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Full time Data Scientist at Red Hat. Part time Data Science Researcher, Part time Teaching Associate at Pune University. Visit https://prasadovhal.github.io/
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Red Hat
Data Scientist
June 16, 2026 – Present
multi-agentic-systems
May 1, 2026 – Present
Understanding multi-agentic systems, and evolution from RAG
View Projectdata-science-masters-work
April 14, 2026 – Present
A collection of projects developed during master's, focused on applied machine learning, optimization, and real-world problem solving.
View Projectdynamic-optimization
April 1, 2026 – Present
Black Hole—White Hole algorithm for dynamic optimization of chemically reacting systems
View Projectfeature-selection-research-papers
March 28, 2026 – Present
A curated repository of feature selection research papers, implementations, and optimization-driven approaches for high-dimensional machine learning
View Projectmcp_server_tutorials
March 17, 2026 – Present
How to create API and MCP servers tutorials from basic to advance
View Projectagentic_ai_tutorials
March 4, 2026 – Present
Understanding Agentic AI and its framework hands on with python
View ProjectA-Simple-Method-of-Solution-For-Multi-label-Feature-Selection
February 9, 2020 – Present
Multi-label learning has been a topic of research interest in multimedia, text & speech recognitions, music, image processing, information retrieval etc. In Multi-label classification (MLC) each instance is associated with a set of multiple class labels. Like other machine learning algorithms, data preprocessing plays an key role in MLC. Feature selection is an important preprocessing step in MLC, due to high dimensionality of datasets and associated computational costs. Extracting the most informative features considerably reduces the computational loads of MLC. Most of the Multi-label feature selection algorithms available in literature involve conversions to multiple single labeled feature selection problems. We proposed an efficient modification of a recent multi-label feature selection algorithm [1] available in literature. Our algorithm consists of two steps: in the first step we decompose the output label space into lower dimensions using simple matrix factorization method; subs
View ProjectCultural Fit Analysis
The candidate's projects show a strong inclination towards research and advanced technical exploration, which could align well with an innovative and problem-solving culture. However, the projects are predominantly personal and academic, with limited evidence of collaborative or team-oriented work. The current role at Red Hat as a Data Scientist suggests industry alignment, but without further details on responsibilities, it's difficult to fully assess cultural fit beyond technical interest.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are technically focused, and no psychometric or English test results are available.