Nathan Cahill

Professor

School of Mathematics and Statistics

College of Science

Director, Mathematical Modeling Program

ndcsma@rit.edu

585-475-5144

Office Hours

Mondays 11:00am - 12:30pm, Wednesdays 12:30pm - 2:00pm

Office Location

GOS-2358

Nathan Cahill

Professor

School of Mathematics and Statistics

College of Science

Director, Mathematical Modeling Program

Education

BS, MS, Rochester Institute of Technology; D.Phil., University of Oxford (United Kingdom)

Bio

Dr. Cahill is an RIT alumnus, earning BS and MS degrees in Applied Mathematics in 1997 and 2000, respectively. He has significant industrial experience: he started working at Eastman Kodak Company as a co-op student in 1996, and he continued working in the Kodak Research Labs and Carestream Health until 2009, eventually attaining the rank of Principal Scientist. During this period, he was awarded 26 US patents in the fields of computer vision and medical imaging analysis. From 2005-2009, while employed at Kodak/Carestream, he earned a DPhil in Engineering Science at the University of Oxford, where he made key theoretical and computational contributions in the field of medical image registration. Since joining RIT as a faculty member in 2009, he has continued to do research in the areas of computer vision and medical imaging analysis, as well as expanding into the areas of data-enabled modeling, computational modeling, and machine learning theory. He has been the Director of RIT's PhD Program in Mathematical Modeling since 2019.

ndcsma@rit.edu

585-475-5144

Personal Links

Personal Website

ORCID

Curriculum Vitae

Google Scholar

Areas of Expertise

Scientific Computing

Machine Learning

Computer Vision

Mathematical Modeling

Select Scholarship

Journal Paper

Korley, Frederick, et al. "Progesterone Treatment Does Not Decrease Serum Levels of Biomarkers of Glial and Neuronal Cell Injury in Moderate and Severe Traumatic Brain Injury Subjects..." Journal of Neurotrauma. (2021): online ahead of print. Web.

Moudgalya, Sanketh S., et al. "Cochlear Pharmacokinetics - Micro-Computed Tomography and Learning-Prediction Modeling for Transport Parameter Determination." Hearing Research 380. (2019): 46-59. Print.

Strang, Alexander, et al. "Generalized Relationships Between Characteristic Path Length, Efficiency, Clustering Coefficients, and Density." Social Network Analysis and Mining 8. 1 (2018): 1-14. Web.

Ek, Bryan, et al. "A Comprehensive Comparison of Graph Theory Metrics for Social Networks." Social Network Analysis and Mining 5. 1 (2015): 1-7. Print.

Gopal, Shruti, et al. "Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis." Schizophrenia Bulletin 42. 1 (2015): 152-160. Print.

Chen, Bin, Anthony Vodacek, and Nathan D. Cahill. "A Novel Adaptive Scheme for Evaluating Spectral Similarity in High-resolution Urban Scenes." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6. 3 (2013): 1376-1385. Print.

Published Conference Proceedings

Hayes, Tyler L., Nathan D. Cahill, and Christopher Kanan. "Memory Efficient Experience Replay for Streaming Learning." Proceedings of the International Conference on Robotics and Automation (ICRA). Ed. None. Montreal, Canada: IEEE, 2019. Web.

Cahill, Nathan D., et al. "Compassionately Conservative Balanced Cuts for Image Segmentation." Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Ed. -. -, -: IEEE, 2018. Web.

Chew, Selene E. and Nathan D. Cahill. "Semi-Supervised Normalized Cuts for Image Segmentation." Proceedings of the International Conference on Computer Vision, December 2015. Ed. Ruzena Bajcsy. Santiago, Chile: IEEE, Print.

Currently Teaching

IMGS-699

Imaging Science Graduate Co-op

0 Credits

This course is a cooperative education experience for graduate imaging science students.

IMGS-790

Research & Thesis

1 - 6 Credits

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

IMGS-890

Research & Thesis

1 - 6 Credits

Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

IMGS-891

Continuation of Thesis

0 Credits

Continuation of Thesis

MATH-421

Mathematical Modeling

3 Credits

This course explores problem solving, formulation of the mathematical model from physical considerations, solution of the mathematical problem, testing the model and interpretation of results. Problems are selected from the physical sciences, engineering, and economics.

MATH-495

Undergraduate Research in Mathematical Sciences

1 - 3 Credits

This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.

MATH-606

Graduate Seminar I

1 Credits

The course prepares students to engage in activities necessary for independent mathematical research and introduces students to a broad range of active interdisciplinary programs related to applied mathematics.

MATH-607

Graduate Seminar II

1 Credits

This course is a continuation of Graduate Seminar I. It prepares students to engage in activities necessary for independent mathematical research and introduces them to a broad range of active interdisciplinary programs related to applied mathematics.

MATH-689

Advanced Special Topics

1 - 4 Credits

Special Topics courses cover content that is not represented in the main curriculum on an experimental or trial basis.

MATH-699

Math & Stats Graduate Co-op

0 Credits

This course is a cooperative education experience for graduate math and stats students.

MATH-790

Research & Thesis

0 - 9 Credits

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

MATH-791

Continuation of Thesis

0 Credits

Continuation of Thesis

MATH-799

MATH GRADUATE Independent Study

1 - 3 Credits

Independent Study

In the News

December 6, 2021

RIT scientists develop machine learning techniques to shed new light on pulsars

New machine learning techniques developed by scientists at Rochester Institute of Technology are revealing important information about how pulsars—rapidly rotating neutron stars—behave. In a new study published by Monthly Notices of the Royal Astronomical Society, the researchers outlined their new techniques and how they applied to study Vela, the brightest radio pulsar in the sky.

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