My goal is to establish a research program in large scale statistical machine learning, data mining, signal processing, compressed sensing, estimation, and the interaction between computational software and mathematics. My main areas of application include anomaly detection, cyber-warfare, and network analysis, with a secondary focus on chemical sensor fusion and analysis of geo-spatial data.
University of Maryland, College Park - 4/1999
Ph.D. in Applied Mathematics
Thesis title: Mathematical Visualization, Parameter Continuation, and Steered Computations
Thesis advisor: Prof. John Maddocks
Boston University - 9/1992
Bachelor of Science, Mathematics
Bachelor of Science, Computer Science
Worcester Polytechnic Institute, Worcester MA 8/2014-Present
Associate Professor of Mathematical Sciences,
Associate Professor of Computer Science, and
Associate Professor of Data Science Program
(Visiting in 2014-2015, permanent position 2015-Present)
Designing and teaching of courses in Data Science and Mathematical Sciences including:
DS501 “Introduction to Data Science” (designed course with Prof. Kong)
DS502/MA543 “Statistical Methods for Data Science” (designed course)
MA542 “Regression Analysis”
MA2611 “Applied Statistics”
MA463X “Data Analytics & Statistical Learning”
Participating in a number of committees:
Committee on Information Technology Policy
High-performance Computing Committee
Data Science Steering Committee
Bioinformatics and Computational Biology Steering Committee
Mathematical Sciences Graduate Program Committee
Actively recruiting and mentoring students with the goal of forming a research group.
Developing several proposal efforts as either PI, Co-PI, or senior personnel.
Numerica Corporation, Loveland CO 9/2006-8/2014
Program Director and Computational Scientist
Leader of a group of research scientists (mostly with Ph.D.'s but also including graduate students and undergraduates).
Select current research topics include unsupervised machine learning for distributed pattern detection in cyber warfare; chemical and biological sensor signal processing and fusion; and compressed sensing algorithms for geospatial data with rigorous error bounds.
The common thread in all of these projects is a principled approach to uncertainty management with areas of application being (unsupervised) machine learning, compressed sensing, estimation, and signal processing.
Has secured the award of research funding from an array of sources:
16 major efforts, each with a value over $50,000, with a total value of more than $5 million dollars (as well as numerous smaller efforts).
Funding sources include the US Air Force, the US Army, the US Navy, the Defense Threat Reduction Agency, NASA, Department of Homeland Security, the Office of the Secretary of Defense, and various industry partners.
California Institute of Technology, Pasadena CA 6/2002-9/2006
Staff Scientist in Applied and Computational Mathematics
MathSys Inc., Pasadena CA
Scientist
Performed research in high-order methods for computational electromagnetics.
Assisted in proposal development.
Mentored undergraduates and collaborated with graduated students and post docs.
Developed software for high-order, parallel calculations using MPI on Beowulf class high performance computers.
California Institute of Technology, Pasadena CA 6/2003-3/2006
Head Coach, Caltech NCAA Fencing Team
Coached a NCAA team of 25-30 undergraduates of all levels.
Taught classes of 5-20 undergraduate and graduate students.
California Institute of Technology, Pasadena CA 6/1999-6/2002
Postdoctoral Researcher in Applied and Computational Mathematics
Performed message passing (MPI and PVM) and multi-threaded (using Pthreads) parallelization of AUTO code for bifurcation analysis applied to large systems of ordinary differential equations.
Performed research in dynamical systems with a focus on parameter continuation methods and applications to spacecraft mission design.
Colorado State University, Fort Collins CO 9/2011-Present
Electrical and Computer Engineering, Faculty affiliate
University of Colorado, Boulder CO 10/2012-9/2014
Department of Electrical, Computer, and Energy Engineering, Visiting Scholar
Colorado State University, Fort Collins CO 9/2010-9/2014
ECE Industrial Advisory Board, Member
Academic year: 2017-2018
MA 463X. “Data Analytics and Statistical Learning” - Undergraduate course in statistical learning Taught, Spring 2018. Instructor rating: TBD
DS502/MA543, “Statistical Methods for Data Science” - Taught, Spring 2018. Instructor rating: DS502, TBD MA543, TBD
DS501, “Introduction to Data Science” - Taught Fall 2017. Instructor rating: 4.95
PIC Math Data Science workshop. Developed and taught four-day Data Science summer course for faculty development at BYU. 5-28-2017 to 5-31-2017
Academic year: 2016-2017
MA 463X. “Data Analytics and Statistical Learning” - Undergraduate course in statistical learning Developed and taught, Spring 2017. Instructor rating: 4.92
DS502/MA543, “Statistical Methods for Data Science” - Taught, Spring 2017. Instructor rating: DS502, 4.85 MA543, 5.00
DS501, “Introduction to Data Science” - Taught Fall 2016. Instructor rating: 4.73
CS534, “Artificial Intelligence”, get lecturer 11-21-2016.
BCB 590 Special Topics: Introduction to Computer Essentials for Bioinformatics Analysis, developed and will teach 3 one-hour classes on Python in Bioinformatics, 1-23-2017, and 1-30-2017, and 2-6-2017.
