ICICLE AI Institute & OSU CSE Distinguished Seminar Series
2025-04-10 11:15 - 12:15 (America/New_York)
Title: Harnessing Responsible AI for Science Discovery, Innovation, and Impact
Speaker: Manish Parashar, University of Utah
When: April 10, 2025 11:15 AM – 12:15 PM EST
Location: DL 260 Dreese Laboratory, 2015 Neil Ave, Columbus, OH 43210
Zoom link: https://osu.zoom.us/j/93195486710?pwd=[REDACTED]

TILOS Seminar: Amplifying human performance in combinatorial competitive programming
2025-04-16 11:00 - 12:00 (America/Los_Angeles) | Zoom webinar

AI-SDM Seminar Series: Using Machine Learning and Psychology to Predict and Understand Human Decisions
2025-04-25 11:30 - 13:00 (America/Halifax) | Zoom webinar
Join the next AI Institute for Societal Decision Making (AI-SDM) seminar on Friday, April 25, 2025. Register now!

Using Machine Learning and Psychology to Predict and Understand Human Decisions

Abstract: Machine learning (ML) methods provide increasingly powerful tools for generating predictions about human behavior. However, simply using off the shelf methods to generate predictions potentially misses opportunities to benefit from and contribute to the psychological literature. This will cover three ways in which theory and data can interact through ML: using theories to pre-train machine learning models; using theories to constrain ML models; and using unconstrained ML models to critique explanatory theories. These cases will be illustrated with examples from the study of human decision-making, discussing risky choice, moral judgments, behavioral game theory, and open-ended decision-making, and highlights from some recent work using large language models (LLMs) to predict human decisions.

Speaker: Tom Griffiths,Professor of Psychology and Computer Science, Princeton University
Tom Griffiths is the Henry R. Luce Professor of Information Technology, Consciousness and Culture in the Departments of Psychology and Computer Science at Princeton University. His research explores connections between human and machine learning, using ideas from statistics and artificial intelligence to understand how people solve the challenging computational problems they encounter in everyday life. He has made contributions to the development of Bayesian models of cognition, probabilistic machine learning, nonparametric Bayesian statistics, and models of cultural evolution, and his recent work has demonstrated how methods from cognitive science can shed light on modern artificial intelligence systems. Tom completed his PhD in Psychology at Stanford University in 2005, and taught at Brown University and the University of California, Berkeley before moving to Princeton. He has received awards for his research from organizations ranging from the American Psychological Association to the National Academy of Sciences and is a co-author of the book Algorithms to Live By, introducing ideas from computer science and cognitive science to a general audience.

Date & Time: Friday, April 25, 2025 | 11:30 a.m. – 1:00 p.m., Eastern Time
Locations:

GHC 6115, CMU
Zoom (After registering, you’ll receive a confirmation email with information about how to join the meeting.)

Dr. Griffiths will also be available to meet throughout the day with anyone who is interested. Click here to sign up for a time slot!
More about the AI-SDM Seminar Series

Visit the Featured AI-SDM Events webpage to learn more about this seminar, see the upcoming seminar schedule, and register for upcoming seminars.

Upcoming AI-SDM Seminar:

Friday, May 9, 2025 | 3:00 – 4:30 pm Eastern Time | Giacomo De Nicola

DynamicsAI Data-Driven Science and Engineering Seminars: Multifidelity, domain decomposition, and stacking for improving training for physics-informed networks
2025-04-25 12:00 - 13:00 (America/Los_Angeles) | Zoom webinar
Join DynamicsAI’s seminar on Friday, April 25, 2025 at 12:00 – 1:00 p.m. Pacific for “Multifidelity, domain decomposition, and stacking for improving training for physics-informed networks.”
This hybrid event features Dr. Amanda Howard of Pacific Northwest National Laboratory (PNNL)!
Register now and get virtual access info.
Physics-informed neural networks and operator networks have shown promise for effectively solving equations modeling physical systems. However, these networks can be difficult or impossible to train accurately for some systems of equations. One way to improve training is through the use of a small amount of data, however, such data is expensive to produce. We will introduce our novel multifidelity framework for stacking physics-informed neural networks and operator networks that facilitates training by progressively reducing the errors in our predictions for when no data is available. In stacking networks, we successively build a chain of networks, where the output at one step can act as a low-fidelity input for training the next step, gradually increasing the expressivity of the learned model. We will finally discuss the extension to domain decomposition using the finite basis method, including applications to newly-developed Kolmogorov-Arnold Networks. Applications will be discussed for accelerating simulations of computational fluid dynamics.
About the Speaker: Amanda Howard is a mathematician at Pacific Northwest National Laboratory specializing in computational fluid dynamics and scientific machine learning (ML). She received her PhD in applied mathematics from Brown University in 2018 and her BS in mathematics from Stanford University in 2012. Her PhD research focused on computational methods for fluid-solid multiphase flows. Her current research includes scientific ML methods for cases with limited data, including multifidelity and physics-informed ML.

