Rate My Professor Alexey Ignatiev
Ratings & reviews
Helps students unlock their full potential.
Challenges students to grow and excel.
Always goes the extra mile for students.
Makes learning a joyful experience.
Encourages open-minded and thoughtful discussions.
Taught by or worked with Alexey?
Add your rating. It takes about 30 seconds.
About Alexey
Alexey Ignatiev is an Associate Professor in the Faculty of Information Technology at Monash University, serving in the Department of Data Science and Artificial Intelligence within the Optimisation research group. He joined Monash as a Senior Lecturer in 2019 and advanced to Associate Professor in 2025. Prior to this, Ignatiev was a postdoctoral researcher at INESC-ID and the Faculty of Sciences, University of Lisbon from 2012 to 2019, and a researcher at the Institute for System Dynamics and Control Theory of the Siberian Branch of the Russian Academy of Sciences from 2009 to 2012. He also worked as a software engineer at Delcam plc from 2007 to 2008. Ignatiev holds a PhD in Computer Science from Irkutsk State University, awarded in 2010, with a thesis on methods of inverting discrete functions using binary decision diagrams under the supervision of Alexander A. Semenov. He earned his Diploma (MSc equivalent) in Applied Mathematics with Honours from the same university in 2006.
Ignatiev's research focuses on developing and improving highly efficient SAT- and SMT-based decision and optimization procedures, with applications in artificial intelligence such as software package upgradability, Boolean formula minimization, model-based diagnosis, software fault localization, and explainable AI (XAI). His contributions have had significant impact, as demonstrated by his MaxSAT solvers RC2 and MSCG achieving gold medals in multiple categories at MaxSAT Evaluations from 2014 to 2020, including both complete and industrial tracks. Additionally, his paper 'Computing Optimal Decision Sets with SAT' was awarded best paper in the CP/ML track at the 2020 International Conference on Principles and Practice of Constraint Programming. Key publications include 'A Formal Explainer for Just-In-Time Defect Predictions' (ACM Transactions on Software Engineering and Methodology, 2024), 'No Silver Bullet: Interpretable ML Models Must Be Explained' (Frontiers in Artificial Intelligence, 2023), 'On Tackling Explanation Redundancy in Decision Trees' (Journal of Artificial Intelligence Research, 2022), 'RC2: An Efficient MaxSAT Solver' (Journal on Satisfiability, Boolean Modeling and Computation, 2019), and 'SuperStack: Superoptimization of Stack-Bytecode via Greedy, Constraint-Based, and SAT Techniques' (PLDI, 2024).
Trending Computer Science Research & Publication News
AI Research Scooping: OpenAI & Anthropic Priority Disputes
Photo by Brecht Corbeel on Unsplash
arXiv Two-Submission Monthly Cap Explained | AcademicJobs
Photo by Kelly Sikkema on Unsplash
AI-Powered Systematic Review Reproducibility | AcademicJobs
Photo by Igor Omilaev on Unsplash
EOSC Federation: Federated Data Publishing | AcademicJobs
Photo by Haberdoedas on Unsplash
Repository Audit Updates: CoreTrustSeal, ISO 16363
Photo by Zulfugar Karimov on Unsplash
Research Software PID Mandates Expand | AcademicJobs
Photo by Google DeepMind on Unsplash