UofT Dept. Computer Science
Researcher Assistant: Sept 2023 - April 2024
Investigating the emergence of sparsity in the development of deep learning models in
solving physics based problems. Also look at the use of Large Language Models and prompting
for novel knowledge generation in physics based problems. Supervised by Prof. Vardan Papyan.
UofT - Dunlap Institute for Astronomy & Astrophysics
Researcher Assistant: Sept 2023 - April 2024
First we look into building faster emulators for dark matter simulations. Look into using JAX for
faster gradient computation when producing forward progating models. Secondly we look symbolic regression
on deep learning models in learning cosmological struture formation.
Supervised by Dr. Keir K. Rogers.
CERN - ATLAS / Large Hadron Collider
Intern Researcher: July 2023 - Sept 2023
Continuing my work at McGill [below] I worked on building deep learning algorithms on low level FPGA's to reconstruct energy readouts
from the Liquid Argon Digital Signal Processor for the ATLAS detector on the future High Luminosity Large Hadron Collider. I also
help around with developing firmware on digital electronics.
McGill University - Experimental Particle Physics Group
Intern Researcher: May 2023 - July 2023
I work on developing high performance FPGA firmware for the Liquid Argon Signal Processing Unit (LASP)
to be attached on the ATLAS detector. I also worked on developing functional hardware test for our LASP
digital electronics.
Supervised by Prof. Brigitte Vachon
California Institute of Technology - LIGO
Intern Researcher: June 2022 - Sept 2022
I work on tackling non-linear dynamic control problems using deep learning. I specifically investigated
attention-based state estimators and reinforcement learning for LIGO's locking acquisition to help detect gravitational waves.
I work with Dr. Gabriele Vajente on this ambitious project.
Our preprint paper can be found here
UofT - Dunlap Institute for Astronomy & Astrophysics
Researcher Assistant: Dec 2021 - Apr 2022
Work on developing novel ensemble machine learning models
for FRB detection with the
CHIME/FRB project. My algorithm now actively runs in production
to help improve the core detection pipeline.
I am grateful to have worked under
Prof. Bryan Gaensler! The goal is to
one day use these detections to help astronomers answer
important questions regarding the origins of these objects and potential cosmological questions.
UC Berkeley SETI Research Center
Co-mentor: Sept 2022 - Dec 2022
I co-mentor with Dr. Steve Croft.
a group of undergraduates from UC Berkeley on developing a deep learning based "reverse image search" method for
radio spectrograms leveraging the techniques from computer vision.
Intern Researcher: [June 2020 - April 2022] and [Sept 2022 - May 2023] On pause
Currently developing attention-based geometric deep learning models for the
MeerKAT telescope to conduct the largest search effort for signs of life beyond Earth, surveying 1 million stars over a span of 2 years.
I am supervised by
Dr. Cherry Ng and
Dr. Steve Croft,.
Previously I explored how deep neural nets like
Disentangled B-VAE's
can search 820 stars for technosignatures. My first paper was published in
Nature Astronomy! Supervisors were
Dr. Cherry Ng,
and Dr. Andrew Siemion
In high school I helped build a distributed cloud
computing platform for Astronomy Research with Dr. Steve Croft
and Yuhong Chen!
AI For Good - Volunteering
Volunteer Researcher: May 2022 - Aug 2022
I work on developing Natural Language Processing models for text classification in building the sustainable development goal (SDG) data catalogue pipeline.
The goal is to help build tools that power data driven policy making in achieving the 17 SDG goals set by the UN.