Dr. Pablo Rivas

Pablo Rivas

Assistant Professor of Computer Science
School of Engineering and Computer Science
Baylor University

Hankamer H330.28
One Bear Place #97141
Waco, Texas 76798-7141, USA
Phone: +1 (254) 710-3385
Fax: +1 (254) 710-3889 alpha@beta.edu
alpha=Pablo_Rivas and beta=Baylor • • • • • • •

I am Dr. Pablo Rivas, an Assistant Professor of Computer Science at Baylor University and former Assistant Professor at Marist College and NASA Goddard Space Flight Center. With a decade of experience in the tech industry as a software engineer and membership as a Senior Member of the IEEE, ACM, and SIAM, I bring a wealth of knowledge to my teaching of machine learning and deep learning courses, focused on natural language processing and computer vision. I am an ally for women in technology and advocate for democratizing AI and machine learning. My research interests include machine learning, computer vision, and AI ethics, and I publish in top machine learning conferences such as NeurIPS, ICML, and ACL. I am also actively developing AI ethics standards with the IEEE Standards Association; I hope the term AI Orthopraxy catches on.

[ Courses | Sched | Students | Papers | Bio ]
 

Education


Research Interests

I am an AI researcher with a strong passion for exploring the possibilities of machine learning and artificial intelligence. My research interests lie in a wide range of topics within this field, including deep learning, neural networks, autoencoders, and adversarial training. I am particularly interested in understanding how these technologies can be leveraged to produce advanced systems, such as chatbots, that can have a significant impact on people's lives. Additionally, I have a keen interest in exploring the ethical implications of AI and machine learning, particularly with regards to standards for AI ethics.

Another area of my research focuses on image processing, particularly in the context of remote sensing and multispectral image segmentation. I am dedicated to understanding the underlying algorithms and models that drive these technologies, and I strive to uncover new and innovative ways to improve their accuracy and performance.

One area of my research that I am particularly passionate about is the optimization of machine learning models. This involves finding the right set of hyper-parameters that will allow a model to perform at its best. I also take a close interest in exploring feature importance estimation, which is a technique used to determine which variables in a dataset are most relevant to a machine learning model's predictions.

Overall, my research interests center around developing advanced machine learning and artificial intelligence technologies that are both accurate and ethically sound. I am constantly searching for new and innovative ways to push the boundaries of this field and bring machine learning closer to its full potential.

Teaching

Introduction to Data Mining - CSI 4352
Introduction to the concepts, techniques, and applications of data mining. Topics include design and implementation of data analysis pipelines; data mining concepts and methods such as association rule mining, pattern mining, classification, regression, and clustering; applications of data mining techniques to complex types of data in various fields.
[ Fall 2020 | Fall 2021 | Fall 2023 ]

Introduction to Algorithms- CSI 3344
This course will provide a comprehensive introduction to computer algorithms taken from diverse areas of application. This course will concentrate on algorithms of fundamental importance and on analyzing the efficiency of these algorithms.
[ Fall 2023 ]

Information Retrieval and Natural Language Processing - CSI 5360
This course introduces fundamental and advanced algorithms in Information Retrieval and Natural Language Algorithms. Topics include Language Modelling, Retrieval Algorithms and Evaluation, and Language Processing techniques such as tagging, parsing, lexical semantics, embeddings and some of most of the most recent machine-learning based approaches to these. Applications and research topics are also covered throughout the course. The Python language will be used to develop applications and projects to showcase its potential.
[ Spring 2022 | Spring 2023 ]

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 | Fall 2022 ]

Introduction to Machine Learning - CSI 5325
Introduction to the major problems, techniques, and issues around the theory of learning from data. Emphasis is placed upon the topics of machine learning and problem solving. The python language is used to illustrate various machine learning techniques.
[ Spring 2021 ]

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 | Spring 2020 ]

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 | Spring 2019 | Fall 2019 | Spring 2020 ]

Data Science Project - DATA 477
Study of project management techniques, review of oral presentation skills, creation of machine learning project, assembly of project teams, selection of a project client, data pipeline analysis and design, and beginning of project implementation.
[ Spring 2020 ]

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 | Spring 2019 | Spring 2020 ]

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 | Spring 2019 | Spring 2020 ]

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 | Fall 2019 ]

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 | Fall 2019 ]

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 | Fall 2019 ]

