ワークショップ | 25th Bio-IT World Conference & Expo 2026 公式サイト

Tuesday, May 19, 2026 9:00 - 12:00 pm

W1: Building Workflows and Advancing FAIR Bioinformatics Practices: A Practical Lab Using the Playbook Workflow Builder (PWB)

This hands-on workshop introduces the Playbook Workflow Builder (PWB), guiding participants through step-by-step recipes to create reusable, transparent, and interoperable bioinformatics workflows aligned with FAIR principles. Attendees will analyze genes, gene sets, and other omics datasets, generate hypotheses, and build shareable workflow components without coding. Working in groups, participants will develop use cases for discovery and leave able to extend PWB, contribute reproducible analyses, and support collaborative research ecosystems.

9:00 am

Chairperson's Remarks

Ishwar Chandramouliswaran, Program Director, Office of Data Science Strategy, NIH

Nick Lynch, PhD, Founder & CTO, Curlew Research; Member, FAIRplus Consortium

9:05 am

Building Workflows and Advancing FAIR Bioinformatics Practices: A Practical Lab Using the Playbook Workflow Builder (PWB)

Daniel Clarke, Biomedical Software Developer, Icahn School of Medicine at Mount Sinai

Allissa Dillman, PhD, CEO & Founder, BioData Sage LLC

Avi Ma'ayan, PhD, Professor & Director, Center for Bioinformatics, Pharmacological Sciences, Icahn School of Medicine at Mount Sinai

Goals: This hands-on workshop will expose participants to the Playbook Workflow Builder (PWB) platform (https://playbook-workflow-builder.cloud/). Participants will follow step-by-step recipes to analyze their data by creating reusable, transparent, and interoperable bioinformatics workflows, which are core principles of FAIR. They will also learn how to build new reusable workflow components (metanodes) to extend the PWB system and support shareable, well-documented analyses that can be reproduced across teams and studies.

Outcomes: Participants will gain hands-on experience analyzing genes, gene sets, and other datasets; forming hypotheses from omics data; and building workflows designed for reuse, provenance tracking, and FAIR compliance. Working in groups, participants will be able to create use cases for drug and target discovery and ask questions that would be answered by community experts. Participants will leave with the ability to conduct analysis and visualization without coding, extend PWB capabilities, and contribute workflows that are discoverable, shareable, and reproducible.

Audience and Pre-requisites: This workshop serves anyone seeking help analyzing omics datasets or contributing to a community ecosystem of FAIR-aligned tools and workflows. No pre-requisites necessary to register for this workshop.

INSTRUCTOR BIOGRAPHIES:

Ishwar Chandramouliswaran, Program Director, Office of Data Science Strategy, NIH

Ishwar Chandramouliswaran is a Program Director and technical lead for the strategy, planning, coordination and oversight of establishing a FAIR data ecosystem at the NIH Office of Director, Office of Data Science Strategy (ODSS).

Nick Lynch, PhD, Founder & CTO, Curlew Research; Member, FAIRplus Consortium

Dr. Lynch has over 25 years’ experience in Data science & Informatics in various start-ups and biopharma. He is interested in making data more accessible for better analysis, and established Curlew Research in 2014, working with pharma/biotech and life science informatics/data science companies.

Daniel Clarke, Biomedical Software Developer, Icahn School of Medicine at Mount Sinai

Daniel J. B. Clarke (MS) is a data scientist in the Ma’ayan Laboratory at the Icahn School of Medicine at Mount Sinai, where he develops computational methods and software systems that support biomedical data analysis. He is a lead developer of Playbook Workflow Builder (PWB), an interactive platform that composes bioinformatics workflows from modular, semantically annotated components spanning APIs, datasets, enrichment tools, and visualization modules. His broader contributions include data-driven methods, open-source research software, and infrastructure that enables scalable and reproducible analyses. He also works on tools that help researchers integrate diverse datasets and apply computational approaches across a range of biomedical research contexts.

Allissa Dillman, PhD, CEO & Founder, BioData Sage LLC

Avi Ma'ayan, PhD, Professor & Director, Center for Bioinformatics, Pharmacological Sciences, Icahn School of Medicine at Mount Sinai

W2: How to Standardize Data Science Ways of Working to Unlock Your Data Science Team’s Creativity

Build high-performing biotech data science teams in an AI-driven world. This interactive workshop covers workflows, delivery models, and hiring strategies that balance best practices with innovation, helping you select team members who thrive alongside AI tools.

9:00 am

How to Standardize Data Science Ways of Working to Unlock Your Data Science Team’s Creativity

Eric Ma, PhD, Principal Data Scientist, Moderna, Inc.

Jackie Valeri, PhD, Data Scientist, Moderna, Inc.

How do you standardize workflows without stifling innovation? How do you hire in an age where AI can write code? This interactive workshop explores strategies for building data science teams in biotech. Through facilitated discussion and examples, we'll examine delivery models, tactics, and hiring approaches that balance standards with creativity. Discover how to make best practices the path of least resistance while selecting members who thrive alongside AI tools.