Academic year: 2015-2016
DS502/MA543, “Statistical Methods for Data Science” - Taught, Spring 2016. Instructor rating: DS502, 4.93 MA543, 4.95
DS501, “Introduction to Data Science” - Taught, Spring 2016. Instructor rating: 4.92
MA2611. “Applied Statistics” - Undergraduate course on statistical analysis. Fall 2015. Instructor rating: Section B01 3.86, Section B02 4.11, Section B03 3.86, Section B04 4.00, Section B05 4.50
Academic year: 2014-2015
DS502/MA543, “Statistical Methods for Data Science” - Taught, Spring 2015. Instructor rating: DS502, 4.94 MA543, 4.90
MA542, “Regression Analysis” - Masters level course focusing on regression algorithms. Developed and taught, Spring 2015. Instructor rating: 4.80
DS502/MA543, “Statistical Methods for Data Science” - Masters level course surveying statistical methods used in Data Science. Topics include regression, classification, dimension reduction, cross-validation, and other methods in statistical analysis. Developed and taught Fall 2014 Instructor rating: DS502, 4.76 MA543, 4.60
DS501, “Introduction to Data Science” - Masters level course that provides an overview of Data Science. Topics include data collection, storage, management, analysis, and visualization. Developed and taught Fall 2014 Instructor rating: Section 191, 4.40 Section 191, 4.61
Organizing committee, Multiscale Mathematical Modeling of Elastic Filaments conference in honor of John Maddocks’ 60th birthday, 2017
Co-Pi, Systems of Lines: Applications of Algebraic Combinatorics, NSF funded workshop, WPI, 8-10-2015 to 8-14-2015
Co-Chair, Activities committee, SciPy 2016, http://scipy2016.scipy.org 7-11-2016 to 7-17-2016.
Co-Chair, Activities committee, SciPy 2017, http://scipy2016.scipy.org 7-10-2017 to 7-16-2017.
University of Massachusetts Dartmouth, Pattern Detection in Computer Networks Using Robust Dimension Reduction (invited), 9-12-2017
University of New Hampshire, Pattern Detection in Computer Networks Using Robust Dimension Reduction (invited), 9-8-2017
ACM KDD 2017, Anomaly Detection with Robust Deep Auto-encoders (with Chong Zhou), 8-15-2017
2017 Mathematics Institute for Secondary Teaching (invited), 7-19-2017
SIAM Annual Meeting, Panel: Data Science in the Applied and Computational Mathematics Curriculum (invited), 7-13-2017
SIAM Annual Meeting, A Global Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics (with Kelum Gajamannage), 7-11-2017
MITRE, DNS and RPCA Working Meeting, 7-6-2017
BBN/Raytheon, Robust Principal Component Analysis Results Presentation, 7-5-2017
PICMath Workshop on Data Analytics (invited), class on Data Science for University faculty, 5-28-17 to 6-2-17
HPC Day 2016 at Umass Dartmouth (invited), Musings on Exabyte Scale Principal Component Analysis, 5-25-17
2017 IEEE International Symposium on Technologies for Homeland Security, Probabilistic Inference of Internet Node Geolocation with Anomaly Detection, 4-26-17
MIT-Lincoln Laboratory (invited), Pattern Detection in Computer Networks Using Robust Dimension Reduction, Processing, Exploitation, and Dissemination Seminar, 3-29-17
UMass Amherst (invited), Pattern Detection in Computer Networks Using Robust Dimension Reduction, Electrical and Computer Engineering Seminar, 2-27-17
WPI, Discrete Math Seminar, Musings On the Restricted Isometry Property and Deterministic Matrices (invited), 2-7-2017
U.S. Army Natick Soldier Systems Center, collaboration presentation, 1-27-2017
BBN/Raytheon, Pattern Detection in Computer Networks Using Robust Principal Component Analysis (invited), 1-5-2017
WPI, Department of Mathematical Sciences, industrial hiring talk, 12-7-2016
New England Security Day, Pattern Detection in Computer Networks Using Robust Principal Component Analysis, 11-29-2016
Asilomar Conference on Signals, Systems, and Computers, Maximum Likelihood Identification of an Information Matrix Under Constraints in a Corresponding Graphical Model (poster), 10-29-1016 to 11-1-2016
WPI, Department of Mathematical Sciences, advising day, 10-24-2016
U.S. Army Natick Soldier Systems Center, collaboration presentation, 10-21-2016
Nanocomp Corporation, final presentation for sponsored project, 10-19-2016
Southern Connecticut State University, Data Science Graduate Education: Content, Projects, and Computer Languages (invited), 10-17-2016
Southern Connecticut State University, Pattern Detection in Computer Networks Using Robust Principal Component Analysis(invited), 10-17-2016
NSF Workshop on Geometry for Signal Processing and Machine Learning, 10-12-2016 to 10-15-2016 INVITATION ONLY
Invited to speak at Collaborative Conference on Big Data 10-10-2016 to 10-14-2016. DID NOT ATTEND.
2016 RMACC HPC Symposium, “Musings on Exabyte Scale Principal Component Analysis” 8-10-2016
U.S. Army Natick Soldier Systems Center, collaboration presentation, 10-4-2016
SIAM Annual Meeting 2016, Education Programs in Data Sciences and Data Analytics, Strategies for Enabling Data Science Research and Education, 7-14-2016
SciPy 2016, “Python at the Intersection of Data Science, Machine Learning and Cyber Anomaly Detection”, 7-13-2015, http://scipy2016.scipy.org
MITRE corporation, collaboration presentation 7-7-2016, invited by Les Servi, small team presentation.
