Yiqun Hu

Case Study: Neuro-Symbolic Learning Algorithms Why And What For?

Neuro-Symbolic AI algorithms help incorporate common sense reasoning and domain knowledge into deep learning. The session will address: • The challenges using instructible neuro-symbolic reasoning systems • How systems can be directly instructed by humans in natural language, resulting in sample-efficient learning in data-sparse scenarios


Experienced Research & Development leader specializing in cutting-edge Data Science and Big Data technologies. He has experience of more than 10 years in software development and technology innovation. He has led the teams consisting of engineers and researchers in two silicon valley companies (eHealthInsurance.com/PayPal). He has successfully led several research innovations and delivered production-ready solutions to impact business. His team was the global winner (1st place) of the global innovation competition of PayPal 2012. He has two patents filed with Microsoft Research Asia and eBay Inc. His data science team currently focuses on delivering large-scale machine learning solutions for e-commerce/payment industry. He has the unique capability and experience of managing a joint team of engineers and researchers to conduct applied research and convert them to production-ready solutions/innovations to impact business. As a computer scientist, he also maintained a successful track record in academic research. He has published 1 book chapter and over 40 scientific papers in the flagship international computer science journals/conferences, i.e. TPAMI/TIP/TMM, CVPR/ICCV/ECCV, etc. His papers have been cited in 1500+ papers in international scientific publications (http://scholar.google.com/citations?user=gIHCye8AAAAJ\​&hl=fr). He is a data hacker in different data science competitions. He is one of few master players in Kaggle from Singapore. Kindly check his record in Kaggle: https://www.kaggle.com/codingneo.

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