HDSI Event,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250602T080000
DTEND;TZID=America/Los_Angeles:20250602T190000
DTSTAMP:20250515T153502Z
CREATED:20250515T153502Z
LAST-MODIFIED:20250515T153502Z
UID:10000520-1748851200-1748890800@datascience.ucsd.edu
SUMMARY:TILOS Industry Day 2025
DESCRIPTION:Our 4th Annual Industry Day will be June 2\, 2025\, at the Halıcıoğlu Data Science Institute at UC San Diego\, the campus hub for data science. This year TILOS Industry Day will feature: \n\nTalks from invited industry speakers sharing their perspectives on challenges in AI + Optimization + Use Domains (chips\, robotics\, networking)\nResearch highlights from TILOS team members\nPanel discussions on AI Challenges for Academia—An Industry Perspective and Building Deep Tech Companies\nPoster session featuring the work of TILOS trainees (students and postdoctoral scholars)\n\nMore information can be found at the event site: https://tilos.ai/tilos-industry-day-2025/ \nDate & Time\nMonday\, June 2\, 2025\n8:00am – 7:00pm \nRegistration\nRegistration is complementary but required as space is limited. Register HERE by Wednesday\, May 28\, 2025. \nVenue\nHalıcıoğlu Data Science Institute Room 123\nUniversity of California\, San Diego\n3234 Matthews Lane\nLa Jolla\, CA 92093\n[ MAP ] \nParking\nGilman Parking Structure (252 Russell Ln\, La Jolla\, CA 92093; 5 minute walk to venue). \nHopkins Parking Structure (9800 Hopkins Dr\, La Jolla\, CA 92093; 10 minute walk to venue). \nParking fees are payable at pay stations or pay-by-phone. Note that many visitor spots are limited to two hours. Even though the app allows you to pay for longer periods\, you will get a ticket after that time if parked in a 2-hour space. \nSchedule (subject to change)\n\n\n\n\n\n\n\n\n8:00 – 8:45am\nRegistration & Breakfast\n\n\n8:45 – 9:00am\nOpening Remarks\nYusu Wang\, TILOS Director\nVijay Kumar\, TILOS Associate Director of Translation\n\n\n9:00 – 10:05am\nSESSION 1 Chair: David Pan\, TILOS & UT Austin\n\n\n9:00 – 9:35am\nIndustry Keynote\nMark Ren\, Director of Design Automation Research\, NVIDIA\n\n\n9:35 – 9:50am\nTILOS Faculty Talk\nTajana Rosing\, UC San Diego\n\n\n9:50 – 10:05am\nTILOS Faculty Talk\nHamed Hassani\, University of Pennsylvania\n\n\n10:05 – 10:15am\nShort Break\n\n\n10:15 – 11:20am\nSESSION 2 Chair: Yusu Wang\, TILOS & UC San Diego\n\n\n10:15 – 10:50am\nIndustry Keynote\nVijay Shirsathe\, VP of Engineering\, Qualcomm\n\n\n10:50 – 11:05am\nTILOS Faculty Talk\nAlejandro Ribeiro\, University of Pennsylvania\n\n\n11:05am – 11:20am\nTILOS Faculty Talk\nFarinaz Koushanfar\, UC San Diego\n\n\n11:20 – 11:30am\nShort Break\n\n\n11:30am – 12:15pm\nPanel Discussion: AI Challenges for Academia—An Industry Perspective\nNageen Himayat\, Senior Principal Engineer\, Intel\nSoonho Kang\, Principal Applied Scientist\, Amazon Web Services\nSubarna Tripathi\, Research Scientist\, Intel Labs\nModerator: TBA\n\n\n12:15 – 1:15pm\nLunch\n\n\n1:15 – 2:30pm\nSESSION 3 Chair: TBA\n\n\n1:15 – 2:00pm\nTILOS Research by Industry Partners\nDavid Gonzalez Aguirre\, Research Scientist\, Intel Labs\nMojan Javaheripi\, Senior Researcher\, Microsoft\nTBA\n\n\n2:00 – 2:30pm\nSpotlight Talks for Poster Session\n\n\n2:30 – 3:15pm\nTILOS Trainee Poster Session + Coffee\n\n\n3:15 – 4:05pm\nSESSION 4 Chair: Vijay Kumar\, TILOS & University of Pennsylvania\n\n\n3:15 – 3:50pm\nIndustry Keynote\nJonathan Hurst\, Co-Founder & Chief Robot Officer\, Agility Robotics\n\n\n3:50 – 4:05pm\nTILOS Faculty Talk\nCamillo J. Taylor\, University of Pennsylvania\n\n\n4:05 – 4:15pm\nShort Break\n\n\n4:15 – 5:00pm\nPanel Discussion: Building Deep Tech Companies\nJohn Black\, SVP of Strategy\, Brain Corp\nHenrik Christensen\, TILOS & UC San Diego\nKatie Vasquez\, Calibrate Ventures\nModerator: Vijay Kumar\, TILOS & University of Pennsylvania\n\n\n5:00 – 7:00pm\nDinner
URL:https://datascience.ucsd.edu/event/tilos-industry-day-2025/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Conference,HDSI Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250402T110000
DTEND;TZID=America/Los_Angeles:20250402T120000
DTSTAMP:20250325T211323Z
CREATED:20250325T211323Z
LAST-MODIFIED:20250325T211323Z
UID:10000515-1743591600-1743595200@datascience.ucsd.edu
SUMMARY:TILOS seminar speaker Michael Mahoney (UC Berkeley)  - Foundational Methods for Foundation Models for Scientific Machine Learning
