Important instructions for students looking to join my lab

The Trustworthy Knowledge-driven Artificial Intelligence Laboratory (TKAI) is seeking highly-motivated Ph.D. student to conduct multidisciplinary research on the problems such as:

  • Encoding knowledge graph with Artificial neural network to construct safe, robust Artificial general intelligence system (AGI) that can be deployed to work on high-risk domains such as medicine, finance. This field is also known as neuro-symbolic AI, where goal is to build trustworthy AI system by encoding interpretable knowledge
  • Design mathematical foundation and derive theoretical limits for these models to work in polynomial time
  • Design predictive coding inspired architectures for sequential modeling, encode multi-modality signals into the systems and evaluate them on critical or high-risk domains
  • Design computational models that can encode various fairness metrics while learning, such that model has a prior and produces fair information by design
  • Design computational models that are capable of generating and recognizing languages when trained on low-resource datasets (low resource NLP)

  • Most importantly we are working on encoding knowledge and build neural architectures that are explainable (XAI) by design and also by visualization.

  • Position Requirements: Master’s/Bachelor’s degree in majors/fields including but not limited to: Mathematics, Applied Physics, Statistics, Electrical Engineering, Computer Science/Engineering, linguistic and other related fields.

  • Preferred Experience: experimental research experience such as, but not limited to Fluency in at least one programming language C/C++ or python, Good working knowledge about Artificial intelligence and Reinforcement learning, Linguistics, formal language theory etc.

Instructions for Undergraduate Students

  • Please refer to instructions highlighted above
  • Interested in working on relevant topics highlighted in our lab’s webpage
  • Self-motivated and eager to explore challenging problems in NLP and AI.
  • Ideally have some background in AI/NLP and working knowledge of linear algebra and stats

How to work with me?

  • Ideally you will/should consider starting as an volunteer and work with me for some-time. In future there with possibility of funding based on performance. Please remember evaluation is based on research conducted in any given semester and not on line of codes you have written.
  • When you volunteer, I would expect you to work for minimal 5-10 hours, however given your obligations are minimal, one can adjust involvement based on schedule and current learning curve.
  • Second approach is to take course credits or independent study and formally work on some challenging problems.

Instructions for Graduate Students seeking me as an advisor

  • Graduate students should satisfy requirements highlighted in the main and under UG Student sections.
  • Graduate students are required to add additional hours and work on thesis and/or research article with me.

Things not to do

  • Asking me about funding without even working with me or taking my courses.
  • Not looking at my papers and website, and sending me an email your work is great in AI/NLP/CV and I wish to solve everything. Such emails won’t be answered. This clearly shows you did not invest any time in reading my work/papers.
  • If you wish to work with me, then your email should have following points
    1. What: What are the problems you wish to explore and how it aligns with our lab’s agenda/interest.
    2. How: How your knowledge/experience would be beneficial for the group.
    3. Most important thing to remember is I can guide you, but I won’t be doing entire job for you.

If you satisfy all criteria and wish to work on challenging topics

  • Please send me an email(remember to mention topic) with your CV and other relevant details.

Interested in working on NLP but do not have prior experience.

  • At TKAI we believe each student is special and offer different prespective of solving any given problem.
  • If student invest time in understanding my work, allocates time to understand NLP and then decides to approach me with interests, then exceptions can be made.