Agentic Machine Learning for Multi-Omics Breast Cancer Subtyping

This event presents a rigorous exploration of Machine Learning and Agentic AI methodologies applied to multi-omics gene expression analysis. We will examine how high-dimensional, heterogeneous biological datasets can be transformed into statistically robust and clinically meaningful predictive models. The discussion will cover classic Machine Learning algorithms alongside emerging Large Language Model (LLM)-augmented pipelines. We will analyze how agentic AI architectures—incorporating retrieval-augmented reasoning, tool orchestration, and autonomous workflow management—can enhance model selection, hyperparameter optimization, validation strategies, and interpretability. Emphasis will be placed on reproducible research practices, including advanced feature engineering, dimensionality considerations, class imbalance mitigation, cross-validation frameworks, and performance evaluation using balanced and stratified metrics. We will further examine deployment considerations, including API-based inference pipelines (FastAPI), model serialization, and scalable biomedical AI system design. This session is intended for researchers, bioinformaticians, computational biologists, AI engineers, and graduate-level students seeking to integrate advanced Machine Learning frameworks with next-generation Agentic AI systems for translational life sciences and precision medicine applications.

Agenda:
5:30 – 6:00 pm: Networking and refreshments
6:00 – 6:10 pm: Welcome message by Ernest Bonat, Ph.D. (Dr.B)
6:10 – 7:30 pm: Presentation and open discussions
7:30 – 8:00 pm: Networking. The building is required to be empty by 8:00 pm

Location:
Entrepreneur Collaborative Center
2101 East Palm Avenue, Tampa FL 33605

Parking:
Free parking is available in the lot located directly north of the ECC building. Please do not park directly adjacent to the ECC facility.

RSVP:
Please RSVP early, since seating is limited to 60 attendees. If you intend to bring a guest, please have them RSVP separately, so we can plan refreshments.

Cost:
The event space is provided at no cost to the local biotech community, courtesy of the Global Online Master's of Science in Artificial Intelligence (AI) and Business Analytics program at the Muma College of Business, University of South Florida. For more info, visit usf.to/global-msaiba.

Refreshments are generously provided by Dr. Matthew Schabath, Moffitt Distinguished Scholar and Program Co-Leader and Senior Member, Cancer Epidemiology Program at the H. Lee Moffitt Cancer Center.

Speakers:

Ernest Bonat, Ph.D.
Senior GenAI Engineer
Specialist in the design and development of Machine Learning systems and Agentic Artificial Intelligence assistants for bioinformatics, computational biology, and healthcare.

Nikolai Fetisov, Ph.D.
Machine Learning Engineer
MLE with ten years of experience spanning academia and industry. Leveraging my extensive expertise in ML/DL, Gen AI, MLOps, and software engineering, I architect end-to-end solutions and drive high-impact projects.

Paul London, M.S.
Molecular Technologist | Data Scientist
Blending 6+ years of lab and biotech experience with applied machine learning and data analytics to build reproducible pipelines and data-driven solutions that bridge science and technology. I’m currently exploring the intersection of computational methods, AI, and life sciences to solve complex problems in the life sciences.