PANINI is a non-parametric continual-learning framework that encodes documents into Generative Semantic Workspaces (GSW)—entity- and event-aware networks of atomic question-answer pairs. A beam-search retrieval procedure (Reasoning Inference Chain Retrieval, RICR) follows multi-hop inference chains through GSWs to assemble compact evidence, achieving top average performance across six QA benchmarks while using 2-30x fewer answer-context tokens than competitive baselines.
@inproceedings{rajesh2026panini,title={Panini: Continual Learning in Token Space via Structured Memory},author={Rajesh, S. and Holur, P. S. and Turali, M. Y. and Duan, C. and Roychowdhury, V.},booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},year={2026},note={Accepted as poster. *Equal contribution by Rajesh and Holur.}}
We customize open-source LLMs to extract quantitative medication attributes (dose, frequency, duration) from heterogeneous electronic health record systems, demonstrating robustness across formats relevant to addiction-medicine deployments.
@inproceedings{fei2025ehrllm,title={Demo: Customizing Open-Source LLMs for Quantitative Medication Attribute Extraction across Heterogeneous EHR Systems},author={Fei, Z. and Turali, M. Y. and Rajesh, S. and Dai, X. and Pham, H. and Holur, P. S. and Zhu, Y. and Mooney, L. J. and Hser, Y.-I. and Roychowdhury, V.},booktitle={NeurIPS 2025 Workshop on GenAI for Health: Potential, Trust, and Policy Compliance},year={2025},note={Accepted as poster. *Equal contribution by Fei and Turali.}}
@article{turali2025bfmo,title={Binary Feature Mask Optimization for Feature Selection},author={Turali, M. Y. and Lorasdagi, M. E. and Kozat, S. S.},journal={Neural Computing and Applications},year={2025},doi={10.1007/s00521-024-10913-9},}
@article{ozturk2025hydravit,title={HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images},author={Öztürk, Ş. and Turalı, M. Y. and Çukur, T.},journal={Biomedical Signal Processing and Control},volume={100},pages={106959},year={2025},doi={10.1016/j.bspc.2024.106959},}
@article{turali2024sgdfilter,title={Optimal Stochastic Gradient Descent Algorithm for Filtering},author={Turali, M. Y. and Koc, A. T. and Kozat, S. S.},journal={Digital Signal Processing},volume={155},pages={104731},year={2024},issn={1051-2004},doi={10.1016/j.dsp.2024.104731},}
@article{turali2024afsbm,title={AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking},author={Turali, M. Y. and Lorasdagi, M. E. and Kozat, S. S.},journal={Signal, Image and Video Processing (SIViP)},year={2024},doi={10.1007/s11760-024-03411-x},}
@article{ilhan2023gbmma,title={Gradient Boosting With Moving-Average Terms for Nonlinear Sequential Regression},author={Ilhan, E. and Turali, M. Y. and Kozat, S. S.},journal={IEEE Signal Processing Letters},volume={30},pages={1182--1186},year={2023},doi={10.1109/LSP.2023.3309577},}
@article{turk2023jointnet,title={JointNET: A Deep Model for Predicting Active Sacroiliitis from Sacroiliac Joint Radiography},author={Turk, S. and Demirkaya, A. and Turali, M. Y.},journal={arXiv preprint},year={2023},doi={10.48550/ARXIV.2301.10769},}