Trong Nghia Hoang | OpenReview

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Trong Nghia Hoang (Preferred), Nghia Hoang

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****@gmail.com (Confirmed), ****@mit.edu (Confirmed), ****@ibm.com (Confirmed), ****@amazon.com (Confirmed), ****@uit.edu.vn (Confirmed), ****@wsu.edu (Confirmed)

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Career & Education History

Assistant ProfessorEECS, Washington State University (eecs.wsu.edu)2023 – Present Senior Machine Learning ScientistAWS AI Labs, Amazon (amazon.com)2020 – 2022 Research Staff MemberMIT-IBM Watson AI Lab, International Business Machines (ibm.com)2018 – 2020 PostdocLIDS, Massachusetts Institute of Technology (mit.edu)2017 – 2018 PostdocSchool of Computing, National University of Singapore (nus.edu.sg)2015 – 2017 

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Advisors, Relations & Conflicts

CoauthorMinh HoangPresent Postdoc AdvisorJonathan How2017 – 2018 PhD AdvisorBryan Kian Hsiang Low2010 – 2017 

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Expertise

Probabilistic Machine Learning, Gaussian Processes, Federated LearningPresent Meta Learning2019 – 2021 Federated Learning2017 – 2021 Variational Inference2015 – 2021 Gaussian Processes2014 – 2021 Machine Learning2013 – 2021 

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Publications

• Principled Design for Trustworthy AI: Interpretability, Robustness, and Safety Across Modalities

  Tsui-Wei Weng, Trong Nghia Hoang, Tengfei Ma, Jake C. Snell, Francesco Croce, Chandan Singh, Subarna Tripathi, Lam M. Nguyen
  • ICLR 2026 Workshop Proposals
  • Readers: Everyone

• Nonparametric Unsupervised Data Condensation for Gigapixel Histological Images

  Duong M. Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Phi Le Nguyen, Minh N. Do
  • ICLR 2026 Conference Withdrawn Submission
  • Readers: Everyone

• ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge

  Manh Cuong Dao, The Hung Tran, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang
  • NeurIPS 2025 spotlight
  • Readers: Everyone

• Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data

  Manh Duong Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Phi Le Nguyen
  • NeurIPS 2025 poster
  • Readers: Everyone

• AutoTransOP: translating omics signatures without orthologue requirements using deep learning

  Nikolaos Meimetis, Krista M Pullen, Daniel Zhu, Avlant Nilsson, Trong Nghia Hoang, Sara Magliacane, Douglas Lauffenburger
  • Greeks in AI 2025 Poster
  • Readers: Everyone

• FROM LOW TO HIGH-VALUE DESIGNS: OFFLINE OPTIMIZATION VIA GENERALIZED DIFFUSION

  Manh Cuong Dao, The Hung Tran, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang
  • Submitted to ICLR 2025
  • Readers: Everyone

• Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship

  Nang Hung Nguyen, Trong Nghia Hoang, Thao Nguyen Truong, Phi Le Nguyen, Masashi Sugiyama
  • Submitted to ICLR 2025
  • Readers: Everyone

• Offline Model-Based Optimization via Policy-Guided Gradient Search

  Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang, Jana Doppa
  • AAAI 2024
  • Readers: Everyone

• Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data

  Pei-Yau Weng, Minh Hoang, Lam M. Nguyen, My T. Thai, Tsui-Wei Weng, Trong Nghia Hoang
  • NeurIPS 2024 poster
  • Readers: Everyone

• Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques

  Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang
  • NeurIPS 2024 poster
  • Readers: Everyone

View all 34 publications

Co-Authors

• Alireza Ghods
• Anoop Deoras
• Aryan Deshwal
• Avlant Nilsson
• Azza Fadhel
• Bryan Kian Hsiang Low
• Carl Kingsford
• Chandan Singh
• Daniel Zhu
• Diane Cook
• Douglas Lauffenburger
• Duong M. Nguyen
• Duy Dinh
• Francesco Croce
• HAO DING
• Hilaf Hasson
• Jake C. Snell
• Jana Doppa
• Jie Chen
• Krista M Pullen
• Kristjan H. Greenewald
• Lam M. Nguyen
• Linbo Liu
• Long Minh Bui
• Luke Huan

View all 63 co-authors

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