Research Interests

Trustworthy Machine Learning (algorithm): Develop reliable, explainable, and scalable models, algorithms, and theory foundations. 

• Robust Optimization: Tackle the out-of-distribution (OoD) challenge that involves dynamic, long-tail, or previously unseen data.

• --- 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 with valid rationales for safety and reliability.

• --- ECCV'24, CVPR'23, NeurIPSW'21 Best Paper, ICLR'21 Spotlight, ICCV'19 Oral, NeurIPS'19, KDD'19 Oral

• Scalable Learning: Optimize for modern HPC platform to manage the scaling law.

• --- ICML'24, ICCV'23, CVPR'22, AAAI'21, CVPR'22, CVPR'21, CVPR'20, CVPR'19

Safe AI System (application): Develop safe learning systems for critical domains where safety and reliability cannot be compromised.

• AI for Science: Extreme-scale seafloor data analytics.

• AI for Medicine: AI-assisted mpMRI interpretation.

• Autonomous Vehicle: End-to-end safe learning system.

Awards

I am honored with prestigious research awards for young investigators:

• NSF CAREER Award (2024)
• DOD DEPSCoR Award (2023)
• Google Faculty Research Award (2022)
• General University Research Award (2022)
• University of Delaware Research Foundation Award (2022) 

My work won a series of paper awards in top-tier AI/ML conferences:

• Strong Double Blind, ECCV 2024, acceptance rate 4.5%
• Best Paper Award, NeurIPSW 2021
• Oral Presentation, CVPR 2021, acceptance rate 4.3%
• Spotlight Presentation, ICLR 2021, acceptance rate 3.8%
• Oral Presentation, ICCV 2019, acceptance rate 4.7%
• Oral Presentation, KDD 2019, acceptance rate 9.2%
• Oral Presentation, BMVC 2018, acceptance rate 4.6%
• Best Student Paper Finalist, ECCV 2016, acceptance rate 0.4%
• Oral Presentation, ICPR 2016, acceptance rate 14.1%
• Oral Presentation Award, ACCV 2010, acceptance rate 3.5%

News and media about my research work:

• UD researchers leverage trustworthy AI to improve seafloor data intelligence. [Link]
• DOD announces awards ($600K) under the Defense Established Program to Stimulate Competitive Research. [Link]
• Interdisciplinary UD team received ($1M) NSF grant to map global illicit trade of energy-critical materials. [Link]
• A vision of the fast-growing Data Science Institute at UD. [Link]

Funding

Since joining UD in 2019, I have secured 15 research grants totaling $6.4 million. I am the Sole or Lead PI on 12 of these grants totaling $3.8 million. These grants come from a diverse range of sources including federal agencies, industrial research labs, and UD internal awards.

Federal Grants: 

• NSF CAREER Award (PI) $572K
• NSF III CORE Small (PI) $600K, with R. Eigenmann (co-PI)
• NSF Safe AI (PI) $1.5M, with W. Shi (co-PI) and C. Yang (co-PI)
• DOD DEPSCoR (PI) $600K, with A. Trembanis (co-PI)
• CDC Contract (PI)
• NSF CMMI (Co-PI) $1M, with J. Klinger (PI)
• NSF HDR (KP) $1.5M, with F. Bianco (PI)

Industrial Grants:

• MSK Cancer Center (PI) $277K
• Google Research Faculty Award (PI)
• Snap Research Award (PI)

Internal Grants: UDRF (PI); GUR (PI); AICoE Seed (PI); DSI Seed (PI); UDRF-SI (Co-PI)

Teaching

My teaching received an average of 4.35 out of 5 according to UD's internal evaluation and 4.8 out of 5 according to RateMyProfessors.com 

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

Service

I serve as the Area Chair, Program Committee, and Reviewers on major AI/ML conferences and journals since 2012:

• Area/Session Chair: BMVC, IISE Annual Conf & Expo, ICIG
• Program Committee / Reviewer: NeurIPS, ICML, ICLR, CVPR, ECCV, ICCV, ACL, AAAI, IJCAI
• Journal Guest Editor: Remote Sensing, Neurocomputing, CVIU
• Journal Reviewer: TPAMI, IJCV, TIP, TNNLS, Pattern Recognition

I serve on Proposal and Grant Review Panels since 2020:

• NSF Review Panel: OAC (2024), III (2024), RI (2023), III (2023), III (2022), CPS (2022), RI (2021), CPS (2020)
• External Proposal Review: Linz Institute of Technology (2024), University of Sydney (2021), University of Central Florida (2020)
• Internal Proposal Review: AICoE (2024), UDRF (2023), GUR (2023)

Students

PhD students:

• Fengchun Qiao, PhD candidate, (2020 Spring-), Previous: Chinese Academy of Sciences
• Mengmeng Ma, PhD candidate, (2020 Fall-), Previous: University of Southern California
• Tang Li, PhD candidate, (2020 Fall-), Previous: George Washington University 
• Qitong Wang, PhD candidate, (2021 Fall-), Previous: Boston University
• Kien X. Nguyen, PhD candidate, (2021 Fall-), Previous: Texas Christian University  
• Jeffrey Peng, PhD candidate, (2024 Spring-), Previous: Columbia University
• Ali Abbasi, PhD candidate, (2024 Fall-), Previous: Amirkabir University of Technology  
• Yanlin Chen, PhD candidate, (2024 Fall-), Previous: Southern University of Science and Technology 
• Mohadeseh Ghafoori, PhD candidate, (2024 Fall-), Previous: Isfahan University of Technology 

Co-advise PhD student:

• Nathaniel Merrill, PhD, (Co-advise: 2020-2023), Advisor: Prof. Paul Huang, Department of Mechanical Engineering

Visiting PhD student:

• Ricardo Santos, PhD candidate, (2023-2024), Universidade NOVA de Lisboa (Portugal)

Undergraduate Researchers:

• VIP Program: 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)

Publication

I have published over 60 papers in AI/ML and computer vision:

• Over 80% of papers are at top conferences such as NeurIPS, ICML, ICLR, CVPR, ECCV, ICCV, AAAI, IJCAI, and KDD
• Over 80% of papers are first-authored by my PhD students

Selected top-tier AI/ML publication (Click here for a full list)

[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 Strong Double Blind] DEAL: Disentangle and Localize Concept-level Explanations for VLM. [PDF] [Code]

[CIKM'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]

[CVPR'18] Jointly optimize data and network training: Adversarial data augmentation in human pose estimation. [PDF]

[CVPR'18] A generative adversarial approach for zero-shot learning from noisy texts. [PDF]

[ECCV'18] Quantized densely connected u-nets for efficient landmark localization. [PDF]

[ECCV'18] Learning to forecast and refine residual motion for image-to-video generation. [PDF]

[IJCAI'18] Cr-gan: Learning complete representations for multi-view generation. [PDF]

[IJCV'18, IF=11.5] Red-net: A recurrent encoder-decoder network for video-based face alignment. [PDF]

[IJCV'18, IF=11.5] Toward personalized modeling: Incremental and ensemble alignment for sequential faces in the wild. [PDF]

[ICCV'17] Reconstruction-based disentanglement for pose-invariant face recognition. [PDF]

[ECCV'16 Best Student Paper Finalist] A recurrent encoder-decoder network for sequential face alignment. [PDF]

[ICCV'15] PIEFA: Personalized incremental and ensemble face alignment. [PDF]

[TIP'12] Robust tracking with discriminative ranking lists. [PDF]