I am a Ph.D. candidate in Computational Science, Engineering, and Mathematics at the Oden Institute for Computational Engineering and Sciences, University of Texas at Austin. My research primarily involves solving PDE-constrained inverse problems in the context of paleoclimate reconstruction over ice sheets. I am broadly interested in data assimilation under uncertainty, machine learning, optimization, and modeling systems.

Recent Research Articles MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade BibTeX @article{GAIKWAD2024107512, title = {MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade}, journal = {Future Generation Computer Systems}, pages = {107512}, year = {2024}, issn = {0167-739X}, doi = {https://doi.org/10.1016/j.future.2024.107512}, url = {https://www.sciencedirect.com/science/article/pii/S0167739X2400476X}, author = {Shreyas Sunil Gaikwad and Sri Hari Krishna Narayanan and Laurent Hascoet and Jean-Michel Campin and Helen Pillar and An Nguyen and Jan Hückelheim and Paul Hovland and Patrick Heimbach}, keywords = {Automatic differentiation, Differentiable programming, Adjoints, Ocean modeling, Data assimilation, Climate science, MITgcm, Tapenade}, abstract = {The Massachusetts Institute of Technology General Circulation Model (MITgcm) is widely used by the climate science community to simulate planetary atmosphere and ocean circulations. A defining feature of the MITgcm is that it has been developed to be compatible with an algorithmic differentiation (AD) tool, TAF, enabling the generation of tangent-linear and adjoint models. These provide gradient information which enables dynamics-based sensitivity and attribution studies, state and parameter estimation, and rigorous uncertainty quantification. Importantly, gradient information is essential for computing comprehensive sensitivities and performing efficient large-scale data assimilation, ensuring that observations collected from satellites and in-situ measuring instruments can be effectively used to optimize a large uncertain control space. As a result, the MITgcm forms the dynamical core of a key data assimilation product employed by the physical oceanography research community: Estimating the Circulation and Climate of the Ocean (ECCO) state estimate. Although MITgcm and ECCO are used extensively within the research community, the AD tool TAF is proprietary and hence inaccessible to a large proportion of these users. The new version 2 (MITgcm-AD v2) framework introduced here is based on the source-to-source AD tool Tapenade, which has recently been open-sourced. Another feature of Tapenade is that it stores required variables by default (instead of recomputing them) which simplifies the implementation of efficient, AD-compatible code. The framework has been integrated with the MITgcm model’s main branch and is now freely available.} } Earth system reanalysis in support of climate model improvements BibTeX @article { Earthsystemreanalysisinsupportofclimatemodelimprovements, author = "Detlef Stammer and Daniel E. Amrhein and Magdalena Alonso Balmaseda and Laurent Bertino and Massimo Bonavita and Carlo Buontempo and Nico Caltabiano and Francois Counillon and Ian Fenty and Raffaele Ferrari and Yosuke Fujii and Shreyas Sunil Gaikwad and Pierre Gentine and Andrew Gettelman and Ganesh Gopalakrishnan and Patrick Heimbach and Hans Hersbach and Chris Hill and Shinya Kobayashi and Armin Köhl and Paul J. Kushner and Matthew Mazloff and Hisashi Nakamura and Stephen G. Penny and Laura Slivinski and Susann Tegtmeier and Laure Zanna", title = "Earth system reanalysis in support of climate model improvements", journal = "Bulletin of the American Meteorological Society", year = "2024", publisher = "American Meteorological Society", address = "Boston MA, USA", doi = "10.1175/BAMS-D-24-0110.1", url = "https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-24-0110.1/BAMS-D-24-0110.1.xml"} Profiling checkpointing schedules in adjoint ST-AD BibTeX @misc{hascoët2024profilingcheckpointingschedulesadjoint, title={Profiling checkpointing schedules in adjoint ST-AD}, author={Laurent Hascoët and Jean-Luc Bouchot and Shreyas Sunil Gaikwad and Sri Hari Krishna Narayanan and Jan Hückelheim}, year={2024}, eprint={2405.15590}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2405.15590}, } SICOPOLIS-AD v2: tangent linear and adjoint modeling framework for ice sheet modeling enabled by automatic differentiation tool Tapenade BibTeX @article{Gaikwad2023, doi = {10.21105/joss.04679}, url = {https://doi.org/10.21105/joss.04679}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {83}, pages = {4679}, author = {Shreyas Sunil Gaikwad and Laurent Hascoet and Sri Hari Krishna Narayanan and Liz Curry-Logan and Ralf Greve and Patrick Heimbach}, title = {SICOPOLIS-AD v2: tangent linear and adjoint modeling framework for ice sheet modeling enabled by automatic differentiation tool Tapenade}, journal = {Journal of Open Source Software} } News I will be working as a Deep Learning RnD Intern at Ansys for Summer 2024, a global leader in multiphysics simulation software. I will be working at the intersection of deep learning, generative AI, and computational physics. I will be giving a talk on "Computational Science To Enable Digital Twins Of The Ocean" at minisymposium "Mathematical and computational foundations of predictive digital twins" at the 6th Annual Meeting of the SIAM Texas-Louisiana Section (TXLA23) to be held at the University of Louisiana at Lafayette, Lafayette, Louisiana. I am going to Qeqertarsuaq, Greenland in August-Septemeber 2023 as part of the ACDC/GRISO summer school 2023! We will be based at the Arctic Station there.

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