Jason Risch is an investor on the enterprise team at Greylock - investing in security, AI/ML, data, infrastructure, and developer tools. Before joining Greylock, he incubated ML companies at AI Fund and was a management consultant at McKinsey. Jason is a Bay Area native, graduated from Stanford, and when not working, can be found reading, hiking, playing Age of Empires, and cheering on Stanford Football.
Datacast Episode 124: The Open-Source Cloud Playbook, The Modular Future of Data and AI Infrastructure, and Meta-Learning as a VC with Casber Wang
Casber Wang is Partner at Sapphire Ventures. He focuses primarily on security, enterprise infrastructure, and data analytics.
He is on the boards of Huntress, JumpCloud, StarTree, Tetrate, Upytcs, and Zesty. For his work, Insider listed Casber as an Enterprise VC Rising Star Investor and as an emerging investor charting the industry’s future on the 2022 EVC List.
Prior to Sapphire, he was part of the technology investment banking group at Bank of America Merrill Lynch, where he worked on a number of high-profile IPO and M&A transactions. He also spent time at Wish, a leading mobile commerce platform in North America and Europe.
Datacast Episode 44: Computer Systems, ML Security Research, and Women in Tech with Shreya Shankar
Shreya Shankar is a computer scientist living in the Bay Area. She is interested in making machine learning work in the real world. She currently works at Viaduct — an applied machine learning startup — but most recently, she researched at Google Brain. She graduated from Stanford University with a B.S. in computer science, concentrating on systems. She's finishing her M.S. in computer science, concentrating on artificial intelligence.
Datacast Episode 40: Biological Aging, Probabilistic Programming, and Private Machine Learning with Matthew McAteer
Matthew McAteer is a Machine learning Researcher at FOR.ai. Before this, he got his career started in biological aging, before moving on to the mission of figuring out ways in which machine learning could be used on large amounts of noisy biomedical data. He has worked for many companies as a machine learning engineer and researcher, ranging from small startups to Google's TensorFlow team. His work on subjects like security and safety in machine learning has also been showcased at top conferences like ICML.