University of California, Irvine<\/strong>, Irvine, CA Scott, C. B.<\/strong>, Mjolsness, E., Oyen, D., Kodera, C.,\nBouchez, D., and Uyttewaal, M. \u201cGraph Metric Learning Quantifies\nMorphological Differences between Two Genotypes of Shoot Apical Meristem\nCells in Arabidopsis”. In press; to appear in in silico Plants<\/em>\n5.1 (2023).<\/p>\n Lewinsohn, D. P., Vigh-Conrad, K. A., Conrad, D., and Scott,\nC. B.<\/strong> \u201cConsensus Label Propagation with Graph Convolutional\nNetworks for Single-Cell RNA Sequencing Cell Type Annotation. \u201d Under\nreview in Bioinformatics<\/em>, extended abstract published in the\nproceedings of the Learning on Graphs Conference. (2023)<\/p>\n Scott, C. B.<\/strong>, and Eric Mjolsness. \u201cGraph diffusion\ndistance: Properties and efficient computation.” PloS one 16.4 (2021):\ne0249624.<\/p>\n Wang, Y., Oyen, D., Guo, W.G., Mehta, A., Scott,\nC.B.<\/strong>, Panda, N., Fern\u00e1ndez-Godino, M.G., Srinivasan, G. and\nYue, X. \u201cStressNet-Deep learning to predict stress with fracture\npropagation in brittle materials.” npj Materials Degradation 5.1 (2021):\n1-10.<\/p>\n Scott, Cory B.<\/strong>, and Eric Mjolsness. \u201cGraph\nprolongation convolutional networks: explicitly multiscale machine\nlearning on graphs with applications to modeling of cytoskeleton.”\nMachine Learning: Science and Technology 2.1 (2020): 015009.<\/p>\n Scott, C.<\/strong>, Dettrick, S., Tajima, T., Magee, R., and\nMjolsness, E. \u201cDetection and prediction of a beam-driven mode in\nfield-reversed configuration plasma with recurrent neural networks.”\nNuclear Fusion 60.12 (2020): 126025.<\/p>\n Mehta, A., Scott, C. B.<\/strong>, Oyen, D., Panda, N., and\nSrinivasan, G. \u201cPhysics-Informed Spatiotemporal Deep Learning for\nEmulating Coupled Dynamical Systems.” AAAI Spring Symposium: MLPS.\n2020.<\/p>\n Scott, Cory B.<\/strong>, and Eric Mjolsness. \u201cMultilevel\nartificial neural network training for spatially correlated learning.”\nSIAM Journal on Scientific Computing 41.5 (2019): S297-S320.<\/p>\n Babinkostova, L., Bombardier, K. W., Cole, M. C., Morrell, T. A., and\nScott, C. B.<\/strong> (2014). Algebraic properties of\ngeneralized Rijndael-like ciphers. Groups Complexity Cryptology, 6(1),\n37-54.<\/p>\n Babinkostova, L., Bombardier, K. M., Cole, M. M., Morrell, T. A., and\nScott, C. B.<\/strong> Elliptic Reciprocity. arXiv preprint\narXiv:1212.1983. (2012).<\/p>\n Machine Learning for Graphs<\/em>. University of Colorado,\nColorado Springs Analysis and Applications Seminar Series. April 27,\n2022<\/p>\n Machine Learning without Structure<\/em>. Colorado College\nDepartment of Mathematics and Computer Science \u201cFearless Friday” seminar\nseries. April 8, 2022.<\/p>\n Graph Neural Networks<\/em>. Gayta Science 5th Anniversary\nSymposium. March 8, 2022.<\/p>\n Morphological Analysis of Biological Images Using Spectral Graph\nTheory and Graph Neural Networks<\/em>. Invited talk at the annual\nmeeting of the Society for Mathematical Biology. June 16, 2021.<\/p>\n Spectral Graph Theory<\/em>. Los Alamos Applied Machine Learning\nSeminar Series. July 2019.<\/p>\n Multigrid Optimization over Graph Lineages, with Applications to\nANN Training.<\/em> (presentation) 15th Copper Mountain Conference on\nIterative Methods, March 2018.<\/p>\n Consensus Label Propagation with Graph Convolutional Networks for\nSingle-Cell RNA Sequencing Cell Type Annotation.<\/em> (with Daniel\nLewinsohn, Katinka Vigh-Conrad, and Don Conrad, presented by D.\nLewinsohn). At the Learning on Graphs Conference (December 2022)<\/p>\n Finding Fake News without the News: Structural Detection of\nMisinformation using Machine Learning<\/em>. (with Max Perozek and Simay\nCural, presented by Max Perozek and Simay Cural). Presented at the Rocky\nMountain Celebration of Women in Computing (September 2022), as well as\nthe Colorado College Student Research Symposium (October 2022).