Hello All,
There was a delay in the routing of this email to the faculty list serve.
Please disregard since the date has already passed.
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Best Regards,
Jonathan Rich
Office Manager
School of Electrical Engineering & Computer Science
Oregon State University
|
1148 Kelley Engineering Center
(541) 737-0046
From: eecs-faculty <eecs-faculty-bounces@engr.orst.edu>
On Behalf Of Mangannavar, Rajesh Devaraddi
Sent: Thursday, May 19, 2022 3:12 PM
To: eecs-grads@engr.orst.edu; eecs-faculty@engr.orst.edu; ai@engr.orst.edu; ai-seminar@engr.orst.edu
Cc: Tadepalli, Prasad <tadepall@engr.orst.edu>
Subject: [eecs-faculty] AI seminar: May 25, 2022
Dear all,
Our next AI seminar on "Toward Addressing Evaluation and Explanation Challenges in Scientific ML Applications" by Shusen Liu is scheduled to be on May 25th, 1-2
PM PST (Add
to google calendar). It will be followed by a 30-minute Q&A session by the graduate students.
Toward Addressing Evaluation and Explanation Challenges in Scientific ML Applications
Shusen Liu
Research Scientist
Machine Intelligence Group
Lawrence Livermore National Laboratory
Abstract:
Although the influence of deep learning in scientific domains is unmistakable, there are still fundamental barriers to utilizing these complex models for scientific discovery
due in part to our inability to directly translate their predictive capabilities into scientific understanding. The root of the problem is twofold: 1) the challenge to evaluate the model in the context of the application; 2) the difficulties of reasoning about
such models in terms that domain scientists can easily understand for knowledge extraction. This talk will provide a closer look at these unique challenges associated with applying deep learning to scientific applications. And cover some of our works for addressing
the evaluation and explanation challenges in Scientific ML. Specifically, we will show how topological data analysis plays a crucial role in evaluating deep surrogate models for fusion science, and how deep generative models allow us to explore hypothetical
materials and obtain actionable explanations that lead to improved material performance.
Speaker Bio:
Shusen Liu is a research scientist with the Machine Intelligence Group at the Lawrence Livermore National Laboratory (LLNL). His interests include
fundamental research in explainable AI and high-dimensional data visualization, as well as their impact on scientific applications for advancing domain understanding. He received a
Ph.D.in computing from the University of Utah in 2017.
Please watch this space for future AI Seminars :
https://eecs.oregonstate.edu/ai-events
Rajesh Mangannavar,
Graduate Student
Oregon State University
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AI Seminar Important Reminders:
-> For graduate students in the AI program, attendance is strongly encouraged.