Edward Chang Email Address

President

HTC United States

Home Page: http://infolab.stanford.edu/~echang/ Edward Chang, a pioneer of data-driven deep learning and parallel machine learning algorithms, is currently serves as an adjunct professor at Stanford CS department and AI/NLP advisor at SmartNews (a Japan's unicorn). His 2010/11 data-driven deep learning patents filed at Google and sponsorship to Stanford ImageNet project contributed to the current AI revolution. From July 2012 to February 2021, Ed served as the President of AI Research and Healthcare (DeepQ) at HTC. He dedicated eight years in developing and applying AI algorithms to enhance healthcare. Ed worked with a team of clinicians, scientists, and engineers to advance precision medicine and precision surgery. His DeepQ Tricorder project entered the Tricorder XPRIZE competition (developing medical IoTs to detect 12 diseases using AI) with 310 other entrants and was awarded the 2nd place with 1M USD in April 2017. Since 2019, he teaches AI for disease diagnosis and information recommendations at Stanford University CS department. In 2020, his team co-developed a COVID19 chatbot with Taiwan CDC to contain pandemic outbreak in Taiwan (only 7 deaths up to March 2021) and received presidential award for this life-saving contribution. Prior to his HTC post, Ed was a director of Google Research for over 6 years, leading research and development in areas including scalable machine learning, indoor localization, Google Q&A, and recommendation systems. His contributions in parallel machine learning algorithms and data-driven deep learning (US patents 8798375 and 9547914) are recognized through many keynote invitations and the developed open-source codes have been collectively downloaded over 30,000 times. His work on IMU calibration/fusion (US patents 8362949, 9135802, 9295027, 9383202, and 9625290) 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 was a full professor of Electrical Engineering at the University of California, Santa Barbara. He joined UCSB in 1999 after receiving his PhD from Stanford University. He is a recipient of the NSF Career Award and Google Innovation Award. He is also an IEEE Fellow for his contributions to scalable machine learning.