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 | Spring 2019 | Fall 2019 ]

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 | Fall 2019 ]

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 ]

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 ]

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 ]

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 ]

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

Monday Tuesday Wednesday Thursday Friday
CSI 4352/5355 - Data Mining 9:05-9:55am 9:05-9:55am 9:05-9:55am
Office Hours 1:30-2:30pm 10:00-11:00am
Baylor.AI Lab 10:00-11:00am

Honors and Thesis Students

Graduate

Undergraduate

Publications

Journal Publications

  1. Ernesto Quevedo, Tomas Cerny, Alejandro Rodriguez, , Jorge Yero, Korn Sooksatra, Alibek Zhakubayev, and Davide Taibi, " Legal Natural Language Processing from 2015-2022: A Comprehensive Systematic Mapping Study of Advances and Applications ", in IEEE Access, 11/2023. [ bib |  .pdf ]
  2. and Mehang Rai, " Enhancing CNNs Performance on Object Recognition Tasks with Gabor Initialization ", in Electronics, vol. 12, no. 19, Special Issue Convolutional Neural Networks and Vision Applications - Volume III, 8/2023. [ bib |  .pdf ]
  3. Korn Sooksatra, Gissella Bejarano, and , " Evaluating Robustness of Reconstruction Models with Adversarial Networks ", in Procedia Computer Science, vol 222, pp. 353-366, 8/2023. [ bib |  .pdf ]
  4. and Liang Zhao, " Marketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology ", in AI, 4, no. 2, pp. 375-384, 4/2023. [ bib |  .pdf ]
  5. Jordan Richard Schoenherr, Roba Abbas, Katina Michael, , and Theresa Dirndorfer Anderson, " Designing AI Using a Human-Centered Approach: Explainability and Accuracy Toward Trustworthiness ", in IEEE Transactions on Technology and Society, vol. 4, no. 1, pp. 9-23, 3/2023 [ bib |  .pdf ]
  6. Bikram Khanal, Javier Orduz, , and Erich Baker, " Supercomputing leverages quantum machine learning and Grover’s algorithm ", in The Journal of Supercomputing, 11/2022. [ bib |  .pdf ]
  7. Tamara Bonaci, Katina Michael, , Lindsay J. Robertson, and Michael Zimmer, " Emerging Technologies, Evolving Threats: Next-Generation Security Challenges ", in IEEE Transactions on Technology and Society, vol. 3 (3), 155-162, 9/2022. [ bib |  .pdf ]
  8. Korn Sooksatra and " Evaluation of adversarial attacks sensitivity of classifiers with occluded input data ", in Neural Computing and Applications, 6/2022. [ bib |  .pdf ]
  9. Olawale Ayoade, , and Javier Orduz " Artificial Intelligence Computing at the Quantum Level ", in Data, 7, 28, 2/2022. [ bib |  .pdf ]
  10. Ziheng Sun, Laura Sandoval, Robert Crystal-Ornelas, S. Mostafa Mousavi, Jinbo Wang, Cindy Lin, Nicoleta Cristea, Daniel Tong, Wendy Hawley Carande, Xiaogang Ma, Yuhan Rao, James A. Bednar, Amanda Tan, Jianwu Wang, Sanjay Purushotham, Thomas E. Gill, Julien Chastang, Daniel Howard, Benjamin Holt, Chandana Gangodagamage, Peisheng Zhao, , Zachary Chester, Javier Orduz, and Aji John " A review of Earth Artificial Intelligence ", in Computers and Geosciences, vol. 159, 2/2022. [ bib |  .pdf ]
  11. Halimin Herjanto, Madysen Byrnes, , " How high can you fly? LCC passenger dissatisfaction " Asian Journal of Business Research, vol. 10, no. 2, 7/2020. [ bib |  .pdf ]
  12. Pamela J. Harper, John C. Cary, William S. Brown, , " Ethics under Pressure: A Study of the Effects of Gender, Religiosity, and Income under the Perception of Pressure " Journal of Leadership, Accountability and Ethics, vol. 16, no. 3, 7/2019. [ bib |  .pdf ]
  13. 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 ]
  14. Juan Cota-Ruiz , Ernesto Sifuentes, and Rafael Gonzalez-Landaeta " A Recursive Shortest Path Routing Algorithm with application for Wireless Sensor Network Localization " in IEEE Sensors Journal, 3/2016. [ bib |  .pdf ]
  15. , 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 ]
  16. , 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, 8/2014. [ bib |  .pdf ]
  17. , 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 ]
  18. 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 ]
  19. , 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 ]
  20. , 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 ]
  21. , 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 ]
  22. , 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 ]
  23. 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 ]
  24. 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 ]
  25. , 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 ]
  26. , 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 ]
  27. 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 ]
  28. 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 ]
  29. 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 ]

