Anthony D. Rhodes Office: FAB 115-06 |

Machine Learning, Artificial Intelligence, Numerical Analysis, Computational Mathematics, Data Science, Intellectual History, Fin de Siecle Studies.

__Education__

**Ph.D.** Mathematical Sciences, Portland State University. (in progress)

<>Focus: Machine Learning, Artificial Intelligence

<>Dissertation: "Efficient Object Localizaton for Situation Recognition in Computer Vision."

<>Advisors: Melanie Mitchell, Computer Science, Portland State University; Bruno Jedynak, Statistics, Portland State University.

**M.S.** Computer Science, Portland State University. (in progress)

**M.S.** Mathematics, Portland State University.

<>Foci: Computational Mathematics, Numerical Analysis, Algebra, Graph Theory.

<>Thesis: "The Algebraic Structure of Cellular Automata."

**M.A.** History, Philosophy and German Studies, Portland State University.

<>Focus: Modern European Intellectual History

<>Thesis: "Jacob Burckhardt: History and the Greeks in the Modern Context."

**G.C.C.I.** Graduate Certificate in Computational Intelligence, Portland State University.

**G.C.A.S.** Graduate Certificate in Applied Statistics, Portland State University.

**B.A.** Mathematics, with distinction, UC San Diego.

**B.A.** History, Minor: Classical Studies, with distinction, UC San Diego.

__Publications__

Rhodes, A. D., (2017). Real-Time Wind Noise Detection and Suppression with Neural-Based Signal Reconstruction for Multi-Channel, Low-Power Devices. Submitted. pdf

Rhodes, A. D., Witte, J., Mitchell, M., and Jedynak, B., (2017). Gaussian Processes with Context-Supported Priors for Active Object Localization. Submitted. pdf

Rhodes, A. D., Witte, J., Mitchell, M., and Jedynak, B. Bayesian optimization for refining object proposals. *Proceedings of the International Conference on Image Processing Theory, Tools & Applications* (IPTA), Montreal, Canada, 2017. pdf

Rhodes, A. D., Quinn, M. H., and Mitchell, M. Fast on-line kernel density estimation for active object localization. *Proceedings of the International Joint Conference on Neural Networks* (IJCNN), Anchorage, AK, 2017. pdf

Quinn, M. H., Rhodes, A. D., and Mitchell, M. (2016). Active object localization in visual situations. arXiv 1607.00548. pdf

<> This project investigates a novel approach to building computer systems that can recognize visual situations;

the approach explored integrates two previously-studied approaches: brain-inspired neural networks for lower-lever

vision and cognitive-level models of concepts and analogy-making.

<> Project is funded by the National Science Foundation (NSF).

<> Advisor: Melanie Mitchell , Computer Science, Portland State University.

<> Content/curriculum creation for large-scale courses for http://www.complexityexplorer.org,

a web-based repository of educational materials related to complex systems science.

<>Advisor: Melanie Mitchell, Computer Science, Portland State University.

*Click image to view my lecture series for 'complexity explorer' MOOC*

<> Directed a study to determine the overall efficacy of the undergraduate placement examination process at Portland State University.

<> Advisor: Mara Tableman (Statistics/Mathematics), Portland State University.

<>“Neural-Based Wind Noise Detection and Suppression for Low-Power Devices”, PSU Seminar in Applied & Computational Mathematics, Winter, 2018.

<>"Bayesian Optimization for Refining Object Proposals", Proceedings of the International Conference on Image Processing Theory, Tools & Applications (IPTA),

November 2017, Montreal, Canada.

<>“Gaussian Processes with Context-Supported Priors for Active Object Localization”, PSU Statistics and Statistical Learning Seminar, May 2017.

<>“Bayesian Optimization for Refining Object Proposals, with an Application to Pedestrian Detection”, PSU Student Research Symposium, May 2017.

<>“Fast On-Line Kernel Density Estimation for Active Object Localization”, International Conference on Neural Networks (IJCNN), May 2017, Anchorage, AK.

<>“Machine Learning Classification & Support Vector Machines”, guest lecture for graduate course in CS: Machine Learning, April 2017.

<>“Graphical Models for Machine Learning”, guest lecture for graduate course in Statistical Learning, PSU, Winter 2017.

<>“A Primer on Bayesian Optimization for Active Object Localization in Computer Vision”, WSU Mathematics and Statistics Seminar, Fall 2016.

<>"Robust Wind Noise Detetion and Suppression for Wearable Glass with Multi-Microphone Array", slides

<>"Bayesian Optimization for Refining Object Proposals, with an Application to Pedestrian Detection", slides

<>"Graphical Models for Machine Learning", lecture notes

<>"Gaussian Process Regression for Visual Situation Recognition", slides

<>"Lectures in Graph Theory and Complex Systems," Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8 Part 9

<>"Lectures in Numerical Analysis," Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part7 Part 8 Part 9 Part 10 Part 11 Part 12 Part 13 Part 14 Part 15

<>"The Algebraic Structure of Cellular Automata," pp. 28. PDF

<>"Jacob Burckhardt: History and the Greeks in the Modern Context," pp. 119. PDF

<>"A Statistical Analysis of Undergraduate Mathematics Placement Scores at Portland State University," pp. 19. PDF

<>"The Case Against Computational Theory of Mind: A Refutation of Mathematically-Contigent Weak A.I.," pp. 27.

CS 250: Discrete Structures I

CS 251: Discrete Structures II

*CS 441/541: Artificial Intelligence

*CS 445/545: Machine Learning

Stat 243: Statistics/Probability I

Stat 244: Statistics II

Math 30: Pre-Algebra

Math 60: Algebra/Geometry I

Math 65: Algebra/Geometry II

Math 95: Intermediate Algebra/Geometry

Math 105: Exploring Mathematics

Math 107: Math & Society

Math 111: Pre-Calculus I

Math 112: Pre-Calculus II/Trigonometry

Math 171: Computational Calculus I

Math 172: Computational Calculus II

Math 221: Finite Mathematics

Math 241: Calculus for Business/Economics

Math 251: Calculus I

Math 252: Calculus II

Math 253: Calculus III

Math 254: Calculus IV

Math 256: Intro to Differential Equations

Math 261: Intro to Linear Algebra

Math 273: Vector Calculus

*Math 300: Computational Mathematics & Numerical Analysis

Math 321: Ordinary Differential Equations

*Math 398: History of Mathematics

*[MOOC]: Matrix Algebra & Applications to Complex Systems

*[MOOC]: Graph Theory/Network Theory & Applications to Complex Systems

*Designed (and taught) curriculum

Listen to my music here: AMAZON SPOTIFY

*Calculus Lecture: Day One