Gati Aher
Machine Learning Ph.D. Student at Carnegie Mellon University School of Computer Science
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolGati Aher is a Machine Learning Ph.D. student at Carnegie Mellon University researching how generative AI can personalize education, advised by Dr. Zachary Lipton. With 11 years of experience spanning research internships and engineering roles at Microsoft Research, Indico Data, MITRE, and startups, he blends ML research with production-grade software engineering. His work ranges from AI safety and adversarial evaluation pipelines to adapting CV models for handwriting and building scalable REST APIs and serverless systems. As a contributor to Microsoft’s PythonProgrammingPuzzles dataset, he’s extended core problem definitions and instance generation code used in AI research. He began as an undergraduate researcher at Olin, applying causal statistics to spectrum policy and compositional analysis in microbiomes—signal that he enjoys interdisciplinary problems and rigorous empirical evaluation. Based in Cambridge, MA, he combines research curiosity with practical tooling that speeds experiments and deployments.
11 years of coding experience
4 years of employment as a software developer
High School Diploma, High School Diploma at Burlington High SchoolBachelor's of Science, Computer Software Engineering, Bachelor's of Science, Computer Software Engineering at Franklin W. Olin College of EngineeringDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Carnegie Mellon University School of Computer Science
Latin, Marathi
Github Skills (5)
solve10
python10
test-automation10
solution10
ai9
Programming languages (2)
Github contributions (5)
microsoft/PythonProgrammingPuzzles
Apr 2021 - Feb 2022
A Dataset of Python Challenges for AI Research
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:2 releases, 4 reviews, 72 commits in 9 months
Contributions summary:Gati added problem definitions and instance generation code to the `problems.py` file, which is the core of the AI puzzle dataset. They also added code for the example solutions, demonstrating their understanding of the problem requirements. Furthermore, they improved the description formatting. Their contributions focused on expanding and refining the dataset of Python challenges for AI research.
programming-competitionspythondata-sciencedatasetmachine-learning
akalai/majulook
Dec 2018 - Apr 2024
Contributions:16 pushes, 1 branch in 5 years 5 months
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