Resume
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As a PhD mathematics student with a passion for optimization and mathematical modeling, I am constantly seeking to push the boundaries of what is possible with numbers and equations. I have always been drawn to the beauty and elegance of mathematics, and my interest in optimization stems from my fascination with finding the most efficient solutions to complex problems.
My graduate work in applied mathematics involved studying the microscopic structure of materials (metals). Most materials have a microscopic structure; just as life forms have cells, metals have crystal grains. In my study, I understood that the orientation of these crystal grains determine the physical and chemical properties of a metal, such as; fracture points, density, elasticity, conductivity, magnetic properties, chemical reactivity, etc. Thus, two metals with different crystal grain orientations can have different physical properties. Simply put, given information about the crystal grain structure of a metal, we can determine the properties of a metal. Cool right?
What if you could do this with more than just metal? Well, you're in luck, because you can. Most metals, ceramics and plastics have a crystal grain structure.
Yes, you heard me right! NO GLASS! Glass crystals are just one giant crystal, and so there is nut much that can be done with it.
Education
[Aug2021 ] - [ May2024 ]
[ Clarkson University, Potsdam, NY]
[ Applied Mathematics, Ph.D.]
Research: Crystallographic Data Reconstruction Using Weighted Total Variation flow and a Hybrid Deep Learning method
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[Aug2019 ] - [ May2021 ]
[ Clarkson University, Potsdam, NY]
[ Applied Mathematics, MS]
Research: Crystallographic Data Reconstruction Using Weighted Total Variation flow
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[Aug2013] - [ May2017 ]
[ University of Ghana, Accra, Ghana]
[ Statistics and Mathematics, BS]
In the summer of 2023, I undertook a 3 month internship at Regeneron Pharmaceuticals as an Statistician where I was trained in standard operating procedures (SOPs) and various assays in laboratory testing and clinical trials. I also worked on experimental designs (DoE) and sampling procedures using JMP and Minitab for selecting samples needed to meet GxP. Furthermore, I created reports on various existing procedures and processes in statistical testings for traceability and reproducibility. The multidisciplinary nature of the Pharmaceutical industry gave me experience in reporting and discussing results concisely with a wide variety of stakeholders.
My Ph.D. research, “reconstructing crystallographic data from electron backscatter diffraction using hybrid machine learning models”, has sharpened my quantitative and analytical skills. This involved collecting crystallographic data of materials in the laboratory using a scanning electron microscope and then reconstructing the diffraction patterns into the grain structure of the material and finally correcting the erroneous data. This was novel to my mathematical background, and so I undertook a period of study of existing literature, and also collaborated with other experts in the field from the National Institute of Standards and Technology (NIST) in order to accomplish the research goals. I developed a metric for identifying erroneous data, reconstructing, and visualizing. I also wrote Python scripts over an available software (Dream3D) to automate data generation for training a partial convolutional neural network. Later, I developed these scripts into python packages to be made available soon. Finally, I incorporated a deterministic (pde model) into our neural network to aid in interpretability. I have a paper in a peer-reviewed journal, details of which are in my resume.
In the summer of 2021, I had the privilege of working as a machine learning engineer for the first time. I usually work from a coffee shop when I am not my office on campus. This coffee shop is part of a local food chain restaurant (The Bagelry - formerly 4 locations, now down to 2). I noticed that they had bad sales on one rainy summer day of our town's annual summer festival. They had to discard large amounts of bagels as a result. Upon observing this, I approached the manager of the Potsdam store and asked for more details. Then I started formulating the problem. After I had a solid problem statement with a proposed solution, I made an appointment with the owner of this food-chain and explained to him my proposed solution with the help of some visualizations, and how it can help him minimize waste and maximize profit. However, ai would need some historical sales data. Rightfully so, was concerned about possible breach of data to competitors, and so I assured him with ways he can transform his data so that the data retains the sales distribution, without the true values. He liked the idea and I got started. I collected historical weather and precipitation data from the Clarkson University weather station database. Then I cleaned and analyzed the data to identify relevant factors using multiple correlations, variance inflation factors (Vif), and other visualization. In summary, I built multiple Machine learning prediction models using; multiple regression (linear, polynomial, and logistic), support vector machines (SVM), XGBoot, Neural Networks, and Decision trees with python (Pandas, Scikit learn and Tensorflow). The neural network model gave the best prediction, with decision trees and Multiple logistic regression not lagging far behind. I presented the results with their respective implications. The neural network model was not worth the computational cost because of the inadequate interpretability in spite of its high prediction efficiency. I recommended the decision tree and logistic regression model for interpretability and efficiency. Unexpectedly, the model also helped to predict the required staffing daily based on the weather forecast and historical sales.
