CV

EDUCATION

University of California, Irvine, Irvine, CA
Dissertation: “Diffusion Distance: Efficient Computation and Applications”
Advisor: Eric Mjolsness
Committee Members: Alex Ilher, Padhraic Smyth, Diane Oyen
Doctor of Philosophy, Computer Science, September 2015 – May 2021 GPA: 3.93
Degree awarded June 2021.
Advanced to Candidacy May 2018.
Completed several Certification Programs (see below).
Master of Science, Computer Science, June 2017GPA: 3.84
Colorado College, Colorado Springs, CO
Bachelor of Arts (with Distinction), Mathematics, May 2013 GPA: 3.65
Bachelor of Arts (with Distinction), Computer Science, May 2013 GPA: 3.91
Received the Florian Cajori Award in Mathematics and Computer Science

PAPERS

Scott, C. B., Mjolsness, E., Oyen, D., Kodera, C., Bouchez, D., and Uyttewaal, M. “Graph Metric Learning Quantifies Morphological Differences between Two Genotypes of Shoot Apical Meristem Cells in Arabidopsis”. In press; to appear in in silico Plants 5.1 (2023).

Lewinsohn, D. P., Vigh-Conrad, K. A., Conrad, D., and Scott, C. B. “Consensus Label Propagation with Graph Convolutional Networks for Single-Cell RNA Sequencing Cell Type Annotation. ” Under review in Bioinformatics, extended abstract published in the proceedings of the Learning on Graphs Conference. (2023)

Scott, C. B., and Eric Mjolsness. “Graph diffusion distance: Properties and efficient computation.” PloS one 16.4 (2021): e0249624.

Wang, Y., Oyen, D., Guo, W.G., Mehta, A., Scott, C.B., Panda, N., Fernández-Godino, M.G., Srinivasan, G. and Yue, X. “StressNet-Deep learning to predict stress with fracture propagation in brittle materials.” npj Materials Degradation 5.1 (2021): 1-10.

Scott, Cory B., and Eric Mjolsness. “Graph prolongation convolutional networks: explicitly multiscale machine learning on graphs with applications to modeling of cytoskeleton.” Machine Learning: Science and Technology 2.1 (2020): 015009.

Scott, C., Dettrick, S., Tajima, T., Magee, R., and Mjolsness, E. “Detection and prediction of a beam-driven mode in field-reversed configuration plasma with recurrent neural networks.” Nuclear Fusion 60.12 (2020): 126025.

Mehta, A., Scott, C. B., Oyen, D., Panda, N., and Srinivasan, G. “Physics-Informed Spatiotemporal Deep Learning for Emulating Coupled Dynamical Systems.” AAAI Spring Symposium: MLPS. 2020.

Scott, Cory B., and Eric Mjolsness. “Multilevel artificial neural network training for spatially correlated learning.” SIAM Journal on Scientific Computing 41.5 (2019): S297-S320.

Babinkostova, L., Bombardier, K. W., Cole, M. C., Morrell, T. A., and Scott, C. B. (2014). Algebraic properties of generalized Rijndael-like ciphers. Groups Complexity Cryptology, 6(1), 37-54.

Babinkostova, L., Bombardier, K. M., Cole, M. M., Morrell, T. A., and Scott, C. B. Elliptic Reciprocity. arXiv preprint arXiv:1212.1983. (2012).

TALKS

Machine Learning for Graphs. University of Colorado, Colorado Springs Analysis and Applications Seminar Series. April 27, 2022

Machine Learning without Structure. Colorado College Department of Mathematics and Computer Science “Fearless Friday” seminar series. April 8, 2022.

Graph Neural Networks. Gayta Science 5th Anniversary Symposium. March 8, 2022.

Morphological Analysis of Biological Images Using Spectral Graph Theory and Graph Neural Networks. Invited talk at the annual meeting of the Society for Mathematical Biology. June 16, 2021.

Spectral Graph Theory. Los Alamos Applied Machine Learning Seminar Series. July 2019.

Multigrid Optimization over Graph Lineages, with Applications to ANN Training. (presentation) 15th Copper Mountain Conference on Iterative Methods, March 2018.

POSTERS

Consensus Label Propagation with Graph Convolutional Networks for Single-Cell RNA Sequencing Cell Type Annotation. (with Daniel Lewinsohn, Katinka Vigh-Conrad, and Don Conrad, presented by D. Lewinsohn). At the Learning on Graphs Conference (December 2022)

Finding Fake News without the News: Structural Detection of Misinformation using Machine Learning. (with Max Perozek and Simay Cural, presented by Max Perozek and Simay Cural). Presented at the Rocky Mountain Celebration of Women in Computing (September 2022), as well as the Colorado College Student Research Symposium (October 2022).