Books

  1. , Book under contract: " AI Technology, Ethics, and Society ." Publisher: Springer. 2024.
  2. Ziheng Sun, Nicoleta Cristea, and , " Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges." Publisher: Elsevier. 2023. [ buy ]
  3. , " Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python." Publisher: Packt, 416 pages. 2020. [ bib |  buy ]
  4. 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 Presentations and Publications

  1. Alejandro Rodriguez Perez, , Javier Turek, Korn Sooksatra, Ernesto Quevedo, Gisela Bichler, Tomas Cerny, Laurie Giddens, and Stacie Petter, " Decoding the Obfuscated: Advanced NER Techniques for Commercial Sex Advertisements ", in the 10th Annual Conf. on Computational Science and Computational Intelligence (CSCI'23), 12/2023. [ bib |  .pdf ]
  2. Ernesto Quevedo, Ana Paula Arguelles, Alejandro Rodriguez, Jorge Yero, Dan Pienta, Tomas Cerny, and , " Creation and Analysis of a Natural Language Understanding Dataset for DoD Cybersecurity Policies (CSIAC-DoDIN V1.0) ", in the 10th Annual Conf. on Computational Science and Computational Intelligence (CSCI'23), 12/2023. [ bib |  .pdf ]
  3. Korn Sooksatra, Bikram Khanal, , and Donald R. Schwartz " Attribution Scores of BERT-Based SQL-Query Automatic Grading for Explainability ", in the 10th Annual Conf. on Computational Science and Computational Intelligence (CSCI'23), 12/2023. [ bib |  .pdf ]
  4. Maisha Binte Rashid and " Scoping Review on Image-Text Multimodal Machine Learning Models ", in the 10th Annual Conf. on Computational Science and Computational Intelligence (CSCI'23), 12/2023. [ bib |  .pdf ]
  5. Ana Paula Arguelles Terron, Jorge Yero Salazar, , Ernesto Quevedo Caballero, and Alejandro Rodriguez Perez " Task-Specific or Task-Agnostic? A Statistical Inquiry into BERT for Human Trafficking Risk Prediction ", in LXAI Workshop @ NeurIPS 2023, 12/2023. [ bib |  .pdf ]
  6. Alejandro Rodriguez Perez, Korn Sooksatra, , Ernesto Quevedo Caballero, Javier S. Turek, Gisela Bichler, Tomas Cerny, Laurie Giddens, and Stacie Petter, " Aligning Word Embeddings from BERT to Vocabulary-Free Representations ", in the 25th International Conference on Artificial Intelligence (ICAI 2023), 7/2023. [ bib |  .pdf ]
  7. , " Deep Learning Evolved: Overcoming Sub-Optimal Local Minima with (μ/ρ + λ)-Evolution Strategies ", in the 25th International Conference on Artificial Intelligence (ICAI 2023), 7/2023. [ bib |  .pdf ]
  8. , Donald R. Schwartz, and Ernesto Quevedo, " BERT Goes to SQL School: Improving Automatic Grading of SQL Statements ", in the 25th International Conference on Artificial Intelligence (ICAI 2023), 7/2023. [ bib |  .pdf ]
  9. Bikram Khanal and , " Evaluating the Impact of Noise on Variational Quantum Circuits in NISQ Era Devices ", in Proc. of the International Conference on Emergent and Quantum Technologies (ICEQT 2023), 7/2023. [ bib |  .pdf ]
  10. Arun Sanjel, Bikram Khanal, , and Greg Speegle, " Non-Invasive Muzzle Matching for Cattle Identification using Deep Learning ", in The 27th International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV 2023), 7/2023. [ bib |  .pdf ]
  11. Korn Sooksatra, Greg Hamerly, and , " Is ReLU Adversarially Robust? ", in The LXAI Workshop @ International Conference on Machine Learning (ICML 2023), 7/2023. [ bib |  .pdf ]
  12. Alejandro Rodriguez Perez, Korn Sooksatra, , Ernesto Quevedo Caballero, Javier S. Turek, Gisela Bichler, Tomas Cerny, Laurie Giddens, and Stacie Petter, " An Empirical Analysis Towards Replacing Vocabulary-Rigid Embeddings by a Vocabulary-Free Mechanism ", in The LXAI Workshop @ International Conference on Machine Learning (ICML 2023), 7/2023. [ bib |  .pdf ]
  13. and Mehang Rai, " Gabor Filters as Initializers for Convolutional Neural Networks: A Study on Inductive Bias and Performance on Image Classification ", in The LXAI Workshop @ International Conference on Machine Learning (ICML 2023), 7/2023. [ bib |  .pdf ]
  14. , Jorge Ortiz, Daniel A. Díaz-Pachón, and Laura N. Montoya, " Bridging Industry, Government, and Academia for Socially Responsible AI: The CSEAI Initiative ", in 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), 5/2023. [ bib |  .pdf ]
  15. Laurie Giddens, Stacie Petter, Gisela Bichler, , Michael Fullilove, and Tomas Cerny, " Navigating an Interdisciplinary Approach to Cybercrime Research ", in the Proceedings of the 56th Hawaii International Conference on System Sciences (HICSS'23), 1/2023. [ bib |  .pdf ]