UNIVERSIDAD NACIONAL DE ASUNCION, Laboratorio de Computacion Científica y Aplicada Asuncion, Paraguay (invited)
WPI Data Science, 6-24-2016
Machine Learning (part 1 of 3 part short course), 6-27-2016
Unsupervised Learning and Computation (part 2 of 3 part short course), 6-28-2016
Research problems: Deep learning, high dimensions, and compressed sensing (part 3 of 3 part short course), 6-29-2016
Presentation to DHS funded project group NetBrain, (invited) “Pattern Detection in Computer Networks Using Robust Principal Component Analysis,” 6-10-2016
MITRE corporation, collaboration presentation 6/7/2016, invited by Les Servi, small team presentation.
National Professional Science Master’s Association, Data Analytics Across Disciplines meeting, (invited) Niagra Falls, NY, 5-18-2016, “Data Science Graduate Education: Content, Projects, and Computer Languages
WPI, Turing Computational Meeting, 4-14-2016 “Reprise: Conversation on Computational Python and Turing”
MITRE corporation, collaboration presentation 3-7-2016, invited by Les Servi, small team presentation.
WPI, Mathematical Sciences Ph.D. lunch, “Research Topics in Compressed Sensing, Matrix Completion, and Robust Principal Component Analysis”, 10-7-2015
WPI, Arts and Sciences Advisory Board, faculty panel, “When a Degree is Just the Beginning”, 10-2-2015
WPI, Statistics Seminar, “Matrix Completion, Robust Principal Component Analysis, and Second Order Problems”, 9-14-2015
WPI, ALAS meeting, “Pattern Detection in Computer Networks Using Robust Principal Component Analysis”, 9-4-2015
Systems of Lines: Applications of Algebraic Combinatorics, Co-Pi, “Musings on sparsity detection, point-wise error bounds, and truncated norms” 8/11/2015 http://users.wpi.edu/~martin/MEETINGS/WPILinesWorkshop.html,
International Workshop on Advanced Computational and Experimental Techniques in Nonlinear Dynamics, invited speaker, Cusco, Peru, 8/3/2015-8/7/2015. DID NOT ATTEND
SENCER Summer Institute, “Interpreting Data: A Cautionary Tale”, 8-1-2015. http://www.sencer.net/Symposia/summerinstitute2015.cfm
ICERM: Mathematics in Data Science, lighting talk, 7/29/2015
SciPy 2015, “Python in Data Science Research and Education”, 7-9-2015, http://scipy2015.scipy.org/ehome/index.php?eventid=115969&,
The 29th New England Statistical Symposium, invited speaker, “Robust Principal Component Analysis for Detecting Sparsely Correlated Phenomena in Computer Networks” 4/25/2015, http://merlot.stat.uconn.edu/ness15/program
WPI Computer Science Colloquium, “Pattern Detection in Computer Networks Using Robust Principal Component Analysis”invited by Prof. Craig Shue, 4/17/2015
WPI, Turing Computational Meeting, 2/24/2015 “Conversation on Computational Python and Turing”
SENCER SCI-New England Fall 2014 Regional Meeting, Panel Discussion: “How do our students experience data analytics?” (with Andres Colubri, Harvard University)
MathFest 2014, The Mathematical Association of America, Invited Paper Session, Fast Algorithms on Large Graphs (and Matroids) (invited), Large Graphs in Internet Tomography and Cyber Defense.
AWARD - Selection as ASCE 2016 outstanding reviewer
AMS – Review for Mathematical Reviews/MathSciNet for “Matrix Completion for the Independence Model”
AMS – Review for Mathematical Reviews/MathSciNet for “Two-Step Proximal Gradient
Algorithm for Low-Rank Matrix Completion”
Publication reviewer for Abstract and Applied Analysis 2017-present
Mathematics-in-Industry Case Studies 2017-present
Cyber Physical Systems journal 2016-present
Talk reviewer SciPy 2016-present
PME Journal reviewer 2016-present
Publication reviewer for SIF Journal of Advances in Information Fusion 2016-present
Publication reviewer for Journal of Aerospace Engineering 2014-present
Book reviewer 5-11-2015 for Springer “Algorithms for Data Science”
Book reviewer 5/15/2015 for Chapman & Hall “Modern Data Science with R”
Book reviewer 12/23/2015 for Cambridge University Press: Compressed Sensing: Algorithms and Applications
Publication reviewer for IEEE Transactions on Aerospace and Electronic Systems 2014
Proposal reviewer for AFOSR (while at Numerica)
Publication reviewer for SPIE (while at Numerica)
Committee on Information Technology Policy (CITP), a permanent subcommittee of COG, 2017-present
COACHE Working Group, 2017-present
Mathematical Sciences, Senior Faculty Hiring Committee, Worcester Polytechnic Institute, Summer 2017
Mathematical Sciences, Personnel Committee, Worcester Polytechnic Institute, 2017-present
WPI GRIE lead judge “Data Science and Cyber-security” 2017
Data Science Steering Committee, Worcester Polytechnic Institute (2014-present)
Bioinformatics and Computational Biology, Steering Committee, Worcester Polytechnic Institute (2015-present)
Data Science Graduate, Admissions and Recruiting Committee, Worcester Polytechnic Institute 2016-present
Mathematical Sciences, Undergraduate Program Committee, Worcester Polytechnic Institute, 2016-2017
Data Science/Mathematical Sciences, Tenure Track Hiring Committee, Worcester Polytechnic Institute 2016-2017
Bioinformatics and Computational Biology, Bootcamp Course Development Committee, Worcester Polytechnic Institute, 2016-present
Seceon Corporation, Board of Advisors 2015-2017
Turing management committee, 2015-present
HPC Steering Group, WPI, 2016-present
Statistics NTT Hiring Committee, WPI Mathematical Sciences 2015-2016
Department Evaluation Committee, WPI Mathematical Sciences 2015-2016
Data Science Promotion Subcommittee, Worcester Polytechnic Institute (2015-2016)
Mathematical Sciences, Graduate Program Committee, Worcester Polytechnic Institute (2015-2016)
Bioinformatics and Computational Biology, Industry Outreach Committee, Worcester Polytechnic Institute (2015-2016)
Predoctoral and doctoral students mentor in the National Alliance for Doctoral Studies in the Mathematical Sciences (limited participation) 12-23-2015-present
Research Strategic Planning/Research Implementation Group, Worcester Polytechnic Institute (2015)
National Science Foundation (NSF), 2015 NSF Graduate Research Fellowship Program (GRFP), panelist.
