Mohammad Alizadeh

About

Mohammad Alizadeh is the NEC Professor of Software Science and Engineering at MIT, serving as an Industry Officer and Director of the 6-A MEng Thesis Program. His research focuses on developing groundbreaking systems in the fields of electrical engineering and computer science, particularly in areas related to software engineering, systems, and networking. As a faculty member, he contributes to advancing knowledge in these domains through his leadership and research activities.

Research topics

• Computer Science
• Information Retrieval
• Computer network
• Real-time computing
• Telecommunications
• Operating system
• Data Mining
• Computer Security
• Distributed computing
• Programming language

Selected publications

• Bao: Making Learned Query Optimization Practical

  Proceedings of the 2022 International Conference on Management of Data · 2021 · 178 citations

  Recent efforts applying machine learning techniques to query optimization have shown few practical gains due to substantive training overhead, inability to adapt to changes, and poor tail performance. Motivated by these difficulties, we introduce Bao (the \underlineBa ndit \underlineo ptimizer). Bao takes advantage of the wisdom built into existing query optimizers by providing per-query optimization hints. Bao combines modern tree convolutional neural networks with Thompson sampling, a well-stu…

  DOI

• Tsunami

  Proceedings of the VLDB Endowment · 2020 · 115 citations

  Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an…

  DOI

• High Throughput Cryptocurrency Routing in Payment Channel Networks

  arXiv (Cornell University) · 2020 · 62 citations

  Senior authorCorresponding

• FactorJoin: A New Cardinality Estimation Framework for Join Queries

  Proceedings of the ACM on Management of Data · 2023-05-26 · 47 citations

  articleOpen access

  Cardinality estimation is one of the most fundamental and challenging problems in query optimization. Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries. They either rely on simplified assumptions leading to ineffective cardinality estimates or build large models to understand the complicated data distributions, leading to long planning times and a lack of generalizability across queries. In this paper, we propose a new…

  PublisherOA PDFDOI

• Longest Chain Consensus Under Bandwidth Constraint

  2022-09-19 · 10 citations

  articleOpen accessSenior author

  Spamming attacks are a serious concern for consensus protocols, as witnessed by recent outages of a major blockchain, Solana. They cause congestion and excessive message delays in a real network due to its bandwidth constraints. In contrast, longest chain (LC), an important family of consensus protocols, has previously only been proven secure assuming an idealized network model in which all messages are delivered within bounded delay. This model-reality mismatch is further aggravated for Proof-o…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: CNS Core: Small: Understanding Per-Hop Flow Control

  NSF · $250k · 2020–2022

• CNS Core: Small: Network Architecture and Routing Protocols for Payment Channel Networks

  NSF · $500k · 2019–2022

• NeTS: Small: Collaborative Research: A Fast and Flexible Transport Architecture for High Speed Networks

  NSF · $250k · 2016–2019

Frequent coauthors

• Hari Balakrishnan

  IIT@MIT

  25 shared

• Balaji Prabhakar

  Jawaharlal Nehru Technological University, Hyderabad

  23 shared

• Prateesh Goyal

  19 shared

• Scott Shenker

  University of California, Berkeley

  16 shared

• Ryan Marcus

  California University of Pennsylvania

  15 shared

• Changhoon Kim

  14 shared

• Tim Kraska

  Amazon (United States)

  14 shared

• Nick McKeown

  Aberystwyth University

  13 shared

Education

• Ph.D., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2008

• M.S., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2004

• B.S., Electrical Engineering

  Sharif University of Technology

  2002

Similar researchers at Massachusetts Institute of Technology

• Regina Barzilayh-94
• Elfar Adalsteinssonh-76