Homepage of Anthony Rhodes

Fariborz Maseeh Department of Mathematics and Statistics        


   

Anthony D. Rhodes
Machine Learning Engineer, University Lecturer
Portland State University

Office:  FAB 115-06
Email: arhodespdx@gmail.com

Maseeh Department of Mathematics and Statistics
Maseeh College of Engineering and Computer Science
1900 SW 4th Ave
Portland OR 97201

         

Academic Fields of Interest
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, Topology.
<>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., Mitchell, M., and Jedynak, B. Scalable Gaussian Processes. (In preparation)

Rhodes, A. D., Nietzsche Contra Burckhardt. (In preparation)

Rhodes, A. D.. 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.. Gaussian Processes with Context-Supported Priors for Active Object Localization. Proceedings of the International Joint Conference
on Neural Networks
(IJCNN), Rio de Janeiro, Brazil, July, 2018. 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


Patents
Rhodes, A. D., Kar, S., Efficient Wind Detection using Multiple Microphones for Headworn Devices. Submitted.

Rhodes, A. D., Kar, S., Neural-Based Signal Reconstruction using Multiple Microphone for Headworn Devices. (In preparation)


Conferences and Presentations
<>“Gaussian Processes with Context-Supported Priors for Active Object Localization.”, International Conference on Neural Networks (IJCNN), July, 2018, Rio de Janeiro, Brazil.
<>“Dimensionality Reduction Methods for Machine Learning”, guest lecture for graduate course in Statistical Learning III, STAT 610, PSU, March, 2018.
<>“Perceptrons and Neural Learning”, guest lecture for graduate course in Statistical Learning II, STAT 610, PSU, February, 2018.
<>“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.

Some Papers and Lecture Materials
<>Machine Learning Course Lectures Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8 Part 9
<>Algorithms & Complexity Course Letures Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8 Part 9
<>A.I. Course Lectures Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8 Part 9 Part 10 Part 11 Part 12 Part 13 Part 14 Part 15 Part 16 Part 17
<>"Neural-Based Wind Noise Detection and Suppression for Low-Power Devices", 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.

University Teaching Experience
CS 250: Discrete Structures I
CS 251: Discrete Structures II
CS 350: Algorithms & Complexity
CS 441/541: Artificial Intelligence
CS 445/545: Machine Learning
CS 446/546: Advanced Topics in 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

Recent Work
Intel Corporation, PhD Technical Intern (2017).

Research Assistantship in Machine Learning and Visual Situation Recognition (Fall 2015-Spring 2017): Abstract
<> 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.

Lectures in Mathematics for Complex Systems Science (2014):
<> 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*

Statistics Consultation for University Mathematics Placement Scores (Spring 2014):
<> 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.




Listen to my music here: AMAZON SPOTIFY


                                                                                                  *Calculus Lecture: Day One