Data Science: Sponsorship Subcommittee, Worcester Polytechnic Institute (2014)
Mostafa El Gamal, WPI, Electrical and Computer Engineering, 2016-2017 (graduated Spring 2017)
Paulo Ferreria, WPI, Electrical and Computer Engineering, 2016-2017 (graduated Spring 2017)
Aaron Birt, WPI, Material Science and Engineering, 2016-2017 (graduated Spring 2017)
Jaroslaw Glowacki , 6-26-2016 Laboratory for Computation and Visualization in Mathematics and Mechanics, Ecole Polytechnic Federal Lausanne, Trip funded by LCVMM.
Kakar Tabassum, WPI, Data Science, 2016-2017
Suhas, Srinivasan, WPI, Data Science, 2016-2017
Xinyuan Sun, WPI, Data Science, 2016
Viseth Sean, WPI Data Science, 2016
Kevin Guth, Aura Ramirez, Erik Sola (with Lane Harrison), Cyber Data Analytics, Fall 2017/Spring 2018
Luke Buquicchio, Juan Louis Estrada, Everett Harding (with Stephan Sturm), Collaboration with Zurich University of Applied Sciences on Deep Learning, Fall 2017/Spring 2018
Emily Bigwood, Owen Chace, Ranier Gran, Stephanie Martin, WPI Career Development Center collaboration, Fall 2017/Spring 2018
Michael Giancola (with Jacob Whitehill), Doubly Stochastic Matrices for Style Analysis in Mechanical Turk Data, Fall 2017/Spring 2018
Casidy Litch (with Lane Harrison), Cyber Data Analytics, Fall 2016/Spring 2017
Ian Jacoway, Gina Rios, Shannon Feeley (with Stephan Sturm), Process Mining for Credit Suisse, Fall 2016/Spring 2017
Sadie Gauthier, Zachery Peters, Emily Weber, Robert Vigeant, WPI Career Development Center collaboration, Fall 2016/Spring 2017
Yao Yuan Chow (with George Heineman), “Application of Data Analytics to Cyber Forensic Data”, Fall 2016
Kathleen Kay, “Inverse Methods for Manifold Learning”, Spring 2016
Maryann VanValkendburg (with Gu Wang), Data for Improving Mathematical Sciences Education, Fall 2017/Spring 2018
Fall 2017:
Bahadur, Nitish, Applications of non-linear dimension reduction to finance, Ph.D. advisor
Baker, Lauren, Machine Learning for Carbon Nano-tube sheets
Gajamannage, Kelum, Non-linear dimension reduction, Postdoctoral mentor
Jutras, Melanie, Anomaly Detection in the Domain Name Service, Master’s thesis
Li, Wenjing, Ensemble methods in machine learning, Ph.D. advisor
Liu, Haitao (co-advisor with Jian Zou), Graphical Models, Ph.D. advisor
Lui, Yingnan, Deep Learning for Financial Time-series, Ph.D. advisor
Medina, Patricia, Deep Learning for LIDAR Processing, Postdoctoral mentor
Reese, Tyler Michael (co-advisor with Joseph D. Fehribach and Brigitte Servatius), Kirchhoff Graphs, Ph.D. advisor
Weiss, Matt, Supervised Machine Learning for Chemical Sensors, Ph.D. advisor
Sun, Fangzheng, Deep Canonical Correlation Analysis, Master’s thesis
Zhou, Chong, Robust Auto-encoders, Ph.D. advisor
Zou, Xiaozhou, Adversarial Neural Networks, Master’s thesis
Spring 2017:
Bahadur, Nitish, Applications of non-linear dimension reduction to finance, Ph.D. advisor
Cinelli, Forrest, Balanced Matrices are Adjacency Matrices for d-regular Graphs, Undergraduate ISP
Gajamannage, Kelum, Non-linear dimension reduction, Postdoctoral mentor
Keller, Josh, Applying Robust Principal Component Analysis to Anomaly Detection in Car Networks, Masters Capstone
Li, Wenjing, Ensemble methods in machine learning, Ph.D. advisor
Liu, Haitao (co-advisor with Jian Zou), Graphical Models, Ph.D. advisor
Reese, Tyler Michael (co-advisor with Joseph D. Fehribach and Brigitte Servatius), Kirchhoff Graphs, Ph.D. advisor
Weiss, Matt, Supervised Machine Learning for Chemical Sensors, Ph.D. advisor
Zhong, Lu, Truncated norms for compressed sensing, Master’s thesis
Zhou, Chong, Robust Auto-encodes, Ph.D. advisor
Zou, Xiaozhou, Adversarial Neural Networks, Master’s thesis
Fall 2016:
Ali Benamara (co-advised with Mohamed Eltabakh <meltabakh@WPI.EDU>) Exabyte Scale Robust PCA
