Upon receiving a Ph.D. (2020), Joseph P. Robinson worked as an AI Engineer at Vicarious Surgical, where we advanced the next generation of surgical robotics. As a member of Team Perception, our Advanced Signal, Data, and AI (ASDAI) group look to deploy state-of-the-art ML models while leveraging various aspects of data science to best assist the surgeon operating with our Vera system. Ultimately, we aim to enhance the ability of surgeons to employ surgical robotics to improve their abilities while providing the best experience for the patient during the procedure and minimizing the time needed to recover.
Dr. Robinson is a research engineer in applied machine vision mainly focused on modern-day, data-driven modeling (i.e., deep networks) based on multimodal inputs. He has a B.S. in Electrical and Computer Engineering from Northeastern University (NEU) class of 2014; Dr. Robinson received his Ph.D. in Computer Engineering from NEU, working under the supervision of Yun (Raymond) Fu and alongside the others of SMILE Lab (class of 2020). As a graduate student, Dr. Robinson also served part-time faculty for several years, teaching Computational Methods for Data Analytics. (voted Best Teacher by students in his final year, 2019-2020). Check out the Research Page and about 40 of his publications with 500+ citations (Scholar). In addition, he has completed two NSF REUs (2010 & 2011); co-oped at Analogic Corporation (2012) & Raytheon BBN Technology; interned at MIT Lincoln Labs (2014), System & Technology Research (2016 & 2017), Snap Inc. (i.e., Snapchat) (2018), and ISMConnect (2019).
Dr. Robinson started his Ph.D. by leading the joint NEU-MIT Lincoln Labs team to their TRECVid debut (MED, placed 3rd of many). He also built numerous image & video-based datasets, most notably Families In the Wild (FIW), which has recently extended with multimedia (i.e., FIW-Multimedia, aka FIW-MM, audio, video, and audio-visual). Balanced Faces in the Wild (BFW) is another face-based labeled dataset to support biased research in facial recognition. His dissertation includes novel work in robust real-time facial recognition, deep learning, and human-computer interaction, emphasizing the social implications of integrating AI in day-to-day society. Nowadays, his focus is on scene-level knowledge of human organs, bringing assistance to surgeons inside the medical room. His particular interests are in exploring the social challenges of integrating computing into surgery – and how this will change the medical field, procedures, and practices in the coming years.
Specifically, Dr. Robinson has published in several journals, such as IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) and Transactions on Multimedia (T-MM), among other top-tier journals. Dr. Robinson also has a variety of conference papers spanning CVPR, ICCV, AAAI, AMFG, ACM MM, and several other renowned publications. Lots of service in the research community: served as the organizing chair and host of several workshops (e.g., NECV2017@ NEU, RFIW2017@ MM [proceedings], RFIW18, 19, 20, 21@FG, AMFG@ CVPR2018-2021, FacesMM @ ICME 2018 -2019), tutorials (ACM-MM18 [slides], FG19 [video], CVPR19), and data challenges (Kaggle, Codalab) at the conferences above and others! Dr. Robinson has a reputation for delivering effective peer reviews, shown in a large number of papers he is assigned regularly and in winning Best Student Reviewer for three consecutive years (2018, 2019, 2020); upon graduating, he earned Best Overall Review (2021). As part of this, Dr. Robinson served as a Program Committee (PC) member for many venues and many years (e.g., CVPR, FG, MIRP, MMEDIA, AAAI, ICCV, ECCV, IJCAI, ACM MM, NIPS), and journal reviewer (e.g., IEEE Trans. on Biomedical Circuits and Systems, Image Processing, TPAMI, TIP). He also held several leadership positions, like the president of IEEE@NEU & Relations Officer of IEEE SAC R1 Region.
I enjoy catching up with family and friends outside of work, playing hockey or rollerblading with Jax (my dog), doing the occasional cook-off, traveling, skiing, and learning. Although I am the type of sports fan that would rather play than watch, except for playoff hockey ;), I am a NEU and Boston sports fan all day!
My research focuses on ML, typically with visual signals, some multi-modal. Experienced data scientist - pattern recognition and understanding, landmark detection, generative modeling, learning with minimal or imbalanced data to mitigate biases in ML with consideration to its impact on society, and multi-modal databases (i.e., acquiring, designing, collecting, organizing, annotating, evaluating). I am incredibly passionate about applying technical skills to improve a concept, product, or system deployed to improve day-to-day society, whether security, entertainment, or advancement in HCI technology. I like playing for a strong team - I know how to take the initiative when delegated yet facilitate when leading. Experience joining diverse groups, team building, handling logistics, and measuring practical significance. Competent writer, especially in the form of research and proposals. Fluent communicator, whether in technical, formal, or informal. I have acquired a vast professional network - familiarized and often personally, with many experts throughout the technological and entrepreneurial worlds. I am big on learning and love teaching and building. It is what I do.
Awards, Scholarships, and Grants
Best Reviewer, IEEE AMFG Conference (2021).
Best Teacher, College of Engineering (2019-20).
Best Student Reviewer, IEEE AMFG Conference (3x, 2018-2020)
DHS ALERT Graduate Student Scholarship (2015-2018).
Best Poster (05/2014).
Huntington 100: the 100 most influential members of the NEU community (05/2014).
1𝑠𝑡 Place ECE Department Senior Capstone (05/2014).
1𝑠𝑡 Place ECE Freshman Remote Control Design Contest (05/2011).
Invest in Tomorrow's Engineering Leaders Scholarship (2011-14).