Overview

Selected funded projects grouped by agency. Work spans neurosymbolic AI, probabilistic inference, explainability, and robust decision-making with applications to vision and task guidance.

DARPA

Perceptually-enabled Task Guidance (PTG)

Summary: Neurosymbolic dynamic probabilistic models for structured task representation and real-time guidance in complex physical procedures.

  • Institution: Center for Machine Learning, The University of Texas at Dallas (UTD)
  • Role: Graduate Research Assistant
  • Timeline: Aug 2021 - May 2025
  • Focus: Neuro-Symbolic Dynamic Probabilistic Models for task representation and reasoning; real-time assistance in complex physical tasks
  • Contributions:
    • Advanced neurosymbolic dynamic models combining structured reasoning with deep learning
    • Improved user performance by expanding skillsets and reducing error rates in task guidance settings
    • Built robust pipelines for perception, inference, and feedback loops

Explainable Artificial Intelligence (XAI)

Summary: Interpretable AI systems that preserve predictive performance while providing faithful, human-understandable rationales.

  • Institution: Center for Machine Learning, UTD
  • Role: Graduate Research Assistant
  • Timeline: Aug 2020 - Aug 2021
  • Focus: Interpretable AI systems that preserve predictive performance while providing faithful explanations
  • Contributions:
    • Delivered high-performance explainable models without sacrificing accuracy
    • Designed methods to increase transparency and user trust for decision support

Assured Neuro Symbolic Learning and Reasoning (ANSR)

Summary: Secure and reliable neurosymbolic learning with a focus on robustness and assurance.

  • Institution: Center for Machine Learning, UTD
  • Role: Graduate Research Assistant
  • Timeline: Aug 2023 - May 2025
  • Focus: Secure, reliable neurosymbolic learning with formal guarantees
  • Contributions:
    • Engineered hybrid AI algorithms integrating symbolic reasoning with data-driven learning
    • Emphasized robustness, assurance, and trustworthy deployment

NSF – National Science Foundation (IIS-1652835)

Summary: AI/ML methodology projects spanning probabilistic inference and interpretable modeling.

  • Institution: Center for Machine Learning, UTD
  • Role: Graduate Research Assistant
  • Timeline: 2021 - 2025
  • Focus: AI/ML methodology spanning probabilistic inference and interpretable modeling
  • Contributions:
    • Co-developed algorithms for scalable inference in graphical models
    • Supported publications recognized through best paper, spotlight, and oral presentations at NeurIPS and AAAI

AFOSR – Air Force Office of Scientific Research

Summary: Human-aware probabilistic logic for learning with fewer labels from multimodal data to model credibility.

  • Institution: University of Texas at Dallas
  • Role: Graduate Research Assistant
  • Timeline: 2021 - 2025
  • Program: Air Force Defense Research Sciences Program (CFDA 12.800)
  • Related Opportunity: FA955021S0001
  • Place of Performance: Texas, United States
  • Funding Agency: Air Force Office of Scientific Research (AFOSR), DoD / USAF / AFMC / AFRL
  • Focus: Human-aware probabilistic logic approach for learning with less labeled multimodal data and modeling credibility
  • Contributions:
    • Supported research on probabilistic logic and credibility modeling for multimodal learning
    • Advanced reliable learning methods designed to reduce label requirements

Recognition & Impact

  • Best Paper Awards; spotlight and oral presentations (NeurIPS, AAAI)
  • Real-time inference algorithms for probabilistic models with improved accuracy and efficiency
  • Practical systems combining reasoning with perception for video understanding and task guidance