World's Best Scientists 2026 revealed!
Sanjeev R. Kulkarni

Sanjeev R. Kulkarni

D-Index & Metrics

Computer Science

D-Index
54
Citations
10466
World Ranking
4615
National Ranking
2145

Overview

Sanjeev R. Kulkarni is affiliated with Princeton University in the United States. Their research primarily falls within the field of Computer Science, with significant contributions in several subfields including Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, and Signal Processing.

Their recent papers reflect a focus on wireless communications, federated learning, and optimization techniques. Notable publications include:

  • Convergence of update aware device scheduling for federated learning at the wireless edge, 2021, IRIS UNIMORE (University of Modena and Reggio Emilia)
  • Blind Federated Edge Learning, 2021, IRIS UNIMORE (University of Modena and Reggio Emilia)
  • Convergence of Federated Learning over a Noisy Downlink, 2022, IRIS UNIMORE (University of Modena and Reggio Emilia)
  • Federated Learning With Quantized Global Model Updates, 2020, arXiv (Cornell University)
  • Convergence of Federated Learning Over a Noisy Downlink, 2021, IEEE Transactions on Wireless Communications

Frequent coauthors collaborating with Kulkarni include Mohammad Mohammadi Amiri, H. Vincent Poor, Deniz Gündüz, Viraj Nadkarni, and Pramod Viswanath. These collaborations suggest interdisciplinary and multi-institutional research efforts.

Kulkarni's publications have appeared in a variety of venues. The most frequent venues are:

  • arXiv (Cornell University)
  • IRIS UNIMORE (University of Modena and Reggio Emilia)
  • IEEE Transactions on Wireless Communications
  • Journal of Medicinal Chemistry
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

The main topics addressed in Kulkarni's research focus on privacy-preserving technologies in data, distributed sensor networks and detection algorithms, stochastic gradient optimization techniques, advanced MIMO systems optimization, network security and intrusion detection, wireless communication security techniques, and cooperative communication and network coding.

  • Privacy-Preserving Technologies in Data
  • Distributed Sensor Networks and Detection Algorithms
  • Stochastic Gradient Optimization Techniques
  • Advanced MIMO Systems Optimization
  • Network Security and Intrusion Detection
  • Wireless Communication Security Techniques
  • Cooperative Communication and Network Coding

Best Publications

  • Machine Learning Methods for Attack Detection in the Smart Grid

    Mete Ozay;Inaki Esnaola;Fatos Tunay Yarman Vural;Sanjeev R. Kulkarni

  • A deterministic approach to throughput scaling in wireless networks

    S.R. Kulkarni;P. Viswanath

  • Divergence Estimation for Multidimensional Densities Via $k$ -Nearest-Neighbor Distances

    Qing Wang;S.R. Kulkarni;S. Verdu

  • Distributed Learning in Wireless Sensor Networks

    Joel B. Predd;Sanjeev R. Kulkarni;H. Vincent Poor

  • Rapid estimation of camera motion from compressed video with application to video annotation

    Yap-Peng Tan;D.D. Saur;S.R. Kulkami;P.J. Ramadge

  • TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking.

    Pingmei Xu;Krista A. Ehinger;Yinda Zhang;Adam Finkelstein

  • Upper bounds to transport capacity of wireless networks

    A. Jovicic;P. Viswanath;S.R. Kulkarni

  • Learning pattern classification-a survey

    S.R. Kulkarni;G. Lugosi;S.S. Venkatesh

  • Divergence estimation of continuous distributions based on data-dependent partitions

    Qing Wang;S.R. Kulkarni;S. Verdu

  • Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

    Mohammad Mohammadi Amiri;Deniz Gunduz;Sanjeev R. Kulkarni;H. Vincent Poor

  • Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models

    M. Ozay;I. Esnaola;F. T. Y. Vural;S. R. Kulkarni

  • Automated analysis and annotation of basketball video

    Drew D. Saur;Yap-Peng Tan;Sanjeev R. Kulkarni;Peter J. Ramadge

  • Probabilistic Coherence and Proper Scoring Rules

    J.B. Predd;R. Seiringer;E.H. Lieb;D.N. Osherson

  • Universal lossless source coding with the Burrows Wheeler transform

    M. Effros;K. Visweswariah;S.R. Kulkarni;S. Verdu

  • Degraded Gaussian multirelay channel: capacity and optimal power allocation

    A. Reznik;S.R. Kulkarni;S. Verdu

  • Bandit problems with side observations

    Chih-Chun Wang;S.R. Kulkarni;H.V. Poor

  • A Collaborative Training Algorithm for Distributed Learning

    J.B. Predd;S.R. Kulkarni;H.V. Poor

  • Rates of convergence of nearest neighbor estimation under arbitrary sampling

    S.R. Kulkarni;S.E. Posner

  • A Nearest-Neighbor Approach to Estimating Divergence between Continuous Random Vectors

    Qing Wang;Sanjeev Kulkarni;Sergio Verdu

  • Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence

    Peng Gao;Fei Shao;Xiaoyuan Liu;Xusheng Xiao

  • Federated Learning With Quantized Global Model Updates

    Mohammad Mohammadi Amiri;Deniz Gunduz;Sanjeev Kulkarni;H. Vincent Poor

Frequent Co-Authors

H. Vincent Poor
H. Vincent Poor Princeton University
Sergio Verdu
Sergio Verdu Princeton University
Gilbert Harman
Gilbert Harman Princeton University
Peter J. Ramadge
Peter J. Ramadge Princeton University
Pan Hui
Pan Hui Hong Kong University of Science and Technology
Prateek Mittal
Prateek Mittal Princeton University
Pramod Viswanath
Pramod Viswanath Princeton University
Edwin K. P. Chong
Edwin K. P. Chong Colorado State University
Deniz Gunduz
Deniz Gunduz Imperial College London
Daniel N. Osherson
Daniel N. Osherson Princeton University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA unlocks pathways to a wide range of related fields. Many students explore other STEM disciplines to broaden their skill sets and career opportunities. Affordable online programs make it easier than ever to diversify your expertise without leaving your current job or relocating.

For example, those interested in expanding their engineering knowledge may consider the cheapest online master's mechanical engineering programs, which equip graduates for advanced roles in design and manufacturing. If your interest is in foundational science, pursuing an online bachelor's degree in physics can open doors to research and analytical careers.

The tech industry is also experiencing strong demand for professionals who understand big data. Accredited data science programs offer training in statistics, programming, and machine learning, leading to data analyst or data scientist roles.

Lastly, the rise of smart technology is generating new jobs for graduates with electrical engineering expertise. Exploring the online electrical engineering career outcomes reveals strong salary potential and diverse job options across industries. These related degrees and career paths complement a Computer Science education, enhancing your employability and long-term growth.

Best Scientists Citing Sanjeev R. Kulkarni

Trending Scientists