Hanoi University of Science and Technology
B.Sc. in Computer Science · GPA: 3.73/4.0
2022 – Jul. 2026
B.Sc. Candidate in Computer Science · Graduating July 2026
Bandits, Reinforcement Learning, and Decision-making under Uncertainty.
I am a B.Sc. candidate in Computer Science at Hanoi University of Science and Technology, graduating in July 2026. My research focuses on bandits, reinforcement learning, offline/robust decision-making, and statistical learning theory. I am first author of an ICML 2026 accepted paper on variance-driven exploration and am seeking Master’s/Ph.D. opportunities and research collaborations.
Research interests
Bandit Theory
Reinforcement Learning
Pure Exploration
Best-Arm Identification
1-Bit Mean Estimation
Offline Contextual Bandits
Distributionally Robust Optimization
Risk-Aware Decision Making
Publications and manuscripts
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Accepted at ICML, 2026. OpenReview
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Nearly Optimal Fixed-Confidence Best-Arm Identification with 1-Bit FeedbackSubmission to NeurIPS, 2026.
Education
Language
English: IELTS 7.0
Experience
HUST Data Science Laboratory
Research Member. Conducted research on machine learning and decision intelligence under uncertainty.
Feb. 2025 – Present
VinDynamics
AI Intern, Robotics. Worked on perception and control pipelines for robotic grasping, hand–object interaction, and vision-based teleoperation.
Jun. 2025 – May 2026
FPT Quantum AI & Cyber Security Institute
Research Member. Conducting research on offline policy evaluation and learning for contextual bandits.
Jun. 2026 – Present
Selected projects
Variance Driven Exploration (VarDE)
HUST Data Science Laboratory
- Derived influence-weighted variance-driven sampling rules for exploration frameworks, providing theoretical guarantees on variance decay and simple regret.
- Applied VarDE to Best-Arm Identification, Monte Carlo Tree Search, and Best-Policy Identification.
Best-Arm Identification with 1-Bit Feedback
HUST Data Science Laboratory
- Developed a BAI algorithm with 1-bit mean estimation, matching the worst-case lower bound for 1-bit feedback.
- Currently extending the analysis to instance-dependent bounds and asymptotically optimal parametric algorithms.
- Applying 1-bit mean estimation to federated learning for privacy, corruption robustness, and communication efficiency.
Offline Policy Evaluation and Learning for Contextual Bandits
FPT Quantum AI & Cyber Security Institute
- Study distributionally robust offline contextual bandits under context shift and reward shift.
- Develop pessimistic offline policy evaluation and learning methods based on optimal-transport DRO objectives.
- Analyze finite-sample guarantees under limited coverage and offline-data constraints.
Vision-Based Robotic Hand Teleoperation and Autonomous Manipulation
VinDynamics
- Developed a vision-based teleoperation system for dexterous robotic hand control.
- Worked on perception and control pipelines for robotic grasping and hand–object interaction.
- Integrated visual perception with control policies for autonomous object manipulation.
Contact
I am interested in conversations with faculty, postdocs, Ph.D. students, and collaborators working on machine learning theory, bandits, reinforcement learning, offline/robust decision-making, and statistical learning theory.