DESCRIPTION:TITLE  Foundational Methods for Foundation Models for Scientific Machine Learning| \nABSTRACT  The remarkable successes of ChatGPT in natural language processing (NLP) and related developments in computer vision (CV) motivate the question of what foundation models would look like and what new advances they would enable\, when built on the rich\, diverse\, multimodal data that are available from large-scale experimental and simulational data in scientific computing (SC)\, broadly defined. Such models could provide a robust and principled foundation for scientific machine learning (SciML)\, going well beyond simply using ML tools developed for internet and social media applications to help solve future scientific problems. I will describe recent work demonstrating the potential of the “pre-train and fine-tune” paradigm\, widely-used in CV and NLP\, for SciML problems\, demonstrating a clear path towards building SciML foundation models; as well as recent work highlighting multiple “failure modes” that arise when trying to interface data-driven ML methodologies with domain-driven SC methodologies\, demonstrating clear obstacles to traversing that path successfully. I will also describe initial work on developing novel methods to address several of these challenges\, as well as their implementations at scale\, a general solution to which will be needed to build robust and reliable SciML models consisting of millions or billions or trillions of parameters. \nBIO Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as head of the Machine Learning and Analytics Group at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning\, including randomized matrix algorithms and randomized numerical linear algebra\, scientific machine learning\, scalable stochastic optimization\, geometric network analysis tools for structure extraction in large informatics graphs\, scalable implicit regularization methods\, computational methods for neural network analysis\, physics informed machine learning\, and applications in genetics\, astronomy\, medical imaging\, social network analysis\, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics\, and he has worked and taught at Yale University in the mathematics department\, at Yahoo Research\, and at Stanford University in the mathematics department. Among other things\, he was on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI)\, he was on the National Research Council’s Committee on the Analysis of Massive Data\, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science\, he ran the Park City Mathematics Institute’s 2016 PCMI Summer Session on The Mathematics of Data\, he ran the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets\, and he was the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/. \nWhen: April 2nd 11am \nLocation: HDSI MPR 123
URL:https://datascience.ucsd.edu/event/tilos-seminar-speaker-michael-mahoney-uc-berkeley-foundational-methods-for-foundation-models-for-scientific-machine-learning/
LOCATION:HDSI 123
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250327T140000
DTEND;TZID=America/Los_Angeles:20250327T150000
DTSTAMP:20250325T192334Z
CREATED:20250325T192334Z
LAST-MODIFIED:20250325T192334Z
UID:10000514-1743084000-1743087600@datascience.ucsd.edu
SUMMARY:Special TILOS Seminar: Claire Boyer | Single location regression and attention-based models
DESCRIPTION:Talk Information \nSpeaker: Claire Boyer (Université Paris-Saclay) \nDate & Time: Thursday\, March 27 @ 2pm PDT \nVenue: HDSI 123 \nTitle: Single location regression and attention-based models \nAbstract: Attention-based models\, such as Transformer\, excel across various tasks but lack a comprehensive theoretical understanding\, especially regarding token-wise sparsity and internal linear representations. To address this gap\, we introduce the single-location regression task\, where only one token in a sequence determines the output\, and its position is a latent random variable\, retrievable via a linear projection of the input. To solve this task\, we propose a dedicated predictor\, which turns out to be a simplified version of a non-linear self-attention layer. We study its theoretical properties\, by showing its asymptotic Bayes optimality and analyzing its training dynamics. In particular\, despite the non-convex nature of the problem\, the predictor effectively learns the underlying structure. This work highlights the capacity of attention mechanisms to handle sparse token information and internal linear structures.
