Ajay D. Sudhir - AI Researcher & Computer Scientist

About Me

I am a passionate AI researcher and computer science student at UNC Charlotte, specializing in neuro-symbolic cognitive architectures and human-AI interaction. My work focuses on bridging the gap between symbolic reasoning and neural learning to create more intelligent and adaptable AI systems.

Education

B.S. Computer Science - AI, Robotics, and Gaming

Minors: Electrical Engineering, Mathematics, and Statistics

Honors: University Honors Program, College of Computing and Informatics Honors

Graduation: May 2026 GPA: 4.0/4.0

Research Interests

🧠Neuro-Symbolic Cognitive Architectures
🧮Lifelong, Continual, and Meta-Learning
🎯Exploration-Exploitation in Reinforcement Learning
🤝Human-in-the-Loop and Feedback-Driven AI Systems
🤖Safe, Explainable, and Ethical Autonomous Agents

Fun Facts

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Multicultural Background

I have lived most of my life in India and America. I speak English, Hindi, Malayalam, and Tamil.

🏎️

Sports Enthusiast

I love watching Formula 1, Soccer, and Basketball. There's nothing like the thrill of competition!

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Entertainment Preferences

Favorite series: Sherlock Holmes, Friends, and Money Heist

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Cinematic Taste

Favorite movies: The Dark Knight, Interstellar, and Inception

Projects

Project Title

Brief description of the project and its key features.

Python PyTorch FastAPI

View All Projects on GitHub

Research

Aug 2025 - Present

AI Algorithms, Domains, and Accountability

Advisor: Dr. Divya Ramesh

Analyzed how algorithmic structures influence model accountability across various AI application domains.
Designed a taxonomy linking model characteristics to transparency and ethical governance frameworks.
Proposed auditing strategies to enhance interpretability, fairness, and accountability in AI systems.

Dec 2024 - Present

Intrinsic vs. Explicit Feedback in Interactive Reinforcement Learning

Advisor: Dr. Minwoo Lee

Engineered and optimized reinforcement learning agents to compare between intrinsic motivation and explicit human feedback mechanisms in interactive environments.
Developed experimental frameworks to evaluate learning adaptability and goal alignment for agents and human collaborators.
Collaborated on integrating BCI EEG signals using OpenBCI and BrainFlow to analyze intrinsic feedback dynamics and their impact on agent learning.

Jan 2025 - Jun 2025

Exploring Relational Authenticity of LLMs for Human-AI Interaction

Advisor: Dr. Elizabeth Johnson

Explored emotional reciprocity and temporal context maintenance in large language models through structured human-AI dialogues.
Conducted sentiment analysis and thematic coding to evaluate relational authenticity of various commercial models.
Developed a comparative framework to assess how model architecture and training paradigms influence affective alignment and long-term conversational coherence.

Publications

The Feel of Friendship: Emotional Presence and Relational Authenticity in Large Language Models

Johnson, L., Sudhir, A. D., & Padmapriya, A. A.

International Journal of Humanities and Social Science, 15, 329-339.

Abstract:
As generative AI systems become increasingly capable of nuanced conversation, a new query arises not whether they can assist humans, but whether AI can engage with us. This study investigates whether large language models (LLMs) can replicate the emotional reciprocity, presence, and anticipatory resonance that are fundamental to human friendship, as envisioned by Aristotle. Over five weeks, four LLMs — ChatGPT, Gemini, Deepseek, and Qwen — engaged in structured, daily dialogue with three human researchers through a mixed-methods approach that combined sentiment analysis and thematic coding. Only ChatGPT consistently mirrored warmth and curiosity, simulating a sense of friendship. The other LLMs relied on text disclaimers and algorithmic function-focused tone. The findings suggest that the relational effect of AI is less about sheer technical horsepower and more about design philosophy, specifically what we choose to make AI for.
This study offers not only data but also a reflection: if friendship is co-created, what does it mean when one party is not human but still manages to be present?

View Publication