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UCI Researchers Receive State Funding to Study Pandemic Forecasting

Led by Statistics Professor Vladimir Minin, UCI is one of four UC system institutions to receive COVID Modeling Consortium funding to help UCI researchers generate more timely and accurate pandemic forecasts.

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Be part of the digital revolution. The Donald Bren School of Information and Computer Science's expertise in computational research is driving meaningful changes to how we work, live, and play. Discover how you can be a part of that today.

Undergraduate Programs

Offering a broad array of undergraduate majors and minors in virtually every principal area, students can pioneer innovation in areas such as artificial intelligence, game design, software engineering and more.

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Want to be part of groundbreaking research? Seeking to advance your skillsets, making an immediate impact in the workforce? Whether it's in research or professional development, our Master's and PhD programs empower and equip graduates to solve today's digital challenges.

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Our Research

ICS faculty and students are changing the world with their research.

Explore the groundbreaking work.

Adaptive Software and Hardware for Distributed, Networked Embedded Systems

The project investigates adaptive strategies to deal with dynamic application environments for distributed, networked embedded systems.

Applying Machine Learning and Advanced Computation to Cancer

In this project, researchers have identified an elusive pocket on the surface of the p53 protein that can be targeted by cancer-fighting drugs.

Machine Learning for Text and Social Network Data Over Time

Researchers are developing new statistical machine learning algorithms that can automatically extract useful information from both large bodies of and large social.

Upcoming Events

May9
Statistics Seminar Series

Reinforcement Learning for Respondent-Driven Sampling

Eric Laber
Professor, Duke University
4:00 PM
6011, Donald Bren Hall

Abstract: Respondent-driven sampling (RDS) is a network-based sampling strategy used to study hidden populations for which no sampling frame is available. In each epoch of…

May10
CS Seminar Series

From Hierarchical Clustering to Phylogenetic CSPs

Evangelos Chatziafratis
Assistant Professor of Computer Science & Engineering, University of California Santa Cruz
11:00 AM
6011, Donald Bren Hall

Abstract: Hierarchical Clustering (HC) is a widely studied problem in unsupervised learning and exploratory data analysis, usually tackled by simple agglomerative procedures like average-linkage, single-linkage…

May16
Statistics Seminar Series

Evaluating the Robustness and Efficiency of Estimators for Informative Covariate Censoring

Tanya Garcia
Associate Professor, University of North Carolina-Chapel Hill
4:00 PM
6011, Donald Bren Hall

Abstract: While right-censored time-to-event outcomes have been studied for decades, handling time-to-event covariates, also known as censored covariates, is now of growing interest. So far,…

May16
ICS Distinguished Lecturer Series

Who’s Afraid of AI? Myths and Realities of Generative AI

Kate Crawford
Research Professor, USC Annenberg School, Senior Principal Researcher, Microsoft Research, Honorary Professor, University of Sydney
5:00 PM
Arnold and Mabel Beckman Center of the National Academies of Sciences & Engineering

RSVP Today! Abstract: There has been a spike in concern about existential risk from artificial general intelligence, or AGI. This fear, commonly associated with terms…

May17
CS Seminar Series

Specifying Goals to Deep Neural Networks with Answer Set Programming

Forest Agostinelli
Assistant Professor, University of South Carolina
11:00 AM
6011, Donald Bren Hall

Abstract: Methods such as DeepCubeA have used deep reinforcement learning to learn domain-specific heuristic functions in a largely domain-independent fashion to solve planning problems. However,…

May17
Informatics Seminar Series

(Virtual) – (Nearly) 40 Years of SE+AI

Gail Kaiser
Professor of Computer Science, Computer Science Department, Columbia University
2:00 PM
Virtual, Zoom

Abstract: Dr. Kaiser will discuss her past work applying AI-based techniques to software engineering problems and applying software engineering techniques to finding bugs in AI…

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