I am an assistant professor in the Department of Computer Science at Rutgers University, where I direct a Machine Learning Lab. Previously I was a Postdoctoral Research Associate at Computer Science & Artificial Intelligence Lab of Massachusetts Institute of Technology, working with Prof. Dina Katabi and Prof. Tommi Jaakkola. I obtained Ph.D degree in CSE department, Hong Kong University of Science and Technology. My supervisor was Prof. Dit-Yan Yeung. I was a visiting scholar working with Prof. Eric Xing's group in Machine Learning department of Carnegie Mellon University. I am also a Microsoft Fellow and received the Baidu Research Fellowship. Before my Ph.D, I got my BS degree from Shanghai Jiao Tong University, 2013 under the supervision of Prof. Wu-Jun Li.

Email: hoguewang AT gmail.com / hw488 AT cs.rutgers.edu / hogue.wang AT rutgers.edu / hwang87 AT mit.edu

Recruiting: I am recruiting PhD students starting from Fall 2025 as well as interns. Send me an email if you are interested in working with me at Rutgers.

My research interest focuses on statistical machine learning, deep learning, and large language models (LLMs). Currently, I mainly work on Bayesian deep learning, probabilistic methods, game-theoretic approaches, and their applications in trustworthy & safe AI (interpretability, robustness, alignment, etc.), healthcare, recommender systems, computer vision (including multimodal LLMs), natural language processing (including LLMs), network analysis, and data mining.

• Our five papers on Bayesian deep learning, language language models, domain adaptation, and their interpretability & safety, "Towards Domain Adaptive Neural Contextual Bandits" "Implicit In-Context Learning" "GenVP: Generating Visual Puzzles with Contrastive Hierarchical VAEs" "NetFormer: An Interpretable Model for Recovering Dynamical Connectivity in Neuronal Population Dynamics" and "On Calibration of LLM-based Guard Models for Reliable Content Moderation" are accepted at ICLR (1/22/25).


• Our paper on benchmarking multimodal large language models, "Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models", is accepted at NAACL (1/22/25).


• Our papers on Bayesian large language models and natural counterfactual inference, "BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models" and "Natural Counterfactuals With Necessary Backtracking" are accepted at NeurIPS (9/25/24).


• Our paper on Bayesian deep learning for interpretable large language models, "Variational Language Concepts for Interpreting Foundation Language Models", is accepted at Findings of EMNLP (9/21/24).


• Grateful to receive the NSF CAREER Award on Robustifying AI with Bayesian Deep Learning (07/10/24).


• Grateful to receive an NIH R01 Award as PI, "Counterfactual Explanations for AI-Assisted Cancer Diagnosis and Subtyping" (07/01/24).


• Our paper on large language models for medical education, "Benchmarking Large Language Models on Communicative Medical Coaching: A Dataset and a Novel System", is accepted at Findings of ACL (5/16/24).


• Our papers on safe & trustworthy large language models and Bayesian deep learning, "Probabilistic Conceptual Explainers: Towards Trustworthy Conceptual Explanations for Vision Foundation Models" and "Delving into Differentially Private Transformer" are accepted at ICML (5/1/24).


• We are organizing the ICML 2024 Workshop on "Foundation Models in the Wild" (3/27/24).


• Our paper on safe & trustworthy large language models, "LatticeGen: Hiding Generated Text in a Lattice for Privacy-Aware Large Language Model Generation on Cloud", is accepted at Findings of NAACL (3/13/24).


• Grateful to receive the Microsoft Research AI & Society Fellowship (03/01/24).


• Our papers on safe & trustworthy large language models, Bayesian deep learning, domain adaptation, and interpretability, "Detecting Text from Large Language Models via Rewriting", "Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations", "Continuous Invariance Learning" are accepted at ICLR (1/16/24).


• Our paper on multi-domain active learning and domain adaptation, "Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees", is accepted at AAAI (11/9/23).


• Our papers on Bayesian deep learning, domain adaptation, and continual learning, "A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm", and "Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing" are accepted at NeurIPS (9/21/23).