AIFS Speaker Series: The Imperative for Open Source Frontier Al - Oumi's Platform & Strategy
2025-05-02 15:30 - 16:30 (America/Los_Angeles) | Zoom webinar
Join us on Zoom to hear how Oumi is fostering open source frontier AI and broad collaboration across academia, industry, and the world. Register now!
WHAT: Free 45-minute event with talk by CEO Emmanouil Koukoumidis plus Q&A
WHEN: Friday, May 2, 2025, 3:30 (PDT)
REMOTE: via Zoom
WHO SHOULD ATTEND: This event is open to all, whether you are a part of the university community or the public.
FEATURED SPEAKER: Emmanouil (Manos) Koukoumidis, CEO of Oumi PBC
The Imperative for Open Source Frontier AI – Oumi’s Platform & Strategy
Open source is the best path to developing the most advanced, trusted, and versatile AI; the AI that would unlock industry applications and truly empower the sciences.
Unfortunately, frontier AI has become increasingly closed giving rise to significant concerns and risks.
Oumi CEO, Dr. Manos Koukoumidis, will discuss the efforts that Oumi, alongside 20+ academics, recently started to put us on a better path by betting on and fostering open source frontier AI. Learn how Oumi empowers sciences, the industries and even AgTech hackathons!
Learn more and register: https://aifs.ucdavis.edu/education-and-outreach/speaker-series

TILOS Seminar: Optimal Quantization for LLMs and Matrix Multiplication
2025-05-23 11:00 - 12:00 (America/Los_Angeles) | Halicioglu Data Science Institute Room 123, 3234 Matthews Ln, La Jolla, CA, 92093, United States

TILOS Seminar: A New Paradigm for Learning with Distribution Shift
2025-10-01 11:00 - 12:00 (America/Los_Angeles) | Halicioglu Data Science Institute Room 123, 3234 Matthews Ln, La Jolla, CA, 92093, United States

Optimization for ML and AI Seminar: High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws
2025-10-24 11:00 - 12:00 (America/Los_Angeles) | UC San Diego and Virtual, 9500 Gilman Dr, La Jolla, CA, 92093, United States

TILOS Seminar: AI Safety Theory: The Missing Middle Ground
2025-11-12 11:00 - 12:00 (America/Los_Angeles) | Halicioglu Data Science Institute Room 123, 3234 Matthews Ln, La Jolla, CA, 92093, United States

TILOS Seminar: Certifiably Correct Machine Perception
2025-11-19 11:00 - 12:00 (America/Los_Angeles) | SDSU and Virtual
Zoom: https://SDSU.zoom.us/j/84993405398

TILOS Seminar: 95 Percent: Bridging the Gap Between Prototype and Product
2025-12-03 11:00 - 12:00 (America/Los_Angeles)
Zoom: https://SDSU.zoom.us/j/82711917109

Optimization for ML & AI Seminar: Training Neural Networks at Any Scale
2025-12-03 13:00 - 14:00 (America/Los_Angeles)
Zoom: https://bit.ly/Opt-AI-ML

Optimization for ML and AI Seminar: Stochastic-Gradient and Diagonal-Scaling Algorithms for Constrained Optimization and Learning
2025-12-05 11:00 - 12:00 (America/Los_Angeles)

TILOS Seminar: Incentivizing Emergent Behaviors for LLMs via Reinforcement Learning
2025-12-08 10:00 - 11:00 (America/Los_Angeles)
Zoom: https://bit.ly/TILOS-Seminars
