Dr. Pablo Rivas

Pablo Rivas

Assistant Professor of Computer Science
School of Computer Science and Mathematics
Marist College

3399 North Road
Poughkeepsie, New York 12601, USA
Phone: +1 (845) 575-2086
Fax: +1 (845) 575-3605
alpha@beta.edu
alpha=Pablo.Rivas and beta=Marist

I am an assistant professor of computer science at Marist College where I teach courses related to programming, data structures, algorithms, and, my favorite, machine learning. Former postdoc teaching and doing research at Baylor University and earlier at NASA Goddard Space Flight Center working on large-scale and deep machine learning with applications in image analysis. On _this_ webpage you will find fewer information than in my CV.


Education


Research Interests

I am interested in deep machine learning and large-scale data mining in big data analytics, large-scale multidimensional multispectral signal analysis, statistical pattern recognition methods, image restoration, image analysis, intelligent software systems, and health-care imaging.

Other areas that have my attention include applied mathematics, numerical optimization, swarm intelligence optimization, evolutionary algorithms, soft computing, fuzzy logic, neural networks, and neurofuzzy systems.

Teaching

Deep Learning for Natural Language Processing - MSCS 688
This course will explore deep learning architectures applied to the problem of natural language processing. We study how to convert text to vectors (word embedding), recursive/recurrent nerual networks, long short-term memory networks, and convolutional neural networks. We will use Keras over Python to implement our models.
[ Fall 2018 ]

Advanced Data Structures - MSCS 502
This course continues the study and implementation of linear and non-linear data structures including linked lists, stacks, queues, trees, heaps, and hashing. Complexity will be considered on sorting algorithms and efficient structures will be covered including balanced binary search trees and priority queues. Advanced Java topics will be covered including abstract classes, class inheritance, and polymorphism.
[ Fall 2018 ]

Emerging Technologies - MSIS 620
This course addresses the management of emerging technologies, how they evolve, how to identify them and the effects of international, political, social, economic, and cultural factors on them. We discuss the management challenges posed by emerging technologies at the point where scientific research reveals a technological possibility and goes all the way to the commercialization of the technology into lead markets.
[ Fall 2017 | Fall 2018 ]

Computer Science Project - CMPT 475
Study of project management techniques, review of oral presentation skills, creation of software documentation, assembly of project teams, selection of a project client, software analysis and design, and beginning of project implementation.
[ Fall 2015 | Fall 2016 | Summer 2017 | Fall 2017 | Fall 2018 ]

Information Technology Project - CMPT 477
Study of project management techniques, review of oral presentation skills, creation of software documentation, assembly of project teams, selection of a project client, software analysis and design, and beginning of project implementation.
[ Fall 2015 | Fall 2016 | Fall 2017 | Fall 2018 ]

Technology, Society, and Ethics - CMPT 305
Examination of the influences of technology on society and the ethical dilemmas presented by technological advances. Study of major ethical theories to provide a framework for analyzing the impact of technology on current legal, social, economic, governmental, religious, and scientific activities.
[ Fall 2015 | Fall 2016 | Fall 2017 | Fall 2018 ]

Theoretical Machine Learning - MSIS 689
The course reviews the theory of learning from data, the study of learning algorithms, as well as machine learning applications. Topics include: supervised and unsupervised learning, regularization methods, cross-validation and model selection.
[ Spring 2018 ]

Machine Learning for Gaming - MSCS 688
Recent developments in machine learning have increased the attention to algorithms capable of learning to react to stimuli without a specific goal or loss function directly related to a game strategy. In this special topics course we will focus on recurrent neural networks (RNNs) with long-short-term memory (LSTM) devices. These have demonstrated a great ability to preserve contextual information about the status of a game while progressively discarding useless information. The Tensorflow platform from Google will be used with its Python interface to illustrate its gaming potential.
[ Spring 2018 ]

Analytics Bootcamp - MBA 665
This course will introduce a range of data driven disciplines and technologies to help enterprise users make better, faster business decisions. Students in this course will be exposed to spreadsheet modeling, data visualization, rudiments of data management and data analysis, and an introduction to data mining and predictive modeling, together with the statistics necessary to use the tools. The course will be hands-on, using state of the art software, with real world examples from different functional areas and business domains.
[ Spring 2018 ]

