Ph. D. 
Department of Computational and Data Science
Indian Institute of Science.
 

Post-Doc 

University of Wisconsin

Invited Seminars

1. “Velocity Inference from blood flow using radiological scans and neural networks”, Department of mathematics and computing, NIT Jalandhar
2. “Data-Driven Stabilization Schemes for Singularly Perturbed Partial Differential Equations”, CRUNCH, Brown University
3. “Data-Driven Stabilization Schemes for Singularly Perturbed Partial Differential Equations”, MOX, Politecnico Di Milano

• Sangeeta Yadav, Sani Yadav, Roshan D’Souza, “Quantum Neural Network-based stabilization parameter for solving Partial Differential Equations”, Advances in Computers, ISSN 0065-2458, https://doi.org/10.1016/bs.adcom.2025.06.006 [Q1, Impact Factor : 5.4]
• Amin Pashaei Kalajahi, Hunor Csala, Zayeed Mamun, Sangeeta Yadav, Omid Amili, Amirhossein Arzani, Roshan D’Souza, “Input parameterized physics informed neural networks for de noising, super-resolution, and imaging artifact mitigation in time resolved three dimensional phase-contrast magnetic resonance imaging”, Engineering Applications of Artificial Intelligence, 2025, 150, 110600. https:// doi.org/ 10.1016/ j.engappai.2025.110600 [Q1, Impact Factor : 7.5]
• Forouzan Naderi, Issac Perez-Raya, Sangeeta Yadav, Amin Pashaei Kalajahi, Zayeed Bin Mamun, Roshan M. D’Souza, “Towards Chemical Source Tracking and Characterization Using Physics-Informed Neural Networks”, Atmospheric Environment, Volume 334, 2024, 120679, ISSN 1352-2310, https:// doi.org/ 10.1016/ j.atmosenv.2024.120679.[Q1, Impact Factor : 9.4]
• Sangeeta Yadav, Sashikumaar Ganesan, “Artificial neural network - augmented stabilized finite element method”, Journal of Computational Physics, Volume 499, 2024, 112702, ISSN 0021-9991, https:// www.sciencedirect.com/ science/ article/ pii/ S0021999123007970 [Q1, Impact Factor : 3.8]
• Sangeeta Yadav, Sashikumaar Ganesan,“ConvStabNet: a CNN-based approach for the prediction of local stabilization parameter for SUPG scheme.” Calcolo 61, 52 (2024).https:// doi.org/ 10.1007/ s10092-024-00597-x [Q1, Impact Factor :1.8]
• Sangeeta Yadav, “QPDE: Quantum Neural Network Based Stabilization Parameter Prediction for Numerical Solvers for Partial Differential Equations.”, 2023, AppliedMath 2023, 3, 552-562. https:// doi.org/ 10.3390/ appliedmath3030029 [Q1, Impact Factor : 3.2]
• ​S Subramanian, R Ramnani, S Sengupta, S Yadav, “Orbit Propagation from Historical Data using Physics-informed Neural ODEs”, International Conference on Machine Learning and Applications (ICMLA), 2023, https:// ieeexplore.ieee.org/ document/ 10459772
• Sangeeta Yadav and Sashikumaar Ganesan,,“Convolutional Neural network for local stabilization parameter prediction for Singularly Perturbed PDEs”, Synergy of Scientific and Machine Learning Modeling, International Conference on Machine Learning (ICML 2023), https:// openreview.net/ pdf ? id=sMULtziWb9
• Sangeeta Yadav and Sashikumaar Ganesan,“Predicting the stabilization quantity with neural networks for Singularly Perturbed Partial Differential Equations”, Synergy of Scientific and Machine Learning Modeling, International Conference on Machine Learning (ICML 2023), https:// openreview.net/ forum? id=hTxcbnu7mV
• Sangeeta Yadav and Sashikumaar Ganesan, “SPDE-ConvNet: Predict stabilization parameter for Singularly Perturbed Partial Differential Equation”, 2022 8th European Congress on Computational Methods in Applied Sciences and Engineering, (ECCOMAS 2022). https:// www.scipedia.com/ public/ Yadav Ganesan 2022ahttps://www.scipedia.com/public/Yadav Ganesan 2022a
• Sangeeta Yadav and Sashikumaar Ganesan, “SPDE-Net: Neural Network based prediction of stabilization parameter for SUPG technique”, 2021 Asian Conference on Machine Learning, Proceedings of Machine Learning Research (ACML 2021), 268-283,https:// proceedings.mlr.press/ v157/ yadav21a.html https://proceedings.mlr.press/v157/yadav21a.html
• Sangeeta Yadav and Sashikumaar Ganesan, “How Deep Learning performs with Singularly Perturbed Problems?”, 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Sardinia, Italy, 2019, pp. 293-297, https:// ieeexplore.ieee.org/ abstract/ document/ 8791725/ ,doi: 10.1109/AIKE.2019.00058.
• Sangeeta Yadav, ‘‘DataWise: Solve Ill Posed Problems in a Data Driven Way”, accepted for ACML 2023
• S. Yadav, A. Kaur and N. S. Bhauryal, “Resolving the celestial classification using fine k-NN classifier”, 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC 2016), Waknaghat, India, 2016, pp. 714-719, doi: 10.1109/PDGC.2016.7913215.

• Artificial Intelligence & Machine Learning, B.Tech-3rd year (CSE), Jul-Dec 2025
• Fundamentals of computer programming, B.Tech-1st year (CSE), Jul-Dec 2025
• Fundamentals of computer programming, B.Tech-1st year (CSE, ECE), Jul-Dec 2024
• Database Management systems, B.Tech-2nd year (CSE), Jul-Dec 2024
• Data Structures, B.Tech-1st year (CSE, ECE), Jan-Jul 2025
• Fundamental of Data Analytics, B.Tech-2nd year (CSE), Jan-Jul 2025

Linkedin: https://www.linkedin.com/in/shongi/
Google Scholar: https://scholar.google.com/citations?user=5HYe3KwAAAAJ&hl=en
Personal Webpage: https://sites.google.com/view/sangeetayadav/home

Department of Computer Science and Engineering