Data Analytics Machine Learning

Oluwaseyi (Sheyi) Jaiyeoba, PhD

**********@*****.*** — +1-765-***-****

— Linkedin: https://www.linkedin.com/in/oluwaseyi-jaiyeoba-9281b7159/

— ORCID: https://orcid.org/my-orcid?orcid=0000-0003-2900-0787

— GitHub: https://github.com/afroomedia

SUMMARY

Passionate technology professional and researcher specializing in generative AI, machine learning, and data analytics. Expe- rienced in developing high-quality synthetic data to enhance model accuracy, with expertise in deep learning, reinforcement learning, and statistical modeling. Proficient in data analytics, leveraging advanced techniques to extract insights, optimize models, and drive data-driven decision-making. Skilled in interpretable AI and data visualization, transforming complex datasets into actionable insights.

• Collaborative Research Leader: Skilled in defining clear research objectives and driving interdisciplinary teams to optimize, improve processes, enhance model performance, and advance innovations.

• Analytical AI specialist: Expert in leveraging tech tools to analyze complex datasets, troubleshoot models, and refine model architectures for improved accuracy and efficiency. Adept at identifying and mitigating systemic challenges in AI models and data pipelines.

• Effective Communicator and Lifelong Learner: Proficient in translating complex data science concepts into ac- tionable insights for diverse audiences, including researchers, industry professionals, and stakeholders. Quick to absorb emerging technologies, continuously expanding expertise in technology systems. TECHNICAL AND RESEARCH SKILLS

• Programming: Python, R, SQL, Java, JavaScript, Flask, FastAPI

• AI Tools: TensorFlow, PyTorch, Scikit-learn, Keras, MATLAB

• Data Visualization: Tableau, Power BI, Matplotlib, Seaborn, Kepler.gl, GIS

• Big Data & Analytics: Hadoop, NoSQL, Databricks, Spark

• Other: Blockchain Development, Statistical Analysis

• Research Skills: Data Collection & Cleaning, Literature Review, Hypothesis Testing, Research Methodology Develop- ment, Quantitative & Qualitative Analysis, Model Evaluation & Validation, Scientific Writing & Publishing

• Soft Skills: Research Communication, Technical Writing, Engaging Project Presentations RESEARCH EXPERIENCE

Purdue University West Lafayette, IN, United States Research and Teaching Assistant January 2021 — August 2025

• Led groundbreaking research in Generative AI at the Data Analytics Lab, generating high-quality synthetic data with improved generative models using TensorFlow and PyTorch. Designed and implemented a novel Reinforcement Learning- based Auxiliary Classifier WaveGAN (RL-AC-WaveGAN) for fine-grained audio synthesis, improving machine learning classification accuracy by 10–30% in low-data scenarios.

• Built GAN-based pipelines in Python to generate synthetic images of dark skin eczema, augmenting underrepresented data and improving classification accuracy by up to 25%. Used TensorFlow and PyTorch to design and train custom generator–discriminator models.

• Spearheaded innovative data science research at the Byrd Visualization Lab, Purdue University, by developing and deploying machine learning-driven visualization tools using Flask to analyze complex lupus patient datasets from Riley Children’s Hospital. Applied advanced Visualization methods to high-dimensional clinical data, enhancing interpretabil- ity and enabling improved diagnostic insights that advanced the understanding of lupus.

• Conducted large-scale analysis of clinical and phenotypic data from the NIH ”All of Us” Research Program to un- cover data-driven insights into lupus disease heterogeneity, utilizing Google Cloud Platform (GCP), Apache Spark and Databricks to build distributed data pipelines for feature engineering, normalization, and multimodal data integration across EHR, laboratory results, and survey responses.

• Applied unsupervised learning techniques and supervised models via Spark MLlib to identify potential lupus subtypes and predict disease trajectories from the NIH ”All of Us” data. This scalable pipeline reduced data processing time by over 60% and supported precision medicine approaches to lupus diagnosis and treatment planning.

• Pioneered an advanced literature mining project, leveraging Natural Language Processing (NLP) to uncover hidden relationships in medical research on lupus. Developed a cutting-edge literature-based discovery system by integrating transformer-based models, Large Language Models (LLMs), and topic modeling techniques to link disparate research findings and identify novel insights for lupus diagnosis and prevention.

• Served as Co-instructor for undergraduate courses in Data Visualization and SQL Database Management at the Depart- ments of Computer Graphics Technology (CGT) and Computer and Information Technology (CIT), Purdue Polytechnic, Purdue University. Guided over 300 students across multiple semesters in mastering practical skills in using SQL to query complex datasets, performing regression analysis, and visualizing results using tools such as Tableau and Kepler.gl, significantly enhancing their statistical reasoning and data interpretation capabilities.

