Grounded in state of the art research

January AI has assembled a multidisciplinary team of computational, food, translational, and medical scientists to tackle unmet needs in people with or at risk for developing diabetes. Our multi-omic approach synthesizes wearable, food, and microbiome data to make personalized recommendations and products to address hyperglycemia.

January’s AI-derived innovations include nutritional labels for local restaurants and glycemic index for 16 million grocery items, recipes, and menus.

Explore our science

Using heart rate and continuous glucose monitors, our Sugar Challenge Study associated the glycemic responses of 1,022 people on the diabetes spectrum with the glycemic load of the foods they ate. The findings led to January’s proprietary algorithms that accurately predict glucose response for 33 hours in participants with type 2 diabetes. Over 70% of all participants improved their time-in-range (TIR)—the amount of time their blood glucose remained within a healthy zone—including 58% of individuals with the highest blood glucose levels, with a median of 13%.

FEATURED WORK

Improvement in Glucose Regulation Using a Digital Tracker and Continuous Glucose Monitoring in Healthy Adults and Those with Type 2 Diabetes

DIABETES THERAPY

Machine-Learned Prediction of Glucose Response For 1000 Subjects

Salar Rahili, Parin Dalal, Mehrdad Yazdani, Solmaz Shariat Torbaghan, and Michael Snyder

Meet Michael Snyder

Dr. Snyder received his Ph.D. training at the California Institute of Technology and carried out postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. Snyder Lab was the first to perform a large-scale functional genomics project in any organism, and has developed many technologies in genomics and proteomics. These include the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks.

Seminal findings from the Snyder laboratory include the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species.

DR. MICHAEL SNYDER

CO-FOUNDER, JANUARY AI

SELECT PUBLICATIONS

Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information

PLOS BIOLOGY

Snyder, Li, Dunn, et al.

Glucotypes reveal new patterns of glucose dysregulation

PLOS BIOLOGY

Snyder, Hall, Perelman, et al.

A longitudinal big data approach for precision health

NATURE MEDICINE

Snyder, Rose, Contrepois, et al.

Personal aging markers and ageotypes revealed by deep longitudinal profiling

NATURE MEDICINE

Snyder, Ahadi, Zhou, et al.

Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California

NATURE COMMUNICATIONS

Snyder, Sailani, Metwally, et al.

Pre-symptomatic detection of COVID-19 from smartwatch data

NATURE BIOENGINEERING

Snyder, Mishra, Wang, et al.

Heart Rate and CGM Feature Representation Diabetes Detection From Heart Rate: Learning Joint Features of Heart Rate and Continuous Glucose Monitors Yields Better Representations

IEEE

Rashtian, Snyder, et al.

LECTURES & INTERVIEWS

Using your genome sequence and big data to manage your health

Fitbit detecting oncoming sickness

Future of individualized medicine

Found My Fitness Podcast with Rhonda Patrick

Moneyball Medicine Podcast: Noosheen Hashemi on Personalized Tech for Blood Glucose Control

Moneyball Medicine Podcast: Michael Snyder on Using Big Data to Keep People Healthy

Jeffrey B. Blumberg, PhD

SCIENTIFIC ADVISOR

Dr. Blumberg is an active Professor Emeritus in the Friedman School of Nutrition Science and Policy at Tufts University. He received undergraduate degrees in pharmacy and psychology from Washington State University and a PhD in pharmacology from Vanderbilt University School of Medicine. His research has been largely focused on the role of antioxidant nutrients in promoting health and preventing disease during the aging process via changes in status of oxidative stress, glucoregulation, and inflammation. He serves on the editorial boards of several scientific journals. He has been cited by Mendeley Data as among the top 1% of published authors in nutrition and dietetics (2020). He has participated in activities relevant to the incorporation of sound nutrition science into public health policy, including work as a member of the Workshop on Health Promotion and Aging in the office of the U.S. Surgeon General, Sports Medicine Committee of the U.S. Olympic Committee, and Food Advisory Committee of the FDA.

