Decoding the Data Ecosystem

This podcast episode features Dr. Ravi Madduri, a senior computer scientist at Argonne National Laboratory, whose work bridges high-performance computing, artificial intelligence (AI), and biomedicine. The conversation explores his research groups’ missions and work, his experience mentoring the next generation of biomedical AI researchers, and their engagement with the Common Fund Data Ecosystem (CFDE) and FAIR Standards. Dr. Maduri emphasizes the importance of respecting the complexity of biological problems and the value of interdisciplinary collaboration.

In this special retrospective episode of Decoding the Data Ecosystem, host LaFrancis Gibson, project manager and contract PI for the CFDE Training Center, sits down with Dr. Allissa Dillman, Training and Engagement Director of the CFDE Training Center, to look back on the podcast's first year. The two reflect on the origins of the podcast and its role in making complex omics data more discoverable and usable across disciplines, discussing the critical importance of interdisciplinary collaboration in tackling the highly complex, multi-expertise nature of biomedical data. The conversation explores why dialogue-based formats like podcasting uniquely capture the nonlinear, relatable realities of research in ways that polished offerings cannot, making complex topics more approachable for listeners. The episode includes a forward-looking discussion on sustainability, with an emphasis that community ownership and engagement are foundational to ensuring data resources and tools remain useful and enduring.

As part of the 34th Conference on Intelligent Systems for Molecular Biology (ISMB), the BOSC 2026 CoFest was a two-day work session to provide an opportunity for anyone interested in open science, biology or programming to meet, talk and work collaboratively. This podcast showcases the final presentations which include a range of topics including AI-assisted metadata generation, protein structure visualization, reproducible research, FAIR data practices and more!

The CFDE Training Center was a sponsor of the Bio-IT World Hackathon, held May 18 – 19, 2026, and facilitated by Dr. Allissa Dillman. The event focused on Open Source and FAIR (Findable, Accessible, Interoperable, Reusable) data principles leveraging CFDE omics data and integrating CFDE tools, with a goal of improving interoperability across datasets to accelerate discoveries. This podcast episode is a showcase of the six final presentations from the 2026 Hackathon. 

Description 

In this month’s podcast, Allissa Dillman chats with Swathi Thaker, Program Manager for the CFDE Integration and Coordination Center (ICC). They discuss how the ICC encourages and facilitates collaboration across institutions, data coordinating centers, and programs, as well as how new CFDE partners and early career researchers can start engaging with the CFDE. 

Guest 

Swathi Thaker, PhD 

Guest Bio 

Swathi Thaker, PhD is the Program Manager for the CFDE Integration and Coordination Center (ICC), as well as the Programs Operation Manager at the Systems Pharmacology AI Research Center at the University of Alabama Birmingham (UAB). Before coming to UAB, she was the Program Manager for the Influenza Division at Centers for Disease Control and Prevention (CDC).  In addition to her professional experience in Program Management, Swathi also has a PhD in Adult Education. 

Description 

In this crossover podcast with “Trends from the Trenches,” Allison Proffitt, executive editor of Bio-IT World, interviews Allissa Dillman and LaFrancis Gibson from the CFDE Training Center about this year’s Bio-IT World Conference & Expo Hackathon. They discuss which tools and datasets from CFDE will be part of the Hackathon, who should participate, learning and collaborating beyond datasets and tools, FAIR principles in an ever-developing AI environment, and a message beyond the teams that are participating. 

