Certificate

Innovation / Design Thinking
Stanford University
2017

Doctor of Philosophy (Ph.D.)

Computer Science
Oklahoma State University
1998

Master of Science (M.S.)

Computer Science
Oklahoma State University
1995

Bachelor of Science (B.S.)

Computer and Information Science / Mathematics
Langston University
1992

Certificate

Cryptology
National Cryptologic School
1991

CS Education

Innovation and Tech Entrepreneurship

CS Diversity, Equity, and Inclusion

Distributed Computing

Operating Systems

CSCI 401 Operating Systems

CSCI 402 Mobile Application Development

CSCI 492 Senior Project I

CSCI 492 Senior Project II

CSCI 493 Bison Startup: Tech Entrepreneurship and Lean Startups

CSCI 494 Bison Accelerate: Launch and Iterate

CSCI 375 Software Engineering

CSCI 410 Software Development Studio

EGPP 501 Bison Innovate: Product Management

CSCI 680 Advanced Operating Systems

CSCI 501 Computer Architecture

BEYA Innovation Award recipient, 2018

American Association for the Advancement of Science Fellow, 2016

Fulbright Scholar, Univ. of South Africa, 2015

Eminent Scholar Tau Beta Pi, 2009

Teacher of the year (Dept. of Systems and Computer Science – CEACS), 2003-2004, 2008-2009

Sigma Xi Honor Society

Upsilon Pi Epsilon Honor Society

Analysing loneliness forum posts, the comments they elicit, and the responses to these comments.

Analysing loneliness forum posts, the comments they elicit, and the responses to these comments.

This article leverages artificial intelligence and machine learning techniques to explore online social interactions related to loneliness. Using BERTopic, a transformer-based topic modeling tool, and the LIWC psycholinguistic framework, the researchers analyzed over three years of Reddit posts and comments on r/Lonely. Their ML-driven analysis identified patterns in language and content that influence whether original posters respond to comments, with features such as second-person pronouns and present-focused language increasing the likelihood of engagement. The study demonstrates how AI-powered language modeling and behavioral analytics can uncover nuanced social dynamics in digital mental health communities and inform the design of more responsive, empathetic AI-driven interventions.

Ensembling and Modeling Approaches for Enhancing Alzheimer's Disease Scoring and Severity Assessment

Ensembling and Modeling Approaches for Enhancing Alzheimer's Disease Scoring and Severity Assessment

The ​paper proposes a machine learning framework for predicting Alzheimer's disease (AD) severity using speech and language data. Utilizing the ADReSS dataset, which includes audio recordings and transcripts from 156 participants, the authors extracted over 13,000 acoustic and linguistic features. They employed various machine learning models, including Random Forest and k-Nearest Neighbors, and introduced an ensemble technique to improve prediction accuracy. The study also explored deep learning approaches, such as AutoKeras and BERT, for comparison. Their ensemble model achieved competitive results in predicting Mini-Mental State Examination (MMSE) scores and classifying AD stages (Early, Moderate, Severe), demonstrating the potential of combining diverse models and features for enhanced AD assessment.

Accurate Identification of Mass Peaks for Tandem Mass Spectra Using MCMC Model

Accurate Identification of Mass Peaks for Tandem Mass Spectra Using MCMC Model

In proteomics, many methods for the identification of proteins have been developed. However, because of limited known genome sequences, noisy data, incomplete ion sequences, and the accuracy of protein identification, it is challenging to identify peptides using tandem mass spectral data. Noise filtering and removing thus play a key role in accurate peptide identification from tandem mass spectra. In this paper, we employ a Bayesian model to identify proteins based on the prior information of bond cleavages. A Markov Chain Monte Carlo (MCMC) algorithm is used to simulate candidate peptides from the posterior distribution and to estimate the parameters for the Bayesian model. Our simulation and computational experimental results show that the model can identify peptide with a higher accuracy.

Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's

Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's

This article ​presents a machine learning approach to predict cognitive decline in Alzheimer's disease (AD) patients using speech data. Utilizing the ADReSS challenge dataset, the study extracted over 13,000 acoustic and linguistic features, both handcrafted and learned, from patient speech recordings. Ten linear regression models were evaluated to predict Mini-Mental Status Exam (MMSE) scores, a standard measure of cognitive function. Feature selection techniques, including recursive elimination and correlation analysis, identified 54 key features that enhanced prediction accuracy, surpassing existing baselines. Notably, the study found that handcrafted linguistic features were more significant predictors than acoustic or learned features, highlighting the potential of natural language processing and machine learning in developing non-invasive tools for early AD detection.

