CV
Interested in frontier AI and its applications to science.
I’m a postdoctoral fellow at the University of Waterloo working at the intersection of AI and science. Previosuly, I was a machine learning engineer at Gandeeva Therapeutics, where I used machine learning to understand and design biomolecules. I was involved in multiple projects at Gandeeva including antibody design, protein affinity maturation, and target discovery.
For my Ph.D., I worked with the computational biology group at Simon Fraser University, while also being funded by UBC. At the Libbrecht lab, I designed representation learning strategies for chromatin structure and the epigenome. Before my Ph.D., I completed my M.A.Sc while working with the information theory group at UBC. During this time, I interned with Skycope Technologies to build their automatic drone detection software. I also have experience using machine learning in a variety of settings like privacy preservation, and variational language models.
Education
- Ph.D. in Electrical and Computer Engineering (Thesis: Representation Learning Strategies for the Epigenome and Chromatin Structure using Recurrent Neural Models), The University of British Columbia, Vancouver, 2023
- M.A.Sc. in Electrical and Computer Engineering, The University of British Columbia, Vancouver, 2018
- B.Tech. in Electronics and Communication Enginnering, National Institute of Technology Karnataka, India, 2017
Work experience
NSERC Postdoctoral Fellow, University of Waterloo (Oct 2024 - Present)
- Consulting on multiple projects in AI for science.
Researcher, Royal Bank of Canada (July 2024 - Present)
- Developing ML tools for spatial optimization of agricultural land use in Canada.
Postdoctoral Fellow, University of Waterloo (Oct 2023 - Present)
- Developing AI models for decision making regarding boreal afforestation in Canada.
AI Consultant, Stealth Startups (Jun 2023 - Sep 2023)
- Designed an AI framework for metal-organic framework (MOF) discovery for carbon capture and storage applications
- Explored mutational landscape of carbonic anhydrase using DL methods
Machine Learning Engineer, Gandeeva Therapeutics (Feb 2023 - May 2023)
- Integrated molecular dynamics based conformation search with deep learning based protein design for protein affinity maturation. Gandeeva will use this platform for their future protein affinity maturation campaigns.
- Tested interface scoring tools from CASP15 to drive in-house ppi prediction, which will significantly improve Gandeeva’s target discovery efforts.
- Tested proof-of-concept target-potential binding partner interface recovery by using protein design to suggest favourable mutations. This will help Gandeeva recover weak but therapeutically relevant interfaces.
Ph.D. Candidate, The University of British Columbia (Jan 2019 - Feb 2023)
- Designed representation learning strategies for the epigenome and chromatin structure.
- Tested representations for tasks like pan-celltype element identification, novel element detection, transfer learning to unseen cell types, inference of 3D chromatin structure, and simulating in-silico alterations.
Machine Learning Intern, Gandeeva Therapeutics (Jun 2022 - Dec 2022)
- Designed antibodies using sequence and structure based Machine Learning, antibody folding tools, rosetta energy metrics, and bayesian optimization.
Machine Learning Intern, Skycope Technologies (May 2018 - Sep 2018)
- Built Skycope’s data and machine learning infrastructure.
- Modified Faster-RCNN, an existing object detection framework, to successfully detect and locate drone signals in the spectrogram.
- Integrated ML into Skycope’s existing software stack, which is now it’s flagship drone detection software.
M.A.Sc. Research Assistant, The University of British Columbia (Sep 2017 - Dec 2018)
- Developed deep learning frameworks for signal detection and hybrid precoding schemes for sequential data.
Skills
- Machine Learning, Artifical Intelligence, Climate Change, Computational Biology
Publications and Patents
Dsouza, K. B., Ofosu, E., Salkeld, J., Boudreault, R., Moreno-Cruz, J., & Leonenko, Y. (2024). Assessing the climate benefits of afforestation: processes, methods, and frameworks. arXiv preprint arXiv:2407.14617.
Dsouza, K. B., Maslova, A., Al-Jibury, E., Merkenschlager, M., Bhargava, V. K., & Libbrecht, M. W. (2022). Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation. Nature Communications, 13(1), 1-19.
Koppisetti, N. R. S. V. P., Dsouza, K. B., Boostanimehr, H., & Mallick, S. (2022). U.S. Patent Application No. 17/825,304.
Dsouza, K. B., Li, A. Y., Bhargava, V., & Libbrecht, M. W. (2021). Latent representation of the human pan-celltype epigenome through a deep recurrent neural network. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
Prasad, K. S. V., D’souza, K. B., & Bhargava, V. K. (2020). A downscaled faster-RCNN framework for signal detection and time-frequency localization in wideband RF systems. IEEE Transactions on Wireless Communications, 19(7), 4847-4862.
Conferences and Talks
December 10, 2021
Talks at 4DN, RECOMB, MLCB, Online
December 13, 2019
Talks at Epigenetics Meeting, MLCB, Online
Teaching
Service
Joint-Secretary at Autism Society of Udupi (ASU), a non-profit organisation in Udupi, India, that aims to create awareness about Autism among parents, teachers, health professionals, students, general public and all the stakeholders so that early diagnosis and early intervention could give the child maximum benefits
Past mentor at Climate Hub, iGEM, UBC, Lets Talk Science, and Geneskool, Genome BC
Projects