• Demonstrated strong research communication skills by presenting original work at leading conferences, including ICB- DAH 2022 (International Conference on Big Data and Analytics in Healthcare) and ICoICT 2019 (International Con- ference on Information and Communication Technology). Effectively conveyed complex ideas to technical and interdis- ciplinary audiences, supported by clear documentation and reproducible workflows. Universiti Sains Malaysia Pulau Pinang, Penang, Malaysia Research Assistant September 2017 — December 2020

• Designed an innovative enhancement to the Proof-of-Work (PoW) algorithm that repurposes wasted computational en- ergy from PoW mining (e.g., Bitcoin mining) for Computer-Aided Drug Discovery. Integrating cryptographic hashing in PoW with molecular docking techniques to accelerate pharmaceutical research using distributed computing in PoW networks. This framework transforms traditionally wasted energy into a resource for advancing molecular simulations in drug development.

• Led a team of undergraduate and graduate computer science students in the end-to-end creation, design, implementa- tion, and testing of a scalable blockchain software application utilizing Docker container tools. This solution significantly enhances the scalability and efficiency of a Proof-of-Work blockchain structure, demonstrating robust performance in distributed computing environments.

• Conducted research on Vehicular Ad Hoc Networks (VANET)-based Connected and Autonomous Vehicles (CAVs), devel- oping vehicle avoidance time models to enhance road safety. Designed and analyzed VANET communication protocols, integrating safety-driven time gap considerations to prevent collisions.

• Researched and developed a novel model for vehicular ad-hoc networks to improve collision avoidance in connected and autonomous vehicles (CAVs). Designed and simulated complex traffic scenarios to evaluate the “Avoidance Time Model,” achieving a 35% reduction in potential collision risk by enhancing communication protocols for emergency braking in vehicle-to-vehicle networks.

• Utilized MATLAB and Python to analyze and validate model performance under various traffic densities and network conditions, providing key insights into CAV safety and informing future intelligent transportation systems. Published the research findings in an IEEE international conference, demonstrating the model’s effectiveness to the broader scientific community.

WORK EXPERIENCES

Farm Clinic Indiana, United States

Data Scientist May 2023 — August 2023

• Increased crop yield by 20% through the implementation of GIS-guided precision agriculture, leveraging remote sensing, geospatial analysis, and data-driven soil analysis techniques to optimize planting strategies and short-term resource al- location.

• Developed a machine learning-based soil diagnostics framework that classified nutrient deficiencies with over 92% accu- racy in simulated results, enabling tailored fertilizer strategies.

• Applied unsupervised clustering to segment farmland by soil health and irrigation demand, improving resource allocation efficiency by 35% in simulated results.

• Integrated probabilistic inference models to forecast environmental variability, reducing planning uncertainty by 28% and supporting data-driven climate-resilient farming strategies. 2

• Conducted a comprehensive analysis of two decades of crop data trends, applying machine learning models, including forecasting algorithms and regression analysis, to identify growth patterns, detect anomalies, and optimize long-term resource allocation. Leveraged these models to simulate yield outcomes, projecting a 40% increase in cumulative crop yield over the next decade.

• Developed and automated a data reporting system using SQL, Python, and Tableau, integrating real-time data analytics and visualization tools to streamline reporting, enhance decision-making efficiency, and provide actionable insights for precision farming.

Indiana Management Performance Hub (MPH), Indiana State Agency Indiana, United States Data Scientist May 2022 — August 2022

• Developed and implemented a comprehensive data governance policy framework for Indiana state agencies, standardiz- ing data collection, normalization, and processing across departments. Integrated data quality and coverage assessment protocols, attribute-level analysis, and performance management metrics to ensure data integrity, interoperability, and regulatory compliance. Streamlined workflows by 50% through automated data management systems, resulting in en- hanced reporting accuracy and more effective evidence-based decision-making.

• Collaborated with the Indiana Arts Commission on advanced statistical analyses of K–12 education datasets. Applied rank statistics, multiple linear regression, and predictive modeling to inform data-driven policy recommendations, ensur- ing the equitable distribution of state funding for arts education across schools. These optimizations improved resource allocation strategies and supported long-term development of arts education programs statewide.

• Led a major data analytics project using Python, Apache Spark and Databricks to analyze over 20 years of Indiana education data for the College Readiness Report. Applied regression models, rank statistics, and probability distribution fitting to forecast college readiness trends and evaluate key factors (academic performance, demographics, family back- ground, resource access). Delivered data-driven insights to optimize educational strategies and improve college readiness programs, with simulations indicating a 70% potential increase in college graduation rates by addressing the identified key factors.

• Incorporated time-series modeling and performance management metrics to track opioid overdose trends and resource response efficiency in Indiana State, contributing to targeted treatment facility placement and informed policy decisions for the state of Indiana.

