SG NTUCollege of Computing and Data Science
Dacheng Tao Distinguished University Professor Applies statistics and mathematics to artificial intelligence, including computer vision, deep learning theory and large models.
2026 A Simple "Motivation" Can Enhance Reinforcement Finetuning of Large Reasoning Models ICLR 2026 AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization AAAI 346 T1501 papersGroup size not listed US UMDComputer Science
Develops machine learning, large-scale optimization and AI methods for biomedical data science and computational health.
2026 An Ensemble Framework for Unbiased Language ModelWatermarking ICLR 2026 Analyzing and Evaluating Unbiased Language Model Watermark ICLR 117 T1141 papersGroup size not listed US StanfordComputer Science Dec 8
Yejin Choi Dieter Schwarz Foundation Professor of Computer Science, Senior Fellow at the Stanford Institute for Human-Centered AI Studies large language models, commonsense reasoning and the limits of language model generalization.
2026 Activation Steering for LLM Alignment via a Unified ODE-Based Framework ICLR 2026 BroRL: Scaling Reinforcement Learning via Broadened Exploration ICML 102 T1170 papers7 members63 alumniCH ETH ZurichComputer Science (D-INFK)
Researches machine learning and causal inference, including causal representation learning, kernel methods and applications in the sciences.
2026 Hyperbolic Busemann Neural Networks CVPR 2026 Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies ICLR 98 T1141 papersGroup size not listed CN ZhejiangCollege of Computer Science and Technology Feb 28
Works on data management and learning-based analytics over graphs, time series and high-dimensional data.
2026 A Unified Federated Framework for Trajectory Data Preparation via LLMs ICLR 2026 Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization SIGMOD 93 T1107 papersGroup size not listed US UIUCSiebel School of Computing and Data Science Dec 1
Studies machine learning theory and algorithms, including optimization, reinforcement learning and alignment of large language models.
2026 "A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions ICLR 2026 "Almost linear convergence under minimal score assumptions: Quantized transition diffusion COLT 89 T1104 papers13 members36 alumniDE TUMDepartment of Computer Science
Develops computer vision methods for 3D reconstruction, visual SLAM and image analysis using convex optimization, statistical learning and neural networks.
2026 BA-T: An Iterative Transformer for Two-View Bundle Adjustment NeurIPS 2026 Combining Projected Uncertainty for Self-Supervised Visual Odometry: From Two-Frame to Multi-Frame IJCV 75 T1138 papers35 members45 alumniUS UC BerkeleyElectrical Engineering and Computer Sciences Dec 1
Jason Lee Associate Professor Studies the theory of deep learning, representation learning and reinforcement learning.
2026 Coverage Improvement and Fast Convergence of On-policy Preference Learning ICML 2026 Improved high-dimensional estimation with Langevin dynamics and stochastic weight averaging ICLR 75 T176 papers8 members12 alumniSG NTUSchool of Electrical and Electronic Engineering
Works on robust control and estimation, networked and cooperative control, distributed optimization and unmanned systems.
2026 Asymptotically Optimal Distributed Consensus of Heterogeneous Multiagent Systems TAC 2026 Data-Driven Cooperative Output Regulation via Output Feedback Control TAC 73 T1196 papers9 members68 alumniUS UCLAComputer Science Dec 15
Develops nonconvex optimization and learning theory, and alignment and reasoning methods for large language models.
2026 Best-of-Majority: Minimax-Optimal Strategy for Pass@k Inference Scaling ICLR 2026 Breaking the Total Variance Barrier: Sharp Sample Complexity for Linear Heteroscedastic Bandits with Fixed Action Set ICLR 73 T191 papers9 members60 alumniCN ZhejiangCollege of Control Science and Engineering Feb 28
Works on network optimization and control and industrial IoT security, and on machine learning for industrial data such as label-free multivariate time-series…
2026 Consistency-Aware Spot-Guided Transformer for Accurate and Versatile Point Cloud Registration TPAMI 2026 DiffPBR: Point-Based Rendering via Spatial-Aware Residual Diffusion ICLR 72 T1210 papersGroup size not listed CH EPFLInstitute of Electrical and Micro Engineering (IEM, School of Engineering STI) Dec 1
Leads the Laboratory for Information and Inference Systems, working on optimization theory, reinforcement learning and robust deep learning.
2026 Diffusion-based Cumulative Adversarial Purification for Vision Language Models TMLR 2026 ESLM: Risk-Averse Selective Language Modeling with Hierarchical Batch Selection TMLR 71 T187 papers7 members25 alumniUS UMDComputer Science
Studies optimization and the reliability of deep learning, including adversarial robustness, watermarking of LLM outputs and diffusion models.
2026 DynaGuard: A Dynamic Guardian Model With User-Defined Policies ICLR 2026 FictionalQA: A Dataset for Studying Memorization and Knowledge Acquisition ICLR 62 T172 papers15 members23 alumniCH ETH ZurichComputer Science (D-INFK)
Leads the Data Analytics Lab, studying deep learning theory, the optimization and generalization of neural networks, and natural language understanding with…
2025 Autoregressive Distillation of Diffusion Transformers CVPR 2025 Avoiding spurious sharpness minimization broadens applicability of SAM ICML 58 T179 papers7 members4 alumniCH ETH ZurichComputer Science (D-INFK)
Niao He Associate Professor Leads the Optimization and Decision Intelligence group, studying large-scale stochastic and minimax optimization, reinforcement learning theory and…
2026 A Convex Framework for Confounding Robust Inference JMLR 2026 A Schrodinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control ICLR 56 T168 papers11 members12 alumniUS MITElectrical Engineering and Computer Science Dec 1
Tommi Jaakkola Thomas M. Siebel Distinguished Professor Develops machine learning methods and theory for generating semi-structured data such as molecules and proteins, including the BoltzGen protein design model.
2026 Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner ICML 2026 Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps ICML 55 T165 papers8 members51 alumniUS NYUComputer Science (Courant) Dec 12
Andrew Gordon Wilson Professor of Computer Science and Data Science Works on Bayesian deep learning, Gaussian processes and generalization in neural networks, with numerical linear algebra for scalable inference.
2026 A Unification of Discrete, Gaussian, and Simplicial Diffusion ICLR 2026 Diverse Dictionary Learning ICLR 53 T158 papers7 members11 alumniSG NTUSchool of Electrical and Electronic Engineering
Works on pattern recognition, computer vision, biometrics and image processing, including statistical pattern classification.
2026 ActivityForensics: A Comprehensive Benchmark for Localizing Manipulated Activity in Videos CVPR 2026 Angle K-Means ICLR 52 T1119 papersGroup size not listed HK CUHKDepartment of Computer Science and Engineering Dec 1
John C. S. Lui Choh-Ming Li Professor of Computer Science and Engineering Works on online learning such as multi-armed bandits and reinforcement learning, network science and quantum networks.
2026 Constraint-Aware Combinatorial Bandits: Theoretical Foundations and Network Applications INFOCOM 2026 Faster, Smaller, and Smarter: Task-Aware Expert Merging for Online MoE Inference INFOCOM 51 T1155 papersGroup size not listed US UCLAComputer Science Dec 15
Studies scalable optimization, adversarial robustness and verification of neural networks, and efficient training of large models.
2025 Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models NeurIPS 2025 Is Your Multimodal Language Model Oversensitive to Safe Queries? ICLR 50 T161 papers13 members18 alumni