Education

• Computing structures and programming concepts, object orientation, data structures, RESTful interfaces, and other SDKs.
• Continuity and differentiability of a function of several variables, gradient vector, Hessian matrices, Taylor approximation, optimization fundamentals, Lagrange multipliers, convexity, gradient descent.
• Machine learning algorithms: decision trees, principal component analysis, k-means, clustering, logistic regression, random forests, boosting, neural networks, kernel methods, and deep learning.
• Techniques in data visualization. Interactive reasoning and exploratory analysis through visual interfaces. Data visualization can be applied in various domains, including science, engineering, and medicine.
• Scalable interactive methods involving exploring big data and visualization methods. Techniques to evaluate the effectiveness and interpretability of analytical products for diverse users to obtain insights supporting assessment, planning, and decision-making.
• Concentration inequalities, Markov processes, ergodicity, martingale inequalities, empirical processes, sparse linear models in high dimensions, principal component analysis in high dimensions, and estimation of large covariance matrices.
• Mathematical foundations of massive data processing, developing and analyzing algorithms. Methods exploration for sampling, sketching, and distributed processing of large-scale databases, clustering, dimensionality reduction, and optimization methods for scalable statistical description, querying, pattern mining, and learning from data.

• Engineering and Managing Data-Driven Change (SQL and additional RDBMS).
• Statistically Measuring and Modeling Social Justice (R, Python).
• Data Storytelling and Visualization (Tableau).
• Data Science and Environmental Justice (Redivis, Python).
• Mastercard Data Science Fellow; Northeast Big Data Hub Howard Chapter President; Women in Data Science Datathon Coordinator; Upsilon Pi Epsilon Honor Society.
• Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) All of Us Traineeship, National GEM Consortium GEM Fellow.

• Applied Data Science with Python and Machine Learning: Artificial, Convolutional, Recurrent, and Deep Neural Networks. Python, TensorFlow, Pandas, Sci-Kit-Learn, Keras.
• Data Visualization: Tableau, Matplotlib, Seaborn, Pyplot, Plotly, Bokeh.
• Scientific and technical computing using the SciPy package and its sub-packages.

• Theory and practical application of Supervised and Unsupervised Learning, Deep Learning, Time-Series Analysis, Neural Networks, Recommendation engines, regression, and Computer Vision.
• Strong foundations in Python, Complex problem-solving, Analytical thinking, and Statistical Modeling.

• Natural sciences including Physics, Analytical Biochemistry, Organic Chemistry, Molecular Genetics, Cellular Biology, Cell Biotechnology, Plant Physiology, and Bacterial Genetics.
• Advanced Mathematics, including Biostatistics, Elementary Statistics, Linear Algebra, and Advanced Calculus.

Employment Experience

• Adjunct faculty in the Center for Applied Data Science and Analytics teaching the Introduction to Data Science foundation course for the Master of Science Data Science program.
• Introduce students to the primary skills to support advanced Machine Learning and Geographic Information Systems studies.
• Instructional design of course materials, assignments, and data science analyses in healthcare, business/economics, and relational databases for data scientists.
• Course content creation in probabilistic programming, statistical analysis, and data storytelling with visualization lectures and practical data science exercises with live coding demonstrations.
• Host office hours to offer one-on-one help to students and ensure support for success.

• Research critical infrastructure points of interest to gain knowledge and insights from high-volume, high-dimensional data and investigate power systems reliability.
• Geospatial analysis, advanced statistical analysis, predictive modeling, and advanced data cleaning to create solutions that enhance emergency operations and preparedness.
• Observed and contributed to building simulation algorithms and optimization using mathematical techniques and statistics.
• Assisted in technical report creation to advise business partners for hardening strategies in critical infrastructure.

• Designed and performed study-related conduct for primary research into neurological connections to scent and empirical analyses that bridge various data sources, including sensor data, scent profiles, and user feedback.
• Collaborate with stakeholders to understand data and reporting requirements and develop streamlined workflows for data intake, validation, engineering, modeling, visualization, and communication.
• Define and refine metrics that measure the success of our products and business initiatives. Continuously evaluate and improve existing metrics to provide more meaningful insights.

• Extracts knowledge and insights from high volume, high dimensional data to investigate complex business problems through a range of data preparation, modeling, analysis, and visualization techniques.
• Advanced statistical analysis, algorithms, predictive modeling, experimentation, and pattern recognition to create solutions that enable enhanced business performance.
• Building predictive, prescriptive, and optimization data models that leverage advanced algorithms, mathematical techniques, and statistics.
• Researching and piloting innovative ideas, testing/evaluating new technologies. Promoting models into production environments and monitoring their operation. Communicating results via presentations, documentation, roadshows, and visualizations.

• Communicating as the Regional Coordinator with FERN Leadership regarding member lab challenges to maintain integration across multiple stakeholders. • Primary research into methodology changes leveraging early decision-making in outbreak situations. This research later translated into Multi-Lab Validation (MLV) and Rapid Methods Deployment to member labs. • Developed new program offerings based on regional needs using projected data for outbreaks to leverage regional markets. Served as lecturer and laboratory methods educator for FERN Methods courses.

Selected Projects

• Detection of seasonal data using a discrete Fourier transform model with a Pearson Correlation Coefficient and MAPE metric to optimize the predictive capacity of electric load forecasting and decision-making for electric power reserves during high consumer demand to the utility-scale electric grid.
“Short-term forecasting of daily precipitation using a SARIMA-XGBoost Model for forecasting rainfall data in Australia.” September 2023 • Creation of a forecasting model that can predict the weather combining historical patterns to predict the likelihood of future weather events. Severe weather are becoming increasingly important as human impact on the climate increases. Confidently forecasting these extreme events is essential for protecting life. “Identification of Plasmodium spp. from digital blood smear slides using Computer Vision.” December 2022 • Build and test a computer vision model that successfully analyzes digital images containing infected and uninfected blood smears for key features identifying Malaria infections at the species level.

Patents and Selected Technical Publications

1. Bacterial identification. The present invention provides for separating bacterial species and serotypes using electrophoretic methods. T Taylor, M Hayes, LK Kemp, P Jones - US Patent 9,185,356, 2015
2. DC-iGDEP device for separating bacterial serotypes. T Taylor, M Hayes, LK Kemp, P Jones - US Patent 9,476,085, 2016
3. Paul V. Jones, Alexa F. DeMichele, LaKeta Kemp, and Mark A. Hayes. Differentiation of Escherichia coli Serotypes Using DC Gradient Insulator Dielectrophoresis. Anal. Bioanal. 2014, 406(1), 183192
4. Crowther CV, Hilton SH, Kemp L, Hayes MA. Isolation and identification of Listeria monocytogenes utilizing DC insulator-based dielectrophoresis. Analytica chimica acta. 2019 Aug 30;1068:41-51.

Current Certifications