Zachary Daniels

Advanced Computer Scientist

Center for Vision Technologies

SRI International

Princeton, NJ 08540

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About Me

I am a computer vision/machine learning research scientist with the Vision Systems Laboratory of the Center for Vision Technologies at SRI International. I received my PhD from the computer science department at Rutgers University in Piscataway, New Jersey. While at Rutgers, I worked in the Center for Computational Biomedicine Imaging and Modeling (CBIM) under the supervision of Prof. Dimitris Metaxas. My main areas of research include computer vision, pattern recognition/machine learning/data mining, and artificial intelligence. Specifically, my PhD research focused on developing semantically-grounded, context-driven, and trustworthy/interpretable mathematical models and algorithms for complex data analysis. My main application focus was on scene understanding: analyzing real world images of scenes using computational methods. I received a bachelor's degree in computer science from Lehigh University's P.C. Rossin College of Engineering in Bethlehem, Pennsylvania and minored in cognitive science. While at Lehigh, I worked with a number of faculty including Prof. Henry Baird, Prof. Jeff Heflin, and Prof. Brian Chen. I also participated in REUs under the supervision of Prof. Mubarak Shah (University of Central Florida), Prof. Niels da Vitoria Lobo (University of Central Florida), and Prof. Saâd Biaz (Auburn University). In the summers of 2018 and 2019, I interned with the Sensors Directorate of the Air Force Research Laboratory (AFRL/RY) through the Autonomy Technology Research Center. I was supervised by Dr. Donald Venable (RYWN) in 2018 and Mr. Christopher Menart (RYAT) in 2019.

Education

• PhD in Computer Science, Rutgers University
  • September 2014-May 2020 (Awarded October 2020)
  • GPA: 3.983
• Bachelor's of Science in Computer Science, P.C. Rossin College of Engineering, Lehigh University
  • Graduated: May 2014
  • Minor in Cognitive Science
  • GPA: 3.89

Work History

• SRI International, Center for Vision Technologies, Vision Systems Laboratory
  • Advanced Computer Scientist (Research Scientist)
  • June 2020-Present
• Rutgers, the State University of New Jersey
  • Graduate Research Assistant, NSF Graduate Fellow, Center for Computational Biomedicine, Imaging, and Modeling
  • September 2014-May 2020
• Air Force Research Laboratory: Sensors Directorate
  • Research Intern, Autonomy Technology Research Center
  • May 2018-August 2018, May 2019-August 2019
• University of Central Florida, Center for Research in Computer Vision
  • NSF Research Experience for Undergraduates on Computer Vision
  • May 2013-August 2013
  • Technical Report of Work Performed
• Auburn University
• NSF Research Experience for Undergraduates on Smart UAVs
• May 2012-July 2012
• Technical Report of Work Performed

News

• June 1, 2020: I will join SRI International's Center for Vision Technologies as an Advanced Computer Scientist.
• April 24, 2020: I successfully defended my PhD!
• February 19, 2019: I'll be interning once again with the Air Force Research Laboratory's Sensors Directorate through the Autonomy Technology Research Center.
• May 7, 2018: I'll be interning with the Air Force Research Laboratory's Sensors Directorate through the Autonomy Technology Research Center.

Publications, Presentations, and Patents

Dissertation

• Explanation-Driven Learning-Based Models for Visual Recognition Tasks
• PhD Thesis
• Computer Science Department, Rutgers University
• Spring 2020
• Committee: Dr. Dimitris Metaxas (Chair), Dr. Konstantinos Michmizos, Dr. George Moustakides, Dr. Fuxin Li (Outside Member)

Journal Articles

• Baker, M. et al. (2023). A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. Neural Networks, Elsevier. Special Issue on Lifelong Learning
• Zhang, X., Song, D., Priya, S., Daniels, Z., Reynolds, K., & Heflin, J. (2014). Exploring Linked Data with Contextual Tag Clouds. Web Semantics: Science, Services and Agents on the World Wide Web, Elsevier. (work done during undergraduate)

