CV

Stephen Scarano Noblis Autonomy Lab

1011 Fairmont St Washington D.C, 20001

978-340-3337

stephenscarano@gmail.com

Noblis Autonomy Lab
- Advisors: Mohammad Goli, Riley White
- Research Summary: Noblis autonomy lab coordinates of autonomous systems, and my work leverages computer vision to facilitate real-time robotics solutions.
- Neuromorphic computing for vision applications in space-based situational awareness and roadside conflict prediction settings. Hazard mapping for lunar navigation of autonomous systems
- Package localization and handoff between quadcopter and rover systems
Socially-Intelligent Media & Systems Lab
- Advisors: Przemyslaw Grabowicz, Mattia Samory, Kaicheng Yang
- Research Summary: Crawling of large-scale Twitter data for signal processing, leveraging machine learning tools and statistical poststratification for election prediction.
University of Massachusetts Computer Vision Lab
- Advisor: Erik Learned-Miller
- Research Summary: Egomotion estimation using hough-transform over optical flow vectors, exceeding accuracy and speed of state-of-the-art in crowded scenes.

Stephen Scarano, Anand Seshadri. Intersection Safety Systems: Classification and Prediction Across Different Road Users and Conditions. Transportation Research Board. Submitted. Scarano, S. et al 2025. Election Polls on Social Media: Prevalence, Biases, and Voter Fraud Beliefs. Proceedings of the International AAAI Conference on Web and Social Media. https://ojs.aaai.org/index.php/ICWSM/article/view/35900
Scarano, S. et al. 2024. Analyzing Support for U.S. Presidential Candidates in Twitter Polls. Journal of Quantitative Description: Digital Media . 4, (May 2024). DOI:https://doi.org/10.51685/jqd.2024.icwsm.4. https://journalqd.org/article/view/5897
Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, and Erik Learned-Miller. 2023. Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes. International Conference on Computer Vision. https://arxiv.org/abs/2309.08588

Best Paper Finalist - International AAAI Conference on Web and Social Media (ICWSM 2025) for “Analyzing Support for U.S. Presidential Candidates in Twitter Polls”
Data Science for the Common Good Fellowship (2023) - Center for Data Science
Bay State Fellowship - University of Massachusetts, Amherst (2021)

Responsible AI Teaching Assistant (2023) Summary: In this course, students will learn techniques for robust model evaluation, model selection, causal discovery, explainable and fair artificial intelligence, and interpretable models. In addition, students will reason about representativeness, transparency, and legal aspects of techno-social systems. The course will review both cutting-edge research and relevant portions of recent open-access textbooks. Coursework includes reading recent research papers, programming assignments, and a final group project. After completing the course, students should be able to develop, investigate, evaluate, and deploy artificial intelligence systems more responsibly.
Introduction to Computation Teaching Assistant (2022) Summary: In addition to basic programming constructs such as looping, conditions, arrays, file handling, and methods, much attention is given to the Java object model as well as to Java’s event model and its relation to graphical user interfaces.