Lake Dai

Adjunct Professor, Carnegie Mellon University

Lake Dai is a venture capitalist and Adjunct Professor at Carnegie Mellon University. She also serves as Chairwoman & Board Member of non-profit organizations.

Lake is Partner at LDV Partners, a deep tech Silicon Valley VC firm, leading investments in early-stage AI/Big Data startups. Prior to that, Lake has 20 years of operating experience of building and scaling product, engineering, and business teams at early stage startups and public companies. She was employee #84 at Alibaba as head of product, and then led the search engine team at Yahoo! China. In the US, she was a founding team member of deep tech startups and tech accelerators. She is a recognized expert in Search Engine, Ad Platform, Marketplace, Analytics, and Mobile Platforms and Applications. She received 5 US/WO patents in search algorithms, search tokenization, mobile data analytics, and mobile monetization.

As Adjunct Professor at Carnegie Mellon University, Lake teaches Applied AI and Product Management at Integrated Innovation Institute, College of Engineering.

Lake is on the Advisory Board of Women In Technology International (WITI), the premiere global organization empowering women in business and technology. She is also Chairwoman of the US Chapter of QCH, a non-profit organization which fosters entrepreneurship for Alibaba alumni worldwide.

WATCH LIVE: November 3rd at 2:00 pm

Lake Dai

This talk is for business leaders interested in optimizing data strategy for AI projects. Data Science background is not required.

Data is King, but does it mean the more the better? Based on a recent survey, over 90% of the data collected by enterprises are estimated not usable in the near term. Over collecting and storing unusable data could be expensive, and may expose your company to potential security and privacy risks.

MVD, Minimum Viable Data, is a new concept referring to using a minimum amount of data for effective AI training and inference. In this session, we will discuss the challenge of data collection, data labelling, data insight generation, and exploring the options to collect smaller sets of data for effective decisions.