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Join Us

The TKAI Lab is seeking highly motivated Ph.D. and undergraduate students to join our multidisciplinary research efforts aimed at addressing foundational and applied challenges in Artificial Intelligence (AI). Our lab's mission is to develop stable, trustworthy, and explainable AI systems inspired by insights from neuroscience, cognitive science, control theory, and formal methods. Below are key areas of research you will have the opportunity to contribute to:

Research Focus Areas

  • Neuro-Symbolic AI for Trustworthy Systems: We are developing methods to encode knowledge graphs into artificial neural networks to create robust and interpretable Artificial General Intelligence (AGI). These systems are designed to operate safely in high-risk domains such as medicine and finance. This aligns with our broader goal of building explainable and trustworthy AI systems by combining symbolic reasoning with neural architectures.

  • Theoretical Foundations of AI Models: Establishing rigorous mathematical frameworks is central to our work. We aim to derive theoretical limits and stability guarantees for AI models, ensuring they function efficiently in polynomial time. This connects to our ongoing efforts to integrate insights from formal methods into AI development for both theoretical rigor and practical reliability.

  • Predictive Coding Architectures: Inspired by cognitive science, we are designing predictive coding-based architectures for sequential modeling and multi-modal signal encoding. These systems are evaluated on high-stakes domains, such as medicine and cybersecurity, to ensure stability, efficiency, and adaptability.

  • Fairness by Design in AI Systems: We aim to create computational models that inherently encode fairness metrics during training. These models are designed with prior constraints to ensure fair and unbiased outcomes, addressing critical ethical concerns in AI deployment.

  • Low-Resource Natural Language Processing (NLP): Our lab is developing systems capable of generating and recognizing languages in low-resource settings. This research focuses on creating efficient and explainable neural architectures that perform well even with limited training data, enabling broader accessibility of AI technologies.

  • Explainability by Design (XAI): At TKAI, we prioritize explainability in AI systems, both through inherent architectural design and visualization tools. This aligns with our mission to create trustworthy AI systems that are transparent to end-users, fostering confidence in their real-world deployment.

Ph.D. Students

We are looking for Ph.D. students who are passionate about tackling these research challenges and contributing to cutting-edge advancements in AI.

Position Requirements:

  • A Master's or Bachelor's degree in fields such as Mathematics, Applied Physics, Statistics, Electrical Engineering, Computer Science/Engineering, Linguistics, or related disciplines.
  • Preferred skills include:
  • Proficiency in at least one programming language (e.g., Python, C/C++).
  • Strong understanding of Artificial Intelligence, Reinforcement Learning, or Linguistics.
  • Knowledge of formal language theory, mathematical modeling, or multi-modal learning is a plus.

Undergraduate Students

Undergraduate students interested in exploring AI and NLP are encouraged to join as volunteers or through independent study.

Requirements:

  • Interest in topics highlighted on the lab’s webpage.
  • Self-motivated with a willingness to tackle challenging problems.
  • Background in AI/NLP, linear algebra, and statistics is preferred but not required.

How to Work with Me

Getting Started:

  • Start as a volunteer to explore research opportunities and establish a working relationship. Depending on performance, funding opportunities may become available. Volunteers should dedicate a minimum of 5–10 hours per week, with flexibility based on obligations and learning curves.
  • Alternatively, take course credits or pursue an independent study under my guidance, focusing on a specific challenging research problem.

Instructions for Graduate Students:

  • Graduate students should meet the general requirements and commit additional hours toward thesis work or publishing research articles.
  • Ensure your research aligns with the lab’s focus areas and be prepared to demonstrate your contributions.

Application Instructions

If you are interested in working with me, your email should include: 1. What: Describe the problems you wish to explore and how they align with the lab’s research agenda. 2. How: Explain how your skills, background, or experience will contribute to the lab’s success. 3. Note: Remember, I can guide you, but I expect you to take ownership of your work.

Include your CV and relevant details, along with the specific research topics you are interested in.

Things to Avoid

  • Asking about funding without prior work or taking relevant courses.
  • Sending generic emails without reading my papers or understanding the lab’s focus. Such emails will not receive a response.

At TKAI, we believe every student brings a unique perspective to solving complex problems. If you take the time to understand our work, explore the field, and approach us with specific research interests, exceptions can always be made for those eager to learn and contribute.