Hyeji Kim
Associate Professor
Works at the intersection of information theory and machine learning, including deep-learning-designed channel codes.
Hyeji Kim's lab at UT Austin works on learning theory & optimization and signal processing & information theory. 30 papers since 2023, 11 at top venues, mostly NeurIPS and ICML. 6 PhD students. 1 alumnus. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 37%
- PhD students
- 6
- MS students
- 0
- alumni listed
- 1
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 4 · collecting
- 5 papers2025 · T1 0
- 10 papers2024 · T1 6
- 5 papers2023 · T1 1
T1 venuesOtherVenues: NeurIPS, ICML, ICC, ISIT, CDC
Research focuspaper titles that use each topic's words
- Learning theory & optimization10 papers
- Signal processing & information theory3 papers
- codes3 papers
- intersection0 papers
- information theory0 papers
Paper list
From the lab homepage.
202610 papers
- IEEE Transactions on Machine Learning in Communications and NetworkingClustered Federated Learning to Support Context-dependent CSI Decoding
- Conference on Neural Information Processing Systems (NeurIPS) SpotlightDynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer
- CVPR (T1)Efficient Weighted Sampling via Score-based Generative Models
- ICML (T1)Fine-Tuning Masked Diffusion for Provable Self-Correction
- TWCGenerating High Dimensional User-Specific Wireless Channels using Diffusion Models
- CDCMinimum Rate For Partially Observable Linear System with Side Information: LQG Plant and Gaussian-Markov Source
- JSACResidual Diffusion Models for Variable-Rate Joint Source Channel Coding of MIMO CSI
- NeurIPS (T1)Structured Masked Diffusion for Joint Multiuser Decoding
- NeurIPS (T1)TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
- Conference on Neural Information Processing Systems (NeurIPS) SpotlightVision Token Pruning via Query-Vision Interaction Decomposition
20255 papers
- International Conference on Learning Representations (ICLR), SpotlightAttention with Markov: A Curious Case of Single-layer Transformers
- JSACEnhancing K-user Interference Alignment for Discrete Constellations via Learning
- JSACFundamental Limits to Exploiting Side Information for CSI Feedback in Wireless Systems
- Conference on Neural Information Processing Systems (NeurIPS), SpotlightGenerating Informative Samples for Risk-Averse Fine-Tuning of Downstream Tasks
- JSACLIGHTCODE: Light Analytical and Neural Codes for Channels with Feedback
202410 papers
- ICML (T1)Clustered Federated Learning via Gradients-based Partitioning
- ICCDeep Learning-based Autodetection of 5G NR mmWave Waveforms
- ICCDeep Learning-Based mmWave Beam Alignment with Only Pilot Channel Measurements
- ICML (T1)DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
- ISITEstimation of Rate-Distortion Function for Computing with Decoder Side Information
- NeurIPS (T1)Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
- ICML (T1)LASER: Linear Compression in Wireless Distributed Optimization
- NeurIPS (T1)Local to Global: Learning Dynamics and Effect of Initialization for Transformers
- NeurIPS (T1)Neural Cover Selection for Image Steganography
- JSAITNeural Distributed Source Coding
20235 papers
- ISITCompressed Error HARQ: Feedback Communication on Noise-Asymmetric Channels
- IEEE Trans on Wireless CommunicationsDeepIC+: Learning Codes for Interference Channels
- ICCInterpreting Neural Min-Sum Decoders
- IEEE Trans. on Information TheoryLinear Coding for AWGN channels with Noisy Output Feedback via Dynamic Programming
- NeurIPS (T1)Task-aware Distributed Source Coding under Dynamic Bandwidth
Where alumni went1 listed
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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Data
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- Papers
- The lab homepage
- Members
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- Lab homepage
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