Research/Areas of Interest:

Computational methods for data analytics, edge AI, machine vision, deep learning, big data, depth perception, and generative modeling.


Joseph P Robinson earned a B.S. in ECE (2014) and a Ph.D. in CE (2020) from Northeastern University, where he also taught Data Analytics (awarded the Outstanding Teacher Award, 2020). His thesis spanned applied machine vision research, emphasizing automatic face understanding, deep learning, multimodal and sensor fusion, and big data. Dr. Robinson has over 30 publications (thesis; see scholar for his paper pile). Previously, led a team to TRECVid debut (MED, 3rd-place). Built many image-based, video, and multi-modal datasets, most notably Families In the Wild (FIW), which recently extended with multimedia in FIW-Multimedia (FIW-MM). The most recent dataset was the Balanced Faces in the Wild (BFW): image-based labeled data to support research on bias in facial recognition. Organized 2020 FG conference, various workshops, and challenges (e.g., NECV 2017, RFIW17@ACM-MM, RFIW18-RFIW19-RFIW20@FG, AMFG@CVPR18-19, FacesMM), tutorials (i.e., ACM-MM, CVPR, FG), PC (e.g., CVPR, FG, MIRP, MMEDIA, AAAI, ICCV), reviewer (e.g., IEEE TBioCAS, TIP, TPAMI), and positions like President of IEEE@NEU & Relations Officer of IEEE SAC R1 Region. Completed two NSF REUs (2010 & 2011); co-ops at Analogic Corporation and Raytheon BBN Technology; interned at MIT Lincoln Labs (2014), System & Technology Research (2016 & 2017), Snap Inc. (i.e., Snapchat) (2018), and ISMConnect (2019). Joseph was the first full-time AI Engineer hired on the Team Perception of Vicarious Surgical (NYSE:RBOT), where he developed depth perception capabilities for their minimally invasive surgery. He is now co-founder, with Dr. Yun Raymond Fu, of the AI startup AEyeZone, and lecturing ML at Tufts University.