  16. Antika Roy, , and Mahee Tayba, " Unsupervised Machine Learning Methods for Diagnosing Autism Spectrum Disorder Using Multimodal Data: A Survey ", in the 9th Annual Conf. on Computational Science and Computational Intelligence (CSCI'22), 12/2022. [ bib |  .pdf ]
  17. and Liang Zhao, " On Unsupervised Reconstruction with Dressed Multilayered Variational Quantum Circuits ", in the 9th Annual Conf. on Computational Science and Computational Intelligence (CSCI'22), 12/2022. [ bib |  .pdf ]
  18. and Liang Zhao, " CLIP-ACQUA 📎 💧 CLIP Autoencoder-based Classic-Quantum Latent Space Reduction ", in The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'22), 11/2022. [ bib |  .pdf ]
  19. Cristian Lazo Quispe, Joe Huamani Malca, Gissella Bejarano Nicho, Manuel Huaman Ramos, , and Tomas Cerny " Impact of Pose Estimation Models for Landmark-based Sign Language Recognition ", in LXAI Workshop @ NeurIPS 2022, 11/2022. [ bib |  .pdf ]
  20. Korn Sooksatra, Bikram Khanal, and , " On Adversarial Examples for Text Classification by Perturbing Latent Representations ", in LXAI Workshop @ NeurIPS 2022, 11/2022. [ bib |  .pdf ]
  21. Ernesto Quevedo Caballero, Mushfika Sharmin Rahman, Tomas Cerny, , and Gissella Bejarano " Study of Question Answering on Legal Software Document using BERT based Models ", in LXAI Workshop @ NAACL 2022, 7/2022. [ bib |  .pdf ]
  22. Alejandro Rodriguez Perez, , and Gissella Bejarano " Distributed Text Representations Using Transformers for Noisy Written Language ", in LXAI Workshop @ NAACL 2022, 7/2022. [ bib |  .pdf ]
  23. Bikram Khanal and " Kernels and Quantum Machine Learning ", in Proc. of the International Conference on Emergent and Quantum Technologies (ICEQT 2022), 7/2022. [ bib |  .pdf ]
  24. Donald Schwartz and , " An Automated SQL Query Grading System Using An Attention-Based Convolutional Neural Network ", in Proc. of The 18th International Conference on Frontiers in Education: Computer Science and Computer Engineering, 7/2022. [ bib |  .pdf ]
  25. Tonni Das Jui, Gissella Maria Bejarano, and , " A Machine Learning-based Segmentation Approach for Measuring Similarity between Sign Languages ", in Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, 6/2022. [ video |  bib |  .pdf ]
  26. Gissella Maria Bejarano, Joe Huamani-Malca, Francisco Cerna-Herrera, Fernando Alva-Manchego, and , " PeruSIL: A Framework to Build a Continuous Peruvian Sign Language Interpretation Dataset ", in Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, 6/2022. [ video |  bib |  .pdf ]
  27. , Gisela Bichler, Tomas Cerny, Laurie Giddens, and Stacie Petter " Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations ", in LXAI Workshop @ International Conference on Machine Learning (ICML 2022), 6/2022. [ bib |  .pdf ]
  28. Mahee Noor Tayba, Abdullah Al Maruf, , Erich Baker, and Javier Orduz " Using Quantum Circuits with Convolutional Neural Network for Pneumonia Detection ", in the The Southwest Data Science Conference 2022, 3/2022. [ bib |  .pdf ]
  29. , Jorge Ortiz, Daniel Diaz, and Laura Montoya " Planning a Center for Standards and Ethics in Artificial Intelligence ", in AIDBEI Workshop @ AAAI 2022: Proceedings of Machine Learning Research (PMLR), 2/2022. [ bib |  .pdf ]
  30. Korn Sooksatra and " Enhancing Adversarial Examples on Deep Q Networks with Previous Information ", in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), 12/2021. [ bib |  .pdf ]
  31. , Liang Zhao, and Javier Orduz " Hybrid Quantum Variational Autoencoders for Representation Learning ", in The 19th International Conference on Scientific Computing (CSC 2021), 12/2021. [ bib |  .pdf ]
  32. Korn Sooksatra and , and Javier Orduz " Evaluating Accuracy and Adversarial Robustness of Quanvolutional Neural Networks ", in The 19th International Conference on Scientific Computing (CSC 2021), 12/2021. [ bib |  .pdf ]
  33. Korn Sooksatra and " On the Practical Uses of Experimental Adversarial Neural Cryptography ", in The 19th International Conference on Scientific Computing (CSC 2021), 12/2021. [ bib |  .pdf ]
  34. Korn Sooksatra and " Adversarial Training Negatively Affects Fairness ", in The 19th International Conference on Scientific Computing (CSC 2021), 12/2021. [ bib |  .pdf ]
