Mohammad Alizadeh - IEEE Xplore Author Profile
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Mohammad Alizadeh

Publications
8
Citations
514
Publications by Year
20182023
Co-Authors:
Show All Co-Authors (23)
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Mohammad Alizadeh

Affiliation

MIT

Publication Topics

Biography

Mohammad Alizadeh received the Ph.D. degree from Stanford University in 2013. He is currently an Associate Professor of computer science with the Massachusetts Institute of Technology. His research interests are in the areas of computer networks, systems, and applied machine learning. His current research focuses on learning-augmented systems, algorithms and protocols for datacenter and wide-area networks, and video streaming. His research on datacenter transport protocols has been implemented in Linux and Windows, and has been deployed by large network operators; his work on adaptive network load balancing algorithms has been implemented in Cisco’s flagship datacenter switching products. He spent two years at Insieme Networks (a datacenter...
Publications
8
Citations
514
Publications by Year
20182023
Co-Authors:
Show All Co-Authors (23)
Author's Published Works

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Unshackling Database Benchmarking from Synthetic Workloads

Parimarjan Negi;Laurent Bindschaedler;Mohammad Alizadeh;Tim Kraska;Jyoti Leeka;Anja Gruenheid;Matteo Interlandi

2023 IEEE 39th International Conference on Data Engineering (ICDE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Introducing new (learned) features into a DBMS requires considerable experimentation and benchmarking to avoid regressions in production (customer) workloads. Using standard benchmarks such as TPC-H and TCH-DS is common practice, but, unfortunately, these do not represent the complexity of real production workloads. To solve this problem, in this demo, we propose a technique that generates a synth...Show More

Unshackling Database Benchmarking from Synthetic Workloads

Parimarjan Negi;Laurent Bindschaedler;Mohammad Alizadeh;Tim Kraska;Jyoti Leeka;Anja Gruenheid;Matteo Interlandi

Year: 2023 | Conference Paper |

Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories

Songtao He;Mohammad Amin Sadeghi;Sanjay Chawla;Mohammad Alizadeh;Hari Balakrishnan;Samuel Madden

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (14)
Traffic accidents cost about 3% of the world’s GDP and are the leading cause of death in children and young adults. Accident risk maps are useful tools to monitor and mitigate accident risk. We present a technique to generate high-resolution (5 meters) accident risk maps. At this high resolution, accidents are sparse and risk estimation is limited by bias-variance trade-off. Prior accident risk ma...Show More

Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories

Songtao He;Mohammad Amin Sadeghi;Sanjay Chawla;Mohammad Alizadeh;Hari Balakrishnan;Samuel Madden

Year: 2021 | Conference Paper |

Real-Time Video Inference on Edge Devices via Adaptive Model Streaming

Mehrdad Khani;Pouya Hamadanian;Arash Nasr-Esfahany;Mohammad Alizadeh

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (29)
Real-time video inference on edge devices like mobile phones and drones is challenging due to the high computation cost of Deep Neural Networks. We present Adaptive Model Streaming (AMS), a new approach to improving the performance of efficient lightweight models for video inference on edge devices. AMS uses a remote server to continually train and adapt a small model running on the edge device, b...Show More

Real-Time Video Inference on Edge Devices via Adaptive Model Streaming

Mehrdad Khani;Pouya Hamadanian;Arash Nasr-Esfahany;Mohammad Alizadeh

Year: 2021 | Conference Paper |

Efficient Video Compression via Content-Adaptive Super-Resolution

Mehrdad Khani;Vibhaalakshmi Sivaraman;Mohammad Alizadeh

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (27)
Video compression is a critical component of Internet video delivery. Recent work has shown that deep learning techniques can rival or outperform human-designed algorithms, but these methods are significantly less compute and power-efficient than existing codecs. This paper presents a new approach that augments existing codecs with a small, content-adaptive super-resolution model that significantl...Show More

Efficient Video Compression via Content-Adaptive Super-Resolution

Mehrdad Khani;Vibhaalakshmi Sivaraman;Mohammad Alizadeh

Year: 2021 | Conference Paper |

Adaptive Neural Signal Detection for Massive MIMO

Mehrdad Khani;Mohammad Alizadeh;Jakob Hoydis;Phil Fleming

IEEE Transactions on Wireless Communications
Year: 2020 | Volume: 19, Issue: 8 | Journal Article |
Cited by: Papers (191)
Traditional symbol detection algorithms either perform poorly or are impractical to implement for Massive Multiple-Input Multiple-Output (MIMO) systems. Recently, several learning-based approaches have achieved promising results on simple channel models (e.g., i.i.d. Gaussian channel coefficients), but as we show, their performance degrades on real-world channels with spatial correlation. We propo...Show More

Adaptive Neural Signal Detection for Massive MIMO

Mehrdad Khani;Mohammad Alizadeh;Jakob Hoydis;Phil Fleming

Year: 2020 | Volume: 19, Issue: 8 | Journal Article |

Cost-Guided Cardinality Estimation: Focus Where it Matters

Parimarjan Negi;Ryan Marcus;Hongzi Mao;Nesime Tatbul;Tim Kraska;Mohammad Alizadeh

2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW)
Year: 2020 | Conference Paper |
Cited by: Papers (19)
The increasing prevalence of machine learning techniques has resulted in many works attempting to replace cardinality estimation, a core component of relational query optimizers, with learned models. The majority of those works have trained models to minimize the prediction error between the model's output for a particular query and the true cardinality of that query. However, when cardinality est...Show More

Cost-Guided Cardinality Estimation: Focus Where it Matters

Parimarjan Negi;Ryan Marcus;Hongzi Mao;Nesime Tatbul;Tim Kraska;Mohammad Alizadeh

Year: 2020 | Conference Paper |

Exploiting Channel Locality for Adaptive Massive MIMO Signal Detection

Mehrdad Khani;Mohammad Alizadeh;Jakob Hoydis;Phil Fleming

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channels with the same or lower computational complexity. MMNet's design builds on the theory of iterative soft-thresholding algorithms and uses a novel training algorithm that leverages temporal and spectral correlation in real channels to accelerate training. These innovations ...Show More

Exploiting Channel Locality for Adaptive Massive MIMO Signal Detection

Mehrdad Khani;Mohammad Alizadeh;Jakob Hoydis;Phil Fleming

Year: 2020 | Conference Paper |

RoadTracer: Automatic Extraction of Road Networks from Aerial Images

Favyen Bastani;Songtao He;Sofiane Abbar;Mohammad Alizadeh;Hari Balakrishnan;Sanjay Chawla;Sam Madden;David DeWitt

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018 | Conference Paper |
Cited by: Papers (231)
Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have...Show More

RoadTracer: Automatic Extraction of Road Networks from Aerial Images

Favyen Bastani;Songtao He;Sofiane Abbar;Mohammad Alizadeh;Hari Balakrishnan;Sanjay Chawla;Sam Madden;David DeWitt

Year: 2018 | Conference Paper |

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