معرفی
Robert Jacob is a Professor of Computer Science at Tufts University's School of Engineering, where he leads research in human-computer interaction with particular focus on implicit brain-computer interfaces. His work bridges computer science, cognitive science, and interface design to create adaptive systems that respond to users' cognitive states without explicit input.
His educational background includes a Ph.D. from Johns Hopkins University. Professional milestones include:
- ACM CHI Academy membership (2007)
- ACM Fellow designation (2016)
- Leadership roles as ACM SIGCHI Vice President and conference chair for CHI, UIST, and TEI
Professor Jacob's research centers on implicit interaction techniques, particularly using fNIRS brain sensing to create adaptive interfaces. His work has evolved from foundational studies in reality-based interaction and tangible programming to current neuroadaptive systems that measure cognitive workload in real-time. This research spans domains including music learning, museum education, and general user interface adaptation.
His publications reveal consistent focus on brain-computer interfaces since 2012, with increasing sophistication in physiological measurement and machine learning techniques. Recent work integrates multiple physiological signals beyond brain data to create comprehensive user state models.
Major recognitions include:
- CHI 2016 Best Paper Award for music learning research
- CHI 2014 and 2012 Best Paper Honorable Mentions
- Keynote addresses at major conferences including Neuroadaptive Technology Conference (2017)
- Extensive media coverage in New Scientist, IEEE Computer, and Boston Globe
Professor Jacob has mentored 17 PhD students who now hold faculty positions at institutions including Worcester Polytechnic Institute, Northwestern University, and Carleton University. His HCI Lab, located in the Joyce Cummings Center, receives funding from NSF and other sources supporting neuroadaptive interface research. Current projects focus on broadening implicit interaction to include multiple physiological measurements while maintaining user privacy and system transparency.
