The digital olfaction company, Osmo, today announced the formation of its Scientific Advisory Board (SAB) composed of leading experts in synthetic chemistry, analytical chemistry, artificial intelligence and sensor technology, to help propel the company’s olfactory innovation across scent detection, digitization and creation.
The SAB brings together distinguished leaders in AI, machine learning, chemistry, olfactory neuroscience, quantum computing, and advanced sensing technologies, each bringing deep expertise and a proven track record of innovation in drug discovery, biological intelligence, and chemical analysis.
“Osmo’s groundbreaking scent digitization mission requires input from the most brilliant minds working in harmony across diverse disciplines. We are so proud to work with the very best in their respective fields” said CEO, Alex Wiltschko. “Just imagine trying to digitize vision in the 1970’s—you’d have to work across half a dozen disciplines, and that’s exactly what we are doing at Osmo.”
Osmo’s SAB will offer guidance on the company’s rapidly advancing olfactory technology and its expanding applications. This valuable resource will support Osmo’s strategic growth in areas like health and wellness (detecting diseases earlier, tracking pandemics faster, and repelling disease-carrying insects), security (identifying fentanyl at borders and security threats at airports), and indoor air pollution and multimedia experiences.
“We have enormous challenges and opportunities ahead of us and I can’t think of a better collective to help shape our scientific vision than this group of individuals.”
Osmo’s newly formed SAB includes the following seven founding members:
Alán Aspuru-Guzik, Ph.D., is a professor of Chemistry and Computer Science at the University of Toronto and is also the Canada 150 Research Chair in Theoretical Chemistry and a Canada CIFAR AI Chair at the Vector Institute. He is a CIFAR Lebovic Fellow co-directing the Accelerated Decarbonization program. Alán also holds a Google Industrial Research Chair in Quantum Computing. Alán is the director of the Acceleration Consortium, a University of Toronto-based strategic initiative that aims to gather researchers from industry, government, and academia around pre-competitive research topics related to the lab of the future. Alán conducts research in the interfaces of quantum information, machine learning and chemistry. He was a pioneer in the development of algorithms and experimental implementations of quantum computers and quantum simulators dedicated to chemical systems. He has worked on molecular representations and generative models for the automatic learning of molecular properties. Currently, Alán is interested in automation and “autonomous” chemical laboratories for accelerating scientific discovery. Alán appeared as one of the top 100 most powerful Canadians in 2024 by Maclean’s Magazine under the AI Category.
Brian Boso, Ph.D., is the former Chief Technology Officer and Chief Scientist for Smiths Detection where he led the international technology teams responsible for all R&D in the development of threat detection and security screening products. Brian joined Smiths Detection in August 2003, having previously held the position of VP of R&D at Scientific-Atlanta, Inc., leading the development of cable TV distribution equipment. Prior to SA, Brian served as VP of Technology at Tektronix, Inc. directing the development of electronic and optical test and measurement products. During his career, Brian has held positions in C-level management, technology/R&D management, basic/applied research, product/process development, marketing/sales management, and manufacturing. Brian received a B.S. degree from The Ohio State University, graduated from the University of Illinois with an M.S. and Ph.D. in physics. He also completed postdoctoral research at Pennsylvania State University before taking a position in industry. He has received multiple patents for his work and has had multiple scientific and technology publications.
Christopher D Brown, Ph.D., is a seasoned technology executive with more than two decades of experience successfully leading the development of breakthrough chemical/biochemical sensing technologies across medical, life science, forensics and consumer markets. Under his leadership, teams have achieved a number of industry firsts in miniaturization and microfabrication of analytical platforms, and the application of machine learning methods in chemical analysis. Dr. Brown has received multiple industry and technology awards and has an impressive record of over 200 patents, papers, and conference presentations in the fields of analytical sensing, instrumentation and statistical/machine learning. He continues to serve on several boards and advisory panels within the technology industry. Currently, he is the co-founder and Chief Product Officer at 908 Devices. Before founding 908 Devices, he was a Platform Architect in health sensing at Apple in California and held leadership roles at several other industry-leading companies.
Joel Mainland, Ph.D., is an olfactory neuroscientist and a Member at the Monell Chemical Senses Center. He has used methods from molecular biology, genetics, neuroscience, and machine learning to examine how humans perceive odors. He developed methods for screening olfactory receptors in cell-based assays to identify ligands, used human genetic variation to examine the role of olfactory receptors in odor perception, and developed models that predict odor perception from molecular structure.
John Patrick Cunningham, Ph.D., is a researcher in machine learning and its application to science and industry, including in particular using the tools of artificial intelligence to understand biological intelligence and other complex processes. He is a Professor of Statistics at Columbia University and an investigator at the Zuckerman Mind Brain Behavior Institute and Center for Theoretical Neuroscience. His education includes an undergraduate degree from Dartmouth, masters and Ph.D. from Stanford, and a fellowship at Cambridge. He has worked with and advised a number of leading companies.
Pat Walters, Ph.D., is Chief Data Officer at Relay Therapeutics in Cambridge, MA. Prior to joining Relay, he spent more than 20 years at Vertex Pharmaceuticals where he was Global Head of Modeling & Informatics. Pat is the 2023 recipient of the Herman Skolnik Award for Chemical Information Science from the American Chemical Society. He is a member of the editorial advisory boards for the Journal of Chemical Information and Modeling and Artificial Intelligence in the Life Sciences, and previously held a similar role with the Journal of Medicinal Chemistry. Pat is co-author of the book “Deep Learning for the Life Sciences”, published in 2019 by O’Reilly and Associates. He received his Ph.D. in Organic Chemistry from the University of Arizona where he studied the application of artificial intelligence in conformational analysis. Prior to obtaining his Ph.D., Pat worked at Varian Instruments as both a chemist and a software developer. He received his B.S. in Chemistry from the University of California, Santa Barbara.
Ryan Adams, Ph.D., is a Professor of Computer Science at Princeton University and Associate Chair of the Computer Science Department. His research investigates machine learning, artificial intelligence, and computational statistics, with applications in science and engineering. Ryan completed his Ph.D. in physics under David MacKay at the University of Cambridge, where he was a Gates Cambridge Scholar and a member of St. John’s College. Following his Ph.D. Ryan spent two years as a Junior Research Fellow at the University of Toronto as a part of the Canadian Institute for Advanced Research. From 2011-2016, he was an Assistant Professor at Harvard University in the School of Engineering and Applied Sciences. In 2015, Ryan sold the company he co-founded, Whetlab, to Twitter and he spent three years in industry at Twitter and Google before joining the faculty at Princeton in 2018. Ryan is co-director of the Princeton Initiative for Accelerating Invention, and is affiliated faculty of the Program in Applied and Computational Mathematics and the Center for Statistics and Machine Learning.
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