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16-17 June, 2026
Mumbai, India
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Note: The schedule is subject to change.

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IMPORTANT NOTE: Timing of sessions and room locations are subject to change.


Wednesday June 17, 2026 3:35pm - 4:15pm IST
AI research is often portrayed as breakthroughs driven by larger models and more compute. In reality, it is an iterative process shaped by uncertainty, failed hypotheses, and refinement. When unstructured, this exploration leads to wasted compute, irreproducible results, and opaque decision-making.

This talk examines AI exploration from a researcher’s perspective, focusing on responsibility and sustainability. From hypothesis formation to experiment design and model comparison, we explore how structured experimentation enables clearer reasoning and accountable research practices.

A central theme is understanding the behavior of every component of an experiment through systematic testing. Models, data pipelines, loss functions, optimization strategies, and training loops interact in complex ways. By isolating and analyzing these components deliberately, researchers can reduce redundant experimentation, improve transparency, and make more responsible use of computational resources.

Attendees will gain practical principles for building reproducible, sustainable, and ethically grounded AI research workflows in open ecosystems.
Speakers
avatar for Bibekananda Hati

Bibekananda Hati

Founder & CEO, ExperQuick.org
I hold an MSc in Data Science. My journey began in my first semester while building PyTorchLabFlow, which later became generalized as PyLabFlow. I recently founded, ExperQuick.org, around it to support researchers across domains.
Wednesday June 17, 2026 3:35pm - 4:15pm IST
Jasmine 2 (Third Floor)
  Open AI + Data

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