Tracey McLaughlin, MD, MS

SCIENTIFIC ADVISOR

Dr. McLaughlin graduated from Stanford University with honors and distinction in Human Biology, obtained an MS in Public Health at UC Berkeley, and MD at UC San Francisco. She completed her residency in Internal Medicine at Santa Clara Valley Medical Center and her fellowship in Endocrinology, Diabetes, and Metabolism at Stanford University School of Medicine. She is currently a clinician investigator and maintains an active clinic for patients with complicated type 1, type 2, or other forms of diabetes, polycystic ovarian disease, and hypoglycemia. Her clinical research program includes human studies on obesity, regional fat distribution, and the role of adipocytes and adipose tissue immune cells in promoting insulin resistance and type 2 diabetes. She also conducts studies on the role of incretin hormones in glucose metabolism and post bariatric hypoglycemia, and the use of continuous glucose monitoring and multi-omics methods to define metabolic sub-phenotypes and precision diets for individuals with prediabetes and type 2 diabetes.

Dariush Mozaffarian, MD, PhD

SCIENTIFIC ADVISOR

Dr. Mozaffarian is a cardiologist, Dean, and Jean Mayer Professor at the Tufts Friedman School of Nutrition Science and Policy, and Professor of Medicine at Tufts Medical School. Dr. Mozaffarian has authored more than 400 scientific publications on dietary priorities for obesity, diabetes, and cardiovascular diseases, and on evidence-based policy approaches and innovations to reduce these burdens in the US and globally. He has served in numerous advisory roles including for the US and Canadian governments, American Heart Association, World Health Organization, and United Nations. His work has been featured in a wide array of media outlets; and Thomson Reuters has named him as one of the World’s Most Influential Scientific Minds.

Dr. Mozaffarian received a BS in biological sciences at Stanford (Phi Beta Kappa), MD at Columbia (Alpha Omega Alpha), residency training in internal medicine at Stanford, fellowship training in cardiovascular medicine at the University of Washington; an MPH from the University of Washington; and a Doctorate in Public Health from Harvard. Before being appointed as Dean at Tufts in 2014, Dr. Mozaffarian was at Harvard Medical School and Harvard School of Public Health for a decade and clinically active in cardiology at Brigham and Women’s Hospital.

Eric Martens, PhD

SCIENTIFIC ADVISOR

Dr. Martens received his PhD in 2005 from the University of Wisconsin-Madison, working with Heidi Goodrich-Blair, PhD on the biology of the entomopathogenic nematode, Steinernema carpocapsae, and its bacterial symbiont, Xenorhabdus nematophila. He then trained with Jeffrey I. Gordon, MD at Washington University School of Medicine, investigating the physiology of beneficial human gut bacteria, especially members of the Bacteroidetes and their interactions with complex carbohydrates. His current research interests include investigating the roles of gut bacteria in human digestive physiology, the gut microbiome in inflammatory bowel disease and colorectal cancer, genetic exchange between environmental and gut bacteria and the mechanism through which gut bacteria break down dietary fiber polysaccharides and mucin glycoproteins.

Justin Sonnenburg, PhD

SCIENTIFIC ADVISOR

Dr. Sonnenburg is an Associate Professor of Microbiology and Immunology at Stanford University and a member of Bio-X. He received his BS in Biochemistry from UC Davis and his PhD from UC San Diego in Biomedical Sciences in 1996. His research program aims to elucidate the basic principles that govern interactions within the intestinal microbiota and between the microbiota and the host. Specifically, the Sonnenburg lab is exploring the effects of perturbations in the intestinal environment, such as changes in diet, microbial community composition, pathogen exposure, host genotype, and microbiota-targeted small molecules. To pursue these aims, his lab studies germ-free mice colonized with simplified, model microbial communities, applies systems approaches and uses genetic tools for the host and microbes to gain mechanistic insight into emergent properties of the host-microbial superorganism.

Parag Mallick, PhD

SCIENTIFIC ADVISOR

Dr. Mallick is an Associate Professor at Stanford University. Originally trained as an engineer and biochemist, his research spans computational and experimental systems biology, cancer biology and nanotechnology. Dr. Mallick received his undergraduate degree in Computer Science from Washington University in St. Louis. He then obtained his Ph.D. from UCLA in Chemistry & Biochemistry, where he worked with Dr. David Eisenberg. He completed postdoctoral studies at The Institute for Systems Biology, in Seattle, WA with Dr. Ruedi Aebersold. Beyond studying fundamental disease mechanisms, his group has been pioneering novel approaches for enabling personalized and predictive medicine. Most recently, his group has been developing model-based and physics-based approaches to machine learning that enable learning over domains that span a wide range of time and length scales.

