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SUMMARY:Data-Driven Engineering Design: Design democratization in the age 
 of machine learning
DTSTART:20190214T090000
DTEND:20190214T100000
DTSTAMP:20260406T183939Z
UID:2ab9aa13ae786ab42d131b4f30585fc43c35f386f0867780c8561bf4
CATEGORIES:Conferences - Seminars
DESCRIPTION:Faez Ahmed\, Mechanical Engineering\, University of Maryland\
 , College Park\, MD\, USA\n\n \nDesign democratization — the ability of
  people all over the world to collaborate together to design physical prod
 ucts — can transform the way we traditionally think about designing prod
 ucts. However\, to enable design democratization\, we need to support it w
 ith machine learning and computing methods. In this talk\, I will talk abo
 ut three problems faced by organizations in gathering and processing ideas
  from distributed teams: 1) How does one form teams to evaluate design ide
 as? 2) How does one reliably measure the creativity of ideas? and 3) How d
 oes one filter good ideas out of hundreds of submissions? I will discuss h
 ow matching\, ranking\, and novelty estimation algorithms developed by me 
 address parts of these problems. The scientific and mathematical work done
  to answer these questions lay the foundation to understand representation
 \, learning\, and optimization of discrete items and apply widely to many 
 other fields.\n\nBio of speaker: Faez Ahmed is a Ph.D. candidate in Mecha
 nical Engineering at the University of Maryland\, College Park. He did his
  undergraduate and master at Indian Institute of Technology\, Kanpur. Prio
 r to his Ph.D.\, he worked as a Reliability Engineer for railways in Weste
 rn Australia. He is a Future Faculty Fellow at the University of Maryland 
 and recipient of the Kulkarni Fellowship. He is also the lead organizer of
  ACM New York non-profit\, to spread computing in the local community. Fae
 z works at the intersection of Machine Learning\, Engineering Design\, and
  Human-Computer Interaction to enable globally distributed teams of design
 ers to participate in the design process.  His interests are in studying 
 principled methods for representation\, learning\, and optimization of dis
 crete problems occurring in design. 
LOCATION:MEB1 B10 https://plan.epfl.ch/?room==ME%20B1%20B10
STATUS:CONFIRMED
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