BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UC Irvine//CML Seminars//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Toward Spatial Intelligence with Limited Data
X-WR-TIMEZONE:America/Los_Angeles
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:2025-02-27-guandao-yang@cml.ics.uci.edu
DTSTAMP:20250227T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20250227T110000
DTEND;TZID=America/Los_Angeles:20250227T120000
SUMMARY:[CML Seminar] Guandao Yang: Toward Spatial Intelligence with Limite
d Data
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Guandao Yang\, Postdoctoral Scholar\, Stanford University\n\nTi
tle: Toward Spatial Intelligence with Limited Data\n\nAbstract: Current su
ccess in artificial intelligence relies heavily on internet-scale data wit
h unified representations. However\, such large-scale homogeneous data is
not readily available for spatial computing applications involving 3D geom
etry. In this talk\, I will present approaches to building spatial intelli
gence systems with limited 3D data by combining existing mathematical mode
ls into existing machine learning pipelines. I will share my work applying
these approaches to develop data-driven methods that can synthesize and a
nalyze 3D geometry. Finally\, I will discuss future opportunities and chal
lenges of data-efficient spatial intelligence.\n\nhttps://cml.ics.uci.edu/
seminars/2025-02-27-guandao-yang
X-ALT-DESC;FMTTYPE=text/html:Guandao Yang\, Postdoctoral
Scholar\, Stanford University
Title: Toward Spatial Intelli
gence with Limited Data
Abstract: Current success in artific
ial intelligence relies heavily on internet-scale data with unified repres
entations. However\, such large-scale homogeneous data is not readily avai
lable for spatial computing applications involving 3D geometry. In this ta
lk\, I will present approaches to building spatial intelligence systems wi
th limited 3D data by combining existing mathematical models into existing
machine learning pipelines. I will share my work applying these approache
s to develop data-driven methods that can synthesize and analyze 3D geomet
ry. Finally\, I will discuss future opportunities and challenges of data-e
fficient spatial intelligence.
https://cml.ics.uci.edu/seminars/2025-02-27
-guandao-yang
URL:https://cml.ics.uci.edu/seminars/2025-02-27-guandao-yang
END:VEVENT
END:VCALENDAR