London Data Science, ML, AI Platform

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Large Language Models (LLMs) and AI Agents are transforming healthcare by automating tasks, supporting diagnosis, and accelerating research.

🔹 What you’ll learn:
How LLMs work in medical workflows
What AI agents are and how they extend LLMs
Real-world applications: clinical note summarization, literature review, patient communication, and diagnosis support

Example:
A patient with chest pain — the LLM summarizes history, the AI agent pulls labs, checks guidelines, and drafts a clinical note.

Agenda:
(PDT) 11:00 am - 11:05 am Arrival and socializing and Opening
(PDT) 11:05 am - 1:00 pm "LLMs & AI Agents in Medicine"
(PDT) 1:00 pm - 1:10 pm Q&A

About Dr. Yasin Ceran:

Yasin Ceran is passionate about all things data and holds a vast experience in data analysis, mathematical modeling and Apache Spark, and in SQL, Python and R. He is currently an associate professor at KAIST, South Korea, as well as teaching at San Jose State University at the heart of Silicon Valley. Yasin has worked rigorously on an array of data-related projects encompassing data mining, statistics, modeling, and is dedicated to sharing his experience and expertise with learners.

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As generative and agentic AI systems move from prototypes to production, builders must balance innovation with trust, safety, and compliance. This talk explores the unique evaluation and monitoring challenges of next-generation AI, with healthcare as a case study of one of the most regulated domains:
Evaluation gaps: why conventional benchmarks miss multi-step reasoning, tool use, and domain-specific workflows—and how contamination and fragile metrics distort results.
Bias and safety: demographic bias, hallucinations, and unsafe autonomy that trigger regulatory, legal, and contractual obligations for fairness and safety assessments.
Continuous monitoring: practical MLOps strategies for drift detection, risk scoring, and compliance auditing in deployed systems.
Tools and standards: open-source libraries like LangTest and HELM, new stress-test and red teaming datasets, and emerging guidance from NIST, CHAI, and ISO.
While the examples draw heavily from healthcare, the lessons are broadly applicable to anyone building and deploying generative or agentic AI systems in highly regulated industries where safety, fairness, and compliance are paramount.

Agenda:
(PDT) 11:50 am - 11:55 am Arrival and socializing and Opening
(PDT) 11:55 am - 1:00 pm "Governing and Evaluating Generative & Agentic AI in Regulated Industries"
(PDT) 1:00 pm - 1:10 pm Q&A

About David Talby

David Talby is the CEO at John Snow Labs and Pacific AI, helping companies apply artificial intelligence to solve real-world problems in healthcare and life science. David has extensive experience building and running web-scale software platforms and teams – in startups, open-source projects, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK. David holds a Ph.D. in Computer Science and Master’s degrees in both Computer Science and Business Administration. He was named USA CTO of the Year by the Global 100 Awards in 2022, Game Changers Awards in 2023, and ACQ5 Global Awards in 2025.

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Data science requires a different type of copilot than software development. But current tools are not fit for purpose. How can you use AI-assisted development to expedite your work, produce better models and more production-ready pipelines?

Tools and standards: open-source libraries like LangTest and HELM, new stress-test and red teaming datasets, and emerging guidance from NIST, CHAI, and ISO.

While the examples draw heavily from healthcare, the lessons are broadly applicable to anyone building and deploying generative or agentic AI systems in highly regulated industries where safety, fairness, and compliance are paramount.

Agenda:
(PDT) 11:50 am - 11:55 am Arrival and socializing and Opening
(PDT) 11:55 am - 1:00 pm "Copilots and Data Science: How is tooling for Data Scientists evolving in the era of AI-assisted development?"
(PDT) 1:00 pm - 1:10 pm Q&A

About Raluca Crisan

I’m a data scientist and co-founder of Etiq. Prior to Etiq I’ve been an ML/data science consultant and I ran and delivered projects for more than 20+ corporates, from mid-sized retailers to ecommerce giants to travel, telco and financial sectors.
At Etiq we are building the data science copilot - a tool to help data scientists document, test and bring their pipelines to production faster and easier.
Etiq is a Techstars portfolio company and has been awarded the prestigious Innovate UK Smart Grant and the Innovate UK Trustworthy AI grant.

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