Predicting Protein-Protein Interactions Based on PPI Networks

Predicting Protein-Protein Interactions Based on PPI Networks 

​This paper introduces a novel computational method for predicting protein-protein interactions (PPIs) by leveraging graph theory and network clustering techniques. Departing from traditional machine learning approaches that focus on individual protein features, we construct dynamic PPI networks using data from online protein databases, encompassing query proteins and their neighboring interactions. These networks are then partitioned into sub-networks through an improved community detection algorithm, inspired by methods such as the Newman algorithm, to identify functional modules within the network. A scoring function is subsequently applied to evaluate potential interactions based on the structural properties of these clusters. The experimental results demonstrate that this network-based approach enhances the accuracy of PPI predictions, underscoring the potential of integrating artificial intelligence and machine learning techniques with graph-theoretical frameworks to address complex biological problems.

Design and Implementation of a Structured Adaptive Individualized Learning System (SAILS) to Assist in the Successful Matriculation of Students in Computer Science

Design and Implementation of a Structured Adaptive Individualized Learning System (SAILS) to Assist in the Successful Matriculation of Students in Computer Science

The paper introduces SAILS, an adaptive educational platform aimed at enhancing learning outcomes for undergraduate computer science students with diverse academic backgrounds. SAILS integrates the Felder-Soloman Learning Style Index (FLSI) and Bloom’s Revised Taxonomy to tailor educational content to individual learning preferences and cognitive levels. A key feature of SAILS is its interactive dashboard, which provides students with visual feedback on their performance, including goal-setting maps and progress tracking tools, thereby promoting self-efficacy and self-regulation. The system employs a quasi-experimental design to deliver personalized interventions, addressing the varying degrees of preparedness among students. While the paper does not explicitly mention the use of artificial intelligence or machine learning, the adaptive nature of SAILS aligns with AI-driven educational technologies that dynamically adjust content based on learner profiles. This approach is particularly pertinent in computer science education, where student retention and engagement are critical amidst the growing demand for skilled professionals in the field.

Mono-isotope Prediction for Mass Spectra Using Bayes Network

Mono-isotope Prediction for Mass Spectra Using Bayes Network

The paper presents a machine learning approach to enhance the identification of mono-isotopic peaks in mass spectrometry data, which is crucial for accurate protein analysis. The authors developed a naïve Bayes classifier that assumes independence among selected features to predict mono-isotope patterns from tandem mass spectra. By utilizing validated theoretical spectra as prior information, the model employs three main features extracted from the dataset as independent variables. When applied to the publicMo dataset, this Bayesian approach demonstrated superior accuracy and sensitivity compared to existing methods, highlighting the potential of integrating probabilistic machine learning models into proteomic data analysis workflows.

APPARATUS AND METHOD FOR CONTEXT-AWARE MOBILE DATA MANAGEMENT

APPARATUS AND METHOD FOR CONTEXT-AWARE MOBILE DATA MANAGEMENT, United States of America, US 8751743, 13/047,992, 2014/6/10, Howard University 

Using Lean Launchpad to Build an Innovation Ecosystem at a Historically Black University

Using Lean Launchpad to Build an Innovation Ecosystem at a Historically Black University

Using Lean Launchpad to Promote Student Engagement and Persistence

Using Lean Launchpad to Promote Student Engagement and Persistence

Opening doors for Black engineers

Opening doors for Black engineers

Google tech exchange: an industry-academic partnership that prepares black and latinx undergraduates for high-tech careers

Google tech exchange: an industry-academic partnership that prepares black and latinx undergraduates for high-tech careers

This paper describes Google Tech Exchange, an industry-academic partnership that involves several Historically Black Colleges and Hispanic Serving Institutions. Tech Exchange's mission is to unlock opportunities in the tech industry for Black and Latinx undergraduates. It is an immersive computer science experience for students and faculty. Participants spend a semester or two at Google in Silicon Valley taking or co-teaching computer science courses, including cutting-edge ones not offered at many universities. The 2018-2019 graduates especially valued the community-building, and a high percentage secured technical internships or jobs.