Talks and presentations

Agentic Evaluation: Assessing Autonomous LLM Workflows

August 15, 2026

Technical Talk, Pune AI Day Q3 2026, Red Hat, Pune, Maharashtra, India

Presented evaluation frameworks specifically designed for agentic AI systems, where standard NLP metrics fall short of capturing agent behavior.
Detailed methodologies for assessing agent execution trajectories — evaluating the sequence of steps an agent takes, not just its final output.
Covered tool-calling accuracy — measuring correctness, relevance, and efficiency of tool selection and parameter construction across multi-step tasks.
Introduced end-to-end task completion metrics for autonomous LLM workflows, including partial credit scoring and failure mode taxonomy.
Discussed hallucination risks specific to agentic contexts — compounding errors across tool calls and reasoning chains.
Shared practical benchmarking setups and open-source frameworks used to evaluate production agentic systems at Red Hat.

May 15, 2026

Workshop, Pune AI Day Q2 2026, Red Hat, Pune, Maharashtra, India

Delivered a hands-on workshop showcasing JIRA’s native AI capabilities for intelligent issue summarization, sprint planning assistance, and backlog prioritization.
Demonstrated how to build custom AI agents on top of JIRA’s API to automate repetitive workflow tasks such as ticket triage, assignment routing, and status tracking.
Walked through end-to-end agent design — tool definitions, task decomposition, and integration with JIRA’s REST API and webhooks.
Showed live examples of agents automating sprint retrospective summaries and dependency detection across linked issues.
Covered best practices for prompt engineering, error handling, and safe deployment of AI agents in project management contexts.

February 15, 2026

Technical Presentation, TechGenie Q1 2026, Red Hat, Pune, Maharashtra, India

Examined the evolving landscape of machine learning in the era of large language models and foundation models.
Challenged the narrative that traditional ML is obsolete — argued for its continued relevance in structured data problems, low-latency inference, and resource-constrained environments.
Discussed when LLMs and generative AI are the right tool versus when classical models (gradient boosting, SVMs, linear models) outperform them in cost, speed, and interpretability.
Highlighted hybrid architectures that combine traditional ML with LLM components for production systems.
Addressed practical trade-offs: compute cost, data requirements, explainability, and regulatory compliance that keep traditional ML indispensable.

October 27, 2020

Webinar, Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Solapur, Maharashtra, India

Delivered a webinar on the current state and emerging trends in machine learning and data science.
Covered the data science lifecycle, essential tools, and the role of visualization in model interpretability.
Introduced core ML paradigms — supervised, unsupervised, and reinforcement learning — with practical examples.
Highlighted career pathways in data science, including industry roles, research tracks, and skill-building strategies.
Engaged students with a live Q&A session on real-world applications and learning resources.

March 12, 2020

Talk, Flame University, Pune, Maharashtra, India

Introduced the family of stochastic optimization algorithms and their motivation over classical gradient-based methods for non-convex, high-dimensional search spaces.
Covered Simulated Annealing — probabilistic hill-climbing inspired by the annealing process in metallurgy, used for combinatorial optimization and hyperparameter search.
Explained Genetic Algorithms (GA) — population-based evolutionary search using selection, crossover, and mutation operators, applied to feature selection and neural architecture search.
Presented Ant Colony Optimization (ACO) — swarm intelligence algorithm modeled on foraging behavior, used for routing, scheduling, and graph-based ML problems.
Detailed the Black Hole Algorithm — a nature-inspired metaheuristic where candidate solutions orbit a best solution (the black hole) and are absorbed if they cross the event horizon, applied to feature subset selection.
Demonstrated comparative performance of these methods on feature selection benchmarks, showing improvements in model accuracy and dimensionality reduction over filter-based baselines.