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X-WR-CALNAME:Propagating Uncertainty from Modeling into Decision-Making for
Trustworthy Autonomy
X-WR-TIMEZONE:America/Los_Angeles
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DTSTART:20070311T020000
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UID:2022-01-24-ransalu-senanayake@cml.ics.uci.edu
DTSTAMP:20220124T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20220124T130000
DTEND;TZID=America/Los_Angeles:20220124T140000
SUMMARY:[CML Seminar] Ransalu Senanayake: Propagating Uncertainty from Mode
ling into Decision-Making for Trustworthy Autonomy
LOCATION:Online (live stream)
DESCRIPTION:Ransalu Senanayake\, Postdoctoral Scholar\, Department of Compu
ter Science\, Stanford University\n\nTitle: Propagating Uncertainty from M
odeling into Decision-Making for Trustworthy Autonomy\n\nAbstract: Autonom
ous agents such as self-driving cars have gained the capability to perform
individual tasks such as object detection and lane following in simple\,
static environments. While advancing robots towards full autonomy\, it is
important to minimize deleterious effects on humans and infrastructure. Fo
r robots to safely operate in the real world\, it is vital to quantify the
multimodal aleatoric and epistemic uncertainty around them and use that u
ncertainty for decision-making. In this talk\, I discuss how we can levera
ge approximate Bayesian inference\, kernel methods\, and deep neural netwo
rks to develop interpretable autonomous systems for high-stakes applicatio
ns.\n\nhttps://cml.ics.uci.edu/seminars/2022-01-24-ransalu-senanayake
X-ALT-DESC;FMTTYPE=text/html:Ransalu Senanayake\, Postdo
ctoral Scholar\, Department of Computer Science\, Stanford University
<
br>Title: Propagating Uncertainty from Modeling into Decision-Makin
g for Trustworthy Autonomy
Abstract: Autonomous agents such
as self-driving cars have gained the capability to perform individual task
s such as object detection and lane following in simple\, static environme
nts. While advancing robots towards full autonomy\, it is important to min
imize deleterious effects on humans and infrastructure. For robots to safe
ly operate in the real world\, it is vital to quantify the multimodal alea
toric and epistemic uncertainty around them and use that uncertainty for d
ecision-making. In this talk\, I discuss how we can leverage approximate B
ayesian inference\, kernel methods\, and deep neural networks to develop i
nterpretable autonomous systems for high-stakes applications.
https:
//cml.ics.uci.edu/seminars/2022-01-24-ransalu-senanayake
URL:https://cml.ics.uci.edu/seminars/2022-01-24-ransalu-senanayake
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