Relevant Work Experience
[ 09/2023 ] - [ present ]
[ Clarkson University - Impetus ]
[ Graduate Research Fellow ]
Lead the design and implementation of STEM-based research projects for middle and high school students, fostering their curiosity and critical thinking skills.
Mentor students in research methodologies, providing guidance in the research process, and supporting them in presenting their findings, cultivating a culture of inquiry and communication.
Instruct students in circuit building and coding, utilizing C++ and Arduino, to enhance their technical proficiency and practical application of coding skills.
Serve as a mentor at coding bootcamps, guiding students in experimental design and setup for STEM projects, contributing to their hands-on experience and problem-solving abilities.
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[ 05/2023 ] - [ 08/2023 ]
[ Regeneron Pharmaceuticals ]
[ Statistician]
Conducted comprehensive cleaning, visualization, and statistical analysis of change control data using advanced tools such as JMP, Alteryx, and Tableau, ensuring data integrity and facilitating informed decision-making.
Initiated the development of a dynamic dashboard for real-time monitoring of change controls and implemented alert mechanisms using Python-Dash, demonstrating a proactive approach to process improvement.
Designed and executed experiments to identify critical factors for statistical tests, utilizing statistical software JMP to enhance the accuracy and reliability of analyses.
Developed a deep understanding of Standard Operating Procedures (SOPs) related to statistical reporting, ensuring compliance and quality in statistical processes.
Collaborated in the review of statistical tests for laboratory tests and clinical trials, contributing to the robustness and validity of statistical methodologies.
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[ 09/2021] - [ 05/2023 ]
[ Clarkson University - Impetus ]
[ Data scientist]
Led a comprehensive analysis of graduation and retention rates, leveraging extensive student data from Clarkson University.
Applied data visualization techniques with ggplot2, uncovering crucial features influencing graduation and retention outcomes.
Spearheaded the evaluation of "Retention measures" impact on graduation and retention through robust statistical inference tools in R.
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[ 05/2021 ] - [ 05/2022 ]
[ Bagelry ]
[ Machine Learning Engineer ]
Spearheaded a data-driven initiative by extracting and analyzing historical weather data and sales information for a local food-chain restaurant, utilizing SQL query tools for efficient data retrieval.
Applied advanced machine learning techniques using Python frameworks such as Scikit-learn and Tensorflow to develop and fine-tune regression models, decision trees, and neural networks for accurate daily sales predictions.
Innovatively visualized complex interactions within the data to uncover patterns and enhance predictive model performance.
Executed comparative analyses between model predictions and actual sales, demonstrating a keen ability to optimize and refine machine learning models.
Translated model outputs into actionable insights, leveraging predictions to estimate foot traffic and provide valuable input for strategic employee shift decisions.
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[ 05/2017 ] - [ 05/2019 ]
[ Science Solutions Center ]
[ Data Analytics & Supply Chain Manager ]
Spearheaded the optimization of the supply chain at the Science Solution Center in Dzorwulu, Ghana.
Developed and maintained a comprehensive schedule using Microsoft Excel to streamline the flow of educational resources to schools and individuals.
Implemented data analytics techniques to identify efficiency gaps in the supply chain, leading to a 20% improvement in resource delivery timelines.
Collaborated cross-functionally to enhance inventory management processes, ensuring timely availability of educational materials for diverse stakeholders.
Played a key role in data-driven decision-making by providing actionable insights through analytics reports, contributing to overall operational excellence.
Relevant Projects
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