Department of Electrical and Computer Engineering Northeastern University, Boston, MA
In a nutshell
My research focus is on applied machine learning, typically vision. My interests in STEM (and sometimes non-STEM) related topics span well beyond that. Outside of work and academia, I enjoy catching up with family and friends, playing hockey or rollerblading with Jax (my dog), doing the occasional cook-off, traveling, skiing, and, more often than not, some light reading.
About Me [Google Scholar] [CURRICULUM VITAE (CV), below] Joseph P. Robinson received a BS in electrical & computer engineering (2014) and is pursuing a Ph. D. in computer engineering at Northeastern University (NEU). I also worked as part-time faculty: designed & taught undergrad course in Data Analytics. Research is in applied machine vision, with emphasis on faces, deep learning, multimedia, and large databases. Previously, I led the team to TRECVid debut (MED, obtained 3rd best accuracy). I also built many image-based & video datasets, most notably Families In the Wild (FIW), which has recently been extended with multimedia (i.e., FIW-Multimedia, aka FIW-MM, with audio, video, and audio-visual). Balanced Faces in the Wild (i.e., BFW) is another face-based labeled dataset to support research in bias in facial recognition [learn more here]. Has served as organizing chair and host of various workshops & challenges (e.g., NECV 2017, RFIW17@ACM-MM, RFIW18-RFIW19-RFIW20@FG, AMFG@CVPR18-19, FacesMM@ICME18-19), tutorials (ACM-MM18, FG19, CVPR19), PC member (e.g., CVPR, FG, MIRP, MMEDIA, AAAI, ICCV, ECCV, IJCAI), reviewer (e.g., IEEE Trans. on Biomedical Circuits and Systems, Image Processing, TPAMI, TIP), and leadership positions like the president of IEEE@NEU & Relations Officer of IEEE SAC R1 Region. Completed two NSF REUs (2010 & 2011); co-ops at Analogic Corporation & BBN Technology; interned at MIT Lincoln Labs (2014), System & Technology Research (2016 & 2017), Snap Inc. (i.e., Snapchat) (2018), and ISMConnect (2019). [Highlighted Research]
NEW! Our paper on facial blending was accepted by ACCV! Stay tuned for camera-ready.
NEW!Recently extended our FIW dataset to contain multimedia (i.e., FIW-MM). The data release is coming soon! [ paper ]
NEW! Our survey on automatic kinship recognition is now available as part of 10th year anniversary [ paper ]
NEW!RFIW Data Challenge White Paper is out [ paper ]
Our Joint Super-Resolution and Alignment of Tiny Faces to be presented at 2020 AAAI in New York, NY
To Recognizing Families In the Wild: A Machine Vision Tutorial presented at ACM-MM 2018 [ slides ]
Visual kinship recognition challenges remain open-- we try to provide platforms to compete and publish consistently (i.e., approximately CFP annually). However, competition portals are still active for registration and submissions for scoring. For challenges on Codalab (Task 1, Task 2, Task 3). Information on previous evaluations: RFIW 2017 I, RFIW 2018, RFIW 2019, and RFIW 2020; also, available on Kaggle! To learn more about visual kinship recognition, see a previous tutorial (e.g., here at ACM MM 2018). Finally, see project page for data downloads, paper pile, links to code, and more. FIW Database for (5) Kaggle! (1) publications;