Biography

My name is Derrick Agyekum, a Computer Science B.S. student at Michigan State University (’27) and University Distinguished Scholar with a Finance minor. I build end-to-end software; from iOS apps (Swift/SwiftUI, UIKit) to full-stack web, and applies machine learning to real data. As a Professorial Assistant in MSU’s Engineering Department, I created data-mining and ML pipelines in Python/SQL/TensorFlow that accelerated research insights and informed faculty decisions. Earlier, I shipped front-end features at Vodafone Ghana that improved access and cut costs. My projects include an iOS Library app, a venue-booking site with a generative-AI chatbot, and Kdad Marketplace. My passion is using research and AI (RAG, model fine-tuning, and practical ML) to solve real-world problems in consumer experience, operations, and education. On campus I mentor in the Honors College and am strengthening my algorithmic foundations through CodePath’s advanced interview prep.

Academic Status

Undergraduate Student - 3rd

Research Area/Department

Computer Science; Data Science; Engineering; Machine Learning/AI

Major/Specialty

B.S. in Computer Science — Specialization: Artificial Intelligence & Machine Learning (minor in Finance).

Degrees Earned or in Progress

B.S., Computer Science / Artificial Intelligence & Machine Learning / Michigan State University — In progress, expected May 2027. Minor: Finance.

Academic Preparation

Here are the most relevant courses and prep I’ve completed for a CS/AI summer internship: Programming + Data Structures/Algorithms: Intro to Programming (Python) and DSA in Java/C++; heavy practice in LeetCode/CodePath Advanced Interview Prep. Math for CS/AI: Discrete Math, Linear Algebra, Calculus I–II, and Probability & Statistics. AI/Data: Foundations of Machine Learning/AI, Data Mining/Data Science projects in Python (Pandas, scikit-learn, TensorFlow). Systems & Product: Database Systems (SQL), Web Development (React/Node), Mobile iOS (Swift/SwiftUI), and Software Engineering (Git). Applied prep: Professorial Assistant research building ML pipelines (Python/SQL/TensorFlow) and shipped app/features in personal and internship projects.

Research/Academic Interests

I’m interested in applied AI/ML that moves from research to real-world impact. My focus areas include NLP and large language models (especially retrieval-augmented generation and agentic workflows), representation learning for multimodal data, and time-series/causal inference for forecasting and decision support. I care deeply about trustworthy AI and the systems side that makes it work in practice (data engineering, evaluation, and MLOps, including on-device/edge deployment). I’m excited to study and build models for personalization and recommendation, demand forecasting, and conversational assistants in domains like consumer analytics, operations/supply chain, education, and public health. I enjoy combining statistical learning with experimentation ot generate actionable insights and close the loop between models and outcomes.

Computational and Data Science Areas

Applied Computer Science; Computer Science; Informatics, Analytics and Information Science; Other Computer and Information Sciences

Motivation

I want to join Sustainable Research Pathways to grow as a researcher while contributing to an inclusive, impact-driven AI community. As a CS major (AI/ML focus) at Michigan State University, I’ve helped faculty as a Professorial Assistant build data-mining and ML pipelines in Python/SQL/TensorFlow and learned how rigorous methods, careful evaluation, and clear communication turn raw data into decisions. SRP’s mission, which include mentoring, collaboration across backgrounds, and hands-on work with NAIRR projects, matches how I like to learn. My research interests are applied AI for public benefit: NLP and retrieval-augmented generation, recommendation/personalization, and time-series/causal methods for forecasting and decision support. I bring solid software skills (iOS, full-stack, SQL), experience shipping features, and a habit of testing ideas with experiments and metrics. Through SRP I hope to deepen my research toolkit (problem formulation, literature synthesis, experimental design, MLOps), contributes to a publishable project, and learn from mentors committed to inclusive excellence. In return I offer energy, humility, and a builder’s mindset, ready to prototype, analyze, write, and present. I’m excited to help SRP’s community connect rigorous AI research to real-world problems where it can improve lives.

Lightning Talk Title

Data-to-Deployment: Shipping Small LLMs With Measurable Impact

Keywords (Maximum 20 words)

LLMs; RAG; Agents; Prompting; Alignment; Guardrails; Retrieval; Tokenization; LoRA; LLMOps; Deployment; Evaluation; Time-series; Causal-inference; Content-Based-Filtering; Feature-engineering; Distillation; Transfer-learning; Vector-databases; SQL/ETL