Security Algorithms and Protocols - MSCS 630
Analysis of the fundamental risks, vulnerabilities and threats to mobile and cloud based applications and the countermeasures that must be implemented to reduce risks to these applications and associated infrastructure with a specific focus on cryptography. Topics covered include risk assessment, foundations of cryptography, symmetric and public key cryptography, stream ciphers, block ciphers and hashing algorithms, public key infrastructures (PKI), cryptographic algorithms (RSA, DES, AES), cryptographic protocols (SSL, TLS, IPSEC), key exchange protocols (Diffie­Hellman, RSA and IKE), authentication protocols (Radius, the Java cryptographic API and government regulations and cryptographic standards).
[ Spring 2016 | Spring 2017 | Spring 2018 ]

Machine Learning - DATA 440
This course provides a broad introduction to automated learning from data. Machine learning is the name given to the collection of techniques that allow computational systems to adaptively improve their performance by learning from past observed data. The course introduces the theoretical underpinnings of learning from data, the study of learning algorithms, as well as machine learning applications. Topics include: supervised learning (linear models, SVMs, MLPs) and unsupervised learning (K-means, GMMs), learning theory (generalization theory, bias/variance tradeoffs; Vapnik - Chervonenkis dimension); regularization methods, validation and models selection.
[ Spring 2018 ]

Algorithm Analysis and Design - CMPT 435
This course continues the study of data structures and algorithm complexity from a more mathematically formal viewpoint. Time complexity of algorithms will be examined using Big Oh notation for worst-, best-, and average-case analyses. The ideas of polynomial-time, NP, exponential, and intractable algorithms will be introduced. Elementary-recurrence relation problems relating to recursive procedures will be solved. Sorting algorithms will be formally analyzed. Strategies of algorithm design such as backtracking, divide and conquer, dynamic programming, and greedy techniques will be emphasized. We will be using the Java programming language as our base language.
[ Spring 2018 ]

Deep Learning with Tensorflow - MSCS 692
This course is an introduction to the major problems, techniques, and issues around deep learning. Emphasis is placed upon the topics of supervised and unsupervised learning for problem solving. This is a field rapidly growing in which we create deep learning models for computers to ``know'' how to make inferences, or make decisions, based on data all around us and even in its absence. The Tensorflow platform from Google will be used with its Python interface to illustrate various deep learning techniques.
[ Fall 2017 ]

Parallel Processing - MSCS 679
This course introduces the concept of multicore and multiprocessor parallel programming. Topics such as Amdhal’s law, speedup, efficiency, hyper-threading, task-level vs. data-level parallelism, shared memory vs. shared-nothing algorithms, concurrent vs. parallel collections, database sharding, and debugging and testing will be discussed. Small student teams analyze, design, and build a parallel computing application using software-development best practices.
[ Fall 2017 ]

Deep Learning with Tensorflow - CMPT 469
This course is an introduction to the major problems, techniques, and issues around deep learning. Emphasis is placed upon the topics of supervised and unsupervised learning for problem solving. This is a field rapidly growing in which we create deep learning models for computers to ``know'' how to make inferences, or make decisions, based on data all around us and even in its absence. The Tensorflow platform from Google will be used with its Python interface to illustrate various deep learning techniques.
[ Fall 2017 ]

Computer Organization and Architecture - CMPT 422
This course provides an understanding and appreciation of a computer system's functional components and their characteristics. Students learn instruction set architecture, the internal implementation of a computer at the register and functional level, and understand how main activities are performed at machine level as well as gain an appreciation for hardware design at micro level.
[ Fall 2017 ]

Natural Language Processing - MSCS 688
NLP deals with the computational modeling of human languages with the purpose of understanding the meaning, predicting, auto-completing, and the production of text. We study how to convert text to vectors (word embedding), recursive/recurrent nerual networks, long short-term memory networks, and convolutional neural networks. Other boosting algorithms such as gradient boosting and parallel tree boosting are also covered. We use TensorFlow and Keras over Python for the most common implementations.
[ Summer 2017 ]

Formal Languages and Computability - CMPT 440
Study of formal languages, automata, and computability provides the theoretical foundation for the design, specification, and compilation of programming languages. The formal languages of the Chomsky Hierarchy, their grammars, and the associated abstract machines or automata will be studied. This leads naturally to consideration of the theory of computability.
[ Spring 2016 | Spring 2017 ]

Software Development 1 - CMPT 220
Introduction to the art and science of software development. Study of software development history and mastering software development skills including but not limited to real-world modeling and multi-language software development.
[ Fall 2015 | Fall 2016 | Spring 2017 ]

