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About Me
About Me
Hi, welcome to my personal webpage! My name is Aniket Jivani, and I recently completed my Ph.D. in Mechanical Engineering and Scientific Computing at the University of Michigan, Ann Arbor. I am very fortunate to have been advised by Professor Xun Huan.
I am interested in the interplay of methods combining surrogate modeling, experimental design techniques and multi-fidelity models for many-query tasks in computationally expensive engineering simulations and more broadly, a small data setting. This typically involves adapting methods from active learning approximations, multi-fidelity estimators and different kinds of surrogate modeling approaches, including neural networks. Examples include bi-fidelity simulations of turbulent jets, propagation of uncertainties and global sensitivity analysis for background solar wind in space weather among others.
I like learning about different domains and their intersections where the most interesting work can be done, so I am happy to talk about anything. Please feel free to drop a line anytime! 😄
When I am not struggling with unwieldy equations, a blank manuscript or buggy code, I:
try / struggle to read a nice book. Check out the Reading section on my site for my stray thoughts on this subject.
walk fast but run like a turtle
cook --- this is the only experimental research I do ; )
nerd out on chess and Formula 1 🏎. The only appropriate response to "What is chess?" is the counter "What is life?"
occasionally write a blog that is mostly incomprehensible to me too - see the Miscellaneous section.
Updates
Updates
August 2023: Our poster on "Towards Uncertainty Quantification for Synthetic White Light Images in the Space Weather Modeling Framework” was one of the winners of the SHINE Student Poster Contest at the SHINE Workshop held in Stowe, VT from August 6-11, 2023. Really excited about this work and recent work-in-progress incorporating ML surrogates for predictions from synthetic white light data.
February 2023: I gave a talk on "Uncertainty Quantification for Random Field Quantities using Multi-fidelity Karhunen-Loève Expansions with Active Learning" (joint work with Cosmin Safta, Beckett Y. Zhou and Xun Huan) at the SIAM CSE 2023 Conference (Feb 26 - Mar 03, 2023) in beautiful Amsterdam, Netherlands. I got to talk to amazing fellow researchers and students and learn about their exciting new work. Check the Photos section for some highlights of this trip.
January 2023: Our work on "Global Sensitivity Analysis for Background Solar Wind Simulations using the Alfvén Wave Solar atmosphere Model" is finally published in Space Weather. Check it out here: https://doi.org/10.1029/2022SW003262.
December 2022: Its the right time of year for the AGU Fall Meeting! This year, I presented a poster on recent work in Uncertainty Quantification, GSA and Data Assimilation for CME simulations in the SWMF at AGU22 in Chicago. Some great interactions with fellow presenters and colleagues at this event.
August 2022: Our new preprint titled "Global Sensitivity Analysis for Background Solar Wind Simulations using the Alfvén Wave Solar atmosphere Model" is now up on the ESSOAr! Check it out here: https://doi.org/10.1002/essoar.10512216.1 . Huge shoutout to the amazing collaborators who made this work possible, and fingers crossed for more!!
June 25 - July 1: I attended the SHINE 2022 workshop held in HI, where I was fortunate to present a poster on our work on Global Sensitivity Analysis for background solar wind simulations. This was a wonderful event with lots of great interactions involving fellow researchers and colleagues, and I came away with many happy memories plus a few new friends.
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