URL:https://datascience.ucsd.edu/event/special-tilos-seminar-claire-boyer-single-location-regression-and-attention-based-models/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Special Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240724T100000
DTEND;TZID=America/Los_Angeles:20240724T110000
DTSTAMP:20240717T171141Z
CREATED:20240717T171141Z
LAST-MODIFIED:20240717T171141Z
UID:10000489-1721815200-1721818800@datascience.ucsd.edu
SUMMARY:HDSI/TILOS Seminar | Rob Nowak | What Kinds of Functions do Neural Networks Learn? Theory and Practical Applications
DESCRIPTION:When Wednesday July 24th 10:00am *Updated\nWhere: HDSI 123 * Updated\nZoom Info: https://ucsd.zoom.us/j/99334315002 *Updated \nTitle: What Kinds of Functions do Neural Networks Learn?  Theory and Practical Applications \nAbstract:  This talk presents a theory characterizing the types of functions neural networks learn from data. Specifically\, the function space generated by deep ReLU networks consists of compositions of functions from the Banach space of second-order bounded variation in the Radon transform domain. This Banach space includes functions with smooth projections in most directions. A representer theorem associated with this space demonstrates that finite-width neural networks suffice for fitting finite datasets. The theory has several practical applications. First\, it provides a simple and theoretically grounded method for network compression. Second\, it shows that multi-task training can yield significantly different solutions compared to single-task training\, and that multi-task solutions can be related to kernel ridge regressions. Third\, the theory has implications for improving implicit neural representations\, where multi-layer neural networks are used to represent continuous signals\, images\, or 3D scenes. This exploration bridges theoretical insights with practical advancements\, offering a new perspective on neural network capabilities and future research directions.\nBio: Robert Nowak is the Grace Wahba Professor of Data Science and Keith and Jane Nosbusch Professor in Electrical and Computer Engineering at the University of Wisconsin-Madison. His research focuses on machine learning\, optimization\, and signal processing. He serves on the editorial boards of the SIAM Journal on the Mathematics of Data Science and the IEEE Journal on Selected Areas in Information Theory.meeting with him. If…
URL:https://datascience.ucsd.edu/event/hdsi-tilos-seminar-rob-nowak-what-kinds-of-functions-do-neural-networks-learn-theory-and-practical-applications/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240618T080000
DTEND;TZID=America/Los_Angeles:20240618T193000
DTSTAMP:20240612T152605Z
CREATED:20240612T152605Z
LAST-MODIFIED:20240612T152605Z
UID:10000485-1718697600-1718739000@datascience.ucsd.edu
SUMMARY:TILOS Industry Day
DESCRIPTION:EVENT WEBSITE \n\n\n\n8:00 – 8:45am\nBreakfast\n\n\n8:45 – 9:00am\nWelcome Remarks and Introduction to TILOS\nDirector Yusu Wang (UCSD) and AD Translation Vijay Kumar (UPenn)\n\n\n9:00 – 10:30am\nSESSION 1\nIndustry Keynote: Nicholas Roy (Zoox and MIT)\nTILOS Faculty Highlights:\nFarinaz Koushanfar (UCSD)\nNisheeth Vishnoi (Yale U)\n\n\n10:30 – 10:45am\nBreak (15 minutes)\n\n\n10:45am – 12:15pm\nSESSION 2\nIndustry Keynote: Nageen Himayat (Intel Labs)\nTILOS Faculty Highlights:\nAlejandro Ribeiro (UPenn)\nSean Gao (UCSD)\n\n\n12:15 – 2:00pm\nTILOS Trainee Poster Lightning Preview Session + Lunch\n\n\n2:00 – 3:00pm\nPanel on Academic-Industry Relations / Collaborations\nInvited Panelists:\nNing Bi (Qualcomm VP Engineering)\nVitaly Feldman (Apple ML Research)\nKatherine Heller (Google Responsible AI)\nTara Javidi (UCSD)\nSomdeb Majumdar (Intel AI/ML Lab)\nModerator: Vijay Kumar (TILOS AD Translation\, UPenn)\n\n\n3:00 – 3:30pm\nBreak (30 minutes)\n\n\n3:30 – 5:00pm\nSESSION 3\nIndustry Keynote: Carolina Parada (Google DeepMind)\nTILOS Faculty Highlights:\nNikolay Atanasov (UCSD)\nMisha Belkin (UCSD)\n\n\n5:00 – 7:30pm\nBuffet Dinner and Trainee Poster Session
