I am currently a Researcher in the Cognitive Computing Lab at Baidu Research working with Dr. Ping Li on generative modeling and its applications in Information Retrieval and AI Security. I am also a member of Prof. Chandan K. Reddy’s lab at VT and the Sanghani Center for Artificial Intelligence & Data Analytics since 2016. From May 2019 to Feb 2020, I was at Criteo AI Lab in Palo Alto, CA, where I worked with Dr. Sathiya Keerthi Selvaraj and Dr. Fengjiao Wang. Before that, I was a Faculty Research Associate of Earth System Science Interdisciplinary Center at UMD and also had a joint appointment at NASA Goddard Space Flight Center, where I worked on high-performance and distributed system research. I received my Ph.D. in Computer Science from Virginia Polytechnic Institute and State University, and MS in Computer Science from University of Maryland, College Park.

Research Interests

My research focuses on understanding the practical limits of using existing ML methods in the real-world. Essential, I seek answers to the following question: How to make ML models simpler & reliable to use in constrained settings? Simplicity refers to the ability to (i) build or implement the method easily, (ii) execute the deployed model efficiently, and (iii) evolve the deployed model with less effort. Reliability relates to (i) whether we can rely on the model to solve the intended task well, (ii) whether this performance is preserved under frequently perturbed environments in practice such as data corruptions or distributional changes, and (iii) whether the model is resilient to (i.e., its performance is not significantly affected by) various forms of security attacks such as adversarial examples and causal attacks. In this sense, I believe that many existing ML methods, including those with complex deep neural networks, are reliable but not yet easy-to-use because they do not satisfy various constraints seen in real-world applications. I also strongly believe the effort to answer this question will help us truly realize the potential of AI/ML methodology in practice.

My goal, therefore, is to develop computational frameworks that enable existing complex/deep models to be more suitable for practical uses. I focus on improving the following aspects of existing models: (i) training/inference, (ii) realistic assumptions, (iii) algorithmic robustness, and (iv) efficiency in constrained settings. Most of my ML/AI solutions center around generative-based approaches that have low computational complexity and require less human effort. Currently, my research activities include, but not limited to, the following themes:

Information Retrieval and Applications

  • Interpretable Graph Similarity Computation via Differentiable Optimal Alignment of Node Embeddings (SIGIR 2021 by Doan et al.)
  • Efficient Implicit Unsupervised Text Hashing using Adversarial Autoencoder (WWW 2020 by Doan et al.)
  • Image Hashing by Minimizing Discrete Component-wise Wasserstein Distance (arxiv 2021 by Doan et al.)
  • Generative Hashing Network (ACCV 2022 by Doan et al.)
  • EBM Hashing Network (Under Submission 2021 by Doan et al.)
  • One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching (CVPR 2022 by Doan et al.)
  • Asymmetric Hashing for Fast Ranking via Neural Network Measures (SIGIR 2023 by Doan et al.)

Generative Models

  • Image Generation Via Minimizing Frechet Distance in Discriminator Feature Space (arxiv 2021 by Doan et al.)
  • Regression via implicit models and optimal transport cost minimization (arxiv 2020 by Manchanda et al.)

AI Backdoor Security with Generative Models

  • Backdoor Attack with Imperceptible Input and Latent Modification (NeurIPS 2021 by Doan et al.)
  • LIRA: Learnable, Imperceptible and Robust Backdoor Attacks (ICCV 2021 by Doan et al.)
  • Adversarial Defenses for Vision Transformers (Under Submission 2022 by Peng et al.)
  • Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class (NeurIPS 2022 by Doan et al.)
  • Defending backdoor attacks on vision transformer via patch processing (AAAI 2023 by Doan et al.)

Professional Service

Program Committee (Invited):

  • International Conference on Learning Representations (ICLR): 2021-2023
  • Annual Conference on Neural Information Processing Systems (NeurIPS): 2020-2023
  • IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 2020-2023
  • International Conference on Machine Learning (ICML): 2020-2023
  • IEEE International Conference on Computer Vision (ICCV): 2021-2023
  • European Conference on Computer Vision (ECCV): 2020-2022
  • AAAI Conference on Artificial Intelligence (AAAI): 2021-2022
  • IEEE International Conference on Big Data (BigData): 2020-2022
  • 1st International Workshop on Industrial Recommendation Systems (IRS): 2020-2021

Conference Reviewer:

  • ACM SIGKDD International Conference on Knowledge discovery and data mining (KDD): 2017-2019
  • ACM InternationalConferenceonInformationandKnowledgeManagement(CIKM):2017- 2019
  • ACM International Conference on Web Search and Data Mining (WSDM): 2017-2019
  • The Web Conference (WWW): 2017-2019
  • International Joint Conference on Artificial Intelligence (IJCAI): 2017-2019

Journal Reviewer:

  • ACM Transactions on Knowledge Discovery from Data (TKDD): 2020
  • ACM Transactions on Internet Technology (TOIT): 2018-2021

Service Awards:

  • 2022: NeurIPS (Top Reviewer)
  • 2021: CVPR (Outstanding Reviewer)
The brick walls are there for a reason. The brick walls are not there to keep us out. The brick walls are there to give us a chance to show how badly we want something -- Randy Pausch