Oyindamola Koleoso

Ph.D. Student, Computer Science
University of Texas at Dallas

About

I am a Ph.D. student in Computer Science at the University of Texas at Dallas, working at the SCALA Lab (Smart Cities Applied Research Lab). My research focuses on multi-agent systems, distributed intelligence, and AI-driven smart infrastructure. I'm passionate about building scalable, data-driven systems that enable autonomous collaboration across intelligent agents.

Contents

News & Updates

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Education

Degree Year
Doctor of Philosophy (Ph.D.), Computer Science, University of Texas at Dallas, Supervisor: Prof. Rym Zalila-Wenkstern 2025 – Present
Master of Science (M.S.), Computer Science, University of Arkansas at Little Rock, Supervisor: Prof. Nitin Agarwal 2023 – 2025

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Research & Experience

Researcher, COSMOS Research Center

August 2023 – May 2025

Developed retrieval-augmented generation (RAG) pipelines using Llama, LangChain, and FAISS. Engineered large-scale data pipelines with Apache Kafka and Spark for distributed processing of research data.

Software Engineer, Flutterwave

February 2023 – June 2025

Scaled distributed payment APIs serving 500,000+ daily transactions with 99.99% uptime. Built fault-tolerant microservices architecture handling mission-critical financial operations across multiple African markets.

Fullstack Engineer, Tolaram Group

March 2022 – February 2023

Built fleet management systems using Node.js, PostgreSQL, and WebSockets for real-time tracking. Deployed cloud-native applications on Google Cloud Platform with containerization and CI/CD pipelines.

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Selected Projects

Autonomous Robot with Custom RTOS

Python-based real-time operating system for an autonomous robot integrating OpenCV vision, environment mapping, and obstacle-aware navigation. The robot perceives its surroundings, detects objects, and navigates autonomously using real-time task scheduling and sensor fusion.

Python OpenCV RTOS

D-CAS: Distributed Content-Addressable Storage

Peer-to-peer distributed storage system implemented in Golang with TCP networking and SHA1-based integrity checks. Features automatic data replication, fault tolerance, and content-addressable architecture for efficient data retrieval.

Go Networking P2P

More projects →

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Publications

  1. Amure, R., Akinnubi, A., Koleoso, O. (2024). Toward Improved Clustering for Textual Data. In: Proceedings of Third International Conference on Computing and Communication Networks. ICCCN 2023. Lecture Notes in Networks and Systems, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-97-0892-5_32
  2. Mbila-Uma, S., Koleoso, O., Umoga, I. et al. (2024). The Multi-attribute impact of hyperlinks in blogs: an emotion-centric approach. Soc. Netw. Anal. Min. 14, 134 (2024). https://doi.org/10.1007/s13278-024-01295-w

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Teaching

During my Masters and Ph.D. studies, I conducted and assisted in teaching several classes, including:

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