  35. and Donald R. Schwartz " Modeling SQL Statement Correctness with Attention-Based Convolutional Neural Networks ", in The 19th International Conference on Scientific Computing (CSC 2021), 12/2021. [ bib |  .pdf ]
  36. Bikram Khanal, , Javier Orduz, and Alibek Zhakubayev " Quantum Machine Learning: A Case Study of Grover's Algorithm ", in The 19th International Conference on Scientific Computing (CSC 2021), 12/2021. [ bib |  .pdf ]
  37. Bikram Khanal, , and Javier Orduz " Human Activity Classification Using Basic Machine Learning Models ", in The 19th International Conference on Scientific Computing (CSC 2021), 12/2021. [ bib |  .pdf ]
  38. Mahee Tayba and " Enhancing the Resolution of Satellite Imagery using a Generative Model ", in The 19th International Conference on Scientific Computing (CSC 2021), 12/2021. [ bib |  .pdf ]
  39. Tonni Jui, Olawale Ayoade, , and Javier Orduz " Performance Analysis of Quantum Machine Learning Classifiers ", in LXAI Workshop @ Neural Information Processing Society Conference (NeurIPS 2021), 12/2021. [ bib |  .pdf ]
  40. Korn Sooksatra and " An Adversarial Neural Cryptography Approach to Integrity Checking: Learning to Secure Data Communications ", in 2021 International Joint Conference on Neural Networks (IJCNN), 7/2021. [ bib |  .pdf ]
  41. Nurul Rafi and " A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing ", in The 25th International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV 2021). 7/2021. [ bib |  .pdf ]
  42. Meghan Bibb and " Predicting Traffic Accident Severity with Deep Neural Networks ", in The 17th International Conference on Data Science (ICDATA 2021). 7/2021. [ bib |  .pdf ]
  43. Nurul Rafi and " A Review on Machine Learning Algorithms for Dust Aerosol Detection using Satellite Data ", in The 23rd International Conference on Artificial Intelligence (ICAI 2021). 7/2021. [ bib |  .pdf ]
  44. Javier Orduz, , and Erich Baker " Quantum Circuits for Quantum Convolutions: A Quantum Convolutional Autoencoder ", in The 23rd International Conference on Artificial Intelligence (ICAI 2021). 7/2021. [ bib |  .pdf ]
  45. Javier Orduz, , and Erich Baker " Quantum Machine Learning Foundations and Applications: A Succinct Literature Review ", in The 19th International Conference on Scientific Computing (CSC 2021). 7/2021. [ bib |  .pdf ]
  46. Timothy Hoang, and , " Memorable Password Generation with AES in ECB Mode ", in Daimi K., Arabnia H.R., Deligiannidis L., Hwang MS., Tinetti F.G. (eds) Advances in Security, Networks, and Internet of Things. Transactions on Computational Science and Computational Intelligence. Springer, Cham. 7/2021. [ bib |  .pdf ]
  47. Pamela Harper, , John Cary, and William Brown, " A Study of the Relationship between Employment Attributes, Ethics and the Dampening Effect of Pressure ", in Northeast Decision Sciences Institute (NESDI) 2021 Conference 3/2021. [ bib |  .pdf ]
  48. , " Working Set Selection to Accelerate SVR Training ", in Proceedings of 2nd Workshop on Diversity in Artificial Intelligence (AIDBEI), PMLR 142:35-38, 2/2021. [ bib |  .pdf ]
  49. and Korn Sooksatra " A Proof of Sparseness, Optimality, and Convergence of an LP-SVR ", 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 12/2020. [ bib |  .pdf ]
  50. Korn Sooksatra and " A Review of Machine Learning and Cryptography Applications ", 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 12/2020. [ bib |  .pdf ]
  51. Mehang Rai and " A Review of Convolutional Neural Networks and Gabor Filters in Object Recognition ", 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 12/2020. [ bib |  .pdf ]
  52. " Accelerating the Training of an LP-SVR Over Large Datasets ", 2020 International Conference on Innovative Techniques and Applications of Artificial Intelligence, 12/2020. [ bib |  .pdf ]
  53. " AI Orthopraxy: Towards a Framework for AI That Promotes Fairness ", 2020 IEEE International Symposium on Technology and Society (ISTAS), 11/2020. [ bib |  .pdf ]
  54. Randy Soper, Karen Bennet, , and Mathana, " Developing Use Cases to Support an Empathic Technology Ethics Standard ", 2020 IEEE International Symposium on Technology and Society (ISTAS), 11/2020. [ bib |  .pdf ]
  55. Michael Guarino, , Casimer DeCusatis, " Towards Adversarially Robust DDoS-Attack Classification ", 2020 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), 10/2020. [ bib |  .pdf ]
