Nikita Kazeev

Leadership

Leading through research, community, and intellectual clarity.

My leadership work spans research teams, scientific events, mentorship, and public communication. The common thread is simple: create structures that help difficult ideas move further, faster, and with more people able to contribute meaningfully.

Leadership principle

Strong leadership in science means setting direction, creating systems for others to do excellent work, and making the field more coherent than you found it.

Scientific community building

  • Speaker search and selection for the 500-person AI4X 2025 conference.
  • Main organizer of the ICLR 2025 workshop on multiscale machine learning.
  • Reviewer for RSC Advances, Machine Learning: Science and Technology, and AI for Accelerated Materials Design workshops at NeurIPS and ICLR.

Research leadership

  • Led a team of 6 on WyckoffTransformer, a symmetry-aware generative model for crystal design.
  • Led a team of 3 on machine learning for defects in 2D materials, including reproducible experimental infrastructure.
  • Led a team of 3 students on uncertainty estimation for generative models in high-energy physics.
  • Co-PI of a 3.4 million dollar AI Singapore grant on multiscale machine learning.

Mentorship and institutional roles

  • Mentored 9 students and 3 interns across research and applied machine learning projects.
  • Student council member from 2013 to 2016, leading several dormitory-scale IT projects.
  • Peer Staff Supporter at NUS, serving as first-line support for mental wellbeing.