INSTRUCTOR BIOGRAPHIES:

Eric Ma, PhD, Principal Data Scientist, Moderna, Inc.

As Principal Data Scientist at Moderna Eric leads the Data Science and Artificial Intelligence (Research) team to accelerate science to the speed of thought. Prior to Moderna, he was at the Novartis Institutes for Biomedical Research conducting biomedical data science research with a focus on using Bayesian statistical methods in the service of discovering medicines for patients. Prior to Novartis, he was an Insight Health Data Fellow in the summer of 2017 and defended his doctoral thesis in the Department of Biological Engineering at MIT in the spring of 2017. Eric is also an open-source software developer and has led the development of pyjanitor, a clean API for cleaning data in Python, and nxviz, a visualization package for NetworkX. He is also on the core developer team of NetworkX and PyMC. In addition, he gives back to the community through code contributions, blogging, teaching, and writing. His personal life motto is found in the Gospel of Luke 12:48.

Jackie Valeri, PhD, Data Scientist, Moderna, Inc.

As a Senior Data Scientist at Moderna, I work on machine learning-guided library design for small molecules and proteins. I obtained my PhD in Biological Engineering from MIT and have worked on sequence-to-function machine learning models for RNA sequences and on graph neural networks for small molecule antibiotics discovery.

W3: Next-Gen AI for Drug Discovery: From LLMs to Multi-Agent Systems

This workshop explores how agentic AI is transforming drug discovery by connecting data, design, and decision-making across R&D. Attendees will learn how multi-agent AI frameworks integrate predictive modeling, generative design, and experimental validation through knowledge graphs, FAIR data, and ELN/LIMS platforms. Real-world examples will demonstrate how AI-ready, interoperable workflows accelerate discovery, improve traceability, and enhance decision quality while ensuring innovation and compliance across the life sciences.

9:00 am

Next-Gen AI for Drug Discovery: From LLMs to Multi-Agent Systems

Parthiban Srinivasan, PhD, Professor and Director, Centre for AI in Medicine, Vinayaka Mission's Research Foundation, India

This workshop explores how artificial intelligence is advancing drug discovery. The focus is on how next-generation, agentic AI frameworks move beyond standalone large language models (LLMs) to connect data, design, and decision-making across the R&D pipeline. Attendees will gain an understanding of how next-generation, agentic AI frameworks integrate predictive modeling, generative design, and experimental validation through connections with knowledge graphs, FAIR data standards, and ELN/LIMS platforms. The session will highlight how agentic AI enables unified, AI-ready workflows spanning molecular design to translational research, driving measurable impact by accelerating discovery, enhancing traceability, and improving decision quality. Through presentations and real-world examples, participants will learn how to assess their organization’s readiness for multi-agent AI and how to apply these technologies to build smarter, reliable, and compliant R&D workflows.

Topics to be Covered:

How drug discovery is evolving from LLM-based tools to agentic, multi-agent AI ecosystems

The role of knowledge graphs, FAIR data, and lab informatics systems in building interoperable discovery pipelines

Practical examples of agentic AI connecting predictive, generative, and experimental workflows

Strategies to assess readiness and integrate multi-agent systems for measurable innovation and compliance

Intended Audience: Drug discovery scientists, computational biologists, data engineers, informatics specialists, and R&D professionals interested in how next-generation agentic AI and multi-agent systems can unify data, design, and decision-making to accelerate innovation across the life-sciences enterprise.

INSTRUCTOR BIOGRAPHIES:

Parthiban Srinivasan, PhD, Professor and Director, Centre for AI in Medicine, Vinayaka Mission's Research Foundation, India

Parthiban Srinivasan, an experienced data scientist, earned his PhD from Indian Institute of Science, specializing in Computational Chemistry. After his PhD, he continued the research at NASA Ames Research Center (USA) and Weizmann Institute of Science (Israel). Then he worked at AstraZeneca in the area of Computer Aided Drug Design for Tuberculosis. Later, he headed informatics business units in Jubilant Biosys and then in GvkBio before he floated the company, Parthys Reverse Informatics and later an AI consultancy, Vingyani. Then he returned to academia as a Professor of Data Science at the Indian Institute of Science Education and Research, Bhopal. Currently, Parthiban is a Professor and Director at the Center for AI in Medicine, Vinayaka Missions Research Foundation, AV Medical College and Hospital, Puducherry, India

Tuesday, May 19, 2026 1:15 - 4:15 pm

W4: Making Data AI-Ready

AI-driven analyses depend on high-quality, accessible data for accurate modeling and decision-making. This workshop digs into strategies and frameworks to ensure that AI models perform reliably, ethically, and within regulatory bounds, while maximizing data’s potential to deliver actionable insights and accelerate pharma R&D.