• Our paper on learning optimization landscape, "Landscape Learning for Neural Network Inversion", is accepted at ICCV (7/14/23).


• Our papers on Bayesian deep learning, causality, interpretable ML, robustness, and domain adaptation, "Self-Interpretable Time Series Prediction with Counterfactual Explanations", "Taxonomy-Structured Domain Adaptation", and "Robust Perception through Equivariance" are accepted at ICML (4/24/23).


• Our paper on Bayesian deep learning for domain adaptation, "Domain-Indexing Variational Bayes for Domain Adaptation", is accepted at ICLR (1/20/23).


• Our Nature Medicine paper on machine learning for health, "Artificial Intelligence-Enabled Detection and Assessment of Parkinson’s Disease Using Nocturnal Breathing Signals", has been selected as one of the Ten Notable Advances in 2022 by Nature Medicine (1/10/23).


• We are organizing the CVPR 2023 Workshop on "New Frontiers in Visual Language Reasoning: Compositionality, Prompts and Causality" (12/15/22).


• Our paper on Bayesian deep learning for speech recognition and education, "Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization", is accepted at TMLR (12/8/22).


• Our paper on Bayesian deep learning for federated learning, "FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation", is accepted at AAAI (11/18/22).


• Our paper on causal and counterfactual recommender systems, "Collaborative Counterfactual Reasoning", is accepted at WSDM (10/18/22).


• Our papers on Bayesian deep learning, continuously streaming domain adaptation, and spatio-temporal forecasting, "Extrapolative Continuous-Time Bayesian Neural Network for Fast Training-Free Test-Time Adaptation" and "Earthformer: Exploring Space-Time Transformers for Earth System Forecasting" are accepted at NeurIPS (09/14/22).


• Our paper on multi-domain imbalanced learning and deep learning for health, "Artificial Intelligence-Enabled Detection and Assessment of Parkinson’s Disease Using Nocturnal Breathing Signals", is accepted at Nature Medicine (8/2/22).


• Our papers on multi-domain imbalanced learning and relational forecasting, "On Multi-Domain Long-Lailed Recognition, Generalization and Beyond" and "Social ODE: Multi-Agent Trajectory Forecasting with Neural Ordinary Differential Equations" are accepted at ECCV (07/03/22).


• Our paper, "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks", is a best paper finalist at CVPR 2022 (6/24/22).


• Our paper on domain adaptation Transformer, "Domain Adaptation for Time Series Forecasting via Attention Sharing", is accepted at ICML (05/13/22).


• Our paper on Bayesian deep learning and interpretable ML for healthcare, "'My Nose is Running.' 'Are you Also Coughing?': Building a Medical Diagnosis Agent with Interpretable Inquiry Logics", is accepted at IJCAI (04/20/22).


• Our three papers on causality, interpretable ML and Bayesian deep learning, "Causal Transportability for Visual Recognition", "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks", and "Bayesian Invariant Risk Minimization", are accepted at CVPR (03/03/22).


• Our paper, "Graph-Relational Domain Adaptation", is accepted at ICLR (1/20/22).


• We are organizing the ICLR 2022 Workshop on "PAIR^2Struct: Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data" (12/06/21).


• Our two papers on uncertainty estimation, "Context Uncertainty in Contextual Bandits with Applications to Recommender Systems" and "Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate", are accepted at AAAI (12/01/21).


• Grateful to receive NSF grant IIS-2127918 as PI, "RI: Small: Enabling Interpretable AI via Bayesian Deep Learning" (08/25/21).


• Our two papers, "Adversarial Attacks Are Reversible with Natural Supervision" and "Paint Transformer: Feed Forward Neural Painting with Stroke Prediction", are accepted at ICCV (07/22/21).


• Our three papers, "STRODE: Stochastic Boundary Ordinary Differential Equation", "Correcting Exposure Bias for Link Recommendation", and "Delving into Deep Imbalanced Regression", are accepted at ICML (05/08/21).


