Sankalp Gilda
I am a machine learning researcher and software developer specializing in time series analysis and constrained optimization. After obtaining my Ph.D. in Astronomy in 2021, I transitioned to industry as an MLE at a V2X SaaS startup. Since then, I've co-founded a consulting firm and am currently enhancing scheduling logistics for an innovative startup.
As an advocate for open-source software, I created tsbootstrap, the first Python library dedicated to time series bootstrapping. I'm actively developing additional libraries focusing on time series analysis and streamlining end-to-end machine learning for astronomers. I thrive on engaging in conferences and workshops within the scientific computing community.
When I'm not immersed in code or data, you'll find me at the gym lifting weights or embracing the thrill of skydiving. I'm always eager to discuss technology, science, or extreme sports – feel free to connect with me during the conference!
Sessions
Explore tsbootstrap and sktime in our 4-hour tutorial, focusing on enhancing time series forecasting and analysis. Discover how tsbootstrap's bootstrapping methods improve uncertainty quantification in time series data, integrating with sktime's forecasting models. Learn practical applications in various domains, boosting predictive accuracy and insights. This interactive session will provide hands-on experience with these tools, offering a deep dive into advanced techniques like probabilistic forecasting and model evaluation. Join us to expand your expertise in time series analysis, applying innovative methods to tackle real-world data challenges.
Explore tsbootstrap, a Python library tailored for time series bootstrapping, which provides specialized methods like Block, Sieve, and Markov Bootstrap for enhanced uncertainty quantification. With only basic Python knowledge required, we will explore tsbootstrap's integration with sktime and its impact on predictive analytics. This talk will cover tsbootstrap's key features, its novel approaches, and practical applications, emphasizing its role in advancing time series analysis and its seamless fit within the Python ecosystem. Attendees will learn how tsbootstrap, with its sklearn-like interface, becomes an essential tool for researchers and practitioners, facilitating sophisticated time series analysis and predictive modeling.