Dr. Xi Peng (Peter), 彭曦
AI/ML Scientist & Educator at University of Delaware
Assistant Professor, Department of Computer & Information Sciences (CIS)
Resident Faculty, Data Science Institute (DSI)
Affiliate Faculty, Delaware Environmental Institute (DENIN)
Email: xipeng at udel dot edu Tel: (302) 831-2876
Office: FinTech 416C, 591 Collaboration Way, Newark, DE 19713
Google Scholar Deep-REAL Lab (Deep Robust & Explainable AI Lab)
Short Bio
Welcome! I am an Assistant Professor at University of Delaware. My research focuses on Machine Learning, Computer Vision, and Safe Learning System.
I'm interested in how to make AI systems safer and more reliable, particularly for high-stake use in science, medicine, and autonomous driving.
I lead the Deep-REAL (Deep Robust & Explainable AI Lab) at UD. My groups publish in top-tier AI/ML venues including NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, AAAI, IJCAI, KDD, and TPAMI. According to csrankings.org, I am the top-ranked individual in the CIS Department and the second-best scholar across the entire university.
My research are generously supported by NSF, DOD, CDC, MSK Cancer Center, Google Research, Snap Research, and UD. My work received prestigious awards for young investigators: NSF CAREER Award, DOD DEPSCoR Award, Google Faculty Research Award, and UD Research Foundation Award.
I earned my Ph.D. in Computer Science from Rutgers University in 2018. My advisor is the Chair and Distinguished Professor Dimitris N. Metaxas. Before that, I received my M.S. degree from the Institute of Automation, Chinese Academy of Sciences in 2011, and my B.S. degree from Beihang University in 2008. I was a full-time software engineer at Baidu (Beijing) from 2011-2012, and summer interns at IBM Watson (NY) in 2015 and NEC Labs America (CA) in 2016.
Research Interests
Safe Learning System: My research focuses on developing reliable, explainable, and scalable models, algorithms, and theory foundations.
Robust Optimization: Tackle out-of-distribution (OoD) data due to distributional shifts in complex and dynamic environments.
--- ICML'24, CIKM'24, ICLR'23, TMAPI'23, CVPR'22, CVPR'21, CVPR'20, NeurIPS'20, NeurIPS'19, CVPR'19, TPAMI'19
Rationale Optimization: Safeguard AI predictions not only accurate but also anchored by valid rationales.
--- ECCV'24, CVPR'23, NeurIPSW'21 Best Paper, ICLR'21 Spotlight, ICCV'19 Oral, NeurIPS'19, KDD'19 Oral
Scalable Learning: Optimize to for modern HPC platform to manage the rapidly growing of data and model.
--- ICML'24, ICCV'23, CVPR'22, AAAI'21, CVPR'22, CVPR'21, CVPR'20, CVPR'19
Safety-critical Applications: The goal is to pioneer safe learning systems for critical domains where safety and reliability cannot be compromised.
AI for Science: Flooding segmentation; Seafloor mapping and characterization; Illicit mining detection.
AI for Medicine: AI-assisted MRI interpretation; Prostate cancer diagnosis; Roofing biomechanics.
Autonomous Driving: Hardware fault tolerant learning system.
[ICML'24] Ensemble Pruning for Out-of-distribution Generalization. [PDF] [Code]
[ICML'24] Beyond Federation: Topology-aware Federated Learning for Generalization to Unseen Clients. [PDF] [Code]
[ECCV'24] DEAL: Disentangle and Localize Concept-level Explanations for VLM. [PDF] [Code]
[CIMK'24] Adaptive Cascading Network for Continual Test-Time Adaptation. [PDF] [Code]
[ICCV'23] Learning from Semantic Alignment between Unpaired Multiviews for Egocentric Video Recognition. [PDF] [Code]
[CVPR'23] Are Data-driven Explanations Robust against Out-of-Distribution Data? [PDF] [Code]
[ICLR'23] Topology-aware Robust Optimization for Out-of-Distribution Generalization. [PDF] [Code]
[TNNLS'23, IF=14.3] Semi-identical Twins Variational AutoEncoder for Few-Shot Learning. [PDF]
[TPAMI'22, IF=24.3] Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach. [PDF] [Code]
[TMM'22, IF=8.2] Region-aware Arbitrary-shaped Text Detection with Progressive Fusion. [PDF] [Code]
[CVPR'22] Are multimodal transformers robust to missing modality? [PDF] [Code]
[CVPR'22] Symmetry and uncertainty-aware object slam for 6dof object pose estimation. [PDF] [Code]