• Received Amazon Faculty Research Award to work on Domain Adaptation, Recommender Systems, Forecasting, and Bayesian Deep Learning. (04/28/21).


• Our paper on causal learning and deep learning, "Generative Interventions for Causal Learning", is accepted at CVPR (02/28/21).


• Our paper on AI and Bayesian deep learning for health, "Assessment of Medication Self-Administration Using Artificial Intelligence", is accepted at Nature Medicine (10/30/20).


• Our Bayesian deep learning survey paper, "A Survey on Bayesian Deep Learning", is accepted and published at ACM Computing Surveys (10/01/20).


• Our work BodyCompass was covered by: MIT News, Engadget, Yahoo, Technology Networks, Sleep Review, TechTimes, and other media outlets (09/30/20).


• Our work on COVID-19 patient monitoring was covered by: CSAIL news, TechCrunch, Engadget, and other media outlets (09/30/20).


• Our two papers, "Continuously Indexed Domain Adaptation" and "Deep Graph Random Process for Relational-Thinking-Based Speech Recognition", are accepted at ICML (06/06/20).


• We released a new TensorFlow implementation for our KDD 2015 paper "Collaborative deep learning for recommender systems" (06/06/20).


• Our paper, "Learning Guided Electron Microscopy with Active Acquisition", is accepted at MICCAI(06/06/20).


• A new project page for an ongoing survey on Bayesian Deep Learning is set up (08/30/19).


• A new project page for our NPN paper is set up with both Matlab and PyTorch code (06/30/19).


• We are organizaing the ICML 2019 Workshop on "Learning and Reasoning with Graph-Structured Representations" (15/03/19).


• We are organizaing the CVPR 2019 Workshop on "Towards Causal, Explainable and Universal Medical Visual Diagnosis" (03/11/19).


• Our paper, "Rethinking Knowledge Graph Propagation for Zero-Shot Learning", is accepted at CVPR (02/24/19).


• Our work on Deep Bayesian Networks is reported by MIT News (01/25/19).


• Our paper, "ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees", is accepted at ICLR (12/22/18).


• Our two papers, "Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling" and "Recurrent Poisson process unit for speech recognition", are accepted at AAAI (11/01/18).


• Our paper, "Deep Learning for Precipitation Nowcasting: A Benchmark and a New Model", is accepted at NIPS (09/05/17).


• A new project page for our CDL paper is set up with a brief list of CDL variants (06/12/17).


• Our paper, "Relational Deep Learning: A Deep Latent Variable Model for Link Prediction", is accepted at AAAI (11/11/16).


• Our survey/review paper on Bayesian deep learning, "Towards Bayesian Deep Learning: A Framework and Some Existing Methods", is accepted in TKDE (08/22/16).


• Two of our papers, "Natural Parameter Networks: A Class of Probabilistic Neural Networks" and "Collaborative Recurrent Autoencoder: Recommend While Learning to Fill in the Blanks", are accepted at NIPS (08/15/16).


• Give the talk "Bayesian Deep Learning for Integrated Intelligence: Bridging the Gap between Perception and Inference" at the Chinese University of Hong Kong (06/17/16). [slides]


• Give the talk "Bayesian Deep Learning for Integrated Intelligence: Bridging the Gap between Perception and Inference" at the Baidu NLP Seminar (06/13/16). [slides]


• We gave a talk about Bayesian Deep Learning at ACML (11/22/15). [slides]


• Give a talk about Bayesian Deep Learning at MSRA (09/11/15) and Baidu (11/05/15). [slides]


• Our paper "Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting" is accepted at NIPS. (09/04/15) [pdf]


• Our paper "Collaborative Deep Learning for Recommender Systems" is accepted at SIGKDD. (05/13/15) [pdf]


• Give a talk about " Relational Stacked Denoising Autoencoder for Tag Recommendation" at HKUST-EPFL Workshop on Data Science. (12/02/14) [slides]


• This homepage is set up. (11/18/14)