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Computation and Neural Systems, Ph.D. student

California Institute of Technology, Pasadena, United States

Honours Bachelor of Science (HBSc)

University of Toronto, Toronto, Canada

• Computer Science Specialist
  • GPA 3.96/4.0
  • Dean's List Scholar (2021-2024)

Research Intern @ Sunnybrook Health Sciences Centre, Toronto, Canada

Supervised by Prof. Maged Goubran

• Leading the development of a 3D deep learning pipeline dedicated to the precise segmentation of neuronal somas in whole-brain light sheet fluorescence microscopy rodent data
• Enhanced existing models by incorporating neuron density, resulting in a balanced approach that aligns more closely with the requirements of neuroscientists and clinicians. Achieved superior results compared to baseline models
• Created a novel UNet-based architecture with integration of small image patch dependencies into the convolutional neural network, delivering optimal performance for processing large size microscopy images

Undergraduate Researcher @ Princess Margaret Cancer Centre

Supervised by Prof. Benjamin Haibe-Kains, University Health Network (UHN), University of Toronto, Canada

• Successfully re-implemented a previously published few-shot learning method for predicting drug responses
• Conducted thorough and comprehensive testing of the developed model across a wide range of datasets, including immortalized cell line data, in vitro patient-derived cell cultures, and clinical trial data, expanding the scope beyond the original publication
• Bridged the gap from cell-line data to real-world clinical data, showcasing the potential of few-shot learning as a robust and promising tool in the medical field

Research Intern @ Data Sciences Institute (DSI), Toronto, Canada

Supervised by Prof. Ting Li and Prof. Joshua Speagle

• Developed a statistical model that incorporated both data and associated uncertainties, a crucial consideration for the analysis of survey data concerning stars at significant distances
• This model achieved remarkable success in the classification of blue horizontal branch stars at vast distances, reaching beyond half a million light-years away, utilizing photometric data from Dark Energy Survey
• Constructed a 3-D model of the stellar halo in the Milky Way by using the predicted stars. The predicted stars were also provided as a valuable catalog for the astrostatistics and astronomy communities for future research

Undergraduate Researcher @ STEM Fellowship, Toronto, Canada

Supervised by Dr. Zongjie Wang

• Conducted research in 3D bioprinting, with emphasis on its role in advancing personalized medicine and exploring prevalent techniques like extrusion printing and digital light processing (DLP)
• Discussed the multifaceted applications of bioprinted constructs, including their utilization as personalized implants for regenerative medicine and as high-throughput drug development models for drug discovery
• Placed 1st in both Intraschool and National Indicium Research Conference Competition out of more than 80 participating teams nation-wide

2023 Stellar Stats Workshop, Toronto, Canada

• Mapping the Milky Way halo with Blue Horizontal Branch stars

2023 Toronto Workshop on Reproducibility

• Evaluating the Reproducibility and Reusability of Transfer Drug Response Workflows

• T-CAIREM AI in Medicine Summer Research Studentship, University of Toronto
• Hurvitz Brain Sciences Summer Student Award, Sunnybrook Research Institute (declined)
• Ruth Reiffenstein Memorial Scholarship, University of Toronto

• Summer Undergraduate Data Science (SUDS) Scholarship, Data Sciences Institute
• First Place in the National Indicium Research Conference 2022, STEM Fellowship
• New college in-course scholarship (2021, 2022)

• University of Toronto Scholars, University of Toronto