Bahadur, Nitish, Applications of non-linear dimension reduction to finance, Ph.D. advisor
Gajamannage, Kelum, Non-linear dimension reduction, Postdoctoral mentor
Li, Wenjing, Ensemble methods in machine learning, Ph.D. advisor
Liu, Haitao (co-advisor with Jian Zou), Graphical Models, Ph.D. advisor
Mukne, Neehar Clustering of Internet Nodes Using Latencies, Directed Research
Reese, Tyler Michael (co-advisor with Joseph D. Fehribach and Brigitte Servatius), Kirchhoff Graphs, Ph.D. advisor
Weiss, Matt, Supervised Machine Learning for Chemical Sensors, Ph.D. advisor
Zhong, Lu, Truncated norms for compressed sensing, Master’s thesis
Zhou, Chong, Robust Auto-encodes, Ph.D. advisor
Zou, Xiaozhou, Adversarial Neural Networks, Master’s thesis
Zou, Chen, Robust non-linear dimension reduction, Directed Research
Spring 2016:
Holmes, Andrew, Steps Toward Constructing Matrices that Satisfy the Restricted Isometry Property, Master's Capstone
El Gamal, Mostafa, Non-linear Dimension Reduction, Research Collaboration (non-credit)
Lama, Suman, Large Scale Processing of PCAP Datga, Directed Research
Li, Nan, Hypothesis Testing for Graphical Models, Master's thesis.
Li, Wenjing, Geolocation of Internets Nodes Using Robust PCA, Research Collaboration (non-credit)
Liu, Haitao (co-advisor with Jian Zou), Graphical Models, Ph.D. advisor
Kay, Kathleen, Inverse Methods for Non-linear Dimension Reduction, MQP.
Mukne, Neehar Clustering of Internet Nodes Using Latencies, Research Collaboration (non-credit)
Orr, Jonathan, Quality Control for Nano-tube Yarns, Master's Capstone (Funded by Nanocomp).
Reese, Tyler Michael (co-advisor with Joseph D. Fehribach and Brigitte Servatius), Kirchhoff Graphs, Ph.D. advisor
Yang, Fan, Using ADMM to Detect K-Sparse Vector, Directed research. (non-credit)
Zhou, Chong, Kernel Based Deep Learning, Master's thesis.
Data Science GQP Advisor, Seceon Corp.
Fall 2015:
Biradar, Rakesh Nagendra, eRPCA for robust prediction in graph communities. Master's thesis. (GRADUATED)
Holmes, Andrew. Steps Toward Constructing Matrices that Satisfy the Restricted Isometry Property, Master's Capstone
Li, Nan, Hypothesis Testing for Graphical Models, Master's thesis.
Pandey, Shivangi, Statistical Methods in Data Science, ISP.
Li, Wenjing, Geolocation of Internets Nodes Using Robust PCA (with Mukne, Neehar) Master's capstone.
Reese, Tyler Michael, Kirchhoff Graphs, Ph.D. advisor (co-advisor with Joseph D. Fehribach and Brigitte Servatius)
Yang, Fan, Using ADMM to Detect K-Sparse Vectors, Master's capstone.
Zhou, Chong, Kernel Based Deep Learning, Master's thesis (and entering Data Science Ph.D. program)
Summer 2015:
Amadeo, Lily Large Scale Matrix Completion and Recommender Systems, Master's thesis. (GRADUATED)
Biradar, Rakesh Nagendra, eRPCA for robust prediction in graph communities. Master's thesis.
Li, Nan, Hypothesis Testing for Graphical Models, Master's thesis.
Liu, Yingnan, Detecting Anomalies in Border Gateway Protocol Data, Master's capstone
Yang, Fan. Using ADMM to Detect K-Sparse Vectors, Directed research (non-credit)
Zhou, Chong. , Kernel Based Deep Learning, Master's thesis)
Spring 2015:
Biradar, Rakesh Nagendra, eRPCA for robust prediction in graph communities. Directed research.
Reese, Tyler Michael, Kirchhoff Graphs, Ph.D. advisor (co-advisor with Joseph D. Fehribach and Brigitte Servatius)
Rosales, Elisa R , Random Forests for prediction in healthcare data. Masters in Applied Statistics capstone project.
Calderon, C., Paffenroth, R. (2013) “Tracking Multiple Particles in Biological Systems”. Submitted to US Patent Office.
Paffenroth, R., Du Toit, P., Scharf, L., Nong. R (2012) “Space-Time Signal Processing for Detecting and Classifying Distributed Attacks in Networks”. Continuation in part. Submitted to US Patent Office.