Artificial Intelligence - MSCS 550
Introduction to the major problems, techniques, and issues of artificial intelligence. Emphasis is placed upon the topics of machine learning and problem solving. The python language is used to illustrate various machine learning techniques.
[ Fall 2016 ]

Competitive Programming - CMPT 192
This special topics course focuses in problem solving and algorithms. It is designed around the idiosyncrasy of the collaborative and competitive model of the ACM ICPC. In class we discuss algorithms and problem solving strategies.
[ Fall 2016 ]

Artificial Intelligence - CMPT 404
Introduction to the major problems, techniques, and issues of artificial intelligence. Emphasis is placed upon the topics of machine learning and problem solving. The python language is used to illustrate various machine learning techniques.
[ Fall 2016 ]

Database Management - CMPT 308
Examination of the theories and concepts employed in database management systems (DBMS). The function of various types of DBMS is described including their purpose, advantages, disadvantages, and applications in business. The course explores the following topics: DBMS architectures, data modeling, the relational model, database normalization, relational algebra, SQL, client/server systems, DB physical design, multiple user environments, database security.
[ Spring 2016 ]

Data Structures and Algorithms - CSI 3334
Software design and construction with abstract data types. Description, performance and use of commonly-used algorithms and data structures including lists, trees, and graphs.
[ Spring 2015 | Fall 2014 | Spring 2014 | Fall 2013 ]

Electric Circuits Lab - EE 2151
Basic and advanced electronic equipment for the design of electric circuits. Construction of the following circuits: Series/Parallel, Voltage/Current Divider, Mesh-Current Node-Voltage, First-Order RC, First-Order RL, Second-Order RLC, and Sinusoidal Steady-State Analysis.
[ Summer 2011 | Spring 2011 | Fall 2010 | Spring 2010 | Fall 2009 | Spring 2009 | Fall 2008 ]

Digital Signal Processing
Discrete-time signals and systems, sampling theory, z-transforms, spectral analysis, filter design, applications, and analysis and design of discrete signal processing systems.
[ Fall 2006 ]

