Nikol Pushkash

Hi everyone. I’m a Bachelor student of Computer Science at Faculty of Mathematics and Physics, Charles University. I like to figure out how things work, math, music and volleyball.

Contact me

Office: CoRE building, room 419

Email: pushnikol@gmail.com

At REU 2025, I work on Classical, Computational, and AI-Powered Social Choice. My mentor in Rutgers University is Professor Lirong Xia, and Professor Farhad Mohsin

Project Description

Week log

Week 1: 05/27–06/01
- I came to the US with two other students from Charles Universty, Hana Salavcová and Martin Černý. On my second day here, I attended an orientation meeting held mostly by Lazaros Gallos and other members of DIMACS family. On third one, I also attended a website creating workshop held by Larry Frolov (special thanks to him for organazing meals on first day, making an office tour and rather funny workshop :))
- Regarding my project, I've got one, something to be proud of already :). I read an introductory article on Social choice and got a basic idea of what I might be working with. At least for now, I do like the topic I have. I had a meeting with Professor Xia and after that with Professor Mohsin, who introduced me to the project and first steps that I should do to get started.
- I also created this website, which I kindly borrowed from a former student of DIMACS REU program. Hope he either would not be mad or would not find out :).
Week 2: 06/02-06/08
- Early in the week, I have prepared and gave a presentation about the problem I am going to try to solve this summer. To sum up, I will try to train a model using machine learning such that it, given different types of data, can find an optimal ranking of the set of objects. I also listened to projects of other students. I do like the variety of topics DIMACS have provided, even though there is relatively smaller number of participants this year.
- After that, I have completed two courses on Coursera platform, ML specialization and Deep Learning specialization, both by Andrew Ng and DeepLearning.AI. I made myself a bit more comfortable with basic concepts of supervised learning and structure of neural networks.
Week 3: 06/09-06/15
- This week I started reading the Lirong Xia's book on Ranking Data and learning from it. I have read first 4 chapters, which gave me a better understanding of Plackett-Luce ranking model which I will be using in my further experiments.
- At the end of the week, I managed to implement my first model, which learns Plackett-Luce parameters of the ranking distribution. I used one of the easiest and most common loss functions in machine learning, and results quite surprised me. With no complicated techniques, they were pretty nice.
Week 4: 06/16-06/22
- This week I implemented 2 other models, which required a bit more of computations for training, which made those slower to train. But the results were slightly better on small number of alternatives. I think they should be better for higher number of alternatives.
- What I also realized was that my method of evaluating the models was incorrect, hence the results might not be that accurate. I will fix this first thing next week.

Acknowledgments

I would like to thank

My supervisors for agreeing working with me
Charles University, expecially Department of Applied Mathematics and Informatics Institute of the University, and also PSJ Foundation for funding my work here
Rutgers University for hosting DIMACS REU 2025 program and giving such a nice opportunity for academic, cultural and social personal and professional development

References