  56. , and Prabuddha Banerjee " Neural-Based Adversarial Encryption of Images in ECB Mode with 16-bit Blocks. ", 22nd Int'l Conf on Artificial Intelligence (ICAI 2020), 7/2020. [ bib |  .pdf ]
  57. Eric Stenton, and " Fine Tuning a Generative Adversarial Network's Discriminator for Student Attrition Prediction. ", 22nd Int'l Conf on Artificial Intelligence (ICAI 2020), 7/2020. [ bib |  .pdf ]
  58. , Chelsi Chelsi, Laharika Ravula, and Nishit Nishit " Chatbot Deployment Considerations for Application-Agnostic Human-Machine Dialogues. ", The Third Workshop on Reasoning and Learning for Human-Machine Dialogues at the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2/2020. [ bib |  .pdf ]
  59. , Michael Guarino, and Alex Shah " DiPol-GAN: Generating Molecular Graphs Adversarially with Relational Differentiable Pooling. ", LXAI Workshop @ Neural Information Processing Society Conference (NeurIPS 2019), 12/2019. [ bib |  .pdf ]
  60. , and Marcus Zimmermann " Empirical Study of Sentence Embeddings for English Sentences Quality Assessment. ", 2019 International Conference on Computational Science and Computational Intelligence (CSCI), 12/2019. [ bib |  .pdf ]
  61. , Chelsi Chelsi, Laharika Ravula, and Nishit Nishit " Application-Agnostic Chatbot Deployment Considerations: A Case Study. ", 2019 International Conference on Computational Science and Computational Intelligence (CSCI), 12/2019. [ bib |  .pdf ]
  62. Laura Montoya and " Government AI Readiness Meta-Analysis for Latin America And The Caribbean. ", 2019 IEEE International Symposium on Technology and Society (ISTAS), 11/2019. [ bib |  .pdf ]
  63. , Casimer DeCusatis, Matthew Oakley, Alex Antaki, Nicholas Blaskey, Steven LaFalce, and Stephen Stone " Machine Learning for DDoS Attack Classification Using Hive Plots. ", 2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), 10/2019. [ bib |  .pdf ]
  64. , Ezequiel Rivas, Omar Velarde, Samuel Gonzalez, " Deep Sparse Autoencoders for American Sign Language Recognition using Depth Images. ", 21st Int'l Conf on Artificial Intelligence (ICAI 2019), 8/2019. [ bib |  .pdf ]
  65. Kaitlyn Mulligan, , " Dog Breed Identification with a Neural Network over Learned Representations from the Xception CNN Architecture. ", 21st Int'l Conf on Artificial Intelligence (ICAI 2019), 8/2019. [ bib |  .pdf ]
  66. Sabrina Bergsten, , " Societal Benefits and Risks of Artificial Intelligence: A Succinct Survey. ", 21st Int'l Conf on Artificial Intelligence (ICAI 2019), 8/2019. [ bib |  .pdf ]
  67. Mackenzie O'Brien, , " Optimizing Cookie Recipes for Ratings Using Machine Learning and Deep Vector-to-Sequence Recurrent Neural Models. ", 21st Int'l Conf on Artificial Intelligence (ICAI 2019), 8/2019. [ bib |  .pdf ]
  68. Amy Pitts, , " Finding Time Series Breakpoints with Fully Connected Neural Networks. ", 21st Int'l Conf on Artificial Intelligence (ICAI 2019), 8/2019. [ bib |  .pdf ]
  69. Brandon Litwin, , " Deep Neural Network Representations for Song Audio Matching and Recommendation. ", 21st Int'l Conf on Artificial Intelligence (ICAI 2019), 8/2019. [ bib |  .pdf ]
  70. Michael Read, , " Deployment and Hyper-Parameter Optimization of Chatbots. ", 21st Int'l Conf on Artificial Intelligence (ICAI 2019), 8/2019. [ bib |  .pdf ]
  71. , " Modeling Five Sentence Quality Representations by Finding Latent Spaces Produced with Deep Long Short-Memory Models. ", Workshop on Widening NLP @ Association of Computational Linguistics (ACL) annual meeting, 7/2019. [ bib |  .pdf ]
  72. , Pamela J. Harper, John C. Cary, William S. Brown, " ML-Based Feature Importance Estimation for Predicting Unethical Behaviour under Pressure. ", LXAI Workshop @ International Conference on Machine Learning (ICML 2019), 6/2019. [ bib |  .pdf ]
  73. , " ASL Hand-Gestures Recognition With Deep Autoencoders over Depth-Images. ", Mathworks Research Summit, Presentation, 6/2019.
  74. , " Ethical Machine Learning: Identifying and Removing Unfairness Caused by Biased Data. ", New York Celebration of Women in Computing (NYCWIC), 4/2019. [ bib |  slides |  colab ]
  75. Claudia Rojas, Casimer DeCusatis, , " Through the looking glass: Exposing the vulnerabilities in smart doorbell systems. ", New York Celebration of Women in Computing (NYCWIC), 4/2019. [ bib ]
