CV

Education

• Ph.D in Artificial Intelligence, Queen’s University, (in progress)
• M.Sc. in Data Science and Machine Learning, Trent University
• B.E. in Engineering Physics/Applied Physics, Cornell University

Research experience

• PhD Student at Queen’s University
  My PhD research is on uncovering interactions in complex networks in order to build better machine learning models. I work on interpreting neural networks, bioinformatics, and neuroevolution/neural architecture search.Projects:
  • Permutation-invariant representation of neural networks (accepted to EvoStar 2022)
  • Evolving modular, interpretable neural networks
  • Reinforcement learning for landmark detection in medical images
• MSc Student at Trent University (2018-2020)
  For my thesis, I designed a new method of incorporating graph neural networks into LSTM neural networks to forecast multiple time series with complex relationships. This solved problems with existing spatiotemporal models on both simulated and real-world sensory data. Other research:
  • Empirical comparison of methods for estimating the uncertainty in machine learning predictions.
  • Mitigating long-term error accumulation in time series forecasting through feature engineering.
  • Built neural network models for forecasting electricity demand (for Lowfoot Inc.)
• Research Assistant at Cornell University (2007-2010)
  • Developed flight and camera code for satellite as member of CUSAT project team. Performed hardware integration tests.
  • Implemented noise reduction algorithms and wrote microcontroller software for Formula SAE team.
• Research Assistant at Trent University (2005-2007)
  • Designed and built signal amplification PCB to collect spectral data.
  • Assembled injection lock for diode laser from schematics.
  • Built simulations using MATLAB to test control theory models and compare with experimental data.

Teaching experience

• Course Instructor at Trent Univeristy (2020)
  • Developed and taught Math 1080H at Trent University. Students learned real-world applications of logic, geometry, financial math and statistics.
• Graduate Teaching Assistant at Queen’s University (2022-)
  • Currently head TA for a team of 7 TAs.
  • Taught, graded, and ran office hours for neural networks, evolutionary computing, and algorithms.
  • Coordinated grading for all TAs and responded to student grade inquiries.
• Graduate Teaching Assistant at Trent University (2017-2019)
  • Taught seminars in multivariable calculus, physics, discrete mathematics and combinatorics.
  • Taught classes in Python and computer algebra systems.
  • Wrote and graded quizzes and assignments, held office hours and provided extra tutoring to students.

Volunteer experience

• Consultant for Little Forest Kingston (2021-2022)
  • Worked with other PhD students to design a toolkit for local youth to assess the climate resilience of their neighbourhoods.
• Information Officer for Graduate Computing Society (2020-2022) I’m the information officer for our graduate student society in the Queen’s School of Computing.
  • Maintain the website and our online presence.
  • Market and advertise events and coordinate with other groups to organize them.
  • Attend departmental meetings such as faculty hiring interviews.