Hello Everyone,
I am writing to address an error in the announcement email regarding the upcoming AI-seminar.
Unfortunately, the date provided was incorrect.
The correct date for seminar is NOV 17, NOT NOV 10 as was previously stated as that is a holiday for Veteran’s Day celebration.
The event will still take place at KEC1001 at 1 PM but on NOV 17. Please find the revised details below:
Event: AI-seminar
Corrected Date: NOV 17
Time: 1-2 PM
Location: KEC 1001
I apologize for any inconvenience this may have caused.
Regards,
Rajesh Mangannavar
From:
Mangannavar, Rajesh Devaraddi <mangannr@oregonstate.edu>
Date: Tuesday, November 7, 2023 at 6:08 PM
To: eecs-grads@engr.orst.edu <eecs-grads@engr.orst.edu>, 'eecs-faculty@engr.oregonstate.edu' <eecs-faculty@engr.oregonstate.edu>, ai-seminar@engr.orst.edu <ai-seminar@engr.orst.edu>, ai@engr.orst.edu <ai@engr.orst.edu>
Cc: Tadepalli, Prasad <prasad.tadepalli@oregonstate.edu>
Subject: AI Seminar : Nov 10
Dear all,
Our next AI seminar on "New Frontiers In Adaptive Experimental Design For Multi-objective Optimization" by Syrine Belakaria is scheduled to be on Nov 10th (Friday),
1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students.
Location: KEC 1001
Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQT09
Learning Interpretable Models on Complex Medical Data
Syrine Belakaria
Data Science Postdoctoral Fellow
Computer Science
Stanford University
Abstract:
Many design optimization problems in science, engineering, and industrial domains are instantiations of the following general problem: adaptive optimization of complex design spaces guided by expensive experiments
where the expense is measured in terms of resources consumed by the experiments. This talk focuses on the problem of multi-objective optimization (MOO) using expensive black-box function evaluations (also referred to as experiments) where the goal is to approximate
the optimal Pareto set of solutions by minimizing the total resource cost of experiments. For example, in drug design optimization, we need to find designs that trade off effectiveness, safety, and cost using expensive experiments. The key challenge is to
select the sequence of experiments to uncover high-quality solutions by minimizing the total resource cost. In this talk, I will describe a general framework for solving MOO problems based on the output space entropy (OSE) search principle: select the experiment
that maximizes the information gained per unit resource cost about the optimal Pareto front. I will also explain how to instantiate the principle of OSE search to derive efficient algorithms for the following four MOO problem settings: 1) The most basic single-fidelity
setting where experiments are expensive and accurate; 2) Handling black-box constraints that cannot be evaluated without performing experiments; 3) The multi-fidelity setting where candidate experiments can vary in the amount of resources consumed and their
evaluation accuracy. I will present experimental results on real-world engineering and science applications to demonstrate the effectiveness of OSE framework in terms of accuracy of MOO solutions and computational efficiency.
Speaker Bio:
Syrine Belakaria is a Data Science Postdoctoral fellow in the Computer Science department at Stanford University working with Professor Stefano Ermon and Professor Barbara Engelhardt. She obtained her PhD in Computer
Science from Washington State University where she was advised by Professor Jana Doppa; MS in Electrical Engineering from the University of Idaho; and Engineering degree in Information Technology from the Higher School of Communication of Tunis, Tunisia. She
won IBM PhD Fellowship (2021-2023) and was a Finalist for Microsoft Research Fellowship (2021), was selected for the MIT Rising Stars in EECS (2021), won the WSU Harriet Rigas Outstanding Woman in Doctoral Studies Award (2023), won Outstanding TA award in
CS (2019), and two Outstanding Reviewer Awards from the ICML conference. She spent time as a research intern at Microsoft Research and Meta Research. Her general research interests are in the broad area of AI for science and engineering with a current focus
on adaptive experiment design to solve real-world problems including hardware design, materials design, electric transportation systems, and Auto ML.
Please watch this space for future AI Seminars :
https://engineering.oregonstate.edu/EECS/research/AI
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