Schedule


Publications

Journal Publications

  1. Erich J. Baker, Nicole A.R. Walter, Alex Salo, , Sharon Moore, Steven Gonzales, Kathleen A. Grant "Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification " in Alcoholism Clinical and Experimental Research, 2/2017. [ bib | .pdf ]
  2. Juan Cota-Ruiz , Ernesto Sifuentes, and Rafael Gonzalez-Landaeta "A Recursive Shortest Path Routing Algorithm with application for Wireless Sensor Network Localization" accepted for publication in IEEE Sensors Journal, 3/2016. [ bib | .pdf ]
  3. , Erich Baker, Greg Hamerly, and Bryan Shaw, "Detection of Leukocoria using a Soft Fusion of Expert Classifiers under Non-clinical Settings", vol. 14, no. 110, in BMC Ophthalmology. 9/2014. [ bib | .pdf ]
  4. , Juan Cota-Ruiz, Jose-Gerardo Rosiles, "Statistical and Neural Pattern Recognition Methods for Dust Aerosol Detection," in International Journal of Remote Sensing, vol. 34, no. 21, 4/2013. [ bib | .pdf ]
  5. Juan Cota-Ruiz, Jose-Gerardo Rosiles, , Ernesto Sifuentes, "A distributed localization algorithm for wireless sensor networks based on the solutions of spatially-constrained local problems", in Sensors Journal, IEEE, vol. 13, no. 6, 4/2013. [ bib | .pdf ]
  6. , Juan Cota-Ruiz, David Garcia Chaparro, Abel Quezada Carreon, Jose-Gerardo Rosiles, "Forecasting The Demand of Short-Term Electric Power Load with Large-Scale LP-SVR", in Smart Grid and Renewable Energy, vol. 4, 2, 4/2013. [ bib | .pdf ]
  7. , Juan Cota-Ruiz, Jose-Gerardo Rosiles, "An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods", in Pattern Recognition Letters, vol. 34, no. 4, pp. 439-451, 3/2013. [ bib | .pdf ]
  8. , Juan Cota-Ruiz, Jose-Gerardo Rosiles, "A nonlinear least squares quasi-Newton strategy for LP-SVR hyper-parameters selection", in International Journal of Machine Learning and Cybernetics, vol. 5, no. 4, 2/2013. [ bib | .pdf ]
  9. , Juan Cota-Ruiz, J. A. Perez Venzor, David Garcia Chaparro, Jose-Gerardo Rosiles, "LP-SVR Model Selection Using an Inexact Globalized Quasi-Newton Strategy", in Journal of Intelligent Learning Systems and Applications, vol. 5, pp. 19-28, 2/2013. [ bib | .pdf ]
  10. , Juan Cota-Ruiz, David Garcia Chaparro, J. A. Perez Venzor, Abel Quezada Carreon, Jose-Gerardo Rosiles, "Support Vector Machines for Regression: A Succinct Review of Large-Scale and Linear Programming Formulations", in International Journal of Intelligence Science, vol. 3, pp. 5-14, 1/2013. [ bib | .pdf ]
  11. Juan Cota-Ruiz, Jose-Gerardo Rosiles, Ernesto Sifuentes, , "A Low-Complexity Geometric Bilateration Method for Localization in Wireless Sensor Networks and Its Comparison with Least-Squares Methods", in Sensors, vol. 12, pp. 839-862, 1/2012 [ bib | .pdf ]
  12. Mario Ignacio Chacon Murguia, Yearim Quezada-Holguin, , Sergio Cabrera, "Dust Storm Detection Using a Neural Network with Uncertainty and Ambiguity Output Analysis", in Pattern Recognition, ed. Jose Francisco Martinez-Trinidad et~al., vol. 6718, Lecture Notes in Computer Science, Springer Berlin Heidelberg, pp. 305-313. 6/2011. [ bib | .pdf ]
  13. , Jose G. Rosiles and Wei Qian, "Subjective Colocalization Analysis with Fuzzy Predicates," in Soft Computing for Intelligent Control and Mobile Robotics, Oscar Castillo, Witold Pedrycz, Janusz Kacprzyk Eds. Computational Intelligence Series of Springer-Verlag. 1/2011. [ bib | .pdf ]
  14. , Jose G. Rosiles, Mario I. Chacon Murguia and James J. Tilton, "Automatic Dust Storm Detection Based on Supervised Classification of Multispectral Data," in Soft Computing for Recognition based on Biometrics, Patricia Melin, Janusz Kacprzyk, Witold Pedrycz Eds. Computational Intelligence Series of Springer-Verlag. 9/2010. [ bib | .pdf ]
  15. M. I. Chacon M., , "Fusion of Fuzzy FFL-KLT and PCNN Features on the Face Recognition Problem," in Dynamics of Continuous, Discrete & Impulsive Systems Journal, Series A: Mathematical Analysis, a Special Issue on Advances in Neural Networks-Theory and Applications, 8/2007. [ bib | .pdf ]
  16. Mario I. Chacon M., , and Graciela Ramirez A., "A Fuzzy Clustering Approach for Face Recognition Based on Face Feature Lines and Eigenvectors," in Engineering Letters Journal, 8/2007. [ bib | .pdf ]
  17. Mario I. Chacon M., Alejandro Zimmerman S., , "Image Processing Applications with a PCNN," in Advances in Neural Networks, Springer LNCS, pp 884-893. 6/2007. [ bib | .pdf ]

Book Chapters

  1. Mario I. Chacon M., , "Face Recognition Based on Human Visual Perception Theories and Unsupervised ANN," Book: "State of The Art in Face Recognition." Publisher: IN-TECH, 436 pages. 2009. [ bib | .pdf ]