MultiPINNs: Using an Ensemble of Physics-Informed Neural Networks to Generalize to Unseen Equilibria. (with Sean Dettrick, Calvin Lau, Laura Galeotti). Poster Presentation at the 63rd Annual Meeting of the APS Division of Plasma Physics. November 9, 2021.

Diff2Dist: Differentiable Graph Diffusion Distance.: Oral presentation at DLG’21: Deep Learning on Graphs. August 14, 2021.

Efficient Calculation of Graph Diffusion Distance: Applications to Molecular Biology and Machine Learning on Graphs. (poster). Presented at Physics Informed Machine Learning, Jan 2020.

Optimization over Graph Lineages with Applications to Multi-Grid Computation and Ferromagnetic Models. (poster) Physics Informed Machine Learning Conference, January 2018.

An Algebraic Multigrid Approach to Scalable Graphs for Multiscale Modeling.
(presentation) (as contributor, presented by Eric Mjolsness) 18th Copper Mountain Conference on Multigrid Methods, March 2017.

Numerical Results on Directed Graph Process Distances.
UCI Data Science Initiative Symposium, October 2016, Improved results presented at SocalML, November 2016.




TEACHING EXPERIENCE

Courses: Computational Thinking (CP115), Computer Science I (CP122), Data Structures and Algorithms (CP307), Computational Graph Theory (CP341) and Senior Software Project (CP499).

Courses: Introduction to Optimization (COMPSCI 169/268) and Intermediate Programming (I&C SCI 33).

Courses: Introduction to Computability (CPSC 313)

Held office hours and problem sessions for a variety of courses in the Mathematics and Computer Science Department. Adapted teaching style to suit the specific challenges of math education on the Block Plan. Helped organize and plan departmental social events.

RESEARCH
EXPERIENCE

Attended classes and performed original research in machine learning (theory and application), graph theory, and model reduction.

Continued work begun the previous summer as Fellow. Worked as part of an interdisciplinary team in material science. In 2020, worked to apply graph comparison algorithms to sparse image recognition problems.

Used Tensorflow, python, and scikit-learn to analyze and predict fast-ion-related instability in a fusion energy experiment. Used Physics-Informed Machine Learning to predict plasma internal state from boundary conditions.

Applied a variety of machine learning methods to predict fracture propagation in brittle materials. Emulated a computationally expensive finite-element code with machine learning. Gained experience in high-performance computing and TensorFlow.

Contributed Gibbs Sampling functionality to the probabilistic programming language Scala. Wrote code and unit tests, following best practices of software engineering.

Performed original research in Cryptography/Abstract Algebra, as part of an REU.

OUTREACH AND SERVICE

  • Served on the Colorado College Diversity and Equity Advisory Board (DEAB) in the 2022-2023 school year; helped create a tool for evaluating the inclusivity of on-campus spaces.

  • Served on the Colorado College Open Education Resources (OER) ad-hoc committee; helped devise a plan to increase adoption of OER at CC.

  • Served on two successful tenure-track searches in the Colorado College Department of Mathematics and Computer Science (in 2022-2023 and 2021-2022).

  • Co-organizer of the Pikes Peak Undergraduate Mathematics Conference (PPRUMC 2022)

  • Co-organizer of a workshop on “Drawings and abstract Imagery: Representation and Analysis” at the European Conference on Computer Vision (ECCV 2022).

  • Organized several departmental on DEI issues and presented on a variety of subjects, including best practices for equitable grading as well as “barrier” courses and how to avoid them.

  • Reviewer for: IEEE Transactions on Neural Networks and Learning Systems, Neural Processing Letters, and IEEE Transactions on Medical Imaging.

HONORS, AWARDS, CERTIFICATES

  • Summer Collaborative Research Grant, awarded to myself and Max Perozek in summer 2022.

  • Cultural Competence in Computing (C3) Fellow, 2nd cohort (2021 – 2023).

  • Participated in a National Science Foundation Research Traineeship (NRT) program: Machine Learning for the Physical Sciences (MAPS). Spent one year as an Honorary Fellow and one year as a Funded Fellow; received a Certificate in Team Science.

  • Participated in the UCI Division of Teaching Excellence and Innovation’s “Certificate in Teaching Excellence” (expected award date: June 2021) and “Certificate in Course Design” (award date: September 2019) programs.

  • Participated in the UCI Office of Inclusive Excellence “Inclusive Excellence Certificate” Program

COMPUTER
SKILLS

Languages and Packages: Python, Tensorflow, pyTorch, Nvidia CUDA, scikit-learn, Mathematica, LaTeX, SLURM.
Applications: bash shell, OpenOffice, GIMP 2.8+, Mathematica, ssh, GNU Parallel, LAMMPS, moltemplate.
Operating Systems: Unix, Linux, Windows, Android.