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AI and Data Science Bootcamp for MEng Students
LOCATION: Wallberg, WB-407
This one-day workshop is for incoming ChemE MEng students of all skill levels.
It is useful even if programming, data science, or AI will not be the main focus of your work. Experimentalists, process engineers, designers, and computational researchers often need to work together, and most technical work produces data somewhere along the way. Understanding how that data will eventually be analyzed can help you design better experiments, improve data quality, document your work properly, and communicate more clearly with the people using that data.
A similar idea applies to programming. You may not need to write large amounts of code yourself, but it is useful to understand how computational problems are formulated, what assumptions are being made, how results are produced, and how to communicate requirements to someone building an analysis or software tool.
Students with little or no programming experience are welcome. Students who already code regularly should also find useful material here. We will cover practical computational work, but also spend time on coding philosophy, debugging, reproducibility, software engineering practices, and how to structure technical work so that it can be understood and reused.
The workshop will mix hands-on exercises with short readings and small-group discussions.
Bring a laptop and charger. Lunch and snacks will be provided.
| Time | Session |
| 10:00-11:20 | Engineering problem formulation, data and AI |
| 11:20-11:30 | Break |
| 11:30-12:50 | Programming and software engineering |
| 12:50-1:50 | Lunch |
| 1:50-3:10 | Generative and agentic AI for technical work |
| 3:10-3:20 | Break |
| 3:20-4:40 | Learning, verification and responsible use |
| 4:40-5:00 | Discussion and wrap-up |
Instructor
Dr. Benjamin Sanchez-Lengeling is an Assistant Professor in the Department of Chemical Engineering & Applied Chemistry at the University of Toronto and leads the Chemical Cognition Lab. His work focuses on using machine learning, data-driven methods, and computational tools to solve problems in chemistry and engineering. He previously worked at Google Brain and Google DeepMind, including on large language models for proteins, and has taught courses and workshops on machine learning, neural networks, software development, and AI for chemical data. He is also involved in science education and outreach through Clubes de Ciencia México and RIIAA.