Alina Vereshchaka

PhD Student, Research Assistant in Artificial Intelligence



I am a PhD candidate advised by Professor Wen Dong in the Computer Science and Engineering Department at the University at Buffalo. I have been deeply working in Reinforcement Learning, and my current research focuses on optimal control in complex systems.


Fall 2017 - Present
PhD student in Computer Science and Engineering
University at Buffalo, the State University of New York (SUNY), NY, USA

Academic Teaching Experience

Instructor, University at Buffalo -- Fall 2020
Instructor, University at Buffalo -- Summer 2020
Instructor, University at Buffalo -- Spring 2020
Instructor, University at Buffalo -- Fall 2019
Instructor, University at Buffalo -- Summer 2019
Teaching Assistant, University at Buffalo




Professional Service


Predicting human behaviors from electroencephalography

Tools & Algorithms: Python3, VAE, NN, Matplotlib
Note: The proposed approach and preliminary results won the second place award at IEEE Brain Data Bank Challenge & Competition, Seattle, USA, Dec 2018

Stabilizing policy optimization by constraining gradient updates in deep reinforcement learning

Tools & Algorithms: Python3, Keras, Tensorflow, Stable Baseline, OpenAI Gym, PPO, TRPO

Learning to navigate in complex environment using deep reinforcement learning

Tools & Algorithms: OpenAI Gym, Keras-RL, CNN, DQN
Note: adapted version of the project was used a main course project in Machine Leaning course at University at Buffalo

Deep residual learning for NIST images recognition (code)

Tools & Algorithms: Keras, CNN

Digits classification (MNIST) using various machine learning and deep learning algorithms

Tools & Algorithms: Python, Numpy, Tensorflow, Numpy, Matplotlib, SVM, CNN, NN

Probabilistic Graphical Models (code)
Making exact inferences about probabilistic graphical models, by constructing the moral graph, triangulated graph and the junction tree. Implementing message passing algorithm to get the cluster marginals.
Tools & Algorithms: Python 2, Bayesian Belief Networks