Page 20 - CEGE Magazine Fall 2024

College of Science & Engineering
Department of Civil, Environmental, and Geo- Engineering
500 Pillsbury Drive S.E.
Minneapolis, MN 55455
Nonprofit Org.
US Postage
PAID
Twin Cities, MN
Permit No. 90155
A new class, CEGE 4160/5180 Special Topic: Applied Machine
Learning for CEGE, will enhance students' preparation to
work with big data and sophisticated models. The course was
developed by three CEGE faculty, Randal Barnes, Seongjin
Choi, and Ardeshir Ebtehaj. It will be offered for the first time in
spring 2025.
The offering of this class is timely as engineers are more and
more expected to be able to collect, analyze, interpret, and
apply large amounts of data to solve big problems and build
and operate complex systems. The problems and solutions
that civil, environmental, and geo- engineers work on require
the assessment of multiple options and risks. Luckily, the
expansiveness of available data—fueled by massive simulation-
based models and real-time data generated from sensors
on everything from structures to roadways to wastewater
systems—supports this large-scale problem solving.
This course will prepare graduate students and upper-level
undergraduates to manage data and leverage machine
learning techniques for applications in civil engineering,
environmental engineering, and geoengineering. It will span
classical methods to state-of-the-art deep learning approaches,
tailored specifically for the problems that CEGE students might
encounter. The course will emphasize hands-on learning, with
one or two practical applications for each topic. By the end
of the course, students will have gained the skills to apply
machine learning techniques to solve real-world problems.
LEADING STUDENTS THROUGH DATA TO SOLUTIONS
(water resources) Currently
working on a NASA-supported
project to provide the longest
and most accurate record of
global snowfall data.
(geotechnical), In addition
to his research, Barnes
is recognized for his
outstanding contributions to
undergraduate teaching.
Randal Barnes
(transportation) Research
interests include urban mobility
data analytics, spatiotemporal
data modeling, deep learning
& artificial intelligence, and
connected automated vehicles
(CAV) & cooperative-ITS.
Seongjin Choi
Ardeshir Ebtehaj