Carter Blair
PhD Candidate
United States
Summary
🤩
Rockstar🎓
Top SchoolCarter Blair is a PhD candidate in computer science with four years of hands-on experience at the intersection of affective computing, personal analytics, and behavioral design. He combines academic rigor—from research roles at Vector Institute and an NSERC-funded visualization project—to applied data science work at NannyML, where he progressed from intern to junior data scientist. Carter has taught machine learning and programming at multiple universities, grounding his research in practical instruction and reproducible software for EEG and behavioral data. His background in both computer science and psychology enables him to design systems that nudge positive personal change while prioritizing measurable outcomes. Notably, he brings a research-driven product mindset to ML problems, blending experimental methods with engineering to move ideas toward real-world impact.
4 years of coding experience
1 year of employment as a software developer
Bachelor of Science - BS, Combined Major in Computer Science and Psychology, Minor in Philosophy, Bachelor of Science - BS, Combined Major in Computer Science and Psychology, Minor in Philosophy at University of VictoriaDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Harvard UniversityMaster of Mathematics - MMath, Computer Science, Master of Mathematics - MMath, Computer Science at University of Waterloo
Github Skills (17)
monitoring10
visualization10
python10
data-science10
machine-learning10
mlops10
deep-learning10
performance-estimation10
jupyter-notebook10
performance-monitoring10
data-analysis10
ml9
reinforcement-learning2
testing2
multi-agent-reinforcement-learning2
Programming languages (1)
Github contributions (5)
cartgr/nannyml
Aug 2022 - Mar 2023
Detecting silent model failure. NannyML estimates performance with an algorithm called Confidence-based Performance estimation (CBPE), developed by core contributors. It is the only open-source algorithm capable of fully capturing the impact of data drift on performance.
Contributions:1 PR, 91 pushes, 31 branches in 7 months
confidencecapturingmachine-learningestimationperformance
cartgr/cartgr.github.io
May 2022 - Jun 2026
Contributions:73 pushes, 2 branches in 4 years 1 month
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