Professor David Madigan is the Provost and Senior Vice President for Academic Affairs at Northeastern University, where he is also a Professor of Statistics. Previously he served as Executive Vice President of Arts and Sciences and Dean of the Faculty at Columbia University, and as Chair of Columbia’s Department of Statistics. He has also held positions at AT&T Inc., Rutgers University, and the University of Washington.

His research spans Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance, and probabilistic graphical models. He holds a B.Sc. in Mathematical Sciences and a Ph.D. in Statistics from Trinity College Dublin. He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the AAAS, and has served as Editor-in-Chief of Statistical Science.

Bio: Mark Chang, PhD, is the founder of AGInception. He is an elected fellow of the American Statistical Association with over 25 years of experience as a statistician in the biopharmaceutical industry and academia, where he previously held various positions from Scientific Fellow to Senior Vice President. As an Adjunct Professor for Boston University, he has guided his students on doctoral thesis topics of Adaptive Clinical Trial Design and Artificial Intelligence. He has broad research interests, including adaptive clinical trials, AI, principles of scientific methods, paradoxes, issues and methods in modern biostatistics, and AI software development. He is the inventor and owner of two US patents on AI. His recent publications include research papers and two books on AI: (1) Artificial Intelligence for Drug development, Precision Medicine, and Healthcare (2020), and (2) Foundation, Architecture, and Prototyping of Humanized AI (2023). Dr. Chang has served on editorial boards for statistical journals and is actively engaged in statistical communities promoting biostatistics, including teaching numerous courses on adaptive clinical trial designs and AI/ML. He is a co-founder of the International Society for Biopharmaceutical Statistics.

For the past 27 years, Gene A. Pennello has been a review and research statistician at the US Food and Drug Administration (FDA), Center for Devices and Radiological Health (CDRH). At FDA, Gene’s official area of expertise is design and analysis of studies concerning diagnostic devices, with particular emphasis on drug-diagnostic co-development and evaluation of imaging systems. In 2020, Gene joined the Division of Imaging, Diagnostics, and Software Reliability (DIDSR), whose research includes addressing emerging challenges in regulatory assessment of artificial-intelligence(AI)-enabled medical devices. Gene is a fellow of the American Statistical Association (ASA).

Jing Huang received her B.A. in Statistics and Probability from Peking University and her Ph.D.in Statistics and M.S. in Epidemiology from Stanford University. She has been working in the biomedical field for over 20 years and her research interest focuses on statistical methodologies in clinical trial design, genomic analysis, and machine learning. She is currently the Chief Data & AI Officer at CareDx, Inc. (Nasdaq: CDNA)- The Transplant Company™. She is responsible for advancing CareDx’s efforts to integrate data science methods and AI into its customer facing products to enhance patient care, and its internal business operations to achieve improved efficiency and scalability. Jing has co-authored more than 30 articles in peer-reviewed scientific journals with over ten thousand citations and is co-inventor of over 20 patent filings. Besides her daily work, she actively promotes data science through many of her volunteer activities: She is the founding president of DahShu, a 501(c)(3) nonprofit organization with the mission of promoting research and education in data science. She is currently the chapter representative of American Statistical Association San Francisco Bay Area Chapter (SFASA). In 2023, Jing was elected as lifetime Fellow of the American Statistical Association to recognize her outstanding contributions to the medical research community in the field of statistics; for numerous statistical innovations in genomic tests; and for exemplary leadership and community service to the profession.

Bi is the Frederick H. Leonhardt Professor of Computer Science and associate head of the department. She holds a Ph.D. from Rensselaer Polytechnic Institute. Her research interests include artificial intelligence, machine learning, data mining, pattern recognition, optimization, computer vision, bioinformatics, medical informatics, drug discovery.

Prof. Jake Y. Chen is the Triton Endowed Professor of Biomedical Informatics and Data Science at the University of Alabama at Birmingham School of Medicine, with joint appointments in Genetics, Computer Science, and Biomedical Engineering. As founding director of UAB’s Systems Pharmacology AI Research Center (SPARC), he has spent over 25 years pioneering AI-driven approaches to computational drug discovery, from network‐based pharmacology models and multi-omics integration frameworks to patient-specific digital twin simulations. His work—drawing on clinical records, genomic assays, and real-world evidence—has led to more than 200 peer-reviewed publications and has directly informed translational studies in precision medicine.

Dr. Chen also serves as Contact MPI for the NIH U54-funded CONNECT consortium (2024–2029), where he leads a multi-institutional effort to build AI-ready biomedical knowledge networks from over $1 billion in NIH-supported data. A trusted advisor to NIH, NSF, FDA, and the U.S. Congress, he has helped shape national and global regulatory frameworks for AI in healthcare. His contributions have earned him fellowships with ACMI, AIMBE, AMIA, and ACM, recognition among the “Top 100 AI Leaders in Drug Discovery & Healthcare,” and the CAST-USA Pioneer Award. As founder of Medeolinx, LLC, he commercialized the GeneTerrain AI platform for pharmaceutical target identification and continues to guide industry and government on integrating AI into drug discovery pipelines.

Dr. Deng is a Professor and Director of Physics Research at the Department of Therapeutic Radiology; a Professor at the Department of Biomedical Informatics and Data Science of Yale University School of Medicine; the Principal Investigator of Yale Smart Medicine Lab; and the President of the Digital Twins for Health Society. Dr. Deng received his PhD from University of Virginia and finished his postdoctoral fellowship at Stanford University. With funding from NIBIB, NSF, NCI, DOE, YCC, and Amazon, Dr Deng’s research has been focused on AI, machine learning, and medical imaging for real-time clinical decision support, digital twins of cancer patients, early cancer detection, as well as AI-empowered mobile health. Dr. Deng has been serving on the editorial board of numerous peer-reviewed journals, on the study sections of NIH, NSF, DOD, ACS, RSNA, ASTRO, and CPRIT since 2005, and as scientific reviewer for various national and international science foundations since 2015. Dr. Deng is an elected fellow of IOP, AAPM, ASTRO, and has been recently selected as one of the Key Thought Leaders of the NCI Cancer AI Accelerator Program, one of the Experts for the NIH AIM-AHEAD PAIR Program, and one of the Mentors for the NIH AIM-AHEAD Research Fellows Program.

Dr. Ani Eloyan is an Associate Professor and Vice Chair in the Department of Biostatistics at Brown University. Broadly, her research focuses on developing statistical methods for analyzing brain imaging data for large groups of subjects, including matrix decomposition methods, machine learning, analysis of longitudinal brain imaging data, among others. Currently, she is developing machine learning algorithms for imaging-based feature extraction from positron emission tomography data, prediction of cognitive decline, and biomarker estimation in Alzheimer’s disease. Dr. Eloyan has served on editorial boards including as Associate Editor of Journal of American Statistical Association and Biostatistics. Her work has been published in leading statistics and imaging outlets including the Journal of Royal Statistical Society (Series B), JASA, Biostatistics, NeuroImage Clinical, and Alzheimer’s and Dementia. She leads the Biostatistics Core of the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) and is the Principal Investigator of an R01 grant from NIA to develop biomarker estimation methods for multimodal-imaging studies in Alzheimer's Disease. Dr. Eloyan received her PhD in Statistics from North Carolina State University in 2010. She completed a postdoctoral fellowship in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health in 2013.

Constantine Gatsonis, PhD, Henry Ledyard Goddard University Professor of Biostatistic was educated at Princeton and Cornell and joined the Brown faculty in 1995. He is the founding Director of the Center for Statistical Sciences and the founding Chair of the Department of Biostatistics. Dr. Gatsonis is a leading authority on the evaluation of diagnostic and screening tests, and has made major contributions to the development of methods for medical technology assessment and health services and outcomes research. He is a world leader in methods for applying and synthesizing evidence on diagnostic tests in medicine and is currently developing methods for Comparative Effectiveness Research in diagnosis and prediction and radiomics. Dr Gatsonis was Network Statistician of the American College of Radiology Imaging Network (ACRIN) since the formation of the Network in 1999. The Center for Statistical Sciences at Brown operates the Biostatistics Center of ACRIN under his direction. With the recent formation of ECOG-ACRIN, Dr Gatsonis serves as a Group Statistician for the combined collaborative group, which conducts NCI-funded multi-center clinical studies across the spectrum of cancer care. He is the lead statistician for several current and past trials, including the Digital Mammography Imaging Screening Trial (DMIST), the National Lung Screening Trial (NLST), and the newly launched Tomosynthesis Mammography Imaging Trial (TMIST). Dr Gatsonis has contributed extensively to statistical methods for the evaluation of diagnostic tests and biomarkers. He has published on methodology for ROC analysis for detection and prediction and on broader issues of study design in diagnostic test and imaging biomarker evaluation and validation. Dr Gatsonis has long-term involvement in the development of Bayesian statistical methods for problems in biomedical research. He developed and applied hierarchical regression models to the analysis variations in utilization, outcomes, and quality of health care, meta-analysis of diagnostic test accuracy studies, and the variability in diagnostic performance among radiologists and institutions. Dr Gatsonis chaired the Committee on Applied and Theoretical Statistics of the National Academies and is a member of the Committee on National Statistics and the Committee on Reproducibility and Replicability in Science. Previously, he co-chaired the Committee on the Needs of the Forensic Sciences Communityand served on the Board of Mathematical Sciences and Applications and several IOM Committees. Since 2016 Dr Garsonis is a statistical consultant for the New England Journal of Medicine. He was the Founding Editor-in-Chief of Health Services and Outcomes Research Methods, and currently serves as Associate Editor of the Annals of Applied Statistics and Academic Radiology. . Dr. Gatsonis was elected fellow of the American Statistical Association and received a Long-Term Excellence Award from the Health Policy Statistics Section of the ASA.

Dr. Bo Huang is Executive Director, Statistics Group Head of Non-malignant Hematology at Pfizer. He has more than 17 years of industry experience across all stages of clinical development in oncology and rare diseases, and successfully led a number of global submissions, including the first Pfizer submission in the FDA’s Real-Time Oncology Review pilot program. He is a recognized scientific leader in developing and applying innovative quantitative methods in drug development, with over 100 scientific publications in medical and statistical journals. In addition, he has been serving as Guest Editor/Associate Editor for several scientific journals, serving as Industry Co-Chair of the 2021 Regulatory-Industry Statistics Workshop, and was the elected 2024 Program Chair for the ASA Biopharmaceutical Section. He was also an elected Board Director of the International Chinese Statistical Association (2016-2019). Bo received his PhD in Statistics from the University of Wisconsin-Madison, and is an elected Fellow of the American Statistical Association (2023).

Dr. Kun Huang received his BS degrees in Biological Science and Computer Science from Tsinghua University in 1996 and his MS degrees in Physiology, Electrical Engineering, and Mathematics all from University of Illinois at Urbana-Champaign (UIUC). He then received his PhD in Electrical and Computer Engineering from UIUC in 2004 with a focus on computer vision and machine learning. He was a faculty member in the Department of Biomedical Informatics at The Ohio State University (OSU) from 2004 to 2017 where he served as the Associate Dean for Genome Informatics in the College of Medicine. He joined Indiana University School of Medicine as Director for Data Science and Informatics of the Precision Health Initiative in 2017. Currently he is the IUSM PHI Endowed Chair for Genomic Data Science, Professor and Chair of the Department of Biostatistics and Health Data Science at Indiana University School of Medicine and Fairbanks School of Public Health. He is also the Associate Director for Data Science of the IU Simon Comprehensive Cancer Center and a member of the Regenstrief Institute. His research interests include bioimage informatics, computational pathology, translational bioinformatics, and heath data science. He is an elected Fellow of American Institute of Medical and Biological Engineering (AIMBE) and published more than 250 research papers.

Dr. Zhongwu Lai is currently an executive director at AstraZeneca, leading a team of data scientists to advance AstraZeneca’s oncology portfolio through advanced data analysis. He has more than 25 years of industry experience in biopharma, and has made significant contributions to the field of bioinformatics, genomics, NGS, and translational science in cancer research. He has authored and co-authored over 50 scientific publications, with over 40k citations. One of his achievements is the development of VarDict, a versatile and novel variant caller designed for both DNA and RNA sequencing data. VarDict is widely recognized for its ability to simultaneously detect single nucleotide variants (SNVs), multi-nucleotide variants (MNVs), insertions and deletions (InDels), and complex variants, making it a critical tool in next-generation sequencing for cancer research. In his early industry career, he also participated in the Human Genome Project, and was one of the authors of the landmark Science publication on the human genome sequence in 2001. Dr. Lai is an expert in cancer genomes across multiple cancer types, including lung, breast, gastric, prostate and pancreatic cancers. His >10 year experience in development of olaparib, a PARP inhibitor, has made him a renowned scholar in BRCA mutation detection and interpretation. He has contributed significantly to the development of olaparib, osimertinib, durvalumab, tremelimumab, and T-DXd.

Qunhua Li is a professor in Dept of Statistics and Huck Institute of life sciences at Penn State University. She received her PhD from University of Washington and had her postdoc trading in University of California at Berkeley before joining Penn State University.

Dr. Zhonghua Liu is an Assistant Professor in the Department of Biostatistics at Columbia University, a member of the Data Science Institute, and an affiliate of the New York Genome Center. His research focuses on developing robust statistical and AI-integrated methods for causal inference in high-dimensional biomedical data. At the intersection of statistics, machine learning, and genomics, Dr. Liu’s work addresses critical challenges in precision medicine, including identifying causal biomarkers and accelerating therapeutic discovery. His recent projects integrate deep learning with semiparametric inference and Mendelian randomization to uncover causal relationships from multi-omics data and to predict 3D structural alterations of disease-associated proteins using AlphaFold3, with applications to Alzheimer’s disease and drug target prioritization. Dr. Liu earned his doctorate in Biostatistics and Epidemiology from Harvard University.

Jianchang Lin, PhD, is an Executive Director, Head Statistical and Quantitative Sciences, Neuroscience & Chief Statistical Office at Takeda Pharmaceuticals with extensive drug development experience across various therapeutic areas in Oncology, Neuroscience, Cell Therapy, Immunology, and Rare Diseases including key contributor for several successful drug global approvals and commercialization in US, EU, Japan and China. He is passionate about innovation and the integration of advanced data and quantitative methodologies—including novel trial designs, real-world data (RWD/RWE) and AI/ML—to accelerate development timelines, reduce risk, and enhance portfolio decision-making. He has published 80+ peer-reviewed articles in top statistical and clinical journals such as Biometrics, Statistics in Medicine, NEJM, JAMA Oncology, JCO, Blood, and Cancer Discovery. In addition, he has served as editor of two Springer books, Industry co-chair for 2024 Regulatory-Industry Statistics Workshop (RISW) and currently serving as Associate Editor for the Journal of Biopharmaceutical Statistics, Statistics in Biosciences, Program Chair for ASA Biopharmaceutical Section, Board of Director for ICSA, etc. Dr. Lin was the Principal Investigator for the MIT–Takeda Program (2020–2024), leading the development of novel AI/ML applications in drug development and an Elected Fellow of the American Statistical Association (2025).

Hana Lee, PhD, is a Senior Statistical Reviewer in the Office of Biostatistics (OB) at the Center for Drug Evaluation and Research (CDER), FDA. She leads and oversees various FDA-funded projects that support the development of the agency’s real-world evidence (RWE) program. She also serves as a co-lead of the RWE Scientific Working Group of the American Statistical Association (ASA) Biopharmaceutical Section, FDA public-private partnership involving scientists from the FDA, academia, and industry to advance the understanding of real-world data (RWD) and RWE to support regulatory decision-making. In 2024, she received the FDA’s most prestigious award for excellence in advancing and promoting statistical innovation in the use of RWD/RWE for regulatory decision-making.

Ying Lu, Ph.D., is Professor in the Department of Biomedical Data Science, and by courtesy in the Department of Radiology and Departement of Health Research and Policy, Stanford University. He is the Co-Director of the Stanford Center for Innovative Study Design and the Biostatistics Core of the Stanford Cancer Institute. Before his current position, he was the director of VA Cooperative Studies Program Palo Alto Coordinating Center (2009-2016) and a Professor of Biostatistics and Radiology at the University of California, San Francisco (1994-2009). His research areas are biostatistics methodology and applications in clinical trials, statistical evaluation of medical diagnostic tests, and medical decision making. He serves as the biostatistical associate Editor for JCO Precision Oncology and co-editor of the Cancer Research Section of the New England Journal of Statistics and Data Science. Dr. Lu is an elected fellow of the American Association for the Advancement of Science and the American Statistical Association. Dr. Lu initiated the Stat4Onc Annual Symposium with Dr. Ji and Dr. Kummar in 2017 and is the PI of the R13 NCI grant for this conference.

Zhengqing Ouyang is an Associate Professor of Biostatistics in the Department of Biostatistics and Epidemiology of the School of Public Health and Health Sciences at the University of Massachusetts Amherst. Dr. Ouyang obtained his PhD degree from Stanford University under the guidance of Prof. Wing Hung Wong followed by a postdoctoral training jointly mentored by Prof. Howard Chang and Prof. Michael Snyder at Stanford University School of Medicine. Dr. Ouyang has received several awards, including the Genome Technology Young Investigators of the Year Award from GenomeWeb, the Research Starter Grant in Informatics Award from PhRMA Foundation, and the NIGMS Maximizing Investigators' Research Award. Dr. Ouyang’s research lies in the development and application of statistical methods for analyzing the spatial interactions and configurations of the genome towards understanding gene regulation and cellular activities.

Bio: Dr. Weishen Pan is a postdoctoral associate in the Department of Population Health Sciences at Weill Cornell Medicine. He received his Ph.D. from Tsinghua University in 2022. His long-term research interest is machine learning and artificial intelligence for biomedicine, with a particular focus on developing algorithms for computational medicine and implementing them in healthcare systems. His recent work has centered on building multi-agent AI systems to support clinical trials. Dr. Pan was the technical lead of the winning team in the 2022 AACC Data Challenge on PTHrP prediction. His research has been published in leading clinical journals, including Nature Communications, NPJ Digital Medicine, and Clinical Chemistry, as well as top computer science conferences such as KDD, ICLR, and NeurIPS.

Alex Sverdlov, PhD is a Senior Director, Statistical Scientist at Novartis. With 18 years of career in the biopharmaceutical industry, Alex has been actively involved in methodological research and applications of innovative statistical approaches in drug development. He has co-authored over sixty refereed articles, edited three monographs ("Modern Adaptive Randomized Clinical Trials”, “Digital Therapeutics”, and “Development of Gene Therapies”), and co-authored a book “Mathematical and Statistical Skills in the Biopharmaceutical Industry: A Pragmatic Approach”.

Dr. Rui (Sammi) Tang is a seasoned drug developer and innovative pharmaceutical leader who has contributed to the successful development and approval of numerous therapies—bringing medicines from research to market that now reach millions of patients every day. With a proven track record of building high-performing teams and driving scientific and operational innovation, she delivers data-driven solutions that accelerate drug development and improve global health outcomes.

As Senior Vice President and Global Head of Quantitative Sciences and Evidence Generation (QSEG) at Astellas Pharmaceuticals, Dr. Tang leads the company’s global data and evidence strategy across quantitative analytics, epidemiology, real-world evidence (RWE), biostatistics, programming, medical writing, scientific communication, data systems & enablement, and data management. She is at the forefront of applying Generative AI in regulatory and clinical documentation, AI/ML-powered analytics, and external data to optimize study design and development efficiency.

She also serves as Site Head of the Astellas Life Sciences Center (ALSC) in Cambridge, where she oversees full site operations and strategic direction across integrated teams including Research, Medical & Development, Business Development, and IT. Under her leadership, the ALSC drives innovation through internal collaboration and external partnerships with incubator labs, biotech start-ups, and academic institutions.

A dedicated scientific leader, Dr. Tang serves on the Executive Committee for Data Science & AI at the American Statistical Association (ASA) and is co-founder of DahShu, a global nonprofit advancing data science research and education with over 5,000 members.

Previously, Dr. Tang was Vice President and Global Head of Biometrics at Servier Pharmaceuticals and Therapeutic Area Head of Biostatistics at Shire. Earlier in her career, she contributed to drug development and statistical innovation at Vertex, Amgen, Mayo Clinic, and Merck—experiences that shaped her cross-functional leadership approach.

Dr. Tang holds a PhD in Statistical Genetics from Michigan Technological University and an Executive MBA from MIT Sloan. She is also an Adjunct Professor at Yale University School of Public Health. With over 50 peer-reviewed publications and multiple patents, she is widely recognized for combining scientific depth with strategic leadership to deliver transformative therapies that improve lives worldwide.

Dr. Tudorascu received her PhD in Biostatistics from University of Pittsburgh, a MS in Computational Mathematics from Duquesne University and a BS in Mathematics from University of Craiova in Romania. She is currently an Associate Professor of Psychiatry and Biostatistics in the Department of Psychiatry at University of Pittsburgh where she Co-Leads the Study Design and Data Analysis Group. She is the Leader of the Biostatistics Core for the “The Role of Astrogliosis in Aging and the Pathological and Clinical Progression of Alzheimer’s Disease” program, Co-Leads the Data Management and Statistics Core for ADRC at Pitt as well as the Biostatistics and Data Management Core for Alzheimer’s Disease Biomarker’s Consortium-Down Syndrome (ABC-DS), an international multisite study. Dr. Tudorascu’s research focuses on developing methods for data harmonization for multi scanner, multi center neuroimaging studies as well as on improving brain tissue classification in presence of white matter lesions and atrophy in neuroimaging studies of Alzheimer’s disease. She is currently principal investigator of a National Institute on Aging-funded R01 grant focused on statistical methods to improve reproducibility and reduce technical variability in multimodal imaging studies of Alzheimer’s disease. Dr. Tudorascu collaborates widely with colleagues in psychiatry, biostatistics, neurology, radiology, among many other departments at Pitt as well as outside institutions. Dr. Tudorascu’s activities include training others in statistical methods used in multimodal imaging studies of neuropsychiatric disorders

Fei Wang is a tenured Professor of Health Informatics in Department of Population Health Sciences at Weill Cornell Medicine (WCM), where he also holds a secondary appointment as a Professor in Department of Emergency Medicine. Dr. Wang holds several administrative roles at WCM, including the Associate Dean for Data Science and AI, and Division Chief of Health Informatics at the Department of Population Health Sciences, as well as the Founding Director of the WCM Institute of AI for Digital Health (AIDH). Dr. Wang is also a Senior Faculty Fellow of Clinical AI at Cornell Tech, a Senior Technical Advisor at New York Presbyterian Hospital, and an Adjunct Scientist at Hospital for Special Surgery (HSS). His research interest is machine learning and artificial intelligence in biomedicine. Dr. Wang has published over 350 papers on the major venues of AI and biomedicine, which have received more than 37K citations to date. His H-index is 86. Dr. Wang is an elected fellow of American Medical Informatics Association (AMIA), American College of Medical Informatics (ACMI) and International Academy of Health Sciences and Informatics (IAHSI), and a distinguished member of Association for Computing Machinery (ACM).

Dr. Hong Yu is an elected Fellow of the American College of Medical Informatics. She is the founding Director of the Center for Biomedical and Health Research in Data Sciences and a tenured Professor in the Miner School of Computer & Information Science at the University of Massachusetts Lowell (UML). She also holds adjunct faculty appointments at the University of Massachusetts Amherst and the UMass Chan Medical School, where she was a tenured full professor prior to joining UML. In addition, Dr. Yu serves as a Research Health Scientist and Principal Investigator at the Center for Healthcare Organization & Implementation Research at the VA Bedford Healthcare System. She earned her Ph.D. in Biomedical Informatics from Columbia University. Dr. Yu holds multiple U.S. patents and has authored more than 250 peer-reviewed publications in the areas of natural language processing, informatics, and artificial intelligence, published in leading journals and conference proceedings in computer science and biomedical informatics. She has mentored over 50 trainees, many of whom have gone on to become faculty members or leaders in industry.

Bio: Dr. Jinfeng Zhang received his Ph.D. in Engineering from the University of Illinois at Chicago in 2004 and completed postdoctoral training in Statistics at Harvard University from 2004 to 2007. He joined the Department of Statistics at Florida State University as a faculty member in 2007, where he conducted research at the intersection of machine learning and biology for over 15 years. In the summer of 2025, Dr. Zhang transitioned full time to his startup, Insilicom LLC, which develops cutting-edge AI systems for drug discovery, pharmacovigilance, and biomedical knowledge integration. His research spans biological information extraction, natural language processing, biomedical knowledge graphs, and AI for science, with a consistent focus on building trustworthy, data-driven methods for advancing life sciences. Dr. Zhang and his team have earned international recognition through multiple top awards in biological NLP and AI challenges, including the LitCoin NLP Challenge organized by NIH and NASA (2022), the BioCreative Challenge VIII Knowledge Graph Track (2023), and the BioASQ 13B Biomedical Question Answering Challenge (2025). At Insilicom, he leads the development of large-scale biomedical knowledge graphs and AI platforms such as IKraph, IKnow, and IDEAL, which are redefining how AI can support scientific discovery and drug development.

Xiang Zhang leads Medical Affairs and HTA Statistics at CSL, co-leading the Forum for Observational Research Excellence. He oversees a team of statisticians, epidemiologists, and RWE scientists to generate RWE throughout the drug life cycle, including clinical development, regulatory submissions, product launches, and commercialization. This team also provides statistical support for HTA submissions and other activities related to market access and the demonstration of value for CSL products. He has authored or co-authored over 40 peer-reviewed publications and a book. He is currently a member of both ASA RWE and HTA scientific working groups.

Dr. Yao Zheng is an Assistant Professor of Statistics at the University of Connecticut. She is a recipient of the NSF CAREER Award, the Makuch Faculty Fellowship, and the IMS New Researcher Travel Award. She is particularly interested in advancing methodologies that address the challenges posed by temporal dependence and dynamic structure in modern datasets. Dr. Zheng earned her BS and PhD from the Department of Statistics and Actuarial Science at the University of Hong Kong. Before joining UConn in 2019, she spent two years at Purdue University as a postdoctoral researcher and Visiting Assistant Professor.