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Bo Peng

PhD Candidate, AI for Earth Observation
Spatial Computing and Data Mining Lab
Department of Geography
University of Wisconsin-Madison
Email: bo.peng AT wisc.edu
413 Science Hall, 550 N Park St
Madison, WI 53706

About

Bo is a Ph.D. Candidate in Geography (GIScience track) with a doctoral minor in ECE Machine Learning at Spatial Computing and Data Mining Lab, University of Wisconsin - Madison, working with Dr. Qunying Huang. He is awarded the Microsoft AI for Earth Grant as the PI in 2020, and the Applied Machine Learning Research Fellowship for Summer 2020 by Los Alamos National Laboratory. He is serving as the Student Co-Director of the Remote Sensing Specialty Group (RSSG) and of the Hazards, Risks and Disasters Specialty Group (HRDSG) - Association of American Geographers (AAG). His research interest lies at the intersection of geospatial data science, machine learning, computer vision, and natural hazards.

He received the M.Sc. from Department of Electrical & Computer Engineering at UW-Madison, the M.Sc. from Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, and the B.E. from School of Remote Sensing and Information Engineering, Wuhan University.

Recent News

  • [04/2021] Elected the Student Co-Director of the Remote Sensing Specialty Group (RSSG) - Association of American Geographers (AAG).
  • [04/2021] Received the 2021 AAG Jeanne X. Kasperson Student Paper Winner Award.
  • [03/2021] Received the 2021 Olmstead Award for Outstanding Publication from UW-Madison Geography Dept. for the paper on real-time self-supervised urban flood mapping without human supervision published in IEEE J-STARS.
  • [03/2021] Gave a talk at the ASPRS Annual Conference: Self-Supervised Learning of Building Image Object Representations Informed by Geospatial Principles.
  • [03/2021] Our new work on spatiotemporal contrastive representation learning for self-supervised satellite image representation learning accepted by IGARSS 2021.
  • [01/2021] New manuscript submitted to IGARSS 2021.
  • [12/2020] New paper on real-time self-supervised urban flood mapping without human supervision published in IEEE J-STARS.
  • More news...

Research

Research topics span geospatial data science, machine learning, computer vision, image processing, and natural hazards. Some example projects include:

Selected Publications

( *research advisor )

Teaching

  • CS/ECE/ME 532 - Matrix Methods in Machine Learning
    Senior Teaching Assistant, Spring 2020
    Teaching Assistant, Fall 2019
  • GEOG 377 - Intro to Geographic Information Systems
    Lecturer / Instructor, Summer 2020
    Teaching Assistant, Spring 2019
  • GEOG 574 - Spatial Database
    Teaching Assistant, Fall 2018