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VERSION:2.0
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SUMMARY:Open-World Machine Learning for Biomedicine
DTSTART:20220511T130000
DTEND:20220511T140000
DTSTAMP:20260511T212658Z
UID:0391f64c00ce575cf1cd11a56e0a2f0e5774d1968e7b853ed637274b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Maria Brbic (Stanford University)\nSV Seminar - Maria Brbic (S
 tanford University)\n\nAbstract\n\nBiomedical data poses multiple hard cha
 llenges that break conventional machine learning assumptions. In this talk
 \, I will highlight the need to transcend our prevalent machine learning p
 aradigm and methods to enable them to become the driving force of novel sc
 ientific discoveries. I will focus on open-world deep learning methods tha
 t generalize to new scenarios never seen during training and demonstrate t
 he impact they have in single-cell genomics. I will first present a method
  that transfers knowledge across heterogeneous datasets generated under di
 fferent distributions\, and then describe the paradigm and methods needed 
 to discover previously unobserved phenomena. I will discuss the biological
  findings enabled by these methods and the conceptual shift they bring in 
 annotating comprehensive single-cell atlas datasets. Altogether\, my work 
 demonstrates that generalization to never-before-seen scenarios is not onl
 y possible\, but it is a necessary component in developing next-generation
  methods that can reveal new scientific insights.\n\nBio\n\nMaria Brbic is
  a postdoctoral researcher in Computer Science at Stanford University. She
  develops new machine learning methods inspired by challenging problems in
  biomedicine and applies her methods to advance biomedical research. Her m
 ethods have been used by global cell atlas consortia efforts aiming to cre
 ate reference maps of all cell types with the potential to transform biome
 dicine\, including the Human BioMolecular Atlas Program (HuBMAP) and Fly C
 ell Atlas consortium.  Previously\, she received her PhD degree from Univ
 ersity of Zagreb while also researching at Stanford University and Univers
 ity of Tokyo. Her work was awarded with the  Fulbright Scholarship\, L’
 Oreal UNESCO for Women in Science Scholarship\, Branimir Jernej award for 
 outstanding publication in biology and biomedicine\,  and the Silver Plaq
 ue Josip Loncar for the best doctoral dissertation. She has been named a R
 ising Star in EECS by MIT. She is a member of the Chan Zuckerberg Biohub a
 t Stanford.\n 
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717 https://epfl.zoom.u
 s/j/66754788304
STATUS:CONFIRMED
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