Conference Publications

  1. , "Machine Learning-Based Chatbots: An Overview, Analysis of Trust, and Ethical Issues.", ECC Conference 2018, Presentation, 6/2018. [ bib | .pdf ]
  2. , Ezequiel Rivas, Deep Dand, and Raul Aragon, "Unsupervised Deep Learning with Stacked Autoencoders on Chameleon ", Chameleon User Meeting 2017, Proceedings of, 9/2017. [ bib | .pdf ]
  3. John Cary "Ethics under Pressure? ", Proceedings of the 2017 Susilo Symposium, Boston University, 6/2017. [ bib | .pdf ]
  4. Akshara Boppidi, Pooja Jadhav Eshwarlal, "Random Forests and SVM for Handwritten Digits Recognition ", Proceedings of the ACM New York Celebration of Women in Computing 2017, 4/2017. [ bib | .pdf ]
  5. , and Juan Cota-Ruiz, "Near Real-Time Dust Aerosol Detection with Support Vector Machines for Regression", 2015 AGU Fall Meeting, Long-Range Transport of Dust and Pollution in the Past, Present, and Future, 12/2015. [ bib | .pdf ]
  6. , Juan Cota-Ruiz, "NERT DADS: A Near-Real-Time Dust Aerosol Detection System", Proceedings of the 2015 Data for Good Exchange (D4GX) Conference, Climate Paper Presentations, 9/2015. [ bib | .pdf ]
  7. , Ryan Henning, Bryan Shaw, and Greg Hamerly, "Finding the Smallest Circle Containing the Iris in the Denoised Wavelet Domain", Proceedings of the Image Analysis and Interpretation (SSIAI), 2014 IEEE Southwest Symposium on, pp. 13-16, 4/2014. [ bib | .pdf ]
  8. Ryan Henning, , Bryan Shaw, and Greg Hamerly, "A Convolutional Neural Network Approach for Classifying Leukocoria" Proceedings of the Image Analysis and Interpretation (SSIAI), 2014 IEEE Southwest Symposium on, pp. 9-12. 4/2014. [ bib | .pdf ]
  9. Mario Ignacio Chacon Murguia, Yearim Quezada-Holguin, , Sergio Cabrera, "Dust Storm Detection Using a Neural Network with Uncertainty and Ambiguity Output Analysis" Proceedings of MCPR 2011, pp 305-313. 5/2011.
  10. , Gerardo Rosiles, "Short Term Electric Power Consumption Forecasting using Linear Programming Support Vector Regression," 1st Southwest Energy Science and Engineering Symposium. 4/2011. [ bib | .pdf ]
  11. , Gerardo Rosiles, "Large-Scale Sonar Target Detection with L_1-Norm SV Regression based on Unfeasible Interior Point Methods," Proceedings of the 2011 ITEA Live-Virtual-Constructive Conference. 1/2011. [ bib | .pdf ]
  12. , Omar Velarde Anaya, Juan De Dios Cota Ruiz, "Performance Evaluation of Classic and Accurate SVD Computation in a Multispectral Image Segmentation Problem," 2010 IEEE CIINDET, 11/2010. [ bib | .pdf ]
  13. ; Rosiles, J. G.; Chacon, M. I.; "Traditional and Neural Probabilistic Multispectral Image Processing for The Dust Aerosol Detection Problem," Image Analysis & Interpretation (SSIAI), 2010 IEEE Southwest Symposium on, pp.169-172, 5/2010. [ bib | .pdf ]
  14. and J. G. Rosiles, "A Probabilistic Model for Stratospheric Soil-Independent Dust Aerosol Detection," in Digital Image Processing and Analysis, Optical Society of America, paper DMD4. 5/2010. [ bib | .pdf ]
  15. Jose G. Rosiles, Mario I. Chacon, M. "A Classic and Neural Probabilistic Approach to Remote Sensing: The Dust Storm Detection Problem," Proceedings of the International Seminar on Computational Intelligence, 1/2010. [ bib | .pdf ]
  16. M. I. Chacon M., and J. G. Rosiles, "A Classic and Neural Probabilistic Approach to the Dust Storm Detection Problem," Proceedings of the 2010 ITEA Live-Virtual-Constructive Conference, 1/2010. [ bib | .pdf ]
  17. J. C. Tilton, and J. G. Rosiles, "Dust Storm Detection Through Moderate Resolution Imaging Spectroradiometer: A Machine Learning Problem," Proceedings of the 2010 ITEA Live-Virtual-Constructive Conference, 1/2010. [ bib | .pdf ]
  18. , Gerardo Rosiles, and Wei Qian, "Fuzzy Predicates from Linguistic Variables for Subjective Quantitative Colocalization Analysis," Proc. 10th U.S. National Congress for Computational Mechanics, 7/2009. [ bib | .pdf ]
  19. , Omar Velarde Anaya, Leonardo Valencia Olvera, Luis Humberto Uribe Chavira, Mario I. Chacon M., Gerardo Rosiles, "Mobile Robot for Face Recognition: A Collaborative Environment," Proc. 2009 High Performance Computing & Simulation Conference, IEEE/ACM/IFIP, 6/2009. [ bib | .pdf ]
  20. , Gerardo Rosiles, and Wei Qian, "Self Organizing Maps for Class Discovery in the Quantitative Colocalization Analysis Feature Space," Proc. 2009 IEEE International Joint Conference on Neural Networks, 6/2009. [ bib | .pdf ]
  21. J.G. Rosiles, W. Qian, "Image Restoration for Quantitative Colocalization: Performance Analysis and Response of Colocalization Coefficients," Proc. 3rd Annual Texas Tech University Health Sciences Center (TTUHSC) Paul L. Foster School of Medicine Research Colloquium, 5/2009.
  22. J.G. Rosiles, W. Qian, "Automatic Quantitative Colocalization Analysis: An Image Restoration and Machine Learning Approach," Proc. 2009 UTEP SACNAS Research Expo, 4/2009.
  23. M. I. Chacon M., and , "Performance Analysis of the Feedforward and SOM Neural Networks in the Face Recognition Problem," Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing (CIISP 2007). pp. 313-318. Honolulu, Hawaii. 7/2007. [ bib | .pdf ]
  24. Mario I. Chacon M., , and Graciela Ramirez A. "A Fuzzy Logic Clustering Approach For Face Recognition Based on Face Feature Lines and Eigenvectors," IEEE International Seminar on Computational Intelligence ISCI 2006. 10/2006. [ bib | .pdf ]
  25. , M. I. Chacon Murguia, "Face Recognition Using Hough-KLT and a Feed-Forward Backpropagation Neural Network," Proc. XXVIII International Congress on Electronics Engineering, 10/2006. [ bib | .pdf ]
  26. , Mario I. Chacon, "Real Time Motion Detection for Fast Human Identification Based on Face Recognition," Proceedings of the XVI Inter-University Congress on Electronics Computation and Electrical - II Congress of Technological Innovation in Electrical and Electronics, pp 172-177, 4/2006. [ bib | .pdf ]
  27. , M. Chacon, "Evaluation of Motion Detection Methods for Person Identification based on Face Recognition," Proc. XXVII International Congress on Electronics Engineering, 10/2005.
  28. , "In Motion Face Recognition Through Multilayer Perceptrons," IEEE/ANaCC/cenidet Proc. of the 11th International Congress on Computer Science Research. 11/2004.
  29. , "Slimmer, a Security Mobile Agent for User Authentication on 802.11 WLAN Environments," IEEE/ANaCC/cenidet Proc. of the 10th International Congress on Computer Science Research, 10/2003.