Conference Articles

• Lomnitz, M. et al. (2023, October). Learning with Local Gradients. In The IEEE International Conference on Systems, Man, and Cybernetics (SMC 2023).
• Sur, I. et al. (2022, October). System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games. In The Second International Conference on AI-ML Systems. [Paper]
• Daniels, Z. A. et al. (2022, August). Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2. In The First Conference on Lifelong Learning Agents (CoLLAs 2022). [Paper]
• Isnardi, M. et al. (2021, March). Hyper-Dimensional Analytics of Video Action at the Tactical Edge. In GOMACTech 2021. [Paper]
• Daniels, Z. A., & Metaxas, D. N. (2020, October). Active Scene Classification via Dynamically Learning Prototypical Views. In The Third International Conference on InfoSymbiotics and Dynamic Data Driven Applications Systems (DDDAS 2020) (Published in Springer's Lecture Notes in Computer Science (LNCS) series). [Paper] [Presentation Video] [One of Three Best Papers/Presentations]
• Daniels, Z. A., Frank, L. D., Menart, C. J., Raymer, M., and Hitzler, P. (2020, April). A Framework for Explainable Deep Neural Models Using External Knowledge Graphs. In SPIE Defense and Commercial Sensing, Track: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. [Paper]
• Daniels, Z. A., & Metaxas, D. N. (2019, April). Exploiting Visual and Report-Based Information for Chest X-Ray Analysis by Jointly Learning Visual Classifiers and Topic Models. In IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1270-1274). [Paper] [Poster]
• Daniels, Z. A., & Metaxas, D. N. (2017, February). Addressing Imbalance in Multi-Label Classification Using Structured Hellinger Forests. In AAAI (pp. 1826-1832). [Code] [Poster]
• Daniels, Z. A., & Baird, H. S. (2013, August). Discriminating Features for Writer Identification. In Document Analysis and Recognition (ICDAR), 2013 12th International Conference on (pp. 1385-1389). IEEE. (work done during undergraduate)

Peer-Reviewed Workshop Long Papers

• Kandaswamy, I., Farkya, S., Daniels, Z. van der Wal, G., Raghavan, A., Zhang, Y., Hu, Y., Lomnitz, M., Isnardi, M., Zhang, D., Piacentino, M. (2022, June). Real-Time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators. In Embedded Vision Workshop (EVW) at CVPR 2022
• Daniels, Z. A., & Metaxas, D. N. (2018, July). ScenarioNet: An Interpretable Data-Driven Model for Scene Understanding. In FAIM/FIX/IJCAI Workshop on Explainable Artificial Intelligence (XAI). (pp.33-39) [Paper] [Poster]
• Zhang, X., Song, D., Priya, S., Daniels, Z., Reynolds, K., & Heflin, J. (2012). Exploring the Linked Data Cloud via Contextual Tag Cloud. Semantic Web Challenge. [First Place] (work done during undergraduate)

Peer-Reviewed Workshop Short Papers, Posters, and Extended Abstracts

• Farkya, S.*, Daniels, Z. A.*, Raghavan, A., Zhang, D., Piacentino, M. (2022, June). Saccade Mechanisms for Image Classification, Object Detection and Tracking. In NeuroVision Workshop at CVPR 2022. [Short Paper] *co-first authors
• Daniels, Z., & Metaxas, D. (2016, June). A Context-Driven Forest-Based Approach to Hierarchical Scene Understanding. In Scene Understanding Workshop at CVPR (SUNw). [Poster]
• Daniels, Z. A., Stinson, S. R., Tian, S., Mulbry, E., & Chen, B. Y. (2014, June). A Gesture-Based Interface for the Exploration and Classification of Protein Binding Cavities. In Proceedings of the 2014 Workshop on Mobile Augmented Reality and Robotic Technology-Based Systems (MARS) (pp. 47-50). ACM. (work done during undergraduate)