<\/p>\n MultiPINNs: Using an Ensemble of Physics-Informed Neural Networks\nto Generalize to Unseen Equilibria<\/em>. (with Sean Dettrick, Calvin\nLau, Laura Galeotti). Poster Presentation at the 63rd Annual Meeting of\nthe APS Division of Plasma Physics. November 9, 2021.<\/p>\n Diff2Dist: Differentiable Graph Diffusion Distance.<\/em>: Oral\npresentation at DLG\u201921: Deep Learning on Graphs. August 14, 2021.<\/p>\n Efficient Calculation of Graph Diffusion Distance: Applications\nto Molecular Biology and Machine Learning on Graphs.<\/em> (poster).\nPresented at Physics Informed Machine Learning, Jan 2020.<\/p>\n Optimization over Graph Lineages with Applications to Multi-Grid\nComputation and Ferromagnetic Models.<\/em> (poster) Physics Informed\nMachine Learning Conference, January 2018.<\/p>\n An Algebraic Multigrid Approach to Scalable Graphs for Multiscale\nModeling.<\/em> Numerical Results on Directed Graph Process Distances.<\/em> Courses: Computational Thinking (CP115), Computer Science I (CP122),\nData Structures and Algorithms (CP307), Computational Graph Theory\n(CP341) and Senior Software Project (CP499). Courses: Introduction to Optimization (COMPSCI 169\/268) and\nIntermediate Programming (I&C SCI 33). Courses: Introduction to Computability (CPSC 313) Held office hours and problem sessions for a variety of courses in\nthe Mathematics and Computer Science Department. Adapted teaching style\nto suit the specific challenges of math education on the Block Plan.\nHelped organize and plan departmental social events.<\/p>\n<\/div>\n Attended classes and performed original research in machine learning\n(theory and application), graph theory, and model reduction.<\/p>\n<\/div>\n Continued work begun the previous summer as Fellow. Worked as part of\nan interdisciplinary team in material science. In 2020, worked to apply\ngraph comparison algorithms to sparse image recognition problems.<\/p>\n<\/div>\n Used Tensorflow, python, and scikit-learn to analyze and predict\nfast-ion-related instability in a fusion energy experiment. Used\nPhysics-Informed Machine Learning to predict plasma internal state from\nboundary conditions.<\/p>\n<\/div>\n Applied a variety of machine learning methods to predict fracture\npropagation in brittle materials. Emulated a computationally expensive\nfinite-element code with machine learning. Gained experience in\nhigh-performance computing and TensorFlow.<\/p>\n<\/div>\n Contributed Gibbs Sampling functionality to the probabilistic\nprogramming language Scala. Wrote code and unit tests, following best\npractices of software engineering.<\/p>\n<\/div>\n Performed original research in Cryptography\/Abstract Algebra, as part\nof an REU.<\/p>\n<\/div>\n Served on the Colorado College Diversity and Equity Advisory\nBoard (DEAB) in the 2022-2023 school year; helped create a tool for\nevaluating the inclusivity of on-campus spaces.<\/p><\/li>\n Served on the Colorado College Open Education Resources (OER)\nad-hoc committee; helped devise a plan to increase adoption of OER at\nCC.<\/p><\/li>\n Served on two successful tenure-track searches in the Colorado\nCollege Department of Mathematics and Computer Science (in 2022-2023 and\n2021-2022).<\/p><\/li>\n Co-organizer of the Pikes Peak Undergraduate Mathematics\nConference (PPRUMC 2022)<\/p><\/li>\n Co-organizer of a workshop on \u201cDrawings and abstract Imagery:\nRepresentation and Analysis” at the European Conference on Computer\nVision (ECCV 2022).<\/p><\/li>\n Organized several departmental on DEI issues and presented on a\nvariety of subjects, including best practices for equitable grading as\nwell as \u201cbarrier” courses and how to avoid them.