Dr. Joseph P. Robinson is a full-time AI Engineer working on the next generation of surgical robotics as a member of the ASDAI group (Team Perception) of Vicarious Surgical.
Dr. Robinson received a BS in Electrical & Computer Engineering (2014) and a Ph.D. in Computer Engineering (2020) at Northeastern University (NEU), while also working as part-time faculty: designed & taught an undergrad course in Data Analytics. Research is in applied machine vision, emphasizing 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 extended with multimedia (i.e., FIW-Multimedia, aka FIW-MM, audio, video, and audio-visual). Balanced Faces in the Wild (i.e., BFW) is another face-based labeled dataset to support bias research 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 & 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's 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 for the better in the years to come.
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 rather play than watch, with exception of playoff hockey ;), I am a Northeastern University, Boston, and New England 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 - 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, love teaching, and building. It is what I do.
2015 - 2020
Earned Ph.D. in Computer Engineering as a member of SMILE Lab and advised by Dr. Yun (Raymond) Fu. Published 20+ papers in top-tier computer vision and machine learning (ML)-based conferences and journals (dissertation). For this, various face recognition tasks were addressed with novel solutions ranging from landmark detection, attribute classification, multi-modal understanding, generative modeling, fairness in ML, video (i.e., visual and speech understanding), and the creation of various large-scale labeled datasets used to host various data challenges, workshops, and tutorials at CVPR, ACM MM, and others. Most renowned was our FIW dataset, which paved the way for the engagement of the community (i.e., RFIW data challenge series held annually, 2017-2021). Also, designed, organized, collected, and facilitated the construction of a multi-modal dataset using student volunteers to perform actions captured by optical and kinect sensors while wearing EMG sensors to acquire the corresponding signal. Became a competent writer, especially in the form of research and proposals; obtained a strong professional network while traveling to multiple conferences per year to present work and connect with others. Furthermore, after teaching the undergraduate course Computational Methods of Data Analytics, which I created and delivered over the course of 7 semesters: the last year being voted the Best Teacher. Under the outstanding guidance of Dr. Fu, I learned to adapt and interrupt highly complex problems with practical solutions backed by rigorous theory in a way that leverages the strong fundamentals learned over many years to reduce the learning curves and path to results.
2011 - 2014
Received BS in Electrical and Computer Engineering in accelerated time and with two summer research experiences for undergraduates and two co-ops (i.e., Analogic and Raytheon BBN in 2012 and 2013, respectively. I was highly engaged, both technically and professionally: held nearly every officer position of the IEEE NEU student chapter (from Student Government Represented to President, and many in between); helped start the Student Research Engagement Committee, where we promoted the benefits of undergraduate research annually to incoming freshman, hosted lab fairs, organized workshops on topics such as resume/research poster building, along with technical programs like statistics for controlled experimental analysis.
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).