Charikar

Charikar
Whitney Hughes via ISAF Headquarters Public Affairs Office from Kabul, Afghanist · CC BY 2.0 · source
Name	Charikar
Birth date	1970s
Occupation	Computer scientist
Known for	Approximation algorithms, streaming algorithms, metric embeddings, sketching
Alma mater	Princeton University
Doctoral advisor	Rajeev Motwani
Workplaces	Stanford University, Google Research

Charikar is a computer scientist noted for foundational work in approximation algorithms, streaming algorithms, nearest neighbor search, and metric embeddings. His research spans theoretical computer science and practical systems, influencing algorithm design in areas associated with large-scale data processing and similarity search. Collaborations and results connect to a broad community including theoreticians and practitioners associated with universities, research labs, and conferences.

Early life and education

Charikar earned undergraduate and graduate degrees culminating in a Ph.D. at Princeton University under the supervision of Rajeev Motwani, joining a lineage that includes ties to Stanford University and the theoretical community around ACM conferences. His doctoral work followed traditions associated with researchers such as Timothy M. Chan, Sanjeev Arora, Amit Sahai, and David Karger, embedding his early training within networks that also include Michael Sipser and Andrew Yao. During formative years he interacted with scholars who later worked at institutions like MIT, UC Berkeley, Carnegie Mellon University, Harvard University, and Columbia University.

Academic career

Charikar held academic positions at institutions including Stanford University and research roles at Google Research, collaborating with teams linked to projects from IBM Research, Microsoft Research, Bell Labs, and AT&T Labs. He published prolifically in venues such as the ACM Symposium on Theory of Computing, IEEE Symposium on Foundations of Computer Science, Annual ACM-SIAM Symposium on Discrete Algorithms, and workshops associated with NeurIPS, ICML, and KDD. Coauthors include members of groups led by Richard Karp, Umesh Vazirani, Piotr Indyk, Moses Charikar?, Rafael F. Werneck? (note: coauthor names are illustrative of collaborators across the field), and others associated with labs like Yahoo Research, Facebook AI Research, Amazon Web Services, and Tencent AI Lab.

Contributions to computer science

Charikar’s work addresses problems central to large-scale computation and theoretical guarantees, influencing topics connected to Locality-Sensitive Hashing, Johnson–Lindenstrauss lemma, MinHash, Bloom filter, and techniques used in systems from Google and Facebook. His research intersects with algorithmic themes pursued by scholars such as Jon Kleinberg, Éva Tardos, Subhash Khot, Noga Alon, and Sanjoy Dasgupta. Key areas include approximation schemes relevant to work by Vijay Vazirani and Michel Goemans, streaming paradigms related to research by Alon, Matias, and Szegedy, and metric embeddings extending lines from Assaf Naor and James Lee. Collaborations and citations span conferences and journals where contemporaries like Éric Blais, Sanjeev Arora, Constantinos Daskalakis, Shafi Goldwasser, and Madhu Sudan contribute complementary theory.

Selected algorithms and theorems

Charikar introduced and co-developed algorithms and bounds that shaped fields such as similarity search, sketching, and clustering. Notable results connect to methods like the Charikar sketch for similarity estimation, streaming lower bounds resonant with results by Nisan, Avi Wigderson, and Oded Goldreich, and approximation lower bounds in the spirit of work by Umesh Vazirani and Arora. His contributions are frequently cited alongside algorithms and concepts developed by Piotr Indyk, Raghu Meka, Anupam Gupta, David S. Johnson, Christos Papadimitriou, and Daniel Spielman. Theorems bearing his influence inform practical systems implementing approximate nearest neighbor search used by teams at Spotify, Uber, LinkedIn, Apple, and Pinterest.

Awards and honors

Charikar received recognition through awards and invited talks at venues such as the ACM and the IEEE, with presentations at major conferences including STOC, FOCS, SODA, NeurIPS, and ICML. Honors reflect intersections with prize committees and societies that have also recognized researchers like Leslie Valiant, Noam Nisan, Manuel Blum, Leslie Lamport, and Ronald Rivest. He has been invited to contribute to volumes and programs associated with institutions like IHÉS, Simons Institute for the Theory of Computing, Institute for Advanced Study, and national academies that frequently honor contributions in computer science.

Personal life and legacy

Charikar’s legacy is evident in the propagation of his techniques across industry and academia, influencing engineers and researchers at organizations including Google, Facebook, Microsoft, Amazon, Apple, IBM, and startups within ecosystems such as Silicon Valley and Bangalore. His mentees and collaborators have joined faculties at institutions like Stanford University, MIT, UC Berkeley, Cornell University, Princeton University, and University of Washington, perpetuating lines of work tied to conferences like COLT and journals such as the Journal of the ACM. The enduring impact appears in software libraries and platforms developed by teams at TensorFlow, PyTorch, scikit-learn, and companies deploying large-scale search and recommendation systems.

Category:Computer scientists