Laknath Ashwin De Silva

PhD Student at Johns Hopkins

prof_pic_2.png

Clark Hall 317

Baltimore MD 21218

ldesilv2 at jhu dot edu

Hello there! My name is Laknath Ashwin De Silva (ලක්නාත් අශ්වි​න් ද සිල්වා in Sinhalese script). I am a third-year PhD candidate in the Department of Biomedical Engineering at Johns Hopkins University, where I am fortunate to be advised by Dr. Joshua Vogelstein, Dr. Pratik Chaudhari (UPenn) and Dr. Carey E. Priebe.

I broadly work on {machine, deep} learning, with an aspiration to strive towards reducing the gap between artificial and natural intelligences. My doctoral research is currently focused on learning under non-stationary distributions, out-of-distribution (OOD) generalization, and robustness to distribution shifts. Outside of theoretical pursuits, I am passionate about tackling challenging problems in computer vision, computational biology, and neuroscience.

I received my Bachelor’s degree in Biomedical Engineering from University of Moratuwa, Sri Lanka in 2020 ( ranked 1st among ~950 undergrads in the Faculty of Engineering). I completed my thesis on “Designing a Cost-Effective Dry Contact sEMG Sensor System for Controlling a Bionic Hand” with Dr. Simon Kappel and Dr. Thilina Lalitharatne. As an undergrad, I interned at The Florey Institute (University of Melbourne) and Center for Advanced Imaging (University of Queensland), where I had the pleasure of working with Dr. Steve Petrou, Dr. Saman Halgamuge, and Dr. Steffen Bollmann.

Outside work, I love spending time with my wife Malsha, playing the piano, hiking, and trying out new recipes. I am an avid fan of astronomy, history, science-fiction, anime, and Formula 1!

news

Feb 5, 2024 Received the MINDS fellowship for Spring 2024
Jan 9, 2024 Passed my doctoral board oral (DBO) exam. Officially a PhD candidate now!
Aug 22, 2023 Attended CoLLAs 2023, Montreal, Cananda.
Jul 22, 2023 Attended ICML 2023, Honolulu, HI in person.
May 15, 2023 Our paper on “Prospective Learning: Principled Extrapolation to the Future” was accepted to CoLLAs 2023! 💫
Apr 24, 2023 “The Value of Out-of-Distribution Data” was accepted to ICML 2023! 💫
Feb 27, 2023 New paper on “Approximately optimal domain adaptation with Fisher’s Linear Discriminant Analysis” (in collaboration with Microsoft Research) out in arXiv! 💫
Dec 3, 2022 Attended NeurIPS 2023, New Orleans, LA.
Oct 23, 2022 Our work on “The Value of Out-of-Distribution Data” won the best short paper award at the OOD-CV workshop, ECCV 2022! 🏆
Oct 20, 2022 1 Paper accepted to the NeurIPS 2022 workshop on Distribution Shift, New Orleans, LA! 💫
Aug 22, 2022 Our paper titled “The Value of Out-of-Distribution Data” got accepted to the OOD Generalization in Computer Vision workshop at ECCV 2022, Tel Aviv, Israel! 💫
Jul 17, 2022 Attended ICML 2022, Baltimore, MD in person.
Jun 19, 2022 Attended CVPR 2022, New Orleans, LA in person.
Apr 5, 2022 Attended NAISys 2022 (From Neuroscience to Artificially Intelligent Systems) held in Cold Spring Harbor Laboratory, NY.
Aug 31, 2021 Started my PhD in Johns Hopkins, Moved to Baltimore, MD.
Jun 6, 2021 Attended ICASSP 2021 virtually.
Jan 30, 2021 Our phase unwrapping paper got accepted to ICASSP 2021, Toronto, Canada.
Jan 6, 2021 Received Prof. Pathuwathawithana Memorial Prize for the Highest GPA at the Faculty of Engineering, University of Moratuwa, Sri Lanka.
Dec 4, 2020 Received a National Merit award from SLAAS for our undergraduate research project.
Aug 24, 2020 Graduated from University of Moratuwa (top of the Faculty of Engineering’s class of 2020).
Aug 20, 2020 One paper accepcted to IEEE International Conference on Systems, Man and Cybernetics (SMC) 2020, Toronto, Canada.
Jul 23, 2020 Completed my tenure as the Chairperson of the IEEE EMBS Student Branch Chapter at University of Moratuwa (term 2019/20).