URL:https://tilos.ai/events/tilos-industry-day-2024/#new_tab
LOCATION:Halıcıoğlu Data Science Institute\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240522T100000
DTEND;TZID=America/Los_Angeles:20240522T110000
DTSTAMP:20240521T224521Z
CREATED:20240521T224521Z
LAST-MODIFIED:20240521T224521Z
UID:10000480-1716372000-1716375600@datascience.ucsd.edu
SUMMARY:TILOS Seminar: Large Datasets and Models for Robots in the Real World
DESCRIPTION:Large Datasets and Models for Robots in the Real World\nNicklas Hansen\, UC San Diego\nHDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: Recent progress in AI can be attributed to the emergence of large models trained on large datasets. However\, teaching AI agents to reliably interact with our physical world has proven challenging\, which is in part due to a lack of large and sufficiently diverse robot datasets. In this talk\, I will cover ongoing efforts of the Open X-Embodiment project–a collaboration between 279 researchers across 20+ institutions–to build a large\, open dataset for real-world robotics\, and discuss how this new paradigm is rapidly changing the field. Concretely\, I will discuss why we need large datasets in robotics\, what such datasets may look like\, and how large models can be trained and evaluated effectively in a cross-embodiment cross-environment setting. Finally\, I will conclude the talk by sharing my perspective on the limitations of current embodied AI agents\, as well as how to move forward as a community. \n\nNicklas Hansen is a Ph.D. student at University of California San Diego advised by Prof. Xiaolong Wang and Prof. Hao Su. His research focuses on developing generalist AI agents that learn from interaction with the physical and digital world. He has spent time at Meta AI (FAIR) and University of California Berkeley (BAIR)\, and received his B.S. and M.S. degrees from Technical University of Denmark. He is a recipient of the 2024 NVIDIA Graduate Fellowship\, and his work has been featured at top venues in machine learning and robotics. Webpage: www.nicklashansen.com
URL:https://datascience.ucsd.edu/event/tilos-seminar-large-datasets-and-models-for-robots-in-the-real-world/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240417T100000
DTEND;TZID=America/Los_Angeles:20240417T110000
DTSTAMP:20240409T185225Z
CREATED:20240409T185225Z
LAST-MODIFIED:20240409T185225Z
UID:10000469-1713348000-1713351600@datascience.ucsd.edu
SUMMARY:TILOS Seminar: Transformers learn in-context by (functional) gradient descent
DESCRIPTION:Transformers learn in-context by (functional) gradient descent\nXiang Cheng\, TILOS Postdoctoral Scholar at MIT\nHDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: Motivated by the in-context learning phenomenon\, we investigate how the Transformer neural network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent\, which enables it to learn linear functions in-context. More generally\, we show that a non-linear Transformer can implement functional gradient descent with respect to some RKHS metric\, which allows it to learn a broad class of functions in-context. Additionally\, we show that the RKHS metric is determined by the choice of attention activation\, and that the optimal choice of attention activation depends in a natural way on the class of functions that need to be learned. I will end by discussing some implications of our results for the choice and design of Transformer architectures.
