January AI | Optimize your blood sugar intelligently

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%.


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.



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

Members of our Scientific and AI Advisory Boards include top experts in machine learning, nutrition, immunology, and microbiology from Carnegie Mellon, Harvard, Tufts, Stanford, and UC Berkeley.

Scientific Advisory Board

  • Jeffrey B. Blumberg, PhD

    Scientific Advisor
  • Tracey McLaughlin, MD, MS

    Scientific Advisor
  • Dariush Mozaffarian, MD, PhD

    Scientific Advisor
  • Eric Martens, PhD

    Scientific Advisor
  • Justin Sonnenburg, PhD

    Scientific Advisor
  • Parag Mallick, PhD

    Scientific Advisor
  • Nima Aghaeepour, PhD

    Scientific Advisor
  • Dalia Perelman, CDE

    Scientific Advisor

AI Advisory Board

  • Pieter Abbeel, PhD

    AI Advisor
  • Jure Leskovec, PhD

    AI Advisor
  • Sergey Levine, PhD

    AI Advisor
  • Zico Kolter, PhD

    AI Advisor
  • Parin Dalal, PhD

    AI Advisor