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 self explanation, small-group learning, learning by teaching, etc., and their applications in healthcare. Here is a summary of research outcome.
I am looking for highly-motivated PhD and master students to join my group. I am also looking for research interns for summer.
PhD Students and Postdocs
Caitlin Aamodt (PhD from UCLA)
Han Guo (from CS at Rice U.)
Ramtin Hosseini (from ME at UC Berkeley)
Mingjia Huo (from CS at Peking U.）
Amirhosein Javadi (from EE at Sharif U. of Tech.)
Youwei Liang (from CS at SCAU)
Xinshuang Liu (from CS at Tsinghua U.)
Sai Somayajula (from EE at IIT Hyderabad)
Li Zhang (from CSE at Zhejiang U.)
Ruiyi Zhang (from CS at Peking U.)
Collaborative PhD Students
Ding Bai (MBZUAI)
Sang Choe (CMU)
Nicholas Ho (CMU)
Blair Jia (UCSD)
Sazan Mahbub (CMU)
Shentong Mo (MBZUAI)
Duy Nguyen (University of Stuttgart)
Publications Since 2020
Publications Before 2020
Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing. Generalized Zero-shot ICD Coding. International Joint Conference on Artificial Intelligence (IJCAI 2020).
Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P Xing. Adversarial Domain Adaptation Being Aware of Class Relationships. European Conference on Artificial Intelligence (ECAI 2020).
B. Huang, K. Zhang, P. Xie, M. Gong, E. P. Xing. Specific and Shared Causal Relation Modeling and Mechanism-based Clustering. Advances in Neural Information Processing Systems (NeurIPS 2019).
K. Xu, M. Lam, J. Pang, X. Gao, C. Band, P. Mathur, F. Papay, A. K. Khanna, J. B. Cywinski, K. Maheshwari, P. Xie, E. P. Xing. Multimodal Machine Learning for Automated ICD Coding. Conference on Machine Learning for Healthcare (MLHC 2019).
Z.Wang, N.Dong, S.Rosario, M.Xu, P.Xie, and E.P.Xing. Ellipse Detection of Optic Disc-and-Cup Boundary in Fundus Image with Unsupervised Domain Adaption. The IEEE International Symposium on Biomedical Imaging (ISBI 2019).
P.Xie, W.Wu, Y.Zhu and E.P.Xing. Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis. The 35th International Conference on Machine Learning (ICML 2018) (Long Oral Presentation).
P.Xie, H.Zhang, Y.Zhu and E.P.Xing. Nonoverlap-Promoting Variable Selection. The 35th International Conference on Machine Learning (ICML 2018) (Short Oral Presentation).
P.Xie, H.Shi, M.Zhang and E.P.Xing. A Neural Architecture for Automated ICD Coding. The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018) (Oral Presentation)
B.Jing, P.Xie and E.P.Xing. On the Automatic Generation of Medical Imaging Reports. The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018).
P.Xie, J.Kim, Q.Ho, Y.Yu and E.P.Xing. Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. Symposium of Cloud Computing (SoCC 2018).
D.Sachan, P.Xie and E.P.Xing. Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition. Conference on Machine Learning for Healthcare (MLHC 2018).
X.Liu, K.Xu, P.Xie and E.P.Xing. Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records. NIPS ML for Healthcare Workshop, 2018 (Spotlight Presentation).
P.Xie, R.Salakhutdinov, L.Mou and E.P.Xing. Deep Conditional Determinantal Point Process for Large-Scale Multi-Label Classification. International Conference on Computer Vision (ICCV 2017).
P.Xie, B.Poczos and E.P.Xing. Near-Orthogonality Regularization in Kernel Methods. Conference on Uncertainty in Artificial Intelligence (UAI 2017) (Plenary Presentation).
H.Zhang, Z.Zheng, S.Xu, X.Liang, W.Dai, Q.Ho, Z.Hu, J.Wei, P.Xie, and E.P.Xing. Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters. 2017 USENIX Annual Technical Conference (ATC 2017) (Oral Presentation).
P.Xie, A.Singh and E.P.Xing. Uncorrelation and Evenness: A New Diversity-Promoting Regularizer. The 34th International Conference on Machine Learning (ICML 2017) (Oral Presentation).
P.Xie, Y.Deng, Y.Zhou, A.Kumar, Y.Yu, J.Zou and E.P.Xing. Learning Latent Space Models with Angular Constraints. The 34th International Conference on Machine Learning (ICML 2017) (Oral Presentation).
H.Zhou, J.Li, P.Xie and Y.Zhang. Improving the Generalization Performance of Multi-class SVM via Angular Regularization. The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017).
P.Xie and E.P.Xing. A Constituent-Centric Neural Architecture for Reading Comprehension. The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017).
Y.Zhou, K.Yuan, Y.Yu, X.Ni, P.Xie, E.P.Xing and S.Xu. Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions. Heredity, 2017.
E.P.Xing, Q.Ho, P.Xie and W.Dai. Strategies and Principles of Distributed Machine Learning on Big Data. Engineering, Transactions of Chinese Academy of Engineering (Engineering 2016).
P.Xie, J.Kim, Y.Zhou, Q.Ho, A.Kumar, Y.Yu and E.P.Xing. Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. The 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016).
P.Xie, J.Zhu and E.P.Xing. Diversity-Promoting Bayesian Learning of Latent Variable Models. The 33rd International Conference on Machine Learning (ICML 2016) (Oral Presentation).
E.P.Xing, Q.Ho, W.Dai, J.Kim, J.Wei, S.Lee, X.Zheng, P.Xie, A.Kumar and Y.Yu. Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Transactions on Big Data (IEEE BigData 2015).
P.Xie. Learning Compact and Effective Distance Metrics with Diversity Regularization. European Conference on Machine Learning (ECML 2015) (Oral Presentation).
P.Xie, Y.Deng and E.P.Xing. Diversifying Restricted Boltzmann Machine for Document Modeling. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015) (Oral Presentation).
E.P.Xing, Q.Ho, W.Dai, J.Kim, J.Wei, S.Lee, X.Zheng, P.Xie, A.Kumar and Y.Yu. Petuum: A New Platform for Distributed Machine Learning on Big Data. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015) (Oral Presentation).
P.Xie, D.Yang and E.P.Xing. Incorporating Word Correlation Knowledge into Topic Modeling. The 2015 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2015).
P.Xie, Y.Pei, Y.Xie and E.P.Xing. Mining User Interests from Personal Photos. The 29th AAAI Conference on Artificial Intelligence (AAAI 2015).
P.Xie and E.P.Xing. Integrating Image Clustering and Codebook Learning. The 29th AAAI Conference on Artificial Intelligence (AAAI 2015) (Oral Presentation).
P.Xie and E.P.Xing. Multi-Modal Distance Metric Learning. The 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013) (Oral Presentation).
P.Xie and E.P.Xing. Integrating Document Clustering and Topic Modeling. Proceedings of the 29th International Conference on Uncertainty in Artificial Intelligence (UAI 2013).
Area Chairs for:
ICML 2021, NeurIPS 2021, CVPR 2021, NAACL 2021, ICCV 2021, AAAI 2021, IJCAI 2021
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”.
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