Jasper Gerigk
Computer Science PhD Student
University of Toronto
I am a first year PhD student at the University of Toronto supervised by Professor Gilitschenski at the Toronto ISL lab. My work focuses on exploiting the abilities of foundational models for robotics with a special attention to controlling uncertainty without losing effectiveness.
For my research during my undergraduate degree at the University of Toronto, I received an honorable mention at the CRA’s Outstanding Undergraduate Researcher Awards (2024). Beyond researching object-centric reinforcement learning at TISL, I combined graph neural networks and reinforcement learning to optimize traffic flow at intersections during an internship at Bosch Corporate Research. We were able to train models that outperformed traditional approaches and generalised well to unseen intersection layouts. Building on this experience, I applied geometric deep learning, under the supervision of Professor Engels, to the problem of learning strategies for the deck-building game Dominion. The resulting agent were able to learn strategies previously only seen in human play on a wider set of game configurations.
- Artificial Intelligence
- Robotics
- Large Models
BSc Computer Science Specialist and Mathematics Major, 2024
University of Toronto, Canada
IB Diploma, 2019
Metropolitan School Frankfurt, Germany
PUBLICATIONS
EXPERIENCE
Worked on combining pre-trained computer vision models with reinforcement learning
Methods applied: Pytorch, JAX, Rainbow, SAM, FastSAM
For the first time, used large-scale customer fleet data to analyze lateral vehicle movement to improve comfort of lane following assistant
Methods applied: Spark, Azure Databricks, Frequentist and Bayesian statistics in Python
Development of multi-agent reinforcement learning algorithms for centralized planning of connected self-driving vehicles using graph neural networks
Methods applied: DQN, TD3, RCGN, GAT implemented in Python using PyTorch
Contributed to backend by integrating machine learning methods using Python
EDUCATION
GPA: 3.96/4.0
Programs:
- Computer Science Specialist
- Focus In Artificial Intelligence
- Mathematics Major
Advanced Courses:
- CSC311: Introduction to Machine Learning
- CSC324: Principles of Programming Languages
- CSC412: Probabilistic Learning and Reasoning
- CSC413: Neural Networks and Deep Learning
- CSC420: Introduction to Image Understanding
- CSC494: Computer Science Project: Developed and published agent for Dominion under supervision of Professor Engels
Awards:
- 2020, 2023 - Dean’s List Scholar
- 2019 - 2020 - Millard Scholarship ($1208)
- 2022 - 2023 - Dr. James A. & Connie P. Dickson Scholarship In Science & Mathematics ($500): Recognizes best University College students in science and mathematics
- 2022 - 2023 - University of Toronto Scholar ($1500): Recognizes the most outstanding students across all three campuses of the University of Toronto
- 2024 Computing Research Association’s Outstanding Undergraduate Researcher Award Honorable Mention
Courses covering:
- Statistical Machine Learning
- Natural Language Processing using Deep Learning
Courses covering:
- Linear Algebra
- Probability and Statistics
- Numerical Methods
