Name: Baboucarr Dibba
Pronouns: he/him/his
Institution: University of Texas Rio Grande Valley
Department: Mathematical Statistics and Interdisciplinary Studies
Biography:
Born on May 5, 1985, in Serrekunda, Gambia, my early education began in Tabokoto and progressed in Kanifing South. The academic trail initiated at Nusrat Senior Secondary School, veered towards an Automotive Engineering diploma at G.T.T.I, but health challenges necessitated a pause. Inspired by the scant local mathematical expertise in Gambia, I pivoted, earning a BSc with Honors in Mathematics from the University of The Gambia. This spurred advanced studies in Mathematical Engineering and Industrial Mathematics at the universities of L’Aquila and Silesia in Italy and Poland, respectively, broadening my intellectual horizons through a thesis on Quantum control of qubits and qutrits. Now, as a Ph.D. candidate at the University of Texas Rio Grande Valley, USA, in Mathematical Statistics and Interdisciplinary Studies, my numerical ardor fuels my exploration into neural networks, Partial Differential Equations (PDEs), and Statistical Methods. This enriched scholarly voyage, peppered with research and teaching engagements, has sharpened my acumen, bolstering my resolve to excel in mathematical modeling. It echoes my unwavering dedication to enhancing Gambia’s mathematical domain, encapsulating the essence of my enduring academic expedition.
Academic Status: PhD Student
Year in program: 3rd
Research Area/Department: Applied Mathematics; Machine Learning/AI; Physics
Other, specify:
Major/Specialty: My major is Mathematical Statistics with a concentration in Interdisciplinary Studies in my doctoral studies. Within this framework, my focus extends to Mathematical Physics and exploring the application of Neural Networks in the field of image processing. This interdisciplinary approach allows me to delve into the intricate relations between mathematical theories, physical principles, and advanced computational techniques, fostering a comprehensive understanding that is poised to drive innovation in image processing technologies.
Degrees Earned or in Progress: University of Texas Rio Grande Valley (UTRGV) Edinburg, TX PhD Candidate in Mathematical Statistics and interdisciplinary Concentration (GPA: 4.0) Expected Graduation Date: May 2024 University of Silesia Katowice, Poland Master of Science in Mathematics for Finance and Economics (GPA: 3.63) Graduation Date: September 2021 University of L’Aquila L’Aquila, Italy Master of Science in Mathematical Engineering (GPA: 3.10) Graduation Date: October 2021 The University of The Gambia Banjul, The Gambia BSc (Hons) in Mathematics (GPA: 3.57) Graduation Date: May 2016
What courses or academic preparation have you completed to prepare for a summer internship experience?
My academic journey to prepare for a summer internship experience has encompassed a robust array of courses that have refined my aptitude in mathematical and computational realms. Among these, I have completed a course in Machine Learning, which has instilled a comprehensive understanding of algorithmic and data analysis techniques. Additionally, my coursework in Ordinary Differential Equations and Partial Differential Equations (PDE) has sharpened my analytical and problem-solving skills, enabling me to tackle complex mathematical models that often underpin real-world phenomena. Furthermore, my engagement with Statistical Learning and Methods has fortified my grasp on data-driven modeling and inference. As I venture into my dissertation phase, the knowledge and skills acquired from these courses provide a substantial foundation for tackling challenges during a summer internship, especially in scenarios demanding a synergistic application of mathematical and computational methodologies.
Have you published any research or worked on research/technical projects? No
Where has your research been published or where have you conducted research/technical projects?
Please describe your research/academic interests:
My research is focused on, the integration of p-adic analysis, fuzzy logic, and Cellular Neural Networks (CNN) can potentially create a novel framework termed p-adic Fuzzy Cellular Neural Networks for image processing. Here, p-adic analysis extends rational numbers providing new mathematical properties, fuzzy logic deals with uncertainty, while CNNs are parallel computing models that have been widely used in image processing. By combining these concepts, one might develop new methodologies or models for tasks like image recognition, noise reduction, and image enhancement. The interdisciplinary research could bridge mathematics, computer science, and engineering, exploring new theoretical models or practical applications in image processing or other related fields. Hence cellular neural networks, delay cellular neural networks, and fuzzy cellular neural networks been extended to the p-adic framework, in image processing and edge detection.
Computational and Data Science Areas:
Applied Mathematics; Data Analytics and Visualization; Machine Learning and AI; Quantum Computing and Information Science
Research Synergy:
My keen interest in computational sciences, AI/machine learning, and data sciences is driven by a desire to solve complex real-world problems and contribute to modern technological advancements. My academic focus on Mathematical Physics and Neural Network applications for image processing aligns well with the innovative pursuits at the DOE labs. Through courses like Machine Learning and Statistical Learning and Methods, I have built a solid foundation for engaging in cutting-edge research. The SRP program’s emphasis on collaborative efforts resonates with my belief in research synergy and offers a valuable platform for intellectual exchange with DOE Lab staff. The prospect of collaborating on projects at the DOE labs excites me as it aligns with my academic and research aspirations, presenting an opportunity to contribute to impactful scientific and technological endeavors while further honing my skills in a mutually enriching environment.
Motivation:
My motivation stems from a deep curiosity and an ambition to contribute to technological advancements through the interdisciplinary lens of Mathematical Statistics, Mathematical Physics, and Neural Networks application in image processing. Courses like Machine Learning and Statistical Learning have further propelled my interest towards computational, AI/machine learning, and data sciences, paving the way for innovative solutions to contemporary challenges. The SRP program’s emphasis on collaboration between students and DOE Lab staff aligns with my belief in research synergy, offering an exhilarating opportunity to merge my skills with the expertise at DOE labs. This prospect resonates with my long-term aspiration to be at the forefront of technological innovation and to contribute meaningfully to cutting-edge research projects, all while nurturing my academic and professional competencies in a mutually enriching environment.
Lightning Talk Title: Constructing Mathematical Theories to Reduce Noisy Data for Image Processing