Qiang Liu
Associate Professor, Faculty Fellowship #7 in Computer Science
Leads the Statistical Learning & AI Group, working on probabilistic inference, Stein variational methods, generative models such as rectified flow and deep reinforcement learning.
Qiang Liu's lab at UT Austin works on learning theory & optimization, probabilistic & causal inference and efficient training & inference. 34 papers since 2023, 33 at top venues, mostly ICLR and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 97%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 3 papers2026 · T1 3 · collecting
- 8 papers2025 · T1 8
- 13 papers2024 · T1 12
- 10 papers2023 · T1 10
T1 venuesOtherVenues: ICLR, NeurIPS, CVPR, ICML, AAAI
Research focuspaper titles that use each topic's words
- Learning theory & optimization9 papers
- Efficient training & inference8 papers
- flow5 papers
- Probabilistic & causal inference0 papers
- variational0 papers
Paper list
From DBLP.
20263 papers
20258 papers
- CVPR (T1)AMO Sampler: Enhancing Text Rendering with Overshooting (opens in a new tab)
- ICCV (T1)Improving Rectified Flow with Boundary Conditions (opens in a new tab)
- ICLR (T1)Longhorn: State Space Models are Amortized Online Learners (opens in a new tab)
- AISTATS (T1)Memory-Efficient Optimization with Factorized Hamiltonian Descent (opens in a new tab)
- ICML (T1)PIPA: Preference Alignment as Prior-Informed Statistical Estimation (opens in a new tab)
- ICML (T1)SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization (opens in a new tab)
- CVPR (T1)Steepest Descent Density Control for Compact 3D Gaussian Splatting (opens in a new tab)
- AISTATS (T1)SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity (opens in a new tab)
202413 papers
- NeurIPS (T1)AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies (opens in a new tab)
- NeurIPS (T1)Communication Efficient Distributed Training with Distributed Lion (opens in a new tab)
- NeurIPS (T1)Enhancing Protein Mutation Effect Prediction through a Retrieval-Augmented Framework (opens in a new tab)
- ICML (T1)Evolution-Inspired Loss Functions for Protein Representation Learning (opens in a new tab)
- ICML (T1)FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames (opens in a new tab)
- ICLR (T1)InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation (opens in a new tab)
- AAAI (T1)Layer Compression of Deep Networks with Straight Flows (opens in a new tab)
- ICLR (T1)Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts (opens in a new tab)
- NeurIPS (T1)Memory-Efficient LLM Training with Online Subspace Descent (opens in a new tab)
- 3DVPathFusion: Path-Consistent Lidar-Camera Deep Feature Fusion (opens in a new tab)
- NeurIPS (T1)PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator (opens in a new tab)
- ECCV (T1)SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow (opens in a new tab)
202310 papers
- ICCV (T1)Efficient Transformer-based 3D Object Detection with Dynamic Token Halting (opens in a new tab)
- NeurIPS (T1)FAMO: Fast Adaptive Multitask Optimization (opens in a new tab)
- CVPR (T1)Fast Point Cloud Generation with Straight Flows (opens in a new tab)
- ICLR (T1)Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow (opens in a new tab)
- CVPR (T1)FlowGrad: Controlling the Output of Generative ODEs with Gradients (opens in a new tab)
- ICLR (T1)HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing (opens in a new tab)
- ICLR (T1)Learning Diffusion Bridges on Constrained Domains (opens in a new tab)
- NeurIPS (T1)LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning (opens in a new tab)
- AAAI (T1)Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning (opens in a new tab)
- ICML (T1)MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UT AustinFall 2027
Computer Science PhD
- Round
- Single round (Dec 15)
- Opens
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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