[MENTEE] Arnav Kapoor
Mentee
Arnav Kapoor
3rd year undergraduate at IISER Bhopal, India
Interested in Bayesian Modeling, Probabilistic ML, and Mathematical Computing
Linkedin
Mentor(s)
Why did you join sktime's mentorship program?
I joined the mentorship program to deepen my understanding of probabilistic modeling, especially from the perspective of mathematical computing and rigorous system design. With a strong inclination toward Bayesian inference, optimization, and scientific computing, I want to sharpen my ability to translate formal statistical theory into clean extensible software abstractions.
The skpro stack provides me an ideal setting to study estimator design invariants, probabilistic APIs, and long-term maintainability. Enabling me to bridge mathematically principled modeling with ecosystem-aligned, production-quality implementation.
What topics are you working on?
Currently focusing on probabilistic and Bayesian estimator design across skpro and skbase, including:
- contributing to probabilistic estimator PRs
- Understanding distribution adapters and
predict_proba interfaces
- Studying architectural trade-offs in base classes and tag systems
- Exploring inference backend abstraction (MAP / VI / MCMC separation)
What are your learning goals?
- Design Bayesian estimator abstractions aligned with skbase invariants
- Improve probabilistic API consistency and posterior handling patterns
- Develop stronger architectural intuition for extensible ML libraries
- Gain experience in high-quality PR review, testing strategy, and ecosystem-aligned design
What's next for you after the mentorship program?
I plan to continue contributing to sktime/skpro, particularly in probabilistic modeling and Bayesian estimator development, and to take on more responsibility in architectural discussions and long-term maintenance.
[MENTEE] Arnav Kapoor
Mentee
Arnav Kapoor
3rd year undergraduate at IISER Bhopal, India
Interested in Bayesian Modeling, Probabilistic ML, and Mathematical Computing
Linkedin
Mentor(s)
Why did you join sktime's mentorship program?
I joined the mentorship program to deepen my understanding of probabilistic modeling, especially from the perspective of mathematical computing and rigorous system design. With a strong inclination toward Bayesian inference, optimization, and scientific computing, I want to sharpen my ability to translate formal statistical theory into clean extensible software abstractions.
The skpro stack provides me an ideal setting to study estimator design invariants, probabilistic APIs, and long-term maintainability. Enabling me to bridge mathematically principled modeling with ecosystem-aligned, production-quality implementation.
What topics are you working on?
Currently focusing on probabilistic and Bayesian estimator design across
skproandskbase, including:predict_probainterfacesWhat are your learning goals?
What's next for you after the mentorship program?
I plan to continue contributing to sktime/skpro, particularly in probabilistic modeling and Bayesian estimator development, and to take on more responsibility in architectural discussions and long-term maintenance.