<\/p><\/li>\n Reviewer for: IEEE Transactions on Neural Networks and Learning\nSystems, Neural Processing Letters, and IEEE Transactions on Medical\nImaging.<\/p><\/li>\n<\/ul>\n Summer Collaborative Research Grant, awarded to myself and Max\nPerozek in summer 2022.<\/p><\/li>\n Cultural Competence in Computing (C3) Fellow, 2nd cohort (2021 –\n2023).<\/p><\/li>\n Participated in a National Science Foundation Research\nTraineeship (NRT) program: Machine Learning for the Physical Sciences\n(MAPS). Spent one year as an Honorary Fellow and one year as a Funded\nFellow; received a Certificate in Team Science.<\/p><\/li>\n Participated in the UCI Division of Teaching Excellence and\nInnovation\u2019s \u201cCertificate in Teaching Excellence\u201d (expected award date:\nJune 2021) and \u201cCertificate in Course Design\u201d (award date: September\n2019) programs.<\/p><\/li>\n Participated in the UCI Office of Inclusive Excellence \u201cInclusive\nExcellence Certificate\u201d Program<\/p><\/li>\n<\/ul>\n Languages and Packages<\/strong>: Python, Tensorflow, pyTorch,\nNvidia CUDA, scikit-learn, Mathematica, LaTeX, SLURM. EDUCATION University of California, Irvine, Irvine, CA Dissertation: \u201cDiffusion 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, […]<\/p>\n","protected":false},"author":1490,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-7","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.coloradocollege.edu\/cbs\/wp-json\/wp\/v2\/pages\/7","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.coloradocollege.edu\/cbs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.coloradocollege.edu\/cbs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.coloradocollege.edu\/cbs\/wp-json\/wp\/v2\/users\/1490"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.coloradocollege.edu\/cbs\/wp-json\/wp\/v2\/comments?post=7"}],"version-history":[{"count":2,"href":"https:\/\/sites.coloradocollege.edu\/cbs\/wp-json\/wp\/v2\/pages\/7\/revisions"}],"predecessor-version":[{"id":155,"href":"https:\/\/sites.coloradocollege.edu\/cbs\/wp-json\/wp\/v2\/pages\/7\/revisions\/155"}],"wp:attachment":[{"href":"https:\/\/sites.coloradocollege.edu\/cbs\/wp-json\/wp\/v2\/media?parent=7"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nDissertation: \u201cDiffusion Distance: Efficient Computation and\nApplications”
\nAdvisor: Eric Mjolsness
\nCommittee Members: Alex Ilher, Padhraic Smyth, Diane Oyen
\nDoctor of Philosophy<\/em><\/span>, Computer Science, September\n2015 – May 2021 GPA: 3.93
\nDegree awarded June 2021.
\nAdvanced to Candidacy May 2018.
\nCompleted several Certification Programs (see below).
\nMaster of Science<\/em><\/span>, Computer Science, June 2017GPA:\n3.84
\nColorado College<\/strong>, Colorado Springs, CO
\nBachelor of Arts (with Distinction)<\/em><\/span>, Mathematics,\nMay 2013 GPA: 3.65
\nBachelor of Arts (with Distinction)<\/em><\/span>, Computer\nScience, May 2013 GPA: 3.91
\nReceived the Florian Cajori Award in Mathematics and Computer\nScience
\n<\/p>\nPAPERS<\/h1>\n
TALKS<\/h1>\n
POSTERS<\/h1>\n
\n(presentation) (as contributor, presented by Eric Mjolsness) 18th Copper\nMountain Conference on Multigrid Methods, March 2017.<\/p>\n
\nUCI Data Science Initiative Symposium, October 2016, Improved results\npresented at SocalML, November 2016.<\/p>\n
\n
\n
\n<\/p>\n<\/div>\nTEACHING EXPERIENCE<\/h1>\n
\n<\/p>\n<\/div>\n
\n<\/p>\n<\/div>\n
\n<\/p>\n<\/div>\nRESEARCH
\nEXPERIENCE<\/h1>\nOUTREACH AND SERVICE<\/h1>\n
\n
HONORS, AWARDS, CERTIFICATES<\/h1>\n
\n
COMPUTER
\nSKILLS<\/h1>\n
\nApplications<\/strong>: bash shell, OpenOffice, GIMP 2.8+,\nMathematica, ssh, GNU Parallel, LAMMPS, moltemplate.
\nOperating Systems<\/strong>: Unix, Linux, Windows, Android.<\/p>\n","protected":false},"excerpt":{"rendered":"