Paffenroth, R., Du Toit, P., Scharf, L., Nong. R (2011) “Space-Time Signal Processing for Detecting and Classifying Distributed Attacks in Networks”. Submitted to US Patent Office.
Leed, W., Lundberg, S., Nong, R., Paffenroth, R., (2011) "Method for Lossy Compression of Point Clouds with Point-wise Error Constraints". Submitted to US Patent Office.
Paffenroth, R. (2016) Pushing the Boundaries of Predictions with Data Analytics, in honor of Math Awareness Month and the “Future of Prediction,” SIAM News, 4-1-2016
Machine Learning for Satellite Communications from LEO to Deep Space Operations: A Multi-Objective Reinforcement Learning Example, Ferreira, P. V. R., Paffenroth, R., Wyglinski, A. M., Hackett, T., Bilen, S., Reinhart, R., Mortensen, D., (2017) IN PREPARATION for Submission to IEEE Communications Magazine.
Ramasmy, S., Paffenroth, R., and Jayasumana, A., (2017) “A Low Complexity Technique for Capture and Characterization of Social Network Topology”, in preparation.
Jayasumana, A., Paffenroth, R., and Ramasmy, S., (2017) “Network Topology Mapping from Graph Geodesics and Partial Virtual Coordinates” ADDRESSING REVIEWER COMMENTS in preparation for re-submission to IEEE/ACM Transactions on Networking.
Gajamannage, K., Paffenroth, R., and Bollt, K., (2017) “A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics”, ADDRESSING REVIEWER COMMENTS in preparation for re-submission to Pattern Recognition.
Shojaeizadeh, M., Djamasbi, S., Paffenroth, R., and Trapp, (2017) “Automatic Detection of Cognitive Demand Via An Eye Tracking Machine Learning System”, SUBMITTED to MIS Quarterly and rejected, in preparation for re-submission to another journal.
Weiss, Matthew, Wiederoder, Michael S., Paffenroth, Randy, Nallon, Eric, Bright, Collin, Schnee, Vincent, McGraw , Shannon, Polcha, Michael, and Uzarski, Josh (2017) “Applications of the Kalman Filter to Chemical Sensors for Downstream Machine Learning”, SUBMITTED to IEEE Sensors Journal.
Partopour, Behnam, Paffenroth, Randy, Dixon, Anthony, (2017) “Random Forests for mapping and analysis of microkinetics models”, SUBMITTED to Computers and Chemical Engineering.
A.M. Birt, R. Paffenroth, D. Apelian, (2017) “Simplifying the Material World: Unsupervised Classification of Feedstock Materials in Metal Powder Manufacturing”, SUBMITTED to Transactions on Knowledge and Data Engineering.
Randy Paffenroth, Kathleen Kay, Les Servi, “Robust PCA for Anomaly Detection in Cyber Networks”, arXiv:1801.01571
Anura P. Jayasumana, Randy Paffenroth, Sridhar Ramasamy “Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics”, arXiv:1712.10063 (Preliminary version of paper of same title above submitted to IEEE/ACM Transactions on Networking).
Kelum Gajamannage, Randy Paffenroth, Erik M. Bollt, “A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics”, arXiv:1707.06757 (Preliminary version of paper of same title above submitted to Pattern Recognition)
Reese, Tyler, Servatius, Brigitte, Paffenroth, Randy, and Fehribach, Joseph, “Matrices over Finite Fields and their Kirchhoff Graphs” (2017), Linear Algebra and its Applications.
Wiederoder, Michael S., Eric C. Nallon, Matt Weiss, Shannon K. McGraw, Vincent P. Schnee, Collin J. Bright, Michael P. Polcha, Randy Paffenroth, and Joshua R. Uzarski. "Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants." ACS sensors 2, no. 11 (2017): 1669-1678. (Note, in the chemical sensor literature, the senior faculty go last in the author list.)
Ferreira, P. V. R., Paffenroth, R., Wyglinski, A. M., Hackett, T., Bilen, S., Reinhart, R., Mortensen, D., (2017). Multi-objective Reinforcement Learning for Cognitive Satellite Communications using Deep Neural Networks Ensembles, IEEE JSAC Special Issue on "Advances in Satellite Communications" (25% acceptance rate).
Ferreira, Paulo Victor Rodrigues, Randy Paffenroth, and Alexander M. Wyglinski. "Interactive multiple model filter for land-mobile satellite communications at Ka-band." IEEE Access 5 (2017): 15414-15427.
Bandara, V. W., Scharf, L. L., Paffenroth, R. C., Jayasumana, A. P., & Du Toit, P. C. (2016). Experimental recovery regions for robust PCA. Signal Processing, 129, 25-32.
Reese, T., Paffenroth, R., & Fehribach, J. D. (2016). “Duality in Geometric Graphs: Vector Graphs, Kirchhoff Graphs and Maxwell Reciprocal Figures”. Symmetry, 8(3), 9.
Calderon, C., Thompson, M., Casolari, J. , Paffenroth, R., and Moerner, W. (2013) Quantifying Transient 3D Dynamical Phenomena of Single mRNA Particles in Live Yeast Cell Measurements. Journal of Physical Chemistry B, 117.49 (2013): 15701-15713.