[NeurIPS'21W Best Paper Award] Deep learning for spatiotemporal modeling of Urbanization. [PDF] [Video-10m]
[ICLR'21 Spotlight] A good image generator is what you need for high-resolution video synthesis. [PDF] [Video-10m] [Code]
[CVPR'21] Uncertainty-guided Model Generalization to Unseen Domains. [PDF] [Video-5m] [Code]
[CVPR'21 Oral] Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization. [PDF] [Video-5m] [Code]
[AAAI'21] Multimodal learning with severely missing modality. [PDF] [Video-60s] [Video-15m] [Code]
[NSDI'21] Adapting Wireless Mesh Network Configuration from Simulation to Reality via Deep Learning-based Domain Adaptation. [PDF]
[IJCV'20, IF=11.5] Towards image-to-video translation: A structure-aware approach via multi-stage generative adversarial networks. [PDF]
[NeurIPS'20] Maximum-entropy adversarial data augmentation for improved generalization and robustness. [PDF] [Code]
[CVPR'20] Learning to learn single domain generalization. [PDF] [Video-60s] [Code]
[CVPR'20] Knowledge as priors: Cross-modal knowledge generalization for datasets without superior knowledge. [PDF] [Video-60s]
[TPAMI'19, IF=24.3] Towards Efficient U-Nets: A Coupled and Quantized Approach. [PDF]
[NeurIPS'19] Semantic-guided multi-attention localization for zero-shot learning. [PDF]
[NeurIPS'19] Rethinking kernel methods for node representation learning on graphs. [PDF] [Code]
[ICCV'19 Oral] AdaTransform: Adaptive Data Transformation. [PDF]
[CVPR'19] Semantic graph convolutional networks for 3d human pose regression. [PDF]
[KDD'19 Oral] Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. [PDF]
Students
PhD students:
Fengchun Qiao, PhD, (2020 Spring-):
Previous: Chinese Academy of Sciences
Publication: CVPR'20, CVPR'21, TPAMI'21, NeurIPS'22W, ICLR'23, CVPR'23, ICML'24, CIKM'24
Awards: Distinguished Graduate Student Award, 2021; Outstanding Graduate Student Award, 2022; NeurIPS Top Reviewer Award, 2022; Outstanding Conference Travel Award 2023 & 2024; Frank A. Pehrson Graduate Student Award 2024 (Most prestigious award for CIS graduates)
Mengmeng Ma, PhD, (2020 Fall-):
Previous: University of Southern California
Publication: AAAI'21, CVPR'22, CVPR'23, ICML'24
Awards: Distinguished Graduate Student Award 2021; University Professional Development Award 2022; Outstanding Conference Travel Award 2024
Tang Li, PhD, (2020 Fall-):
Previous: George Washington University
Publication: NeurIPS'21W Best Paper, CVPR'23, ICML'24
Awards: Best Paper Award of NeurIPS ML4PH Workshop 2021; Distinguished Graduate Student Award 2022; Outstanding Conference Travel Award 2023; Distinguished Graduate Student Award 2024
Qitong Wang, PhD, (2021 Fall-):
Previous: Boston University
Publication: CVPR'20W, ICIP'21, CVIU'22, TMM'22, ICCV'23
Awards: Outstanding Conference Travel Award 2023; Distinguished Graduate Student Award 2024
Kien X. Nguyen, PhD, (2021 Fall-):
Previous: Texas Christian University
Publication: IPCCC'20, ICME'21, CVIU'23, CIKM'24
Awards: Outstanding Graduate Teaching Assistant Award 2024
Jeffrey Peng, PhD, (2024 Spring-)
Previous: Columbia University
Ali Abbasi, PhD, (2024 Fall-)
Previous: Amirkabir University of Technology
Yanlin Chen, PhD, (2024 Fall-)
Previous: Southern University of Science and Technology
Mohadeseh Ghafoori, PhD, (Defer to 2025 Spring-)
Co-advise students:
Nathaniel Merrill, PhD:
Advisor: Prof. Paul Huang, Department of Mechanical Engineering
Co-advise: 2020 -- 2023
Pub: ICRA'21, CVPR'22
Visiting student:
Ricardo Santos, PhD, Universidade NOVA de Lisboa (Portugal), 2023-2024
Undergraduate students:
VIP Research (2024 Spring): Jakeb Miburn, Coleman Walsh, Furdeen Hasan, Jonathan Ma, Michael Lutz
Amani A. Kiruga (Junior): Paul D. Amer Meritorious Award; MIT 2023 Summer Research
Wenxuan Li (Senior): Dean's list; now JHU
Ruoxi Jin (Senior): Dean's list
Jonathan Ma (Junior)
Teaching
Undergraduate-Level: CISC484: Intro to Machine Learning
2019 Fall; 2021 Spring; 2021 Fall; 2022 Fall; 2022 Spring
Graduate-Level: CISC684: Intro to Machine Learning
2022 Fall; 2023 Fall; 2024 Spring; 2024 Fall
Advanced Graduate-Level: CISC889: Advanced Topics in Machine Learning and Deep Neural Networks
2020 Spring; 2020 Fall; 2022 Spring