Name: William Marfo
Pronouns:
Institution: The University of Texas at El Paso
Department:
Biography:
I am a third-year Ph.D. student in computer science at the University of Texas at El Paso, where I graduated with a Master’s in Big Data Analytics and Applied statistics. My research interests include cyber-physical systems, data science, federated learning, distributed computing, and cybersecurity. I have participated in several research projects relating to cybersecurity, machine learning, and AI. My current research involves monitoring critical infrastructure and cyber-physical systems using deep learning techniques. The primary motivation for my latest research is to design a robust technique to detect threats on a network to prevent cyber-attacks, breaches of personally identifiable information, and data loss while reducing risks. My focus is on utilizing machine learning techniques in a distributed approach for faster training, reducing overhead, and improving performance while reducing errors. This opportunity is a way to contribute to an exciting and critical modern aspect of technology. I want to be a researcher in using deep learning and cybersecurity for national security. Outside class, I like to participate in discussions and debates and volunteer for my community. As a volunteer, I contributed by teaching high school students the basics of cybersecurity and ensuring safety at home and school.
Academic Status: PhD Student
Year in program: 3rd
Research Area/Department: Computer Science; Data Science; Machine Learning/AI
Other, specify:
Major/Specialty: Computer science , Data science , Cybersecurity.
Degrees Earned or in Progress: Ph.D. /Computer Science /2021 – present Master of Science / Big Data Analytics and Applied Statistics/2021. B.S. / Computer Science /2017.
What courses or academic preparation have you completed to prepare for a summer internship experience?
Theory of Computation, Advanced Algorithms, Computer Security, Computer Networks, Software Requirements Engineering, Computational Linear Algebra, Data mining, Data visualization, Experimental Design, Post-Genomic Analysis.
Have you published any research or worked on research/technical projects? Yes
Where has your research been published or where have you conducted research/technical projects? 1. 2022 17th Annual System of Systems Engineering Conference (SOSE),Location: Rochester, NY, USA https://doi.org/10.1109/SOSE55472.2022.9812638 2. 2022 Military Communications Conference (accepted to present in November 2022)
Please describe your research/academic interests:
My research involves monitoring critical infrastructure and cyber-physical systems using machine learning techniques. The primary motivation for my latest research is to design a robust technique to detect threats on a network to prevent cyber-attacks, breaches of personally identifiable information, and data loss while reducing risks. My focus is on utilizing machine learning techniques in a distributed approach for faster training, reducing overhead, fault tolerance and improving performance while reducing errors.
Computational and Data Science Areas:
Computer Science; National Security; Data Analytics and Visualization; High-Performance Computing; Machine Learning and AI
Research Synergy:
From the onset of my academic journey, I have been deeply immersed in understanding the interplay between distributed systems, cybersecurity, and the vast domain of machine learning. My specific research foci, including Distributed ML, Federated Learning, Cybersecurity, Artificial Intelligence, and Data Science, align with the cutting-edge initiatives undertaken at DOE labs. My intrigue in distributed systems resonates with DOE’s endeavors to efficiently process vast datasets. Similarly, my exploration of Distributed ML and Federated Learning parallels DOE labs’ pioneering efforts in optimizing these paradigms for high-performance computational platforms. Moreover, my dedication to cybersecurity, especially in network anomaly detection, complements the lab’s mission to fortify our nation’s digital defenses. Lastly, my commitment to leveraging AI and extracting profound insights from data synergizes with DOE’s emphasis on advanced computational research. In summary, my research aspirations and the goals of DOE labs are intertwined, and I am fervently enthusiastic about the prospect of collaborative endeavors that push the frontiers of computational research, AI, and data sciences.
Motivation:
I am excited to participate in this program, which perfectly aligns with my research interests. I am a third-year computer science Ph.D. student at The University of Texas at El Paso, where I have participated in several research projects relating to cybersecurity, machine learning and AI. I am highly passionate about research relating to the monitoring of critical infrastructure and cyber-physical systems. We are preparing a manuscript describing distributed machine learning concerning parallel scientific computing. I enjoyed this experience of using computational and federated learning skills in monitoring networks to help address some of the world’s security problems. I am enthusiastic about the program and intend to work hard for the projects as they will provide me with the opportunity to obtain many social and research skills that will help me in my future career.
Lightning Talk Title: Detecting both fabrication/masquerade attacks in the controller area network bus