Paffenroth, R., Du Toit, P., Nong, R., Scharf, L., Jayasumana, A., Bandara, V. (2013) Space-time signal processing for distributed pattern detection in sensor networks. IEEE Journal of Selected Topics in Signal Processing, Issue on Anomalous Pattern Discovery for Spatial, Temporal, Networked, and High-dimensional Signals, Vol 7., Issue. 1, February 2013.
Bruno, O., Elling T., Paffenroth, R., Turc, C., (2009). Electromagnetic integral equations requiring small numbers of Krylov-subspace iterations. Journal of Computational Physics
Volume 228, Issue 17, 20 September 2009, Pages 6169-61 (Cited 30 or more times on Google Scholar)
Doedel, E., Romanov, V., Paffenroth, R., Keller, H., Dichmann, D., Galan-Vioque, J., Vanderbauwhede, A. (2007). Elemental periodic orbits associated with the libration points
in the Circular Restricted 3-Body Problem. Int. J. Bifurcation and Chaos 17 #8, 2625-
2677. (Cited 40 or more times on Google Scholar)
Doedel, E., Paffenroth, R., Keller, H., Dichmann, D., Gal´an, J., Vanderbauwhede, A. (2003). Continuation of periodic solutions in conservative systems with application to the 3-Body
problem. Int. J. Bifurcation and Chaos, Volume 13 #6, 1-29. (Cited 40 or more times on Google Scholar)
Dichmann, D., Doedel, E., Paffenroth, R. (2003). The computation of periodic solutions of
the 3-Body problem using the numerical continuation software AUTO, in: ”Libration Point
Orbits and Applications”, G. G´omez. M. W. Lo, J. J. Masdemont, eds., World Scientific, 489-528. (Cited 10 or more times on Google Scholar)
Hoffman, K., Manning, R., Paffenroth, R. (2002). Calculations of the Stability Index in Parameter-Dependent Calculus of Variations Problems: Buckling of a Twisted Elastic Strut. SIAM Journal on Applied Dynamical Systems, 1 (1). pp. 115-145. ISSN 1536-0040 (Cited 10 or more times on Google Scholar)
Maddocks, J., Manning, R., Paffenroth, R., Rogers, K., Warner, J. (1997), Interactive Computation, Parameter Continuation, and Visualization. Int. J. Bifurcation and Chaos, Volume 7, 1699-1715
Zhou, Chong, Paffenroth, R. (2017) Anomaly Detection with Robust Deep Auto-encoders. ACM SIGKDD 2017, August 5 13-17, 2017 (Paper accepted for oral presentations (8.56% acceptance rate). A total of 748 papers were submitted to the Research track, of which 64 were accepted for oral presentation and 67 were accepted as posters.)
Ferreira, Paulo Victor R., et al. "Multi-objective reinforcement learning-based deep neural networks for cognitive space communications." Cognitive Communications for Aerospace Applications Workshop (CCAA), 2017. IEEE, 2017.
Mukne, Neehar, and Randy Paffenroth. "Probabilistic inference of Internet node geolocation with anomaly detection." Technologies for Homeland Security (HST), 2017 IEEE International Symposium on. IEEE, 2017. (3 peer reviewers of 4 page abstract)
Randy Paffenroth, Nan Li, Louis Scharf, Myung Hee Lee (2016) Maximum Likelihood Identification of an Information Matrix Under Constraints in a Corresponding Graphical Model, Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers. (http://www.asilomarsscconf.org/)
Ferreira, P., Paffenroth, R., Wyglinski, A., Hackett, T. M., Bilén, S., Reinhart, R., & Mortensen, D. (2016). Multi-Objective Reinforcement Learning for Cognitive Radio--Based Satellite Communications. In 34th AIAA International Communications Satellite Systems Conference (p. 5726).
AJayasumana, A. P., Paffenroth, R., & Ramasamy, S. (2016, May). Topology maps and distance-free localization from partial virtual coordinates for IoT networks. In 2016 IEEE International Conference on Communications (ICC), (pp. 1-6).
Mukne, N. and Paffenroth R. (2015). Probabilistic Inference of Internet Node Geolocation with Anomaly Detection. Extended (4 page) abstract submitted to 15th annual IEEE Symposium on Technologies for Homeland Security, submitted 11/30/15, ACCEPTED 2/17/2016 (3 peer reviewers of 4 page abstract) (PAPER WITHDRAWN)
Doedel, E., Dichmann, D., Gal´an-Vioque, J., Keller, H., Paffenroth, R., Vanderbauwhede, A. (2005). Elemental periodic orbits of the CR3BP: A brief selection of computational results, Proc. Equadiff 2003, Hasselt Belgium, pp. 163-168, World Scientific, Singapore.
Doedel, E., Dichmann, D., Paffenroth, R. (2004). Boundary Value Methods for Computing
Periodic Solutions of Conservative Systems with Application to the CR3BP, Proc. Third
International Workshop on Scientific Computing and Applications, Hong Kong, 2003, Lu, Y.
Y., Sun, W., Tang, T., eds., Nova Publishers, 2004, 14 pp.
