Publications and Reports

Published Works

Johnston, Liam, Patel, Vivak, Cui, Yumian and Balaprakash, Prasanna. Revisiting the problem of learning long-term dependencies in recurrent neural networks. Published in Neural Networks in 2025. (DOI) AI 

Patel, Vivak and Berahas, Albert S. Gradient descent in the absence of global lipschitz continuity of the gradients. Published in SIAM Journal on Mathematics of Data Science in 2024. (DOI) Optimization Data Science AI 

Pritchard, Nathaniel and Patel, Vivak. Solving, tracking and stopping streaming linear inverse problems. Published in Inverse Problems in 2024. (DOI) RLA 

Pritchard, Nathaniel and Patel, Vivak. Towards practical large-scale randomized iterative least squares solvers through uncertainty quantification. Published in SIAM/ASA Journal on Uncertainty Quantification in 2023. (DOI) RLA 

Patel, Vivak, Jahangoshahi, Mohammad and Maldonado, D. Adrian. Randomized block adaptive linear system solvers. Published in SIAM Journal on Matrix Analysis and Applications in 2023. (DOI) RLA 

Patel, Vivak. Counterexamples for noise models of stochastic gradients. Published in Examples and Counterexamples in 2023. (DOI) Data Science AI 

Patel, Vivak, Zhang, Shushu and Tian, Bowen. Global convergence and stability of stochastic gradient descent. Published in Advances in Neural Information Processing Systems in 2022. (Proceedings) Optimization Data Science AI 

Patel, Vivak. Stopping criteria for, and strong convergence of, stochastic gradient descent on Bottou-Curtis-Nocedal functions. Published in Mathematical Programming in 2021. (DOI) Optimization Data Science 

Patel, Vivak, Jahangoshahi, Mohammad and Maldonado, D. Adrian. An implicit representation and iterative solution of randomly sketched linear systems. Published in SIAM Journal on Matrix Analysis and Applications in 2021. (DOI) RLA 

Johnston, Liam and Patel, Vivak. Second-order sensitivity methods for robustly training recurrent neural network models. Published in IEEE Control Systems Letters in 2020. (DOI) AI 

Wang, Jinyi and Patel, Vivak. Reduced-memory Kalman-based stochastic gradient descent. Published in Proceedings of 12th OPT Workshop on Optimization and Machine Learning in 2020. (Proceedings) Optimization Data Science AI 

Maldonado, D. Adrian, Rao, Vishwas, Anitescu, Mihai and Patel, Vivak. Sequential Bayesian parameter estimation of stochastic dynamic load models. Published in Electric Power Systems Research in 2020. (DOI) Power Systems 

Saikai, Yuji, Patel, Vivak and Mitchell, Paul D. Machine learning for optimizing complex site-specific management. Published in Computers and Electronics in Agriculture in 2020. (DOI) Optimization AI 

Patel, Vivak, Maldonado, D. Adrian and Anitescu, Mihai. Semiparametric estimation of solar generation. Published in Proceedings of the IEEE Power and Energy Society General Meeting in 2018. (DOI) Power Systems 

Patel, Vivak. Identification of dynamical systems: identifiability to stochastic optimization. Published in the University of Chicago ProQuest Dissertations and Theses in 2018. (ProQuest Archive) Optimization Data Science 

Maldonado, D. Adrian, Patel, Vivak, Anitescu, Mihai and Fluek, Alex. A statistical approach to dynamic load modelling and identification with high frequency measurements. Published in Proceedings of the IEEE Power and Energy Society General Meeting in 2017. (DOI) Power Systems 

Patel, Vivak. Kalman-based stochastic gradient method with stop condition and insensitivity to conditioning. Published in SIAM Journal on Optimization in 2016. (DOI) Optimization Data Science 

Technical Reports

Patra, Abani, Thomas, Mary, Bou-Harb, Elias, Carver, Jeffrey, Guo, Yuebin, Kumar, Ratnesh, Langou, Julien, Lu, Guoyu, Patel, Vivak, Safronova, Marianna, Simpson, Isla, Chakravorty, Dhruva, Combs, Jane, Cui, Hantao, Prasad, Sushil, Rajib, Adnan, Rathbun, Susan, Saule, Erik, Simpson, Isla, Sussman, Alan, Wang, Shaowen, Zhang, Sarina (Zhe), Brown, Ben, Chandola, Varun, Crawford, Daniel, Foster, Ian, Hart, Dave, Heroux, Mike, Leung, Mary Ann, Lynch, Benjamin, Negrut, Dan, Panda, D. K., Parashar, Manish, Kline-Struhl, Melissa and Thiruvathukal, George K.. 2024 NSF CSSI-Cybertraining-SCIPE PI Meeting August 12 to 13, 2024, Charlotte, NC. Technical Report in arXiv in 2025. (arXiv)

Patel, Vivak, Maldonado, D. Adrian, Melnichenko, Maksim, Pritchard, Nathaniel , Rao, Vishwas, Rebrova, Elizaveta, Sankararaman, Sriram and Schweitzer, Marcel. Scientific applications leveraging randomized linear algebra. Technical Report in arXiv in 2025. (arXiv) RLA 

Patel, Vivak and Varner, Christian. Recent advances in non-convex smoothness conditions and applicability to deep linear neural networks. Submitted in TBA in 2024. (arXiv) Optimization AI 

Varner, Christian and Patel, Vivak. The challenges of optimization for data science. Technical Report in arXiv in 2024. (arXiv) Optimization Data Science AI 

Varner, Christian and Patel, Vivak. A novel gradient methodology with economical objective function evaluations for data science applications. Technical Report in arXiv in 2024. (arXiv) Optimization Data Science 

Patel, Vivak, Jahangoshahi, Mohammad and Maldonado, D. Adrian. Convergence of adaptive, randomized, iterative linear solvers. Technical Report in arXiv in 2021. (arXiv) RLA 

Patel, Vivak and Zhang, Shushu. Stochastic gradient descent on nonconvex functions with general noise models. Technical Report in arXiv in 2021. (arXiv) Optimization Data Science AI 

Zhang, Shushu and Patel, Vivak. Stochastic approximation for high-frequency observations in data assimilation. Technical Report in arXiv in 2020. (arXiv) Optimization Power Systems 

Patel, Vivak. On SGD's failure in practice: characterizing and overcoming stalling. Technical Report in arXiv in 2017. (arXiv) Optimization Data Science AI 

Patel, Vivak. The impact of local geometry and batch size on stochastic gradient descent for nonconvex problems. Technical Report in arXiv in 2017. (arXiv) Optimization Data Science AI