About Me

Owen Dugan Headshot

Hi! I'm Owen, an undergraduate student at MIT researching at the intersection of AI and Physics. Human and artificial intelligence fascinate me, and in my research I study and design better machine learning systems, with real-world applications to physics and other sciences.

I aspire to use techniques from physics to model the workings of neural networks and to develop more capable and reliable AI systems. In the process, I apply my work to help push beyond current computational limits in physics and other sciences.

I work with Professor Marin Soljačić. I also research with Professor Hong Liu.

Contact

Feel free to contact me regarding any of my research.

Select Awards, Accomplishments, and Affiliations

NSF Graduate Research Fellow 2024
National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP)
Knight-Hennessy Scholars Finalist 2024
Stanford University Knight-Hennessy Scholarship
Hertz Fellowship Finalist 2024
Fannie and John Hertz Foundation
Phi Beta Kappa Member 2024
The Phi Beta Kappa Society
Sigma Pi Sigma Member 2024
The Sigma Pi Sigma Society
Outstanding UROP (Undergraduate Research) Award 2023
MIT School of Science
U.S. Patent Nos. 11,688,045 and 11,756,304 2023
US Patent & Trademark Office
164th place (top 5%) 2022
William Lowell Putnam Mathematical Competition
329th place (top 12%) 2021
William Lowell Putnam Mathematical Competition
US Presidential Scholar 2021
US Department of Education
Talentplace Network Member 2023
Andreessen Horowitz
Received credit for 8 MIT classes 2021
MIT Advanced Standing Examinations
Neo Scholar 2021
Neo Venture Capital Firm
Davidson Fellows Scholar 2021
Davidson Institute
STS Scholar 2020
Regeneron Science Talent Search
ISEF Finalist 2020
International Science and Engineering Fair
DoD Scholar 2020
Department of Defense
RSI Scholar 2020
Research Science Institute
SAT Perfect Score (1600) 2020
College Board
National Merit Scholar 2019
National Merit Scholarship Corporation
Caroline D. Bradley Scholar 2016
Institute for Educational Advancement

Publications

Expect more soon!

8) Owen Dugan, Peter Lu, Rumen Dangovski, Di Luo, Marin Soljačic,2023
“Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows”
Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii. PMLR 202.arXiv:2302.12235
7) Owen Dugan, Rumen Dangovski, Allan Costa, Samuel Kim, Pawan Goyal, Joseph Jacobson, Marin Soljačić,2023
“OccamNet: A Fast Neural Model for Symbolic Regression at Scale.”arXiv:2007.10784
6) Julia Balla, Sihao Huang, Owen Dugan, Rumen Dangovski, Marin Soljačić,2023
“AI-Assisted Discovery of Quantitative and Formal Models in Social Science.”arXiv:2210.00563
5) Owen Dugan,2021
“QiskiFT: Quantum Error Correction and Quantum Fault Tolerance Development Kit.”druidowm.github.io/qiskift
4) Owen Dugan,2020
“Astronomy Will Not Trail Off: Novel Methods for Removing Satellite Trails from Celestial Images.”
Journal of the American Association of Variable Star Observers, vol. 48, no. 2, p. 262.Abstract
3) Peyton Robertson, Connor Espenshade, Jay Sarva, Owen Dugan, Kalée Tock,2020
“An Automated Approach to Modeling Jupiter’s Synchrotron Radiation from Radio Telescope Observations.”
Astronomy Theory, Observations and Methods, vol. 1, no. 1, pp. 24-33.PDF Link
2) Owen Dugan, Thomas Robinson, Finnian Carmeci, Kalée Tock,2019
“CCD Measurements and Reclassification of WDS 07106+1543 to an Optical Double.”
Journal of Double Star Observations, vol. 15, no. 1, pp. 119–129.PDF Link
1) Owen Dugan, James Krasner,2022
“Soup, Bones, and Shakespeare: Literary Authorship and Allusion in Middle-earth.”
Mythlore, vol. 40, no. 2, pp. 105-120.PDF Link

Research Presentations

6) Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows March 2024
American Physical Society (APS) March Meeting
5) Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows July 2023
International Conference on Machine Learning
4) OccamNet: A Feed-Forward Neural Model for Symbolic Regression April 2023
MIT Conference on Artificial Neuroscience
3) Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows April 2023
NSF Institute for AI and Fundamental Interactions External (IAIFI) Advisory Board Review
2) Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows February 2023
NSF Institute for AI and Fundamental Interactions External (IAIFI) Mini Symposium
1) Astronomy Will Not Trail Off: Novel Methods for Removing Satellite Trails from Celestial Images May 2020
Joint Meeting, Society for Astronomical Sciences (SAS) and the American Association of Variable Star Observers (AAVSO 108th Spring Meeting)