Paffenroth, R., Doedel, E., Dichmann, D. (2001). Continuation of periodic orbits around
Lagrange points and AUTO2000, AAS paper 01-303, Proc. AAS/AIAA Astrodynamics Specialist Conference, Session on ”Lagrange Point Missions” organized by M. Lo and K. Howell,
Quebec City, 20 pages. (Cited 20 or more times on Google Scholar)
Paffenroth, R. (2015) Python in Data Science Research and Education. Proceedings of SciPy 2015. (abstract peer reviewed) 40% acceptance rate for abstract, 50% of abstracts to papers
Paffenroth, R., Nong, R., Du Toit, P., (2013) On covariance structure in noisy, big data. Proceedings Vol. 8857, Signal and Data Processing of Small Targets, October 2013, Oliver E. Drummond; Richard D. Teichgraeber, Editors.
Paffenroth, R., Du Toit, P., Scharf, L., Jayasumana, A., Banadara, V., Nong, R., (2012) Distributed pattern detection in cyber networks. Proceedings Vol. 8408, Cyber Sensing 2012, Igor V. Ternovskiy; Peter Chin, Editors.
Lundberg, S., Calderon, C., Paffenroth, R., (2012) Detecting clustered chem/bio signals in noisy sensor feeds using adaptive fusion. Proceedings Vol. 8393 Signal and Data Processing of Small Targets 2012, Oliver E. Drummond; Richard D. Teichgraeber, Editors.
Trawick, D., Du Toit, P., Paffenroth, R., Norgard, G., (2012) Ambiguous data association and entangled attribute estimation. Proceedings Vol. 8393 Signal and Data Processing of Small Targets 2012, Oliver E. Drummond; Richard D. Teichgraeber, Editors.
Paffenroth, R., Du Toit, P., Scharf, L., Jayasumana, A., Banadara, V., Nong, R., (2012) Space-time signal processing for distributed pattern detection in sensor networks. Proceedings Vol. 8393 Signal and Data Processing of Small Targets 2012, Oliver E. Drummond; Richard D. Teichgraeber, Editors.
Coult, N., Knight, J., Leed, W., Danford, S., Paffenroth, R., Poore, A., (2012) Information-based data prioritization in distributed tracking systems. Proceedings Vol. 8393 Signal and Data Processing of Small Targets 2012, Oliver E. Drummond; Richard D. Teichgraeber, Editors.
Paffenroth, R., Du Toit, P., Scharf, L., Jayasumana, A., (2011) Space-Time Signal Processing for Detecting and Classifying Distributed Attacks in Networks. Proceeding of 2011 MSS National Symposium on Sensor & Data Fusion.
Calderon, C., Jones, A., Lundberg, S., Paffenroth, R., (2011). A data-driven approach for processing heterogeneous categorical sensor signals. Submitted. SPIE, Conference on Signal and Data Processing of Small Targets (Vol. 8137).
Lundberg, S., Paffenroth, R., Yosinski, J., (2010). Algorithms for distributed chemical sensor fusion. In O. E. Drummond (Ed.), Proceedings of the SPIE, Conference on Signal and Data Processing of Small Targets (Vol. 7698).
Lundberg, S., Paffenroth, R., Yosinski, J., (2010). Analysis of CBRN Sensor Fusion Methods. 13th Conference on Information Fusion (FUSION).
Trawick, D., Slocumb, B., Paffenroth, R., (2010). A tracker adjunct processing system for reconsideration of firm tracker decisions. In O. E. Drummond (Ed.), Proceedings of the SPIE, Conference on Signal and Data Processing of Small Targets (Vol. 7698).
Yosinski, J., Paffenroth, R., (2010). Nonlinear estimation for arrays of chemical sensors. In O. E. Drummond (Ed.), Proceedings of the SPIE, Conference on Signal and Data Processing of Small Targets (Vol. 7698).
Yosinski, J., Coult, N., Paffenroth, R., (2009). Network-centric Angle-only Tracking. Proceedings of the SPIE, Conference on Signal and Data Processing of Small Targets (Vol. 7445).
Chan, S., Paffenroth, R., (2008). Out-of-sequence measurement updates for multi-hypothesis tracking algorithms. Proceedings of SPIE, 69691H-69691H-12.
Yosinski, J., Paffenroth, R., (2008). A distributed database view of network tracking systems. Proceedings of SPIE, 696915-696915-12.
Paffenroth, R., Novoselov, R., Danford, S., Teixeira, M., Chan, S., Poore, A., (2007). Mitigation of biases using the Schmidt-Kalman filter. In O. E. Drummond (Ed.), Proceedings of the SPIE, Conference on Signal and Data Processing of Small Targets (Vol. 6969). (Cited 10 or more times on Google Scholar)
Paffenroth, R., Vrajitoru, D., Stone, T., Maddocks, J., (2002). DataViewer: A Scene Graph Based Visualization Tool. The 20th Eurographics UK Conference, IEEE ComputerSociety Publications, 147-148.
Paffenroth, R., Vrajitoru, D., Stone, T., Maddocks, J., (2002). DataViewer: A Scene Graph Based Visualization Library. The 5th IASTED Conference on Computer Graphics and Imaging (CGIM 2002), ACTA Press, 200-205.
Paffenroth, R., Doedel, E., (2001). The AUTO2000 command line user interface, Ninth International Python Conference, Long Beach, CA, March 2001, 233-241.
Paffenroth, R., (1998). VBM and MCCC: Packages for Objected Visualization and Computation of Bifurcation Manifolds. Proceedings of the 1998 SIAM Workshop: Object Oriented Methods for Interoperable Scientific and Engineering Computing p. 255-263.
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