[IISE Faculty List] IISE QCRE&DAIS Webinar Reminder- November 28th - Dr. Ashif Iquebal

[Xiaowei Yue]

Dear Colleagues,

Hope you had a wonderful Thanksgiving holiday!

The IISE Data Analytics and Information Systems (DAIS) Division and the
Quality Control and Reliability Engineering (QCRE) Division will jointly
organize a webinar series after the break. The webinar titled "Advancing
and Accelerating Qualification and Characterization through Stochastic
Inverse Modeling" will be given by Dr. Ashif Iquebal on Tuesday November 28
from 1pm-2pm EST.

Bio of presenter: Dr. Ashif Iquebal is an assistant professor of Industrial
Engineering in the School of Computing and Augmented Intelligence at ASU.
Prior to this, he obtained his B.S in Industrial Engineering from IIT
Kharagpur, India and M.S. in Statistics and Ph.D. in Industrial Engineering
from Texas A&M University. His research aims to bridge the gap between
advanced manufacturing and statistical learning. More specifically, he is
interested in stochastic inverse problems, active learning, and graphical
models for accelerating materials characterization, discovering process
physics, and generating causal inference. He received the NIH Trailblazer
Award 2023, Finalist for NSF Blue Sky Competition 2022, Pritzker Doctoral
Dissertation Award from the Institute of Industrial and Systems Engineering
in 2021. His research papers have been winners/finalists for six best
student paper/poster awards at INFORMS, IISE, IEEE and the American
Statistical Association conferences. His research is funded by MxD-DoD,
NIH, and industry.


Short description of webinar:
A wide range of problems in science and engineering necessitates estimating
critical quantities of interest (QoIs) through indirect measurements. A
pertinent example lies within advanced manufacturing, where pursuing
comprehensive structure (including microstructure and geometrical
dimensions) and properties for part qualification and certification
involves either exorbitantly expensive experiments limited to laboratories
or costly destructive testing. For instance, the definitive method for
appraising elastoplastic properties entails destructive tensile testing,
while microstructure characterization demands intricate electron
backscatter diffraction with high fidelity. These challenges fueled the
research on estimating QoIs using indirect measurements, leading to
developments in solving ill-posed inverse problems. Yet, a fundamental
limitation of classical inverse problems is that they consider material
properties to be deterministic, lacking uncertainty quantification.
Bayesian inverse models attempt to overcome this issue but assume that the
variability in the indirect measurements arises from measurement noise,
thereby failing to account for the variability in the QoIs.
In this talk, we will explore the existing research on inverse problems and
how they are limited in accurately estimating the QoIs and their
variabilities. Subsequently, we will present our research on stochastic
inverse problems that reformulates the classical inverse problem by
considering the variability in the QoIs. This new approach leads to
accurately estimating not just the QoIs but also the variabilities therein.
Advances in stochastic inverse problems also open venues beyond material
characterization, such as discovering the physics of complex processes via
indirect measurements. We will show examples to demonstrate these
applications.


To register for this event, please visit: https://us06web.zoom.us/webinar
/register/WN_bdP93N7YTYGZJl6liPjbUg
More webinars and recordings can be found on:  https://www.iise
.org/details.aspx?id=643

For more information about this webinar, please feel free to contact the
event organizers:

Yu (Chelsea) Jin: yjin at binghamton.edu
Syed Hasib Akhter Faruqui: shf006 at shsu.edu
Xiaowei Yue: yuex at tsinghua.edu.cn
You are welcome to redistribute this announcement to your networks.

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