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.
Email: odugan at mit . edu
Select Awards, Accomplishments, and Affiliations
Hertz Fellow2024 Fannie and John Hertz Foundation
Knight-Hennessy Scholar2024 Stanford University Knight-Hennessy Scholarship
NSF Graduate Research Fellow2024 National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP)
Phi Beta Kappa Member2024 The Phi Beta Kappa Society
Sigma Pi Sigma Member2024 The Sigma Pi Sigma Society
Outstanding UROP (Undergraduate Research) Award2023 MIT School of Science
U.S. Patent Nos. 11,688,045 and 11,756,3042023 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 Scholar2021 US Department of Education
Received credit for 8 MIT classes2021 MIT Advanced Standing Examinations
Neo Scholar2021 Neo Venture Capital Firm
Davidson Fellows Scholar2021 Davidson Institute
STS Scholar2020 Regeneron Science Talent Search
ISEF Finalist2020 International Science and Engineering Fair
DoD Scholar2020 Department of Defense
RSI Scholar2020 Research Science Institute
SAT Perfect Score (1600)2020 College Board
National Merit Scholar2019 National Merit Scholarship Corporation
Caroline D. Bradley Scholar2016 Institute for Educational Advancement
Publications
Expect more soon!
11) Owen Dugan, Donato Manuel Jimenez Beneto, Charlotte Loh, Zhuo Chen, Rumen Dangovski, Marin Soljačić,2024 “OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step”arXiv:2406.06576
10) Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić,2024 “QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation”arXiv:2406.00132
9) Owen Dugan,2024 “Machine Learning for Physics: from Symbolic Regression to Quantum Simulation” MIT Undergraduate Physics ThesisThesis Link
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 FlowsMarch 2024 American Physical Society (APS) March Meeting
5) Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing FlowsJuly 2023 International Conference on Machine Learning
4) OccamNet: A Feed-Forward Neural Model for Symbolic RegressionApril 2023 MIT Conference on Artificial Neuroscience
3) Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing FlowsApril 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 FlowsFebruary 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 ImagesMay 2020 Joint Meeting, Society for Astronomical Sciences (SAS) and the American Association of Variable Star Observers (AAVSO 108th Spring Meeting)