1:15 pm

Making Data AI-Ready

Fernanda Foertter, MSc, Oak Ridge National Lab

INSTRUCTOR BIOGRAPHIES:

Fernanda Foertter, MSc, Oak Ridge National Lab

Fernanda Foertter is currently the Director of Developer Relations at Voltron Data. She previously held roles as the Senior Scientific Consultant for BioTeam and GPU Developer Advocate for Bioinformatics at NVIDIA in the Healthcare group where she fostered an emerging community in AI and GPU computing. Before NVIDIA, Foertter held roles as an HPC Data Scientist in the Biomedical Sciences and Engineering group and was an HPC Programmer and Training Coordinator at the Oak Ridge National Lab's Leadership Computing Facility. She participated in the CORAL project that selected Summit as the next supercomputer to replace Titan, was co-PI of Kokkos Exascale Computing Project, served in OpenACC and OpenMP language standards, and is the “inventor” of the GPU Hackathon training series. Other interests include the intersection of HPC and AI, facilitating data integration workflows, and productivity in scientific application development.

W5: Quantum Computing in Life Sciences: From Fundamentals to Future Applications

Quantum computing is set to reshape how we approach complex problems in life sciences. In this workshop, Christopher Bishop will provide an accessible overview of quantum computing, explore its potential for accelerating computational biology and drug discovery, and highlight emerging applications across research and technology. Attendees will leave with a clear understanding of quantum principles and how they may transform the life sciences landscape.

1:15 pm

Quantum Computing in Life Sciences: From Fundamentals to Future Applications

Christopher Bishop, Chief Reinvention Officer, Improvising Careers

This workshop introduces the essentials of quantum computing and its emerging role in life sciences. Participants will learn how quantum methods differ from classical approaches and explore early applications in drug discovery, molecular modeling, and complex data analysis. The session highlights realistic timelines, current capabilities, and future opportunities, offering a clear foundation for understanding quantum’s potential impact on research and industry.

INSTRUCTOR BIOGRAPHIES:

Christopher Bishop, Chief Reinvention Officer, Improvising Careers

I describe myself as a nonlinear multimodal careerist-I have had 8 so far-from touring rock musician in the 70s to 15 years at IBM-and with several others along the way. My most recent career finds me engaged as a deep tech MC for various quantum events in Silicon Valley, Montreal, Washington, D.C, New York, London, and Singapore. I also host the Quantum Tech Pod, where I have interviewed over 55 senior execs at leading quantum companies. I am also passionate about helping people reinvent themselves and prepare for the future of work-focusing on career guidance and life design skills. Based on how I successfully navigated my own atypical career path, I have developed a Future Career Toolkit designed to enable learners to be successful when pursuing their own nonlinear, multimodal career path. I conduct workshops titled "How to succeed at jobs that don't exist yet" using the toolkit to excite and empower students at universities as well as in Millennial/Gen Z workplaces. With over 14 years of experience as a Chief Reinvention Officer at Improvising Careers, I help learners of all ages and stages prepare for the 21st century's global borderless workplace.

W6: AI Upskilling for Computational Biology Teams

AI is rapidly transforming how computational biology teams design experiments, analyze data, and generate insights-but most scientists haven’t had the opportunity to build hands-on proficiency with these new tools. This interactive workshop bridges that gap, providing practical guidance on how to apply AI and agentic coding frameworks in real-world research settings. Participants will learn how to build, test, and deploy simple AI agents, connect biological data to large language models, and explore no-code or low-code platforms that accelerate productivity. Designed for computational biologists, bioinformaticians, and data scientists, this session focuses on applied learning to help teams confidently integrate AI into their daily workflows.

1:15 pm

AI Upskilling for Computational Biology Teams

Sonia Timberlake, PhD, R&D Strategy Consultant, Timberlake & Maclsaac Biopharma Consulting

AI is rapidly transforming how computational biology teams design experiments, analyze data, and generate insights-but most scientists haven’t had the opportunity to build hands-on proficiency with these new tools. This interactive workshop bridges that gap, providing practical guidance on how to apply AI and agentic coding frameworks in real-world research settings. Participants will learn how to build, test, and deploy simple AI agents, connect biological data to large language models, and explore no-code or low-code platforms that accelerate productivity. Designed for computational biologists, bioinformaticians, and data scientists, this session focuses on applied learning to help teams confidently integrate AI into their daily workflows.

INSTRUCTOR BIOGRAPHIES:

Sonia Timberlake, PhD, R&D Strategy Consultant, Timberlake & Maclsaac Biopharma Consulting

Sonia Timberlake has spent the last 15 years building biotech startups, as a head of computational biology and head of Research. Sonia currently consults for biotechs and VC on leveraging novel high throughput technologies and AI to accelerate R&D. As head of translational research at a cell therapy biotech, she built a discovery platform based on a novel beside-to-bench translation of genomic data for target ID and hit ID, and is transitioning it to the clinic. Sonia has a BS from Caltech and a PhD from MIT.