Abraham Matta

About

Abraham Matta is a Professor in the Computer Science Department at Boston University, where he served as Chair of the department during 2018-2024. He received his Ph.D. in computer science from the University of Maryland at College Park in 1995. His research focuses on the design of network protocols and architectures based on principles such as inter-process communication, decomposition, and recursion, as well as mathematical techniques including probabilistic analysis, queuing theory, optimization, and control theory. His work encompasses performance evaluation tools like simulation and emulation, with application domains including the Internet, wireless, mobile, sensor, and disruption-tolerant networks, as well as cloud and distributed systems. He has published over 150 peer-reviewed technical papers and has received numerous awards, including the NSF CAREER award in 1997, a patent in 2011, and several best-paper awards for work on wireless ad hoc and sensor networks, cloud computing, and experimental work on the GENI and FABRIC testbeds. He has been actively involved in projects such as GENI since 2013, contributing to outreach, education, and collaboration efforts in cyberinfrastructure. He serves on various scientific advisory boards and has held leadership roles in technical program committees and organizing committees for major conferences. He is a senior member of the ACM and IEEE, leads the DASNet group, and is an Associate Editor for IEEE Networking Letters.

Research topics

• Computer Science
• Artificial Intelligence
• Computer Security
• Machine Learning
• Distributed computing
• Embedded system
• Operating system

Selected publications

• COSE: Configuring Serverless Functions using Statistical Learning

  IEEE INFOCOM 2022 - IEEE Conference on Computer Communications · 2020 · 87 citations

  Senior authorCorresponding

  Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud computing with a simplified programming model, that aims to abstract away most operational concerns. Running serverless functions requires users to configure multiple parameters, such as memory, CPU, cloud provider, etc. While relatively simpler, configuring such parameters correctly while minimizing cost and meeting delay constraints is not trivial.…

  PublisherDOI

• Federated or Split? A Performance and Privacy Analysis of Hybrid Split and Federated Learning Architectures

  2021 · 61 citations

  Senior authorCorresponding

  Mobile phones, wearable devices, and other sensors produce every day a large amount of distributed and sensitive data. Classical machine learning approaches process these large datasets usually on a single machine, training complex models to obtain useful predictions. To better preserve user and data privacy and at the same time guarantee high performance, distributed machine learning techniques such as Federated and Split Learning have been recently proposed. Both of these distributed learning…

  PublisherDOI

• Privacy and Efficiency of Communications in Federated Split Learning

  IEEE Transactions on Big Data · 2023-05-29 · 54 citations

  articleSenior author

  Every day, large amounts of sensitive data are distributed across mobile phones, wearable devices, and other sensors. Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make valuable predictions. Distributed machine learning techniques such as Federated and Split Learning have recently been developed to protect user data and privacy better while ensuring high performance. Both of these distributed learning architectures have advant…

  PublisherDOI

• Combining split and federated architectures for efficiency and privacy in deep learning

  2020-11-23 · 37 citations

  articleOpen accessSenior author

  Distributed learning systems are increasingly being adopted for a variety of applications as centralized training becomes unfeasible. A few architectures have emerged to divide and conquer the computational load, or to run privacy-aware deep learning models, using split or federated learning. Each architecture has benefits and drawbacks. In this work, we compare the efficiency and privacy performance of two distributed learning architectures that combine the principles of split and federated lea…

  PublisherOA PDFDOI

• Configuration and Placement of Serverless Applications Using Statistical Learning

  IEEE Transactions on Network and Service Management · 2023-03-08 · 22 citations

  article

  In the last decade, serverless computing emerged as a new compelling paradigm for the deployment of cloud applications and services. It represents an evolution of cloud computing with a simplified programming model, that aims to abstract away most operational concerns. Running serverless applications requires users to configure multiple parameters, such as memory, CPU, cloud provider, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . Whil…

  PublisherDOI

Recent grants

• CNS-NeTS:Medium: A Recursive Internet Architecture

  NSF · $559k · 2010–2015

• CNS Core: Small: Collaborative Research: HEECMA: A Hybrid Elastic Edge-Cloud Application Management Architecture

  NSF · $299k · 2019–2023

Frequent coauthors

• Azer Bestavros

  57 shared

• Flavio Esposito

  Saint Louis University

  25 shared

• Nabeel Akhtar

  Akamai (United States)

  23 shared

• Yuefeng Wang

  Akamai (United States)

  20 shared

• A. Udaya Shankar

  18 shared

• Liang Guo

  17 shared

• Hany Morcos

  Boston University

  15 shared

• Mina Guirguis

  Texas State University

  14 shared

Education

• Ph.D., Computer Science

  University of Maryland at College Park

  1995

Awards & honors

• NSF CAREER award (1997)
• two best-paper awards (2008 and 2010)
• best-paper award for cloud computing work (2021)
• awards for experimental work on the GENI testbed (2018)
• awards for work on the FABRIC testbed (2023)

Similar researchers at Boston University

• Kate Saenkoh-89
• Ran Canettih-78
• Stan Sclaroffh-69