Bhaskar Krishnamachari

About

Bhaskar Krishnamachari is a Professor and Ming Hsieh Faculty Fellow in Electrical Engineering at the Viterbi School of Engineering, University of Southern California. He received his B.E. in Electrical Engineering from The Cooper Union and his M.S. and Ph.D. in Electrical Engineering from Cornell University. He has been a faculty member in the Department of Electrical Engineering since 2002 and also holds a joint appointment in the Department of Computer Science. He is the Director of the Autonomous Networks Research Group and Co-Director of the Ming Hsieh Institute. His research interests are focused on the design and analysis of algorithms, protocols, and applications for next-generation wireless networks, internet of things, distributed systems, blockchain technologies, artificial intelligence, machine learning, and network economics. His work spans from theoretical analysis of algorithms to prototype software implementations of network protocols and applications. He has co-authored over 300 technical articles and has received numerous awards, including being named an IEEE Fellow in 2023, and recognition in Popular Science Magazine's 'Brilliant Ten' list in 2015 and the TR-35 list in 2011. He has also received awards such as the ASEE Terman Award, IEEE-HKN Outstanding Young Electrical and Computer Engineer Award, and the NSF CAREER award. Additionally, he has served as an editor for several prominent journals and co-edited special issues on sensor network algorithms and…

Research topics

• Computer Science
• Computer Security
• Medicine
• Data science
• Telecommunications
• Artificial Intelligence
• World Wide Web
• Simulation
• Computer engineering
• Internal medicine

Selected publications

• Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities

  IEEE Internet of Things Magazine · 2022 · 358 citations

  Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that may contain users' private information will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional eco-system of centralized over-the-cloud learning and processing for IoT platforms. Fed…

  DOI

• A Survey of Blockchain-Based Strategies for Healthcare

  ACM Computing Surveys · 2020 · 267 citations

  Blockchain technology has been gaining visibility owing to its ability to enhance the security, reliability, and robustness of distributed systems. Several areas have benefited from research based on this technology, such as finance, remote sensing, data analysis, and healthcare. Data immutability, privacy, transparency, decentralization, and distributed ledgers are the main features that make blockchain an attractive technology. However, healthcare records that contain confidential patient data…

  DOI

• Fast and Accurate Streaming CNN Inference via Communication Compression on the Edge

  2020 · 60 citations

  Senior authorCorresponding

  Recently, compact CNN models have been developed to enable computer vision on the edge. While the small model size reduces the storage overhead and the light-weight layer operations alleviate the burden of the edge processors, it is still challenging to sustain high inference performance due to limited and varying inter-device bandwidth. We propose a streaming inference framework to simultaneously improve throughput and accuracy by communication compression. Specifically, we perform the followin…

  DOI

• Simulating COVID-19 classroom transmission on a university campus

  Proceedings of the National Academy of Sciences · 2022 · 16 citations

  We study the airborne transmission risk associated with holding in-person classes on university campuses for the original strain and a more contagious variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We adopt a model for airborne transmission risk in an enclosed room that considers room properties, mask efficiency, and initial infection probability of the occupants. Additionally, we study the effect of vaccination on the spread of the virus. The presented model has been e…

  DOI

• PISA: An Adversarial Approach to Comparing Task Graph Scheduling Algorithms

  2025-06-03 · 2 citations

  articleSenior author

  Scheduling a task graph representing an application over a heterogeneous network of computers is a fundamental problem in distributed computing. It is known to be not only NP-hard but also not polynomial-time approximable within a constant factor. As a result, many heuristic algorithms have been proposed over the past few decades. Yet it remains largely unclear how these algorithms compare to each other in terms of the quality of schedules they produce. We identify gaps in the traditional benchm…

  PublisherDOI

Recent grants

• NeTS: Small: GOALI: Information Centric Networking on Wheels (IC NoW) - Architecture and Protocols

  NSF · $350k · 2012–2016

• ECCS - EARS: Collaborative Research: Enhanced Radio Spectrum via Information Acquisition and Learning

  NSF · $250k · 2012–2016

• EAGER: Towards an Open Source Backpressure Protocol Stack for Wireless Networks

  NSF · $240k · 2010–2013

Frequent coauthors

• Tarek Abdelzaher

  192 shared

• Viktor K. Prasanna

  136 shared

• Jie Gao

  130 shared

• Sotiris Nikoletseas

  130 shared

• Magnús M. Halldórsson

  Reykjavík University

  129 shared

• Luca Mottola

  Politecnico di Milano

  128 shared

• Arvin Hekmati

  University of Southern California

  85 shared

• Eugenio Grippo

  University of Southern California

  74 shared

Education

• Ph.D., Electrical Engineering

  University of California, Los Angeles

  1995

• M.S., Electrical Engineering

  University of California, Los Angeles

  1991

• B.S., Electrical Engineering

  Indian Institute of Technology, Madras

  1988

Awards & honors

• IEEE Fellow (2023)
• Popular Science Brilliant 10 (2015)
• TR-35, MIT Technology Review's Top 35 Innovators Under 35 (2…
• IEEE - Eta Kappa Nu Outstanding Young Electrical and Compute…
• ASEE Frederick E. Terman Award (2010)

Similar researchers at University of Southern California

• Andreas Molischh-132
• Demetrios Christodoulidesh-113
• Shrikanth Narayananh-81