March 13, 2024
MAD Games - Multi-Agent Dynamic
Games: What can you learn from
Autonomous Racing?
6:30 PM PST
online
Speaker: Rahul
Mangharam
Register at:
www.ieee-bv.org/meet/2024-03-cs
Balancing performance and
safety are crucial to deploying autonomous
vehicles in multi-agent environments. In
particular, autonomous racing is a domain
that penalizes safe but conservative
policies, highlighting the need for
robust, adaptive strategies. Current
approaches either make simplifying
assumptions about other agents or lack
robust mechanisms for online adaptation.
In this talk we will explore research
themes on perception, planning and control
at the limits of performance. We explore:
(1) How to generate the most competitive
agents who dynamically balance safety and
assertiveness by using distributionally
robust online adaptation and
Game-theoretic planning
(2) How to be better-than-the-best using
imitation learning with multiple imperfect
experts
(3) Using invertible neural networks to
solve inverse problems in localization and
SLAM
(4) How to build the most efficient
autonomous race car with Multi-domain
optimization across vehicle design,
planning and control;
We realize all our research in the
autonomous race car platform that is 10th
the size, but 10x the fun! The main
takeaway from this talk is how you can get
involved in very exciting research on safe
autonomous systems. I will also present
projects on AV Gokart that we are doing in
the Autoware Center of Excellence for
Autonomous Driving.
About the Speaker
Rahul
Mangharam
builds safe autonomous
systems at the intersection of formal
methods, machine learning and controls.
He applies his work to safety-critical
autonomous vehicles, urban air mobility,
life-critical medical devices, and AI
Co-designers for complex systems. He is
the Penn Director for the Department of
Transportation's $20MM Safety21 National
UTC [2023-2028] which focuses on
technologies for safe and efficient
movement of people and goods. Rahul is
the Director of the Autoware Center of
Excellence for Autonomous Driving, a
consortium of 70+ companies and
universities focused on open-source AV
software for open-standards EV
platforms.
Rahul received the 2016 US Presidential
Early Career Award (PECASE) from
President Obama for his work on
Life-Critical Systems. He also received
the 2016 Department of Energy’s
CleanTech Prize (Regional), the 2014
IEEE Benjamin Franklin Key Award, 2013
NSF CAREER Award, 2012 Intel Early
Faculty Career Award and was selected by
the National Academy of Engineering for
the 2012 and 2017 US Frontiers of
Engineering. He has won several ACM and
IEEE best paper awards in Cyber-Physical
Systems, controls, machine learning, and
education.
Presented by:
IEEE Buenaventura Computer
Society Chapter
March 14, 2024
Task-oriented Communications for
Edge AI
5:30 PM PST
online
Speaker: Dr.
Jun Zhang, IEEE Fellow
Register at: https://events.vtools.ieee.org/m/406837
Discover
the future of edge AI in our upcoming
talk by Dr. Jun Zhang, an IEEE Fellow
and Associate Professor at the Hong Kong
University of Science and Technology.
Delve into the shift from traditional
data-oriented communications to
task-oriented approaches, optimizing
data transmission for specific inference
tasks. Learn about the development of
effective feature encoders and the
introduction of EdgeGPT, an autonomous
edge AI system. This presentation will
highlight innovations in edge video
analytics and mobile robotics, offering
insights into achieving high accuracy
and low latency in resource-constrained
devices. Join us to explore cutting-edge
strategies for enhancing edge computing
solutions.
Abstract
Deep
learning has achieved remarkable
successes in many application domains,
such as computer vision, image
processing, and natural language
processing. However, deploying powerful
deep learning models on
resource-constrained mobile devices
(e.g., wearable or IoT devices) faces
great challenges. Recently, edge AI
techniques that rely on the emerging
mobile edge computing platforms have
been proposed, which forward
intermediate features to be processed by
a powerful edge server. To achieve
high-accuracy and low-latency inference,
effective feature encoders with low
complexity and high compression
capability will be needed. This calls
for a paradigm shift in wireless
communications, from “data-oriented
communications”, which maximize data
rates, to “task-oriented
communications”, where the data
transmission is an intermediate step to
be optimized for the downstream
inference task. This talk will introduce
recent progresses on task-oriented
communication for edge inference. An
effective design principle based on
information bottleneck will be firstly
introduced, which will then be extended
to multi-device cooperative perception
based on a distributed information
bottleneck framework. Use cases on edge
video analytics and edge-assisted
localization for mobile robots will be
presented, followed by introduction of
EdgeGPT, an autonomous edge AI system
empowered by large language models.
About the Speaker
Jun Zhang received his Ph.D. degree in
Electrical and Computer Engineering from
the University of Texas at Austin. He is
an IEEE Fellow and an IEEE ComSoc
Distinguished Lecturer. He is an
Associate Professor in the Department of
Electronic and Computer Engineering at
the Hong Kong University of Science and
Technology. His research interests
include wireless communications and
networking, mobile edge computing and
edge AI, and cooperative AI. Dr. Zhang
co-authored the book Fundamentals of LTE
(Prentice-Hall, 2010). He is a
co-recipient of several best paper
awards, including the 2021 Best Survey
Paper Award of IEEE Communications
Society, the 2019 IEEE Communications
Society & Information Theory Society
Joint Paper Award, and the 2016 Marconi
Prize Paper Award in Wireless
Communications. He also received the
2016 IEEE ComSoc Asia-Pacific Best Young
Researcher Award. He is an Editor of
IEEE Transactions on Communications and
IEEE Transactions on Machine Learning in
Communications and Networking, and was
an editor of IEEE Transactions on
Wireless Communications (2015-2020).
Presented by:
IEEE Oregon Communications
Society Chapter
Volunteers Needed: IEEE
Spring STEM Event for Girls
“Girls Make STEM with
Heart”, the IEEE Buenaventura spring
STEM event for middle-school girls, will
be on Saturday, April 13 at Isbell
Middle School in Santa Paula. If you
were involved in previous events, you
know what a wonderful experience it is
for the students and volunteers. Here is
some background:
-
Students can choose
from a variety of workshops covering
topics such as chemistry, circuits,
light, sound, solar energy, and
numbers.
-
We have at least three
mentors per workshop. Each student was
able to get plenty of attention.
-
At the end of the day,
students get to show what they learned
to their parents.
-
Isbell Middle School
graciously lets us use their
classrooms and food service. Each
classroom is equipped with a computer
projector, movable tables and chairs,
and Wi-Fi connectivity.
-
Lunch and materials are
provided by IEEE.
-
See the IEEE
Foundation Newsletter
for an excellent write-up of a
past event, and our YouTube channel
for a photo
montage.
Planning for the event
is already underway. If you are
interested in volunteering, please
contact
us at stem2024@ieee-bv.org
Presented by:
IEEE Buenaventura Section
Skoolcade Seeking
Volunteers to Serve as Judges for Game
Design
Skoolcade
is a video game design competition for
students in grades 3 - 12. Students code
original games and submit them
digitally. The first round of judging
narrows down the competitors. Round two
is a live competition, which will be at
the Ventura County Office of Education
on Saturday, April 27 from 9:00 AM to
1:00 PM.
Skoolcade was
initiated in order to attract students
to coding and create interest in
pursuing educational and career goals
related to coding. Judges can do either
the digital judging, competition
judging, or both. Anyone interested in
judging should contact Anne Jenks <ajenks.jenks9@gmail.com>.
Presented
by: Skoolcade
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