Aleksandar Nikolov (computer scientist)
 |
Aleksandar Nikolov | |
|---|---|
| Born | |
| Education | St. Peter's University |
| Alma mater | Rutgers University |
| Scientific career | |
| Fields | Differential privacy, discrepancy theory |
| Institutions | University of Toronto |
| Thesis | New computational aspects of discrepancy theory (2014) |
| Doctoral advisor | S. Muthukrishnan |
| Website | www |
Aleksandar Nikolov is a Bulgarian and Canadian theoretical computer scientist working on differential privacy, discrepancy theory, and high-dimensional geometry. He is a professor at the University of Toronto.
Nikolov obtained his Ph.D. from Rutgers University in 2014 under the supervision of S. Muthukrishnan (Thesis: New computational aspects of discrepancy theory).[1]
Nikolov is the Canada Research Chair in Algorithms and Private Data Analysis.[2]
Early Life and Education
[edit]Aleksandar Nikolov was born in Varna, Bulgaria to Bulgarian parents of Banat origin. After attaining primary and secondary education in Varna, he was awarded a presidential scholarship to St. Peter's University in Jersey City, NJ. There he majored in Computer Science, graduating as the valedictorian of his class.[3] He later pursued graduate studies at Rutgers University, obtaining his PhD in Computer Science under the supervision of S. Muthukrishnan.
Academic and Research Career
[edit]Following his doctoral studies, Nikolov joined the University of Toronto as an Assistant Professor in the Department of Computer Science.[4] His research interests include differential privacy, optimization, and the design and analysis of algorithms. Nikolov has made significant contributions to understanding the mathematical foundations of privacy and the development of efficient algorithms with strong theoretical guarantees.[5]
Research Contributions
[edit]Nikolov's work on differential privacy has been particularly influential. He has explored various aspects of privacy-preserving data analysis, including mechanisms for ensuring privacy in statistical queries and optimization problems. His research has advanced the theoretical understanding of how to balance data utility with privacy guarantees.[5]
In addition to privacy, Nikolov has contributed to the field of optimization, focusing on algorithms for high-dimensional data and the development of efficient approximation algorithms. His work often intersects with machine learning, where he addresses problems related to data representation and complexity.[5]
Publications and Citations
[edit]Aleksandar Nikolov has published numerous papers in prestigious conferences and journals. His work has been widely cited, reflecting the impact of his research on the computer science community. Some of his notable publications include contributions to conferences such as STOC (Symposium on Theory of Computing), FOCS (Foundations of Computer Science), and SODA (Symposium on Discrete Algorithms).[6][7][8][9]
Teaching and Mentorship
[edit]At the University of Toronto, Nikolov is also recognized for his dedication to teaching and mentorship. He teaches courses on algorithms, data privacy, and theoretical computer science, inspiring a new generation of computer scientists. His approach to teaching emphasizes both the theoretical underpinnings of computer science and their practical applications.[10]
Professional Activities and Recognition
[edit]Nikolov is actively involved in the academic community, serving on program committees for major conferences and reviewing for top journals. His contributions have earned him recognition, highlighting his role as a leading figure in his areas of expertise.[5]
Recent Work and Future Directions
[edit]In recent years, Nikolov has focused on developing a 21st-century framework for addressing contemporary problems in computer science. He advocates for interdisciplinary approaches that combine insights from computer science, mathematics, and other fields to tackle complex challenges. His recent initiatives aim to bridge the gap between theoretical research and practical applications, ensuring that advancements in algorithms and privacy have a meaningful impact on society.[11]
External Links
[edit]Aleksandar Nikolov's Google Scholar Profile
Aleksandar Nikolov's Research Page at the University of Toronto
Aleksandar Nikolov's Personal Homepage at the University of Toronto
Article on the Need for a 21st-Century Framework for 21st-Century Problems
References
[edit]- ^ Nikolov, Aleksandar (2014). New computational aspects of discrepancy theory (Thesis). Rutgers University - Graduate School - New Brunswick.
- ^ "U of T gains 34 new Canada Research Chairs". University of Toronto News. Retrieved 2019-10-23.
- ^ Saint Peter's University (2008-06-10). SPC '08 Valedictorian, Aleksandar Nikolov. Retrieved 2024-07-08 – via YouTube.
- ^ "Aleksandar Nikolov". www.cs.toronto.edu. Retrieved 2024-07-08.
- ^ a b c d "Aleksandar Nikolov". scholar.google.com. Retrieved 2024-07-08.
- ^ "Aleksandar Nikolov". Simons Foundation. Retrieved 2024-07-08.
- ^ Andoni, Alexandr; Naor, Assaf; Nikolov, Aleksandar; Razenshteyn, Ilya; Waingarten, Erik (2018-06-20). "Data-dependent hashing via nonlinear spectral gaps". Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing. STOC 2018. New York, NY, USA: Association for Computing Machinery. pp. 787–800. doi:10.1145/3188745.3188846. ISBN 978-1-4503-5559-9.
- ^ Kush, Deepanshu; Nikolov, Aleksandar; Tang, Haohua (2021-05-10). "Near Neighbor Search via Efficient Average Distortion Embeddings". arXiv:2105.04712 [cs.DS].
- ^ "dblp: SODA 2021". dblp.org. Retrieved 2024-07-08.
- ^ "Teaching: Courses". www.cs.toronto.edu. Retrieved 2024-07-08.
- ^ "We need a 21st century framework for 21st century problems". Schwartz Reisman Institute. Retrieved 2024-07-08.
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