  76. , Aishwarya Pagalla, " Rule-Based Sentence Quality Analysis using Deep LSTM Feature Representations. ", Causal Learning Workshop @ Neural Information Processing Society Conference (NeurIPS 2018), 12/2018. [ bib |  .pdf |  video ]
  77. , Deep Dand, Ezequiel Rivas, Omar Velarde, and Samuel Gonzalez, " A deep learning approach to sign language recognition using stacked sparse autoencoders. ", LXAI Workshop @ Neural Information Processing Society Conference (NeurIPS 2018), 12/2018. [ bib |  .pdf |  video ]
  78. , Sharon Moore, Urszula T. Iwaniec, Russell T. Turner, Kathy Grant, and Erich Baker " Optimizing Support Vector Machine Analysis in Low Density Biological Data Sets. ", 2018 International Conference on Computational Science and Computational Intelligence (CSCI), 12/2018. [ bib |  .pdf ]
  79. , Kerstin Holzmayer, Cristian Hernandez, and Charles Grippaldi " Excitement And Concerns about Machine Learning-Based Chatbots and Talkbots: A Survey. ", IEEE International Symposium on Technology and Society (ISTAS 2018), 11/2018. [ bib |  .pdf ]
  80. , " Ethics under Pressure: A Study of the Effects of Gender, Religiosity, and Income under the Perception of Pressure. ", 2018 International Vincentian Business Ethics Conference (IVBEC), Presentation, 10/2018. [ bib ]
  81. , " Rule-Based Sentence Quality Analysis using Deep LSTM Feature Representations. ", Presentation @ CS Colloquium, 10/2018.
  82. , Aishwarya Pagalla, " Writing Advisor Project. ", Presentation to IBM Watson Education @ IBM Thomas J. Watson Research Center, 9/2018.
  83. , Aishwarya Pagalla, " Writing Advisor Project. ", Presentation to IBM and Marist Executives at the IBM Joint Study Dinner @ IBM in Poughkeepsie, 9/2018.
  84. , P. Handley, and R. Aragon Franco, " Encrypting ImageNet with Chaotic Logistic Maps And AES in ECB Mode. ", 2018 International Conference on Image Processing, Computer Vision, and Pattern Recognition, (IPCVPR), 7/2018. [ bib |  .pdf ]
  85. , " Machine Learning-Based Chatbots: An Overview, Analysis of Trust, and Ethical Issues. ", ECC Conference 2018, Presentation, 6/2018. [ bib |  .pdf ]
  86. , 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 ]
  87. John Cary " Ethics under Pressure? ", Proceedings of the 2017 Susilo Symposium, Boston University, 6/2017. [ bib |  .pdf ]
  88. 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 ]
  89. , 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 ]
  90. , 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 ]
  91. , 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 ]
  92. 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 ]
  93. 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.
  94. , 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 ]
  95. , 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 ]
  96. , 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 ]
  97. ; 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 ]
  98. 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 ]
  99. 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 ]
  100. 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 ]
  101. 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 ]
  102. , 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 ]
  103. , 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 ]
  104. , 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 ]
  105. 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.
  106. J.G. Rosiles, W. Qian, " Automatic Quantitative Colocalization Analysis: An Image Restoration and Machine Learning Approach ," Proc. 2009 UTEP SACNAS Research Expo, 4/2009.
  107. 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 ]
  108. 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 ]
  109. , 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 ]
  110. , 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 ]
  111. , M. Chacon, " Evaluation of Motion Detection Methods for Person Identification based on Face Recognition ," Proc. XXVII International Congress on Electronics Engineering, 10/2005.
  112. , " In Motion Face Recognition Through Multilayer Perceptrons ," IEEE/ANaCC/cenidet Proc. of the 11th International Congress on Computer Science Research. 11/2004.
  113. , " 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: $575,616)


