Nathan Cahill
Professor
School of Mathematics and Statistics
College of Science
Director, Mathematical Modeling Program
585-475-5144
Office Hours
T 3:00 - 4:30pm in person W 3:00 - 4:30pm via Zoom (Zoom link provided in myCourses or by email request)
Office Location
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 area of machine learning theory. He has been the Director of RIT's PhD Program in Mathematical Modeling since 2019.
585-475-5144
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-799
Imaging Science Independent Study
1 - 4 Credits
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their graduate studies.
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-241
Linear Algebra
3 Credits
This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course.
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