URL:https://datascience.ucsd.edu/event/tilos-seminar-transformers-learn-in-context-by-functional-gradient-descent/
LOCATION:Virtual
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240320T100000
DTEND;TZID=America/Los_Angeles:20240320T110000
DTSTAMP:20240318T224925Z
CREATED:20240318T224758Z
LAST-MODIFIED:20240318T224925Z
UID:10000461-1710928800-1710932400@datascience.ucsd.edu
SUMMARY:TILOS Seminar: How Large Models of Language and Vision Help Agents to Learn to Behave
DESCRIPTION:Roy Fox\, Assistant Professor and Director of the Intelligent Dynamics Lab at UC Irvine\nHDSI 123 and Zoom (Link below) \nAbstract: If learning from data is valuable\, can learning from big data be very valuable? So far\, it has been so in vision and language\, for which foundation models can be trained on web-scale data to support a plethora of downstream tasks; not so much in control\, for which scalable learning remains elusive. Can information encoded in vision and language models guide reinforcement learning of control policies? In this talk\, I will discuss several ways for foundation models to help agents to learn to behave. Language models can provide better context for decision-making: we will see how they can succinctly describe the world state to focus the agent on relevant features; and how they can form generalizable skills that identify key subgoals. Vision and vision–language models can help the agent to model the world: we will see how they can block visual distractions to keep state representations task-relevant; and how they can hypothesize about abstract world models that guide exploration and planning. \nBio: Roy Fox is an Assistant Professor of Computer Science at the University of California\, Irvine. His research interests include theory and applications of control learning: reinforcement learning (RL)\, control theory\, information theory\, and robotics. His current research focuses on structured and model-based RL\, language for RL and RL for language\, and optimization in deep control learning of virtual and physical agents.
URL:https://datascience.ucsd.edu/event/tilos-seminar-how-large-models-of-language-and-vision-help-agents-to-learn-to-behave/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240308T120000
DTEND;TZID=America/Los_Angeles:20240308T130000
DTSTAMP:20240318T225308Z
CREATED:20240226T212933Z
LAST-MODIFIED:20240318T225308Z
UID:10000447-1709899200-1709902800@datascience.ucsd.edu
SUMMARY:TILOS Webinar: AI Ethics in Research
DESCRIPTION:The Ethics and Early Career Committee would like to invite you to our upcoming webinar on AI Ethics in Research. This will take place virtually through Zoom on Friday\, March 8th at noon Pacific\, 2pm Central\, 3pm Eastern (https://nu.zoom.us/j/2183621123). \nPlease join Dr. Nisheeth Vishnoi from Yale and Dr. David Danks from UC San Diego who will discuss their Research in AI Ethics. Professor Danks develops practical frameworks and methods to incorporate ethical and policy considerations throughout the AI lifecycle\, including different ways to include them in optimization steps. Bias and fairness have been a particular focus given the multiple ways in which they can be measured\, represented\, and used. Professor Vishnoi uses optimization as a lens to study how subjective human and societal biases emerge in the objective world of artificial algorithms\, as well as how to design strategies to mitigate these biases. \nThis event is a great opportunity to learn about the constantly evolving issues of AI Ethics in research and the societal impact of AI. It will also provide a platform for students to gain insights and valuable advice that can help them in their future career pursuits. \nNisheeth Vishnoi is the A. Bartlett Giamatti Professor of Computer Science and a co-founder of the Computation and Society Initiative at Yale University. He studies the foundations of computation\, and his research spans several areas of theoretical computer science\, optimization\, and machine learning.  He is also interested in understanding nature and society from a computational viewpoint. Here\, his current focus includes understanding the emergence of intelligence and developing methods to address ethical issues at the interface of artificial intelligence and humanity. \nDavid Danks is Professor of Data Science & Philosophy and affiliate faculty in Computer Science & Engineering at University of California\, San Diego. His research interests range widely across philosophy\, cognitive science\, and machine learning\, including their intersection. Danks has examined the ethical\, psychological\, and policy issues around AI and robotics across multiple sectors\, including transportation\, healthcare\, privacy\, and security. He has also done significant research in computational cognitive science and developed multiple novel causal discovery algorithms for complex types of observational and experimental data. Danks is the recipient of a James S. McDonnell Foundation Scholar Award\, as well as an Andrew Carnegie Fellowship. He currently serves on multiple advisory boards\, including the National AI Advisory Committee.
