Nickvash Kani

Nickvash Kani is a Teaching Assistant Professor in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign since August 2023. He is affiliated with the Coordinated Science Lab and leads research in Mathematical Language Processing, focusing on developing systems that can process mathematical data with the sophistication currently applied to natural language.

His educational background includes:

• Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2017)
• M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2013)
• B.S. in Computer Engineering from Boston University (2010)

Dr. Kani's research centers on Mathematical Language Processing, which he describes as essential for achieving true artificial intelligence since mathematics represents axiomatic knowledge. His work bridges natural language processing with mathematical reasoning systems, developing algorithms to extract knowledge from mathematical literature. He emphasizes that "true intelligence is axiomatic" and believes focusing on mathematical literature enables automated collection of axiomatic knowledge from unlabeled sources.

His publication record shows a clear evolution from traditional electrical engineering topics like nanomagnetics and spintronics to cutting-edge AI research. Recent publications (2023-2025) focus on mathematical expression representation, formula search, and enhancing mathematical reasoning in language models. His work spans multiple venues including Transactions on Machine Learning Research, IEEE/ACM Transactions, and NeurIPS workshops, demonstrating interdisciplinary impact across computer science and engineering.

His notable achievements include:

• George Anner Excellence in Undergrad Teaching Award (September 2023)
• 1st Place - NL4Opt-Task2 challenge at NIPS 2022 (December 2022)
• 2023 Engineering Council Outstanding Advisors award

Dr. Kani actively mentors students and has built a research group dedicated to Mathematical Language Processing. His graduate teaching assistants, including Neeraj Gangwar and Suyuan Wang, have been instrumental in his research and teaching. He has taught ECE 374 (Introduction to Algorithms and Models of Computation) to 200-300 students and developed ECE 364 (Data Science and Engineering), focusing on implementation with auto-differentiation tools like PyTorch.

He leads the Mathematical Language Processing Group, which is dedicated to developing algorithms that process mathematical data and literature, with the long-term goal of enabling AI systems to reason axiomatically. His teaching philosophy emphasizes "slow, iterative changes" to improve course accessibility and support student learning objectives in STEM education.