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  • Seminar: Machine Learning for Force Field Parameterization – Application to 2D Materials

    Title:  Machine learning for force field parameterization -- Application to 2D materials Speaker:  Horacio Espinosa (Northwestern University) Abstract: The parameterization of interatomic potentials for molecular dynamics (MD) simulations has long been a highly-specialized endeavor requiring strong domain expertise and in most cases deep chemical intuition. We propose a robust approach incorporating multi-objective genetic algorithms and […]

  • HDSI Open House Fall 2021

    HDSI Open House is an event that will provide attendees with an in-depth look at our undergraduate data science talent and the various opportunities to engage with them. This event will be particularly relevant to those involved in talent acquisition as well hiring managers and leaders considering the addition of data science talent to their […]

  • Data Science Insights Speaker Series: Arya Mazumdar

    In partnership with the San Diego Machine Learning Meetup Group, we are excited to be launching this monthly speaker series. The intent for this series is to highlight faculty and data science related research from the Institute and UC San Diego to the broader community. Our next monthly event will be taking place on Wednesday, […]

  • E4E Summer 2021 Research Forum

    Summer Research Presentations Friday August 27, 2021, 9:30 - 10:30am Pacific Time Registration: https://ucsd.zoom.us/j/96150602602    Join us to hear about our Engineers for Exploration (E4E) research projects. This summer, E4E researchers made impressive contributions to develop technologies that help understand critical ecosystems and monitor endangered species.  Acoustic Species Identification: Leverage machine learning and digital signal […]

  • Statistical Learning and Market Design

    We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori and must be learned from data. Taking the two-sided matching market as a running example, we focus on the decentralized setting, where agents do not share their learned preferences with a central authority. Our approach is based on the representation of preferences in a reproducing kernel Hilbert space, and a learning algorithm for preferences that accounts for uncertainty due to the competition among the agents in the market. Under regularity conditions, we show that our estimator of preferences converges at a minimax optimal rate. Given this result, we derive optimal strategies that maximize agents' expected payoffs and we calibrate the uncertain state by taking opportunity costs into account. We also derive an incentive-compatibility property and show that the outcome from the learned strategies has a stability property. Finally, we prove a fairness property that asserts that there exists no justified envy according to the learned strategies. This is a joint work with Michael I. Jordan.