Guests 

Allissa Dillman, PhD 

LaFrancis Gibson, MPH, MBA 

Guest Bios 

Allissa Dillman, PhD, is the founder and CEO of BioData Sage, which helps integrate strategic data science practices into the biomedical and biological sciences. She holds a doctorate in computational neuroscience from Karolinska Institutet and has over a decade of experience spanning government, academia, and nonprofit work, with a focus on strategic planning, community engagement, and workforce training. She is also co-PI of the NIH Common Fund Data Ecosystem Training Center, leading efforts to develop training resources, podcasts, and community events, such as hackathons, to support biomedical research. Dr. Dillman is an adjunct faculty member at Montgomery College, where she teaches courses in data science and bioinformatics. Previously at NIH, she led workforce development and international training initiatives and organized hackathons, bootcamps, and community-building programs. Her work centers on building inclusive, reproducible, and impactful data science ecosystems through partnerships across major research and public institutions. 

LaFrancis Gibson, MPH, MBA serves as a manager for health promotion at ORAU and has created a unique skill set and understanding of community-based programs and how to manage them to maximize success. With more than 15 years of experience in managing outreach initiatives, training development, and program evaluation, she ensures project success through her expertise in budget control, risk mitigation, and strategic communication with stakeholders from government agencies such as the National Institutes of Health (NIH), National Library of Medicine, and Centers for Disease Control and Prevention. Ms. Gibson is experienced in working with multiple stakeholders and partners across federal agencies, Minority-Serving Institutions, community and faith-based organizations, and national organizations. She currently serves as the contact principal investigator for the NIH Common Fund Data Ecosystem Training Center, providing oversite, coordination, and compliance in delivering bioinformatics training programs that align with NIH mission. 

Description

In this podcast, Allissa Dillman talks with Raja Mazumder, René Ranzinger, and Michael Tiemeyer about their journeys in glycoscience and the goals of the GlyGen program. GlyGen integrates glycan and glycoinformatics data from various sources, making it an accessible and comprehensive online resource for researchers. They also discuss the challenges of working with glycan data, including its structural complexity and variability. They cover several workflows using GlyGen resources, how researchers can contribute data to GlyGen and suggestions for new users on how to get started.

Guests

Raja Mazumder, PhD

René Ranzinger, PhD

Michael Tiemeyer, PhD

Guest Bios

Raja Mazumder, PhD is a Professor of Biochemistry and Molecular Medicine and Co-Director of The McCormick Genomic Proteomic Center at George Washington University. His background is in evolutionary biology and bioinformatics. He has previously worked at the Oak Ridge National Laboratory, Merck, NIH, and Georgetown University. While working on UniProt at Protein Information Resource, and leading projects such as BioCompute, GlyGen, OncoMX, and ARGOS, Dr. Mazumder worked closely with colleagues in developing international molecular biology resources and using these resources to identify therapeutics, diagnostics and vaccines targets. Through NIH, NSF, FDA and industry funding he is involved in genomic and bioinformatics research in cancer biology, glycobiology, metagenomics and bioinformatics standards development.

René Ranzinger, PhD is an Associate Research Scientist in the Complex Carbohydrate Research Centre at the University of Georgia. His work focuses on GlycoBioinformatics. As a research scientist, he worked on the implementation of new software tools for the interpretation of glycomics mass spectrometric data. He and his team were also involved in the design and implementation of glycomics databases such as GlyTouCan and GlyGen. Another focus of his work is the standardization of glycomics data reporting as well as standardization of data representation and data exchange. In this context he was organizer and member of several international working groups including the working group to define minimum reporting standards for Glycomics (MIRAGE). 

Michael Tiemeyer, PhD, is the Joint Director of the Complex Carbohydrate Research Center and the Center for Molecular Medicine at the University of Georgia, where he facilitates the research programs of 24 active faculty and 300 research trainees and staff who generate between 20-25 million USD in total grants and contracts each year. He also holds the title of Distinguished Research Professor in the Department of Biochemistry and Molecular Biology at the University of Georgia. His own active research areas are focused on the identification and characterization of bioactive glycans in neural development and disease as well as on the development of glycoinformatic strategies and platforms for capturing knowledge in the glycoscience domain. 

