Pengtao Xie

Assistant Professor, Department of Electrical and Computer Engineering

Affiliated Faculty, AI Group in the Department of Computer Science and Engineering

University of California San Diego


I obtained my PhD from the Machine Learning Department, School of Computer Science, Carnegie Mellon University. My research interests mainly lie in machine learning inspired by humans' learning skills (especially classroom learning skills), such as learning by passing tests, interleaving learning, learning by self-explanation, small-group learning, learning by teaching, learning by ignoring, etc., and their applications in healthcare. Please refer to this monograph for details.


pengtaoxie2008@gmail.com Twitter Weibo


I am looking for highly-motivated PhD and master students to join my group. I am also looking for (remote) research interns for Summer 2021.

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Current Students

  • Arda Bati

  • Bhanu Garg

  • Ramtin Hosseini

  • Sai Somayajula

  • Tushar Sircar (CSE)

  • Jeremy Wink (CSE)

  • Xingyi Yang

  • Ruisi Zhang

  • Shengzhe Zhang


Past Students

Undergraduate Students

  • Ruisi Zhang (2020 --> PhD student at UCSD ECE)

  • Matt Hong (2020 --> PhD student at UCSD CSE)

  • Jiayuan Huang (2020 --> Master student at CMU CS)

  • Jiaqi Zeng (2020 --> Master student at CMU CS)

  • Meng Zhou (2020 --> Master student at CMU CS)

  • Yuhong Chen (2020 --> Master student at CMU INI)

  • Yue Yang (2020 --> Master student at Georgia Tech CS)

Master Students

  • Jiachen Li (2020 --> PhD student at UCSB)

  • Xuehai He (2020 --> PhD student at UCSC)


Teaching


Recent Works on Machine Learning Inspired by Humans' Learning Skills

Publications Since 2020

Google Scholar

Publications Before 2020


Professional Activities

Area Chairs for:

  • ICML 2021, NeurIPS 2021, CVPR 2021, NAACL 2021, ICCV 2021, AAAI 2021, IJCAI 2021

Co-organizer for:

  • NeurIPS 2020 workshop "Self-Supervised Learning -- Theory and Practice".

  • AAAI 2021 workshop "Trustworthy AI for Healthcare".

  • ICLR 2021 workshop “Machine Learning for Preventing and Combating Pandemics”.

Member for:

  • Machine Learning for Signal Processing Technical Committee

  • ACM, IEEE, AMIA

Program Committee Members or Reviewers for:

  • Conferences: ICML (2014, 2018-2019), NIPS (2016, 2018), AISTATS (2017-2019), UAI (2018), ICLR (2019), AAAI (2019), CVPR (2016-2019), ICCV (2015, 2017), ECCV (2016, 2018), ACL (2015-2018), EMNLP (2015), KDD (2015), ECML (2016-2017), ACCV (2016), BMVC (2017)

  • Journals: TPAMI (2018), TKDE (2015-2018), TMM (2016-2017), PLOS ONE (2017-2018), TNNLS (2015-2016, 2018), JASA (2015)


Selected Awards and Honors

  • Finalist (top 5) for AMIA Doctoral Dissertation Award.

  • Amazon AWS Research Award.

  • Tencent AI-Lab Faculty Award.

  • Tencent Faculty Award.

  • Innovator Award, 2018 (presented by the Pittsburgh Business Times).

  • Google Cloud research credits.

  • 1st Place (out of 400+ participating teams) in both Defenses and Targeted Attacks, 3rd Place in Untargeted Attacks, in NIPS Adversarial Vision Challenge, 2018.

  • Siebel Scholarship, 2014 (85 graduate students from around the world).

  • National Scholarship of China, 2009.

  • National First Prize in China Undergraduate Mathematical Contest of Modeling, 2008.

  • Goldman Sachs Global Leader Scholarship, 2008 (150 undergraduate students from around the world).


Recent Invited Talks

  • Machine Learning for Medical Decision Support

    • Nov 2019, Department of Biomedical Informatics, University of Pittsburgh

    • Oct 2019, AI NEXTCon Developer Conference

    • Apr 2019, New York University

    • Apr 2019, University of Massachusetts Amherst

    • Mar 2019, University of California San Diego

    • Feb 2019, Columbia University

    • Feb 2019, Johns Hopkins University

    • Feb 2019, University of California Los Angeles

    • Jan 2019, University of Wisconsin-Madison