Baris Askin

Pittsburgh, PA, USA

baskin [at] andrew.cmu.edu

Hi, I am Baris 👋

I am a fifth-year PhD candidate in the Department of Electrical and Computer Engineering at Carnegie Mellon University. I am advised by Dr. Gauri Joshi and Dr. Carlee Joe-Wong. My research interests broadly lie in language models, federated/distributed learning, and their intersection.

I graduated as the valedictorian from my undergraduate studies in the Department of Electrical and Electronics Engineering at Bilkent University, where I was fortunate to work with Dr. Tolga Cukur on deep learning applications in medical imaging.

news

Jun 2026	Emergent and Subliminal Misalignment Through the Lens of Data-Mediated Transfer was accepted to ICML 2026 Workshop on Foundations of Deep Generative Models! [paper]
Jun 2026	PubSwap: Public-Data Off-Policy Coordination for Federated RLVR was accepted to ICML 2026 Workshop on RL from World Feedback! [paper]
Jun 2026	Started a Quant Research internship at IMC Trading for Summer 2026.
Jan 2026	Our paper Internal Planning in Language Models: Characterizing Horizon and Branch Awareness was accepted to ICLR 2026! [paper]
Sep 2025	Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning was accepted to NeurIPS 2025! [paper]
Jun 2025	Started a research internship at NVIDIA for Summer 2025, working on asynchronous federated learning algorithms with knowledge distillation.
Jan 2025	Federated Communication-Efficient Multi-Objective Optimization was accepted to AISTATS 2025! [paper] [code]
Apr 2024	FedAST: Federated Asynchronous Simultaneous Training was accepted to UAI 2024! [paper] [code]
Sep 2023	Honored to receive the Ben Cook Presidential Graduate Fellowship in Electrical and Computer Engineering at CMU for the 2023–2024 academic year.

selected publications

ICML FoGen

Emergent and Subliminal Misalignment Through the Lens of Data-Mediated Transfer

B. Askin^*, M. Ustaomeroglu^*, A. Nayak^*, G. Joshi, G. Qu, and C. Joe-Wong

2026, Initial version at ICML 2026 Workshop on Foundations of Deep Generative Models: Understanding Memorization, Generalization, and Reasoning

arXiv Bib

@misc{askin2026emergentsubliminalmisalignmentlens,
      title={Emergent and Subliminal Misalignment Through the Lens of Data-Mediated Transfer},
      author={Baris Askin and Muhammed Ustaomeroglu and Anupam Nayak and Gauri Joshi and Guannan Qu and Carlee Joe-Wong},
      year={2026},
      eprint={2605.12798},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2605.12798},
}

ICLR

Internal Planning in Language Models: Characterizing Horizon and Branch Awareness

M. Ustaomeroglu^*, B. Askin^*, G. Joshi, C. Joe-Wong, and G. Qu

In International Conference on Learning Representations (ICLR), 2026

arXiv Bib

@inproceedings{
ustaomeroglu2026internal,
title={Internal Planning in Language Models: Characterizing Horizon and Branch Awareness},
author={Muhammed Ustaomeroglu and Baris Askin and Gauri Joshi and Carlee Joe-Wong and Guannan Qu},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=dqGWQdFdTC}
}

Federate the Router: Learning LM Routers with Sparse and Decentralized Evaluations

B. Askin^*, S. Patel^*, A. Nayak^*, A. Vigano, J. Woo, G. Joshi, and C. Joe-Wong

2026, Preprint, under review

arXiv Bib

@misc{askin2026federaterouterlearninglanguage,
      title={Federate the Router: Learning Language Model Routers with Sparse and Decentralized Evaluations},
      author={Baris Askin and Shivam Patel and Anupam Nayak and Andrea Vigano and Jiin Woo and Gauri Joshi and Carlee Joe-Wong},
      year={2026},
      eprint={2601.22318},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2601.22318},
}

Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning

B. Askin, H. R. Roth, Z. Sun, C. Joe-Wong, G. Joshi, and Z. Xu

2026, Preprint, under review

arXiv Bib

@misc{askin2026revivingstaleupdatesdatafree,
      title={Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning},
      author={Baris Askin and Holger R. Roth and Zhenyu Sun and Carlee Joe-Wong and Gauri Joshi and Ziyue Xu},
      year={2026},
      eprint={2511.00655},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2511.00655},
}

ICML RLxF ICML DEMO

PubSwap: Public-Data Off-Policy Coordination for Federated RLVR

A. Nayak^*, B. Askin^*, M. Ustaomeroglu, C. Joe-Wong, and G. Joshi

2026, Initial version at ICML 2026 Workshop on RL from World Feedback and ICML 2026 Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning

arXiv Bib

@misc{nayak2026pubswappublicdataoffpolicycoordination,
      title={PubSwap: Public-Data Off-Policy Coordination for Federated RLVR},
      author={Anupam Nayak and Baris Askin and Muhammed Ustaomeroglu and Carlee Joe-Wong and Gauri Joshi},
      year={2026},
      eprint={2604.12160},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2604.12160},
}

NeurIPS

Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning

A. Raje, B. Askin, D. Jhunjhunwala, and G. Joshi

In Conference on Neural Information Processing Systems (NeurIPS), 2025

arXiv Bib

@article{raje2026ravan,
  title={Ravan: Multi-head low-rank adaptation for federated fine-tuning},
  author={Raje, Arian and Askin, Baris and Jhunjhunwala, Divyansh and Joshi, Gauri},
  journal={Advances in Neural Information Processing Systems},
  volume={38},
  pages={48564--48592},
  year={2026}
}

AISTATS

Federated Communication-Efficient Multi-Objective Optimization

B. Askin, P. Sharma, G. Joshi, and C. Joe-Wong

In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), May 2025

arXiv Bib Code Poster

@inproceedings{askin2025fedcmoo,
  title = {Federated Communication-Efficient Multi-Objective Optimization},
  author = {Askin, B. and Sharma, P. and Joshi, G. and Joe-Wong, C.},
  booktitle = {Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS)},
  pages = {4627--4635},
  year = {2025},
  volume = {258},
  series = {Proceedings of Machine Learning Research},
  month = may,
  publisher = {PMLR},
  url = {https://proceedings.mlr.press/v258/askin25a.html},
}

UAI

FedAST: Federated Asynchronous Simultaneous Training

B. Askin, P. Sharma, C. Joe-Wong, and G. Joshi

In Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI), Jul 2024

arXiv Bib Code Poster

@inproceedings{askin2024fedast,
  title = {{FedAST}: Federated Asynchronous Simultaneous Training},
  author = {Askin, B. and Sharma, P. and Joe-Wong, C. and Joshi, G.},
  booktitle = {Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI)},
  pages = {138--172},
  year = {2024},
  volume = {244},
  series = {Proceedings of Machine Learning Research},
  month = jul,
  publisher = {PMLR},
  url = {https://proceedings.mlr.press/v244/askin24a.html},
}

ICML Position

Position: Federated Learning is a Lens towards a Democratized Future for the Scaling Law Era

H. H. Jiang, B. Askin, G. Joshi, and C. Joe-Wong

In International Conference on Machine Learning (ICML) Position Track, 2026

Link Bib

@inproceedings{
jiang2026position,
title={Position: Federated Learning is a Lens towards a Democratized Future for the Scaling Law Era},
author={Harry H. Jiang and Baris Askin and Gauri Joshi and Carlee Joe-Wong},
booktitle={Forty-third International Conference on Machine Learning Position Paper Track},
year={2026},
url={https://openreview.net/forum?id=td1fQFeIiI}
}