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Spring 2022

The graduate seminar series occurs autumn, winter, and spring quarter. The seminars feature lectures on current research within the field of industrial and systems engineering. MSIE and Ph.D. students are required to register for the entire seminar series (3 credits total).

IND E 593 schedule

Quarter:
Spring 2022
Time:
Tuesdays, 1:30 p.m. - 2:20 p.m.
Location:
Zoom or MEB 234
 
Date Speaker

March 29

Zoom

Dr. Ahmed Aziz Ezzat, Industrial & Systems Engineering, Rutgers University

Enabling Offshore Wind Energy via Physics-Guided Spatio-Temporal Data Science: Towards Accurate Probabilistic Forecasting with AIRU-WRF

April 5

Zoom

Dr. Trung (Tim) Le, Industrial & Manufacturing Engineering, North Dakota State University

Sensor-Based Modeling & Nonlinear Stochastic Dynamic Systems Approach for Health Analytics and Smart Connected Systems

April 12

Zoom

Tianchen Sun, Ph.D. candidate, Industrial and Systems Engineering, University of Washington

Modeling students’ procrastination in university classrooms

April 19

MEB 234 

Dr. Xuegang (Jeff) Ban, Civil & Environmental Engineering, University of Washington

Future Urban Transportation with Connected and Automated Vehicles

April 26

MEB 234

Dr. Xiao Liu, Industrial & Systems Engineering, University of Arkansas

Domain-Aware Statistical Learning for Natural and Engineering Processes 

May 3

MEB 234

Dr. Baosen Zhang, Electrical Engineering, University of Washington

Safe and Efficient Reinforcement Learning for Energy Systems

May 10

Zoom 

Dr. Chang-Han Rhee, Industrial Engineering & Management Sciences, Northwestern University

Eliminating Sharp Minima from SGD with Truncated Heavy-Tailed Noise

May 17

Zoom

Dr. Ryan Qi Wang, Civil & Environmental Engineering, Northeastern University

Human Mobility and Urban Resilience

May 24

Zoom

Dr. Don McKenzie, Civil & Environmental Engineering, University of Washington

Planning EV Infrastructure: Theory and Practice

May 31

Zoom

Dr. Andrea Civan Hartzler, Biomedical Informatics & Medical Education, University of Washington

Battling bias in healthcare interactions:  What we can learn from computers, patients, and providers