Alesia Chernikova

Alesia Chernikova (she/her) is a Postdoctoral Research Associate working with Tina Eliassi-Rad. Her research interests are in the intersection of responsible AI and network science, and she is also interested in the security of GenAI ecosystems.

Alesia completed her Ph.D. in Computer Science at Northeastern University, where she was advised by Alina Oprea. She was affiliated with the NDS2 lab and was a part of the Cybersecurity and Privacy Institute. During her doctoral years, her research interests were in the intersection of deep learning, network science, and cybersecurity, starting on the level of cybernetwork resilience against adversarial behavior and going to the level of evasive behavior against machine learning models in critical environments. Therefore, she was interested in the mathematical modeling of self-propagating malware with a rigorous assessment of its essential characteristics and proactive methods for network resilience against it. Another research area that fascinated her was adversarial machine learning, where she studied the effect of adversarial attacks against deep neural networks in cybersecurity and self-driving cars domains.

Before joining Northeastern, Alesia received her BS degree in Applied Mathematics from Belarusian State University, where she was affiliated with the Mathematical Modeling and Data Analysis Department under the supervision of Vladimir Malugin. Her research focus included the design of hedging algorithms based on derivative contracts. Additionally, she was a part of the Research Institute of Applied Mathematics and Information Technology Problems, where she participated in the project for credit rankings estimation and evaluation of national enterprises using mathematical, statistical, and econometric methods and models.

Alesia's other interests include hiking and yoga, film photography and visual art, music and soundscapes.

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