معرفی
Aravindan Vijayaraghavan is an Associate Professor in the Department of Computer Science at Northwestern University (affiliated with McCormick School of Engineering). He also holds courtesy appointments in the Industrial Engineering and Management Sciences (IEMS) department.
- Research interests include theoretical computer science, machine learning algorithms, quantum information, and combinatorial optimization under non-worst-case paradigms.
- He leads IDEAL (Institute for Data, Economics, Algorithms and Learning) as Site Director at Northwestern and former Institute Director (2023-24).
Academic Background:
- PhD in Computer Science from Princeton University (advisor: Moses Charikar)
- Bachelor's Degree in Computer Science from Indian Institute of Technology Madras
- Postdoctoral work at Courant Institute (NYU) and Carnegie Mellon University via Simons Collaboration grants
Research Contributions:
- Developed smoothed analysis frameworks for random matrices with dependent entries
- Created sum-of-squares certificates for anti-concentration beyond Gaussian distributions
- Advanced quantum entanglement certification algorithms for subspaces
- Improved weak-to-strong generalization theory with data distribution expansion properties
- Designed error-tolerant e-discovery protocols for legal document classification
Scientific Recognition:
- NSF CAREER Award
- NSF AITF Award (CCF-1637585, CCF-2154100)
- Google Research Scholar Program grant
- Amazon Research Awards program support
- Simons Postdoctoral Fellowship
Academic Leadership:
- General Chair for FOCS 2024
- Co-organizer of Junior Theory Workshop and Northwestern QTW series
- Active in program committees for COLT, ICML, NeurIPS, and STOC conferences
Teaching Portfolio:
- CS262: Mathematical Foundations of CS (Continuous Mathematics for Computer Science)
- CS212: Mathematical Foundations of Computer Science (multiple offerings since 2015)
- CS496: Graduate Algorithms (since 2016)
- CS396/496: Quantum Computation & Information (co-taught with S. Rao)
- CS497: Machine Learning Theory (Spring 2025 offering)
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