Industrial machine learning software
Our mission at Fero Labs is to bring actionable machine learning to the industrial sector. We believe that factories around the world can use machine learning to optimize production, reduce waste, and improve quality. We are building Fero to support the next leap in industrial productivity.
Berk holds a Ph.D. in electrical engineering and computer science from Columbia University. His academic research focused on optimizing wireless and optical networks with efficient cross-layer algorithms. He developed scheduling algorithms for LTE-A and fiber-optic networks. He holds a patent in IoT systems for resilient fiber-optic networks.
Alp is an expert in machine learning and probabilistic programming. He has published multiple papers with David Blei and Andrew Gelman. He is also an adjunct professor of computer science at Columbia University. Alp received his Ph.D. from Yale University, where he won the best thesis award.
Pamir has worked as a strategy consultant and sales engineer for industry leaders including Toshiba-Mitsubishi-General Electric and Siemens. He has extensive international experience supporting customers in the metals, paper, mining, oil and gas, renewables, logistics, and transportation industries. Pamir holds an MBA from the London Business School and an M.S. in electrical engineering from Columbia University.
Todd is a veteran software engineer with experience leading companies from their early stage to acquisitions by sector giants, such as Dropbox. He previously led infrastructure teams at Google, where he was responsible for managing big data storage. Todd is excited about the application of machine learning in industrial IoT.
Tim has extensive experience in the automotive and robotics sector, ranging from startups to Fortune 20 companies. He holds a joint Bachelors and Masters degree in mechanical engineering and business administration from RWTH Aachen University. He is also a lecturer in Industrie 4.0 and the application of machine learning to this field.