Nima Aghaeepour, PhD

SCIENTIFIC ADVISOR

Dr. Aghaeepour is an Assistant Professor at Stanford University. Dr. Aghaeepour’s graduate research focused on bioinformatics analysis of single cell data. Dr. Aghaeepour and colleagues have developed a pipeline that identifies cellular correlates of clinical outcomes from high-dimensional flow cytometry datasets. His team has also established the very first objective benchmark for evaluation of algorithms that could automatically identify cell-types (and, eventually, correlate them with clinical outcomes). Dr. Aghaeepour is interested in the intersection of data sciences, immunology, and clinical phenotyping. His lab develops machine learning/artificial intelligence methods to study the immune system in clinical settings. This includes integrative multi-omics analysis across genomics, proteomics, and single-cell technologies, as well as quantitative clinical phenotyping using wearable devices, to produce a holistic understanding of immunity.

Dalia Perelman, CDE

SCIENTIFIC ADVISOR

Dalia Perlman is health educator and a research dietician at Stanford University in the department of Medicine. Dalia received her bachelor’s degree in microbiology and her Master’s degree in Nutritional Sciences and completed her graduate research in Molecular Biology at Stanford University. Dalia is a certified diabetes educator and worked at the Palo Alto Medical Foundation for 14 years counseling patients.

Pieter Abbeel, PhD

AI ADVISOR

Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Dr. Abbeel’s research strives to build ever more intelligent systems, which has his lab pushing the frontiers of deep reinforcement learning, deep imitation learning, deep unsupervised learning, transfer learning, meta-learning, and learning to learn, as well as studying the influence of AI on society. His lab also investigates how AI could advance other science and engineering disciplines. Dr. Abbeel’s Intro to AI class has been taken by over 100,000 students through edX, and his Deep RL and Deep Unsupervised Learning materials are standard references for AI researchers. He has founded three companies: Gradescope (AI to help teachers with grading homework and exams), Covariant (AI for robotic automation of warehouses and factories), and Berkeley Open Arms (low-cost, highly capable 7-dof robot arms). Dr. Abbeel advises many AI and robotics start-ups, and is a sought after speaker worldwide for C-suite sessions on AI future and strategy. He has received many awards and honors, including the PECASE, NSF-CAREER, ONR-YIP, Darpa-YFA, and TR35. His work is frequently featured in the press, including The New York Times, The Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review. Dr. Abbeel received his PhD in Computer Science from Stanford University and MS in Electrical Engineering from KU Leuven, Belgium.

Jure Leskovec, PhD

AI ADVISOR

Dr. Leskovec is an Associate Professor of Computer Sciences at Stanford University and member of Bio-X. Dr. Leskovec’s research focuses on the analyzing and modeling of large social and information networks and the study of phenomena across the social, technological, and natural worlds. He focuses on statistical modeling of network structure, network evolution, and spread of information, influence and viruses over networks. Problems he investigates are motivated by large scale data, the Web and other on-line media. He also does work on text mining and applications of machine learning. Dr. Leskovec received his BSc and PhD in Computer Science from the University of Ljubljana and Carnegie Mellon University, respectively.

Sergey Levine, PhD

AI ADVISOR

Dr. Levine is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. Dr. Levine received a BS, MS and PhD in Computer Science from Stanford University.

Zico Kolter, PhD

AI ADVISOR

Dr. Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, with appointments in the Computer Science Department, the Institute for Software Research (in the Societal Computing program), and affiliated appointments with the Machine Learning Department, the Robotics Institute, and the Electrical and Computer Engineering Department. His work focuses on machine learning and optimization, with a specific focus on applications in smart energy systems. From an algorithmic standpoint, he has worked on fast optimization algorithms for a number of problems and for general convex programs, large-scale probabilistic modeling, stochastic optimization, and deep learning. On the application side, he has worked on energy disaggregation, probabilistic forecasting for energy systems, and model predictive control techniques for industrial control in the electrical grid.

Parin Dalal, PhD

AI ADVISOR

Dr. Dalal is the Vice President of Advanced AI at Varian Medical Systems. He has more than 20 years of industry experience holding various positions of increasing responsibility leading multidisciplinary teams. Dr. Dalal has extensive expertise and interest in reinforcement learning, human biomedical modeling, sparse data machine learning, hardware design for AI, non-traditional computational paradigms, high speed circuit design and Quantum Information Theory, and theoretical physics. Dr. Dalal received his bachelor’s degree from UC Berkeley and PhD from UC San Diego.