Projects where I am not the principal investigator (Total: $945,954)


Professional Associations


Professional Service


Awards


Short Autobiography

Pablo Rivas (Senior Member, ACM and IEEE) received the BS in Computer Systems Engineering degree from the Nogales Institute of Technology, Mexico, in 2003; the MS in Electrical Engineering from the Chihuahua Institute of Technology, Mexico, in 2007; and his Ph.D. in Electrical and Computer Engineering from The University of Texas at El Paso, in 2011. He has been an assistant professor of computer science at the School of Engineering and Computer Science at Baylor University since 2020. Before that, Dr. Rivas was with the School of Computer Science and Mathematics at Marist College (2015-2020). Pablo has more than eight years of industry experience as a Software Engineer and has been recognized for his creativity and academic excellence. In 2011, he was inducted into the international honor society for IEEE Eta Kappa Nu; in 2021, Dr. Rivas was inducted into Upsilon Pi Epsilon, the international honor society for the computing and information disciplines; and in 2022, he was elevated to Senior Member of ACM. He has published several peer-reviewed papers and authored a book on deep learning in 2020. Prof. Rivas predominantly researches artificial intelligence and its ethical and social implications, focusing on computer vision, natural language processing, and quantum machine learning. He is a member of the IEEE Standards Association and is involved in the working groups developing the P70XX series standards for AI ethics. He is currently in the planning phase of the Center for Standards and Ethics in Artificial Intelligence with funding from the National Science Foundation.

Work Experience

Assistant Professor of Computer Science @ Baylor University ~ 2020 - to-date

Assistant Professor of Computer Science @ Marist College ~ 2015 - 2020

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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Pablo Rivas

Copyright © 2020 Pablo Rivas
School of Engineering and Computer Science
Baylor University
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