AI for Scientific Research (GY)

  • Building an innovative and collaborative future for science

AIfSR Logo

AI for Scientific Research (AIfSR) is a student-managed group made up of consulting-like teams developing and delivering AI-enabled products of real value to scientific labs. Since our founding in Spring 2021, we have developed and delivered AI/ML-driven solutions to multiple collaborators (more information below).

We invite bachelors and masters students who have interest/skills in:


2024 AI Meets Science Conference

In three years of operation at AIfSR, we have learned that there is a significant need for greater awareness of the potential that AI can offer to scientific research. AI practitioners should recognize the immense amount of unique datasets and tasks available to them. Scientists need access to a larger number of specialists trained to apply AI methodologies specifically to scientific tasks. It is time to bring together education, science, and industry mindsets in NYC.

This conference, taking place on April 26th, 2024, will explore the dynamic interplay between AI and various scientific disciplines. The one-day agenda is packed with insightful talks, inspiring projects at poster presentations, discussions with experts applying AI to advance research, and of course, opportunities to present your own AI + Science work.



Each semester, we work on a unique set of projects. Here are some we have worked on/ are working on currently.


  • Vaccination response prediction​
  • Sleep patterns classification in EEG data​
  • Microscopy images segmentation: cells​
  • Tracking of cells moving under microscope​
  • Image-based prediction for worms embryonic lethality ​
  • Symptoms of Malaria​
  • Genetics​


  • Inverse task: current in batteries from magnetic field​
  • Material properties prediction from structure​


  • Focused Ion Beam (FIB) profiles​
  • Trajectory of particles under microscope​
  • Spectroscopy for microdiamonds​


  • A Generative Model for Neurolinguistics of Morphology​
  • Generic gender categories labeling in text​

Scientific Software

  • Automatic annotation of scientific figures​
  • Parsing scientific papers from PDF​
  • Benchmarking of CloudMask project on NYU HPC​



For each of our projects, we have a collaborator (professor/researcher). We work(ed) with collaborators from NYU CAS, NYU Abu Dhabi, ML Commons, Semantic Scholar just to name a few! 


Methods & Technologies

  • Data mining / analysis / visualization
  • Statistics
  • Data Science
  • Machine learning
  • Artificial Intelligence
  • Development of interdisciplinary connections
  • Coding / Implementation 

View detailed description of our methods

Faculty Advisor

  • Sergey Samsonau, PhD

Contact Information

  • Student Leadership: