Tom Chittenden, PhD, DPhil, PStat is the President of the United Sigma Intelligence Association.
Dr. Chittenden is Chief Technology Officer for HiberCell. He is the President and Founding Director of the Genuity AI Research Institute (GAIRI), the $100-million R&D arm of HiberCell. In these roles, Dr. Chittenden is responsible for development and execution of HiberCell’s global AI/ML R&D strategy. This R&D initiative includes development of advance integrative deep learning, statistical machine learning, probabilistic programming, and structural causal modeling strategies aimed at furthering scientific understanding of human disease initiation and progression, knowledge that can be directly applied in innovative products for better care and medicine in a range of disease areas.
With the launch of GAIRI in the first quarter of 2021, Dr. Chittenden has established several large technology-based R&D partnerships to create and power the world’s first Exascale Causal AI Computing Platform for the biomedical sciences. Dr. Chittenden believes this advanced computing platform will afford biological validation of his “Unified Theory of Molecular Evolution,” and thus eventually lead to eradication of human disease.
Dr. Chittenden was named the Business Intelligence Group (BIG) AI Excellence Award Winner for 2021. In 2019, Forbes named him among the top 100 AI Pioneers in Drug Discovery and Advanced Healthcare. Tom is regarded as one of the world’s leading authorities on causal AI/ML in the biomedical sciences. He has also been recognized as one of the world’s top minds in the Global Genius Directory.
Dr. Chittenden is an Omega Society Fellow with over 25 years of experimental and theoretical research experience. He is an Accredited Professional Statistician™ with the American Statistical Association. In addition to his responsibilities at HiberCell, Tom is the Chairman, President, and Chief Scientist for the Complex Biological Systems Alliance (CBSA), a non-profit global research consortium dedicated to furthering scientific understanding of the nature and origins of human disease. In 2014, Dr. Chittenden established the CBSA as a recognized Extreme Science and Engineering Discovery Environment (XSEDE) Campus. Through the XSEDE Campus Champions Program, the Alliance provides its investigators with direct access to a national consortium of supercomputing facilities supported by the National Science Foundation.
Dr. Chittenden also serves as the Chairman of the Scientific and Medical Advisory Board for Bio-AI Health, a digital pathology company aimed at solving the most pressing issues in healthcare and precision medicine. He is Senior Research Fellow and the Chief Statistical Sciences Advisor for the Global Strategic Initiatives and Planning Committee of the International Society for Philosophical Enquiry.
Dr. Chittenden held an academic appointment as a Visiting Lecturer in the Department of Biological Engineering at the Massachusetts Institute of Technology from 2016 to 2018. Since October 2015, Dr. Chittenden has continuously held faculty appointments in the Division of Genetics and Genomics, Department of Pediatrics at Boston Children’s Hospital and the Harvard Medical School. His research has been published in top-tier scientific journals, including featured articles in Nature and Science. A major focus of his work involves development and application of causal AI/ML and integrated systems biology models to investigate evolutionary factors of human disease. Dr. Chittenden’s overall objective is to understand how genetic variation and somatic mutation influence aberrant gene activity and subsequent disease biology. Tom is currently applying quantum information theory to decipher an elemental molecular code regulating patterns of biological complexity to better understand human disease initiation and progression.
Through a formal academic research collaboration with the USC-Lockheed Martin Quantum Computation Center, Dr. Chittenden’s AI team has access to quantum computing hardware. His team has recently developed novel quantum machine learning (qML) strategies, which provide competitive classification of human cancer types and associated molecular tumor subtypes and ‘superior’ performance with smaller patient training dataset sizes, thus providing compelling empirical evidence for the potential of this emerging field. Dr. Chittenden’s team is also investigating the utility of neuromorphic computing in the biomedical sciences. Their newly developed deep spiking neural networks indicate even greater potential than qML. Moreover, via formal academic research collaborations with the Harvard, Yale, and Washington University Medical Schools, Dr. Chittenden and his colleagues are pioneering the field of single cell science.
Dr. Chittenden holds a PhD in Molecular Cell Biology and Biotechnology from Virginia Tech and a DPhil in Computational Statistics from the University of Oxford. His multidisciplinary postdoctoral training includes experimental investigations in molecular and cellular cardiology from the Dartmouth Medical School; biostatistics and computational biology from the Dana‐Farber Cancer Institute and the Harvard School of Public Health; and computational statistics, statistical methodology and statistical machine learning from the University of Oxford.