Funding

Projects where I am the principal investigator (Total: $120,837.20)


Projects where I am not the principal investigator (Total: $131,455.60)


Professional Associations


Professional Service


Awards


Short Autobiography

Nice to meet you, I am Pablo Rivas. I am a professional member of the ACM and IEEE. My degrees are in computer science (B.S. ’03), electrical engineering (M.S. ’07), and electrical and computer engineering (Ph.D. ’11 from the University of Texas at El Paso). I am an assistant professor of computer science at the School of Computer Science and Mathematics at Marist College. Prior to that, I worked as a postdoc and adjunct professor of computer science at the Computer Science Department at Baylor University.
At Marist College I have the opportunity to work in different aspects of machine learning, data science, big data, and large-scale pattern recognition. Perhaps you have heard on NPR about one of the projects I most recently worked with Baylor University on the detection of leukocoria (see leuko.net for more info), where we used deep learning and image-processing techniques, which is fascinating for me. Another recent research project originated after an internship at NASA Goddard Space Flight Center where I worked in the detection of a particular kind of atmospheric particle using different machine learning methods. I currently work to make this remote sensing project available on-line in real time.
In the past I worked as Software Engineer for about 8 years; thus, I am familiar with databases and SQL queries, as well as with programming languages, particularly C++, visual basic, but I like to use MATLAB in order to save time when developing the prototypes of my algorithms.
I am humbled by the recognition I have received for my creativity and academic excellence; for instance, I received the IEEE Student Enterprise Award in 2007, and the Research Excellence Award from the Paul L. Foster Health Sciences School of Medicine of Texas Tech University in 2009. In 2011, I had the honor of being inducted to the International Honor Society Eta Kappa Nu (HKN).
When I am not having fun doing research or teaching, I also like to play basketball, code, eat pizza with friends, or go to the movie theater with my beautify wife Nancy.
Please feel free to contact me if you have any questions or want to collaborate.

Work Experience

Assistant Professor of Computer Science @ Marist College ~ 2015 - to-date

Adjunct Professor of Computer Science @ Baylor University ~ 2013 - 2015

Post-Doctoral Research Scientist @ Baylor University ~ 2012 - 2015

Teaching Assistant @ The University of Texas at El Paso ~ 2008 - 2010

Graduate Research Intern @ NASA Goddard Space Flight Center ~ 2009

Systems Integrator Engineer @ TRW Automotive ~ 2000 - 2008

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Copyright © 2015 Pablo Rivas
School of Computer Science and Mathematics
Marist College
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