Episode 14: Investigating Acute to Chronic Pain Signatures (A2CPS) for Advancing Pain Prevention Strategies 

Description 

In this episode, Allissa Dillman chats with Margaret Taub, a Senior Scientist with the Johns Hopkins Bloomberg School of Public Health on her journey from mathematics to statistical genetics, highlighting her current role with the Acute to Chronic Pain Signatures (A2CPS) program. The A2CPS study aims to develop predictive biomarkers for chronic pain by collecting comprehensive genomic, psychosocial, and functional data from patients. To coordinate diverse data sources, Margaret emphasized the importance of collaboration and interdisciplinary teamwork, drawing parallels between her experiences in teaching, music, and Ultimate Frisbee to her approach in science. Margaret and Allissa also discuss the study's progress and future plans. 

Guest Bio 

Margaret Taub, PhD, is a Senior Scientist at Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health. She works collaboratively with clinicians and epidemiologists to use genetic and genomic data to understand the causes of complex diseases. She is also an Investigator with the Data Integration and Resource Center for the Acute to Chronic Pain Signatures (A2CPS) program. 

Description

In this episode, Allissa Dillman talks with John Kwagyan about the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in genomics and personalized medicine as well as the promise of these technologies in analyzing complex biological data to advance disease prediction, prevention, and personalized treatments. They also discuss machine learning models, the differences between machine learning and statistical learning, explainable AI, and ethical considerations, as well as the skills future researchers will need to thrive in the AI-genomics landscape.

Guest Bio

John Kwagyan, PhD, is a Statistician and Graduate Associate Professor of Public Health at Howard University College of Medicine, and serves as co-Director of Biostatistics, Epidemiology and Research Design (BERD) at the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS). He is co-PI of the Public Health Informatics and Technology program for District of Columbia (PHIT4DC), and PI (Data Science Core) of the recently funded Howard-Hopkins Comprehensive Alliance in Cancer Research and Education (H2CARE). His research interests include statistical genetics and predictive modelling of clustered data with applications to clinical and public health outcomes.

Description 

In this video podcast, Allissa Dillman and Andreas Bueckle discuss and demonstrate the Human Reference Atlas (HRA) Organ Gallery, a virtual reality (VR) application that lets users explore the HRA in immersive 3D. Andreas also presents the “HRA Powers of Ten,” a data integration module in development that uses the HRA Common Coordinate Framework (CCF) to harmonize, visualize, and explore data from the small/large intestine, lymph node, skin, and liver. The HRA Organ Gallery is available, for free, to anyone at https://www.meta.com/experiences/5696814507101529. A paper describing the concept of the HRA Organ Gallery is available at https://doi.org/10.3389/fbinf.2023.1162723. 

Single-cell atlassing portals such as the Human BioMolecular Atlas Project (HuBMAP), the Cellular Senescence Network (SenNet), and the Human Tumor Atlas Network (HTAN) publish datasets across scales for multiple organs of the adult human body in health, aging, and disease. This data can be interrogated semantically and spatially, usually on 2D screens with limited 3D affordances. Imagine, for example, examining the 3D location of a cell in the context of a tissue or zooming across multiple orders of magnitude. To be tackled effectively, 3D problems require 3D platforms. 

Guest Bio 

Andreas Bueckle, Ph.D. (https://andreas-bueckle.com), is the Research Lead in the Cyberinfrastructure for Network Science Center at the Luddy School of Informatics, Computing, and Engineering (SICE) at Indiana University in Bloomington, IN. His research interest is interactive information visualization in XR. Andreas has a TEDx talk titled “Living and Learning in the Metaverse” (available on YouTube and on the TED website). From 2023-2025, he was awarded two JumpStart Fellowships by the National Institutes of Health (NIH) to advance multiscale exploration of the human body in VR with the Human Reference Atlas Organ Gallery. In 2025, he received an R03 award, also by the NIH, to advance the integration of 3D reference organs with data visualizations of cell type populations for Common Fund datasets in 3D and VR.