EDUCATION

Purdue University, West Lafayette, IN, United States January 2021 — August 2025 Doctor of Philosophy in Technology

Dissertation Title: RL-AC-WAVEGAN: A Novel Reinforcement Learning-Based Auxiliary Classifier WaveGAN For Fine- Grained Multi-Class Audio Synthesis

Purdue PhD Research Teams: Data Analytics, Technologies, and Applications Lab & Mobile Artificial Intelligence Laboratory Universiti Sains Malaysia, Pulau Pinang, Penang, Malaysia September 2017 — September 2018 Master of Science in Computer Science

Thesis Title: Avoidance Time Model For Connected and Autonomous Vehicles ad-hoc Network Bingham University, Karu, Nasarawa, Nigeria September 2013 — September 2016 Bachelor of Science in Computer Science

Project Title: Academic Transcript Alert System Application PUBLICATIONS AND PATENTS (Google Scholar)

• Jaiyeoba, Oluwayemisi, Emeka Ogbuju, Grace Ataguba, Oluwaseyi Jaiyeoba, James Daniel Omaye, Innocent Eze, and Francisca Oladipo. ”State-of-the-art skin disease classification: a review of deep learning models.” Network Modeling Analysis in Health Informatics and Bioinformatics 14, no. 1 (2025): 9.

• Jaiyeoba, Oluwayemisi, Oluwaseyi Jaiyeoba, Emeka Ogbuju, and Francisca Oladipo. ”Ai-based detection techniques for skin diseases: A review of recent methods, datasets, metrics, and challenges.” Journal of Future Artificial Intelligence and Technologies 1, no. 3 (2024): 318-336.

3

• Jaiyeoba, Oluwaseyi, and Vetria Byrd. ”Literature-Based Discoveries in Lupus Treatment”. World Academy of Sci- ence, Engineering and Technology, Open Science Index 189, International Journal of Health and Medical Engineering

(2022), 16(9), 124 - 132.

• Babarinsa, Olayiwola, Azfi Zaidi Mohammad Sofi, Asrul Hery Mohd, Akinola Eluwole, Imoni Sunday, Wakili Adamu, Benson Onojhojobi, Oluwaseyi Jaiyeoba et al. ”Note on the history of (square) matrix and determinant.” FUDMA JOURNAL OF SCIENCES 6, no. 3 (2022): 177-190.

• Jaiyeoba, Oluwaseyi, Azman Samsudin, and Suhaila Sulaiman. ”Utilising the Computational Power of Blockchain Proof-of-Work in Computer-Aided Drug Design.” International Journal of Emerging Technology and Advanced Engi- neering 12, no. 10 (2022): 37-50.

• Jaiyeoba, Oluwaseyi, Azizul Rahman Mohd Shariff, and Suzi Iryanti Fadilah. ”Passenger Vehicle Avoidance Time Model For Connected and Autonomous Vehicles.” In 2019 7th International Conference on Information and Communi- cation Technology (ICoICT), pp. 1-6. IEEE, 2019.

• [Under Review] Jaiyeoba, Oluwaseyi, Vetria Byrd, John Springer, Sudip Vhaduri, and Chad Laux. ”Evolution of Healthcare Data Analytics” ACM Transactions on Computing for Healthcare

• [Under Review] Jaiyeoba, Oluwaseyi, Sayanton Dibbo, Sudip Vhaduri, and John Springer “RL-AC-WaveGAN: A Novel Reinforcement Learning-based Auxiliary Classifier WaveGAN for Fine-Grained Multi-Class Audio Synthesis.” submitted to the Association for the Advancement of Artificial Intelligence, 2026 (AAAI, 2026).

• Provisional patent application

PRF Ref.: 71034-01

Title: SYSTEM AND METHODS GENERATE FINE-GRAINED AUDIO DATA US App. No.: 63/766,736

Filing Date: March 4, 2025

AWARDS AND CERTIFICATIONS

Certificate of Best Paper Award

ICBDAH 2022: XVI. International Conference on Big Data and Analytics in Healthcare September 2022 Best Poster Presentation, Graduate Research Award

Graduate Student researchers for Future Work and Learning projects, Purdue Polytechnic, Purdue University April 2022 Ross-Lynn Graduate Fellowship

Ross-Lynn Graduate Fellowship to support a research assistantship for FY22 (FA21/SP22), National Science Foundation, Purdue University 2021 August 2021 Malaysia Digital Economy Corporation (MDEC) - Intel Artificial Intelligence Academy certification October 2019 Cisco Networking Academy

CCNA Exploration: Network Fundamental July 2014

RESEARCH CONFERENCES AND TALKS

• Research talk at the Department of Computer and Information Technology (CIT), Purdue University mAI lab February 2025

Topic: Generative AI

• ICBDAH 2022: XVI. International Conference on Big Data and Analytics in Healthcare September 2022 Topic: Literature-Based Discoveries in Lupus Treatment

• 2019 7th International Conference on Information and Communication Technology (ICoICT) July 2019 Topic: Passenger Vehicle Avoidance Time Model For Connected and Autonomous Vehicles 4

• Computer Science Postgraduate Colloquium, Universiti Sains Malaysia September 2020 Topic: Enabling Technologies and Infrastructure Track

• Research talk at the Computer Science Journal club, Universiti Sains Malaysia February 2020 Topic: Proof-of-Work: Blockchain Consensus Method

5

Contact this candidate