Anna Choromanska
Associate Professor
Studies deep learning optimization and machine learning for autonomous driving, including world models and joint embedding predictive architectures.
Anna Choromanska's lab at NYU works on learning theory & optimization, generative & foundation models and embodied & active vision. 16 papers since 2023, 7 at top venues, mostly AISTATS and UAI. 2 PhD students. 33 alumni, most often to NVIDIA and Google. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 44%
- PhD students
- 2
- MS students
- 0
- alumni listed
- 33
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 4 · collecting
- 1 papers2025 · T1 0
- 6 papers2024 · T1 2
- 2 papers2023 · T1 1
T1 venuesOtherVenues: AISTATS, UAI, AAAI, EMNLP, ICRA
Research focuspaper titles that use each topic's words
- Learning theory & optimization10 papers
- Generative & foundation models3 papers
- learning optimization1 paper
- joint1 paper
- Embodied & active vision0 papers
Paper list
From the lab homepage.
20267 papers
- AISTATS (T1)Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization (opens in a new tab)
- UAI (T1)Communication-Efficient Distributed Training for Collaborative Flat Optima Recovery in Deep Learning (opens in a new tab)
- HiDLOuter-Momentum Restarting in High-Dimensional Two-Phase Optimization (opens in a new tab)
- AAAI (T1)Self-Supervised Representation Learning with Joint Embedding Predictive Architecture for Automotive LiDAR Object Detection (opens in a new tab)
- PMLRStreamlining Industrial Contract Management with Retrieval-Augmented LLMs (opens in a new tab)
- EMNLP (T1)Understanding Quantization of Optimizer States in LLM Pre-training: Dynamics of State Staleness and Effectiveness of State Resets (opens in a new tab)
- HiDLWorker Disagreement Reveals Sharp Directions in Local SGD (opens in a new tab)
20251 paper
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Domain Generalization: Evolution, Breakthroughs, and Future HorizonsTask-Level Contrastiveness for Cross-Domain Few-Shot Learning (opens in a new tab)
20246 papers
- ECAIAdjacent Leader Decentralized Stochastic Gradient Descent (opens in a new tab)
- UAI (T1)AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop (opens in a new tab)
- AISTATS (T1)GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models (opens in a new tab)
- IEEE Intelligent Vehicles SymposiumMulti-View Radar Autoencoder for Self-Supervised Automotive Radar Representation Learning (opens in a new tab)
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Continual Learning in Computer VisionTAME: Task Agnostic Continual Learning using Multiple Experts (opens in a new tab)
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Continual Learning in Computer VisionWake-Sleep Energy Based Models for Continual Learning (opens in a new tab)
20232 papers
- Neural Computing and ApplicationsAutomatic Document Classification via Transformers for Regulations Compliance Management in Large Utility Companies
- ICRA (T1)ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals (opens in a new tab)
Where alumni went33 listed, 80% to industry
Industry 4Academia 1
Most often to
- NVIDIA
- Huawei
Also to
- NYU
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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- Papers
- The lab homepage
- Members
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