Welcome everyone to the Conversations on Applied AI podcast. Today we're speaking with Sruthi Pisipati. Shruthi is a software engineer turned data scientist with more than eleven years of experience across the consulting, IT, insurance, manufacturing, and medical device industries. She holds a Master's in Analytics from Georgia State University and a Bachelor's in Computer Science.

In her current role as a principal data scientist at Boston Scientific, Shruthi builds AI-powered solutions that drive innovation in healthcare and manufacturing. Her expertise spans computer vision, generative AI, time series forecasting, and uncertainty quantification, with a focus on deploying trustworthy, scalable models in high-impact environments.

Shruthi is a recognized speaker and thought leader in the AI space, with talks focused on optimization techniques, responsible AI practices, and real-world applications. Thank you, Shruthi, for taking the time to be on our podcast today. 

If you are interested in learning how AI is being applied across multiple industries, join us at a future AppliedAI Monthly meetup and help support us as we plan future Emerging Technologies North non-profit events!

Resources and Topics Mentioned in this Episode

  1. "Attention Is All You Need" — The transformer architecture paper by Vaswani et al. (2017)
    https://arxiv.org/abs/1706.03762
  2. ChatGPT — OpenAI's conversational AI
    https://chatgpt.com
  3. Claude Code — Anthropic's AI coding assistant
    https://www.anthropic.com/claude-code
  4. Chris Gregory's LinkedIn article: "Code is cheap now, but software isn't". -https://www.chrisgregori.dev/opinion/code-is-cheap-now-software-isnt
  5. Hugging Face — Open-source model hub (visual-language models, etc.)
    https://huggingface.co
  6. Conformal prediction / Uncertainty quantification — A statistical framework for calibrated prediction intervals. A good introductory resource:
    https://en.wikipedia.org/wiki/Conformal_prediction
  7. Time series foundation models — This is an emerging topic, not a single resource. Shruthi's upcoming talk compares these transformer-based models against classical approaches like ARIMA. For an overview:
    https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average

Bonus links from the episode:


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