Joaquin Carbonara at computer

Joaquin Carbonara

Associate Director, Data Science Science And Math Complex 379
Phone: (716) 878-6423


Ph.D., Mathematics - Combinatorial Algorithms and Representation Theory (University of California San Diego)

M.S., Computer Science (SUNY University at Buffalo)

M.A., Mathematics (San Diego State University)

B.A., Mathematics with a minor in Italian (San Diego State University)

Extensive musical training as part of El Systema (Caracas, Venezuela)

Research Focus

  • Transdisciplinary applications of Mathematics in Machine Learning
    • Mathematics as a tool for explainability
    • Data Ethics, Culture, Strategy and Governance
    • Modern Mathematical tools in ML that go beyond traditional math approaches
    • Translation and infusion of Mathematical DSA tools to other disciplines
  • Dissemination of Data Science and Analytics (DSA) as the literacy of the 21st century
    • Creation of effective and relevant DSA curriculum across disciplines in academia
    • Connections between academia and industry
    • Creation of learning communities, centers, startups and more
    • Future Thinking and scenarios based on current and relevant discoveries in DSA
  • Combinatorial Algorithms
  • Fractals theory and applications
    • Fractal dimensions as features in a data set
    • Discrete Mathematical models

Modes of Research

  • Computational paradigm applications: Machine Learning, Convoluted Neural Networks, Natural Language Processing, Reinforced Learning, Explainability, AI Ethics.
  • Computational tools: Python, Cloud Computing.
  • Transdisciplinary Team Discovery.

CISNN-specific Research:

Dr. Carbonara focuses is on accelerated research, discovery and visualization on a transdisciplinary environment leveraging the AI tools (ML, CNN, etc.).

CISNN specific publications, white papers, grants, and other resources:

  • Carbonara, Ahmed*, et al. “A Machine Learning Approach to Delineate Impact of Materials Properties on Solar Cell Device Physics”, ACS Omega, JUST ACCEPTED, May 2022. {Clean Energy: Device Physics, Computation, ML, Ethics}
  • Carbonara, Ahmed*, et al. “Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI3−xBrx)”, ACS Applied Materials and Interfaces, published December 2021. {Clean Energy: Device Physics, Computation, ML, Ethics}
  • Carbonara, Ahmed, Biswas* “Superoxide Mediated Transition Metal Oxides for Aerobic Oxidation Reactions”, DOE White Paper, 2022 (did not ultimate get invited) {Catalysis, Energy and Reaction Engineering: Experiment, Computation, ML}
  • Ettestad, David; Carbonara, Joaquin, The Sierpinski Triangle Plane, Fractals Vol. 26, No. 01 (2018).

Synergistic activities in DSA


  • Chair of the Data Science and Analytics MS at SUNY Buffalo State (2019 – current)

Conference organizer