Executive Briefing: Rigorous application of domain insights in AI projects
Domain insights are crucial for successful AI/ML initiatives. This talk discusses three areas of concerns: clarification of business context, awareness of nuances of data sources, and navigating organizational structure.
To clarify business context, we discuss the importance of understanding organizational vision and mission, the maturity stage of the organization, product, and feature, and techniques for crystalizing a project’s description, meaning, relevance, value, and purpose.
To be aware of the nuances of data sources, we discuss the biases, inaccuracies, incompleteness that inevitability exists in data, and provide examples in web session data, geolocation data, and financial transaction data.
To navigate organizational structures in specific industries, we interpret the stage of product development, the maturity of the data science infrastructure, and examples for being flexible to adapt to industry norms.
LinkedIn | Tsinghua University
Dr. Jike Chong is an accomplished executive and professor with experience across industry and academia.
Jike currently heads Data Science, Hiring Marketplace at LinkedIn. He was most recently the chief data scientist at Acorns, the leading micro-investment app in US with over four million verified investors, which uses behavioral economics to help the up-and-coming save and invest for a better financial future. Previously, Jike was the chief data scientist at Yirendai, an online P2P lending platform with more than $7B loans originated and the first of its kind from China to go public on NYSE; established and headed the data science division at Simply Hired, a leading job search engine in Silicon Valley; advised the Obama administration on using AI to reducing unemployment; and led quantitative risk analytics at Silver Lake Kraftwerk, where he was responsible for applying big data techniques to risk analysis of venture investment.
Jike is also an adjunct professor and PhD advisor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, where he established the CUDA Research Center and CUDATeaching Center, which focus on the application of GPUs for machine learning. Recently, he also developed and taught a new graduate level course on machine learning for Internet finance at Tsinghua University in Beijing, China, where he is serving as an adjunct professor.
Jike holds MS and BS degrees in electrical and computer engineering from Carnegie Mellon University and a PhD from the University of California, Berkeley. He holds 11 patents (six granted, five pending).
Yue Cathy Chang
Yue Cathy Chang is a business executive recognized for sales, business development, and product marketing in high technology.
Cathy co-founded and is currently the CEO of TutumGene, a technology company that aims to accelerate disease curing by providing solutions for gene therapy and regulation of gene expression. She was most recently with Silicon Valley Data Science, a startup (acquired by Apple) that provided business transformation consulting to enterprises and other organizations using data science- and engineering-based solutions. Prior to that, Cathy was employee #1 hired by the CEO at venture-funded software startup Rocana (acquired by Splunk), where she served as Senior Director of Business Development focusing on building and growing long-term relationships, and notably increased sales leads 2x through building and managing indirect revenue channels.
Prior to Rocana, Cathy held multiple strategic roles at blue chip software enterprise companies as well as startups, including Corporate and Business Development at FeedZai and Datameer; Senior product management, product marketing and sales at Symantec and IBM; and Strategic Sourcing Improvement Consulting at Honeywell.
Cathy holds MS and BS degrees in Electrical and Computer Engineering from Carnegie Mellon University, MBA and MS degrees as a Leaders for Global Operations (LGO) duel-degree fellow from MIT, and two patents for her early work in microprocessor logic design.
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