I am a fifth-year PhD candidate in the Department of Electrical and Computer Engineering at Carnegie Mellon University, advised by Gauri Joshi and Carlee Joe-Wong. My research work spans large language models (LLMs), including alignment, interpretability, post-training, and model routing, as well as federated/distributed learning. I have also been a research intern at NVIDIA and a quant research intern at IMC Trading.
@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
@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}}
NeurIPS
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
In Conference on Neural Information Processing Systems (NeurIPS), 2026
@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},}
NeurIPS
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
In Conference on Neural Information Processing Systems (NeurIPS), 2026
@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
@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
@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}}
@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
@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
@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}}
Federate the Router: Learning LM Routers with Sparse and Decentralized EvaluationsIn Conference on Neural Information Processing Systems (NeurIPS), 2026
