Funded Projects

Overview

My funded research spans neurosymbolic AI, probabilistic inference, neural optimization, and structured artificial intelligence.

Funded Research as PI / Co-PI

Research grants, sponsored research agreements, and other external support for projects where I serve in an investigator role.

Industry-Sponsored Research and Gifts

Lambda Research Grant Program

  • Role: Principal Investigator
  • Status: Awarded
  • Award Date: September 2025
  • Support: $1,000 in cloud computing credits

Prior Externally Funded Research Contributions

Before joining NJIT, I contributed as a graduate researcher to several federally funded research projects at The University of Texas at Dallas. These projects supported research in neurosymbolic reasoning, probabilistic modeling, explainable AI, multimodal learning, and intelligent task guidance.

DARPA

Perceptually-enabled Task Guidance (PTG)

  • Role: Graduate Research Assistant
  • Period: August 2021 - May 2025
  • Institution: Center for Machine Learning, The University of Texas at Dallas
  • Research Objective: Develop neurosymbolic probabilistic models for structured task representation, reasoning, and real-time guidance in complex physical procedures.

  • Key Contributions:

    • Developed structured neurosymbolic models combining deep perception with probabilistic reasoning for multi-step task understanding.
    • Built perception, inference, and feedback pipelines for real-time predictive task guidance.

Explainable Artificial Intelligence (XAI)

  • Role: Graduate Research Assistant
  • Period: August 2020 - August 2021
  • Institution: Center for Machine Learning, The University of Texas at Dallas
  • Research Objective: Develop interpretable AI systems that preserve predictive performance while producing faithful and human-understandable explanations.
  • Contribution: Contributed to explainable learning methods designed to improve transparency while maintaining predictive performance.

Assured Neuro Symbolic Learning and Reasoning (ANSR)

  • Role: Graduate Research Assistant
  • Period: August 2023 - May 2025
  • Institution: Center for Machine Learning, The University of Texas at Dallas
  • Research Objective: Develop secure and reliable neurosymbolic learning and reasoning methods with an emphasis on robustness, assurance, and trustworthy deployment.
  • Contribution: Developed hybrid learning approaches that integrate symbolic reasoning with data-driven models for reliable structured decision-making.

National Science Foundation

NSF IIS-1652835

  • Role: Graduate Research Assistant
  • Period: 2021 - 2025
  • Institution: Center for Machine Learning, The University of Texas at Dallas
  • Research Objective: Advance AI and machine learning methods for probabilistic inference and interpretable modeling.

  • Key Contributions:

    • Developed algorithms for scalable inference in probabilistic models.
    • Supported publications recognized through best paper, spotlight, and oral presentations at NeurIPS, AAAI, and UAI TPM.

Air Force Office of Scientific Research

Air Force Defense Research Sciences Program

  • Role: Graduate Research Assistant
  • Period: 2021 - 2025
  • Institution: The University of Texas at Dallas
  • Research Objective: Develop human-aware probabilistic logic methods for learning from limited labeled multimodal data while modeling information credibility.
  • Contribution: Contributed to probabilistic reasoning methods for reliable multimodal learning under limited supervision.