Quality, cost, and accessibility form an iron triangle that has prevented healthcare from achieving accelerated advancement in the last few decades. Improving any one of the three metrics may lead to degradation of the other two. However, thanks to recent breakthroughs in artificial intelligence (AI) and virtual reality (VR), this iron triangle can finally be shattered. In this talk, I will share the experience of developing DeepQ, an AI platform for AI-assisted diagnosis and VR-facilitated surgery. I will present three healthcare initiatives we have undertaken since 2012: Healthbox, Tricorder, and VR surgery, and explain how AI and VR play pivotal roles in improving diagnosis accuracy and treatment effectiveness. And more specifically, how we have dealt with not only big data analytics, but also small data learning, which is typical in the medical domain. The talk concludes with roadmaps and a list of open research issues in signal processing and AI to achieve precision medicine and surgery.
Edward Chang currently serves as the President of Research and Healthcare (DeepQ) at HTC. Ed's most notable work is co-leading the DeepQ project (with Prof. CK Peng at Harvard), working with a team of physicians, scientists, and engineers to design and develop mobile wireless diagnostic instruments. Such instruments can help consumers make their own reliable health diagnoses anywhere at any time. The project entered the Tricorder XPRIZE competition in 2013 with 310 other entrants and was awarded second place in April 2017 with 1M USD prize. The deep architecture that powers DeepQ is also applied to power Vivepaper, an AR product Ed's team launched in 2016 to support immersive augmented reality experiences (for education, training, and entertainment).
Prior to his HTC post, Ed was a director of Google Research for 6.5 years, leading research and development in several areas including scalable machine learning, indoor localization, social networking and search integration, and Web search (spam fighting). His contributions in parallel machine learning algorithms and data-driven deep learning (US patents 8798375 and 9547914) are recognized through several keynote invitations and the developed open-source codes have been collectively downloaded over 30,000 times. His work on IMU calibration/fusion with project X was first deployed via Google Indoor Maps (see XINX paper and ASIST/ACM SIGIR/ICADL keynotes) and is now widely used on mobile phones and VR/AR devices. Ed's team also developed the Google Q&A system (codename Confucius), which was launched in over 60 countries.
Prior to Google, Ed was a full professor of Electrical Engineering at the University of California, Santa Barbara (UCSB). He joined UCSB in 1999 after receiving his PhD from Stanford University, and was tenured in 2003 and promoted to full professor in 2006. Ed has served on ACM (SIGMOD, KDD, MM, CIKM), VLDB, IEEE, WWW, and SIAM conference program committees, and co-chaired several conferences including MMM, ACM MM, ICDE, and WWW. He is a recipient of the NSF Career Award, IBM Faculty Partnership Award, and Google Innovation Award. He is also an IEEE Fellow for his contributions to scalable machine learning.