URL:https://datascience.ucsd.edu/event/tilos-webinar-ai-ethics-in-research/
LOCATION:Virtual
CATEGORIES:Webinar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240118T100000
DTEND;TZID=America/Los_Angeles:20240118T110000
DTSTAMP:20240111T172743Z
CREATED:20240111T172743Z
LAST-MODIFIED:20240111T172743Z
UID:10000422-1705572000-1705575600@datascience.ucsd.edu
SUMMARY:TILOS Seminar: The Dissimilarity Dimension: Sharper Bounds for Optimistic Algorithms
DESCRIPTION:Title: The Dissimilarity Dimension: Sharper Bounds for Optimistic Algorithms\n\nSpeaker: Aldo Pacciano\, Assistant Professor\, Boston University Center for Computing and Data Sciences \nZoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: The principle of Optimism in the Face of Uncertainty (OFU) is one of the foundational algorithmic design choices in Reinforcement Learning and Bandits. Optimistic algorithms balance exploration and exploitation by deploying data collection strategies that maximize expected rewards in plausible models. This is the basis of celebrated algorithms like the Upper Confidence Bound (UCB) for multi-armed bandits. For nearly a decade\, the analysis of optimistic algorithms\, including Optimistic Least Squares\, in the context of rich reward function classes has relied on the concept of eluder dimension\, introduced by Russo and Van Roy in 2013. In this talk we shed light on the limitations of the eluder dimension in capturing the true behavior of optimistic strategies in the realm of function approximation. We remediate these by introducing a novel statistical measure\, the “dissimilarity dimension”. We show it can be used to provide sharper sample analysis of algorithms like Optimistic Least Squares by establishing a link between regret and the dissimilarity dimension. To illustrate this\, we will show that some function classes have arbitrarily large eluder dimension but constant dissimilarity. Our regret analysis draws inspiration from graph theory and may be of interest to the mathematically minded beyond the field of statistical learning theory. This talk sheds new light on the fundamental principle of optimism and its algorithms in the function approximation regime\, advancing our understanding of these concepts. \nBio: Aldo Pacchiano is an Assistant Professor at the Boston University Center for Computing and Data Sciences and a Fellow at the Eric and Wendy Schmidt Center of the broad institute of MIT and Harvard. He obtained his PhD under the supervision of Profs. Michael Jordan and Peter Bartlett at UC Berkeley and was a Postdoctoral Researcher at Microsoft Research\, NYC. His research lies in the areas of Reinforcement Learning\, Online Learning\, Bandits and Algorithmic Fairness. He is particularly interested in furthering our statistical understanding of learning phenomena in adaptive environments and use these theoretical insights and techniques to design efficient and safe algorithms for scientific\, engineering\, and large-scale societal applications.
URL:https://datascience.ucsd.edu/event/tilos-seminar-the-dissimilarity-dimension-sharper-bounds-for-optimistic-algorithms/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240117T100000
DTEND;TZID=America/Los_Angeles:20240117T110000
DTSTAMP:20240116T232430Z
CREATED:20240111T171853Z
LAST-MODIFIED:20240116T232430Z
UID:10000421-1705485600-1705489200@datascience.ucsd.edu
SUMMARY:TILOS Seminar: Medical image reconstruction via deep learning: new architectures\, data reduction and theoretical guarantees
DESCRIPTION:Title: Medical image reconstruction via deep learning: new architectures\, data reduction and theoretical guarantees \nSpeaker: Mahdi Soltanolkotabi\, Director of the Center on AI Foundations for the Sciences (AIF4S) at USC \nZoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: In this talk I will discuss the challenges and opportunities for using deep learning in medical image reconstruction. Contemporary techniques in this field rely on convolutional architectures that are limited by the spatial invariance of their filters and have difficulty modeling long-range dependencies. To remedy this\, I will discuss our work on designing new transformer-based architectures called HUMUS-Net that lead to state of the art performance and do not suffer from these limitations. In the next part of the talk I will report on techniques to significantly reduce the required data for training. Finally\, I will briefly discuss our recent attempts to develop rigorous theory for simple end-to-end training methods used in image reconstruction problems which is surprisingly quite challenging even for simple target functions. Notability\, our theory will be in the rich (or beyond NTK regime) that conforms with practical choice of hyperparameters. Time permitting I will discuss other exciting directions for the use of deep learning in MR. \nBio: Mahdi Soltanolkotabi is the director of the center on AI Foundations for the Sciences (AIF4S) at the University of Southern California. He is also an associate professor in the Departments of Electrical and Computer Engineering\, Computer Science\, and Industrial and Systems engineering where he holds an Andrew and Erna Viterbi Early Career Chair. Prior to joining USC\, he completed his PhD in electrical engineering at Stanford in 2014. He was a postdoctoral researcher in the EECS department at UC Berkeley during the 2014-2015 academic year. Mahdi is the recipient of the Information Theory Society Best Paper Award\, Packard Fellowship in Science and Engineering\, an NIH Director’s new innovator award\, a Sloan Research Fellowship\, an NSF Career award\, an Airforce Office of Research Young Investigator award (AFOSR-YIP)\, the Viterbi school of engineering junior faculty research award\, and faculty awards from Google and Amazon. His research focuses on developing the mathematical foundations of modern data science via characterizing the behavior and pitfalls of contemporary nonconvex learning and optimization algorithms with applications in deep learning\, large scale distributed training\, federated learning\, computational imaging\, and AI for scientific and medical applications.