Patents

• Raghavan A. et al. (Pending; Filed March 2022). "Reconfigurable, Hyperdimensional, Neural Network Architecture". Worldwide Patent [Patent Application]
• Metaxas, D. and Daniels, Z. A. (Granted May 2022). "Image Processing Neural Network Systems and Methods with Scene Understanding". United States Patent [Patent Application; More Information]

Technical Reports/Under Review

• Daniels, Z. A. and Metaxas, D. N. (2020). A Dynamic Data-Driven Approach for Explainable Scene Understanding. Technical Report. [Paper]

Other Talks and Presentations

• Poster on lifelong learning using Eigentasks presented at DARPA Electronics Resurgence Initiative (ERI) Summit 2021
• Abstract and poster on "Aligning Human Semantics with Neural Networks" presented at "Naval Applications of Machine Learning (NAML2020)" by Mr. Christopher Menart

Academic Honors and Awards

• Fellow, NSF Graduate Research Fellowship Program (GRFP)
• National Science Foundation
• Recipient, Rizvi Family Award
  • Rutgers Computer Science Department
• Recipient, Best Reviewer Prize (Awarded to 2/943 Conference Reviewers)
  • British Machine Vision Conference (BMVC 2019)
• Recipient, Best Student Paper/Presentation (Awarded to 3 Papers)
  • Third International Conference on InfoSymbiotics and Dynamic Data Driven Applications Systems (DDDAS 2020)
• Member, First Place Team, Semantic Web Challenge 2012: Billion Triples Track
  • International Semantic Web Conference (ISWC 2012)
• Recipient, Dean's Scholarship
  • Lehigh University
• Member, Tau Beta Pi
• Outstanding Reviewer, CVPR 2021

Professional Membership

• IEEE, Member
• SPIE, Member

Service and Outreach

• PC Member/Reviewer: CVPR, ICCV, ECCV, ACCV, BMVC, WACV, AAAI, ICLR, EMBC, SMC, IEEE Transactions on Multimedia, Medical Image Analysis (MEDIA), International Journal of Intelligent Systems (Wiley), SPIE Journal of Electronic Imaging, SPIE Journal of Remote Sensing
• BMVC 2019 Best Reviewer Prize (Awarded to 2/943 Conference Reviewers)
• Outstanding Reviewer: CVPR2021
• Member, Rutgers Machine Learning and Deep Learning Reading Group
• Member/Presenter, Rutgers Learning and Vision Reading Group
• Visual Explanations from Deep Neural Networks (02/28/2018)
• Bayesian Deep Learning with Applications to Multi-Task Learning (10/01/2018)
• Guest Lecturer, Rutgers University CS580: Topics in Biomedicine
  • Introduction to Visual Recognition with Neural Networks (04/10/2019, 09/18/2019)
  • Interpreting and Explaining the Predictions of Deep Neural Networks (03/27/2019)
  • Beyond Image Classification with Neural Networks: Interpretability, Unsupervised and Generative Modeling, Detection, and Segmentation (11/20/2019)
• I served as the primary teacher and curriculum designer for a middle school summer introductory computer science program at Moravian Academy entitled "Take a Byte".
• I have judged, helped mentor students, and chaperoned for the Pennsylvania Junior Academy of Science (PJAS) local and state science fairs.
• I have judged for the Army Educational Outreach Program's ECyberMission Competition.
• I have worked with Moravian Academy's FIRST Robotics Lego League team.
• I helped run an Ozobot demonstration booth (where I taught elementary school children about coding and robotics) at the Bethlehem Area School District's Science Fun Night.
• I was a mentor with Lehigh University's STAR Academy outreach program where I tutored underprivileged local area students.
• While at Lehigh, I served as secretary of the student ACM chapter.

Copyright 2023 Zachary Daniels