BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Halıcıoğlu Data Science Institute - UC San Diego - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Halıcıoğlu Data Science Institute - UC San Diego
X-ORIGINAL-URL:https://datascience.ucsd.edu
X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251017T100000
DTEND;TZID=America/Los_Angeles:20251017T113000
DTSTAMP:20260530T075801
CREATED:20251002T162628Z
LAST-MODIFIED:20251002T162628Z
UID:10000525-1760695200-1760700600@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Anupam Datta - What is your Agent’s GPA? Toward Trustworthy Data Agents 
DESCRIPTION:Speaker: Anupam Datta\nDate & Time: Friday Oct 17th\, 10am\nLocation: HDSI Multipurpose Room 123\, 1st floor \nTalk title: What is your Agent’s GPA?\nSubtitle: Toward Trustworthy Data Agents \nAbstract: We introduce the Agent GPA (Goal-Plan-Action) framework: an evaluationparadig m based on an agent’s operational loop of setting goals\, devising plans\, and executing actions. The framework includes five evaluation metrics: Goal Fulfillment\, Logical Consistency\, Execution Efficiency\, Plan Quality\, and Plan Adherence. Logical Consistency checks that an agent’s actions are consistent with its prior actions. Execution Efficiency checks whether the agent executes in the most efficient way to achieve its goal. Plan Quality checks whether an agent’s plans are aligned with its goals; Plan Adherence checks if an agent’s actions are aligned with its plan; and Goal Fulfillment checks that agent’s final outcomes match the stated goals. Our experimental results on two benchmark datasets – the public GAIA dataset and an internal dataset for a production-grade data agent – show that this framework (a) provides a systematic way to cover a broad range of agent failures\, including all agent errors on the GAIA benchmark dataset; (b) exhibits strong agreement between human and LLM judges\, ranging from 80% to over 95%; and (c) localizes errors with 86% agreement with human annotations to enable targeted improvement of agent performance. \nBio: Anupam Datta is a Principal Research Scientist and Snowflake AI Research Lead at Snowflake. He joined Snowflake as part of the acquisition of TruEra where he served as Co-Founder\, President\, and Chief Scientist from 2019-2024. Datta was on the faculty at Carnegie Mellon University from 2007-2022\, most recently as a tenured Professor of Electrical & Computer Engineering and Computer Science. Datta’s current research focuses on Trustworthy AI\, spanning evaluation\, explainability\, fairness\, and adversarial robustness of ML models and GenAI applications. Specific results include early work on Shapley Values & gradient-based explanations\, fairness assessments\, robustness of classical machine learning and deep learning models for natural language processing and computer vision\, and the TruLens open source project for evaluation and experiment tracking of GenAI apps. These research results have had a significant impact on products at TruEra and Snowflake. Datta has published over 100 research papers\, served as Chair of the National Academies Workshop on Assessing and Improving AI Trustworthiness\, on the Steering Committee of of the ACM Conference on Fairness\, Accountability\, and Transparency\, and the IEEE Computer Security Foundations Symposium\, and as an Editor-in-Chief of Foundations and Trends in Privacy and Security. He received the 2018 David P. Casasent Outstanding Research Award from the CMU College of Engineering\, a 2020 Young Alumni Achiever Award from IIT Kharagpur\, a 2021 Google Faculty Research Award\, and several awards for top papers at conferences. Datta obtained a B.Tech. from IIT Kharagpur\, and Ph.D. and M.S. degrees from Stanford University in Computer Science\, where he currently teaches a course on Trustworthy AI.
URL:https://datascience.ucsd.edu/event/hdsi-seminar-anupam-datta-what-is-your-agents-gpa-toward-trustworthy-data-agents/
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:20250521T150000
DTEND;TZID=America/Los_Angeles:20250521T170000
DTSTAMP:20260530T075801
CREATED:20250422T202557Z
LAST-MODIFIED:20250422T202557Z
UID:10000519-1747839600-1747846800@datascience.ucsd.edu
SUMMARY:HDSI UG Scholarship Showcase
DESCRIPTION:HDSI UG Scholarship Showcase Registration\n\nThe HDSI UG Scholarship at UC San Diego supports multidisciplinary student-led projects. Students choose their own research topics and lead the research process with guidance from a faculty or industry mentor. These opportunities allow students to deepen analytical skills\, develop data science portfolios\, and foster novel data-driven approaches to problem solving. \n\nThis showcase will highlight the projects of the 2024-2025 HDSI UG Scholarship recipients in an interactive poster presentation session\, open to HDSI and the public. The event will take place on Wednesday\, May 21 2025\, from 3:00 pm – 5:00 pm. Please RSVP to confirm your attendance below. \n\n\n  \nClick here for RSVP Link
URL:https://datascience.ucsd.edu/event/ugshowcase25/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Showcase,Special Seminar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2025/04/HDSI-Undergrad-Scholarship-Showcase.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250402T110000
DTEND;TZID=America/Los_Angeles:20250402T120000
DTSTAMP:20260530T075801
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:20260530T075801
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
END:VCALENDAR