Workshops at Bio-IT World Expo 2026

W1 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

9:10 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

WORKSHOP OBJECTIVES:Â The Playbook Workflow Builder (PWB) is a web-based platform that facilitates the interactive construction of bioinformatics workflows by enabling users to utilize an ever-growing network of input datasets, semantically annotated API endpoints, and data visualization tools. Via a user-friendly web-based user interface, workflows can be constructed from contributed building blocks without technical expertise. Workflows can be alternatively created using a chatbot. The output of the workflows includes an interactive online report, publication-like PDF file, and a YouTube video. The workshop will demonstrate how to use the platform via interactive sessions, as well as introduce ways for the community to contribute workflow building blocks to the platform.Â

OUTCOMES:Â 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.Â

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.Â

AGENDA:

9:10â€“10:00 Presentations: Tour of the Platform and Use Cases

10:00â€“11:00 Hands on Session*: Building and Publishing Workflows with PWB

User guide: https://github.com/MaayanLab/Playbook-Workflow-Builder/blob/main/docs/user/index.mdÂ

11:00â€“12:00Â Hands on Session*: Creating Building Blocks to Add to the PWB Platform

Developer guide: https://github.com/MaayanLab/Playbook-Workflow-Builder/blob/main/docs/index.mdÂ

*PleaseÂ bring your laptopÂ to participate in the interactive sessions.

The Playbook Workflow Builder project is partially funded by the NIH grant OT2OD036435 for the CFDE Data Coordination Center (DRC) of the CFDE. Please visit the CFDE Workbench website for more details about the CFDE and the DRC: https://info.cfde.cloud/.

PWB Website:Â https://playbook-workflow-builder.cloud/Â

PWB Publication:Â https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012901

INSTRUCTOR BIOGRAPHIES:

Photo of Avi Ma'ayan, PhD, Professor & Director, Center for Bioinformatics, Pharmacological Sciences, Icahn School of Medicine at Mount Sinai — **Avi Ma'ayan, PhD, Professor & Director, Center for Bioinformatics, Pharmacological Sciences, Icahn School of Medicine at Mount Sinai**
Dr. Maâ€™ayan is a Mount Sinai Endowed Professor in Bioinformatics, Director of the Mount Sinai Center for Bioinformatics, Professor in the Department of Pharmacological Sciences, Professor in the Department of Artificial Intelligence and Human Health, and faculty member of the Icahn Genomics Institute. Dr. Ma'ayan is also a Principal Investigator of the NIH Common Fund Data Resource Center (DRC) for the Common Fund Data Ecosystem (CFDE), a NCI-funded ITCR resource center, a NIDDK-funded diabetes hypothesis platform, and the NCI-funded Mount Sinai Proteogenomic Data Analysis Center. The Ma'ayan Laboratory applies computational methods to study the inner workings of regulatory networks in mammalian cells. His research team applies machine learning and other statistical mining techniques to study how intracellular regulatory systems function as networks to control cellular processes such as differentiation, dedifferentiation, apoptosis and proliferation. The Ma'ayan Laboratory develops bioinformatics software applications to enable experimental biologists to form novel hypotheses from high-throughput omics datasets, while aiming to better understand the structure and function of regulatory networks in mammalian cellular and multi-cellular complex systems.

Photo of Daniel Clarke, Biomedical Software Developer, Icahn School of Medicine at Mount Sinai — **Daniel Clarke, Biomedical Software Developer, Icahn School of Medicine at Mount Sinai**
Daniel J. B. Clarke (MS) is a data scientist in the Maâ€™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.

Photo of Ishwar Chandramouliswaran, Program Director, Office of Data Science Strategy, NIH — **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).

Photo of Nick Lynch, PhD, Founder & CTO, Curlew Research; Member, FAIRplus Consortium — **Nick Lynch, PhD, Founder & CTO, Curlew Research; Member, FAIRplus Consortium**
Dr. Lynch has over 25 yearsâ€™ 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.

W2 W2: How to Standardize Data Science Ways of Working to Unlock Your Data Science Teamâ€™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 Teamâ€™s Creativity

Eric Ma, PhD, Principal 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:

Photo of Eric Ma, PhD, Principal Data Scientist, Moderna, Inc. — **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.

Photo of Jackie Valeri, PhD, Data Scientist, Moderna, Inc. — **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 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

Petrina Kamya, PhD, Global Head of AI Platforms & Vice President, Insilico Medicine; President, Insilico Medicine Canada

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 organizationâ€™s readiness for multi-agent AI and how to apply these technologies to build smarter, reliable, and compliant R&D workflows.Â

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 complianceÂ

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:

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

Photo of Petrina Kamya, PhD, Global Head of AI Platforms & Vice President, Insilico Medicine; President, Insilico Medicine Canada — **Petrina Kamya, PhD, Global Head of AI Platforms & Vice President, Insilico Medicine; President, Insilico Medicine Canada**
Petrina Kamya, PhD, is the Head of AI Platforms and President of Insilico Medicine, Canada an end-to-end artificial intelligence-driven drug discovery company. Before joining Insilico, Dr. Kamya spent eight years in various roles at Chemical Computing Group that involved scientific and business-related aspects of preclinical drug discovery. In addition to establishing the corporate strategy for the sales and business development of molecular modeling software for academia, she also played an active role as an application scientist working on real-world discovery projects and finally in a senior role in strategy and business development for pharma and biotech companies. Following her time at CCG, Petrina moved to Certara as a Market Access Manager, where she learned first-hand the challenges of getting drugs to market. Petrina has been with Insilico Medicine since August 2020. She holds a PhD in Chemistry (specializing in computational chemistry) from Concordia University.

W4 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 dataâ€™s potential to deliver actionable insights and accelerate pharma R&D.

1:15 PM Making Data AI-Ready

Fernanda Foertter, MSc, Executive Director, The University of Alabama High Performance Computing and Data Center

INSTRUCTOR BIOGRAPHY:

W5 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 quantumâ€™s potential impact on research and industry.

INSTRUCTOR BIOGRAPHY:

Photo of Christopher Bishop, Chief Reinvention Officer, Improvising Careers — **Christopher Bishop, Chief Reinvention Officer, Improvising Careers**
I describe myself as a nonlinear multimodal careeristâ€”I have had 8 so farâ€”from touring rock musician in the 70s to 15 years at IBMâ€”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 workâ€”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 W6: AI Upskilling for Computational Biology Teams

AI is rapidly transforming how computational biology teams design experiments, analyze data, and generate insightsâ€”but most scientists havenâ€™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 insightsâ€”but most scientists havenâ€™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.