URL:https://datascience.ucsd.edu/event/tilos-seminar-medical-image-reconstruction-via-deep-learning-new-architectures-data-reduction-and-theoretical-guarantees/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 2154\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2023/10/TILOS-Square_HDSI-Website-e1712854679822.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231011T100000
DTEND;TZID=America/Los_Angeles:20231011T110000
DTSTAMP:20231011T171144Z
CREATED:20231002T224704Z
LAST-MODIFIED:20231011T171144Z
UID:10000402-1697018400-1697022000@datascience.ucsd.edu
SUMMARY:TILOS Seminar: Towards Foundation Models for Graph Reasoning and AI 4 Science
DESCRIPTION:TILOS Seminar: Towards Foundation Models for Graph Reasoning and AI 4 Science\n\n\nMichael Galkin\, Research Scientist at AI Lab\nHDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: Foundation models in graph learning are hard to design due to the lack of common invariances that transfer across different structures and domains. In this talk\, I will give an overview of the two main tracks of my research at Intel AI: creating foundation models for knowledge graph reasoning that can run zero-shot inference on any multi-relational graphs\, and foundation models for materials discovery in the AI4Science domain that capture physical properties of crystal structures and transfer to a variety of predictive and generative tasks. We will also talk about theoretical and practical challenges like scaling behavior\, data scarcity\, and diverse evaluation of foundation graph models. \nBio: Michael Galkin is a Research Scientist at Intel AI Lab in San Diego working on Graph Machine Learning and Geometric Deep Learning. Previously\, he was a postdoc at Mila – Quebec AI Institute with Will Hamilton\, Reihaneh Rabbany\, and Jian Tang\, focusing on many graph representation learning problems. Sometimes\, Mike writes long blog posts on Medium about graph learning.
URL:https://datascience.ucsd.edu/event/tilos-seminar-towards-foundation-models-for-graph-reasoning-and-ai-4-science/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231004T093000
DTEND;TZID=America/Los_Angeles:20231004T103000
DTSTAMP:20231004T165423Z
CREATED:20231002T164337Z
LAST-MODIFIED:20231004T165423Z
UID:10000400-1696411800-1696415400@datascience.ucsd.edu
SUMMARY:Fireside Chat: Theory in the age of modern AI
DESCRIPTION:TILOS Fireside Chat on “Theory in the age of modern AI“\, which will be a conversation led by TILOS team members: Misha Belkin (UCSD)\, Arya Mazumdar (moderator\, UCSD)\, Tara Javidi (UCSD)\, Visheeth Vishnoi (Yale U). The focus will be on the implications to\, and the roles played by theory\, in modern AI (especially with the recent exciting development in LLMs).  \n***************** \nTitle: TILOS Fireside Chat on Theory in the age of modern AI \nPanelists: Misha Belkin (UCSD)\, Arya Mazumdar(moderator\, UCSD)\, Tara Javidi (UCSD)\, Visheeth Vishnoi (Yale U) \nTime: Oct 4 (Wed) @ 9:30am — 10:00am PT / 12:30pm — 1:30pm ET
URL:https://datascience.ucsd.edu/event/tilos-fireside-chat-theory-in-the-age-of-modern-ai/
LOCATION:3234 Matthews Ln\, La Jolla\, 92093\, United States
CATEGORIES:Fireside Chat
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