M. Hadi Amini (0000-0002-2768-3601)

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Biography

M. H. Amini is an Assistant Professor of Computer Science in the Knight Foundation School of Computing and Information Sciences (KFSCIS) and founding director of Sustainability, Optimization, and Learning for InterDependent Networks Laboratory (www.solidlab.network) at Florida International University (FIU) since August 2019. He also serves as the Director and PI of ADvanced education and research for Machine learning-driven critical Infrastructure REsilience (ADMIRE) Center, and an Associate Director of the USDOT National University Transportation Center for Transportation Cyber Security and Resiliency. Prior to that, he received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2019. He conducts research in (decentralized) learning and optimization algorithms, their security and privacy vulnerabilities, and applications in cyber-physical systems security, resilience, and homeland security. He has extensively and sustainably secured external grants from various competitive funding agencies, including DHS, NSF, and USDOT. He is the recipient of the DHS Science and Technology Directorate, Scientific Leadership Award, Best Paper Award from “2019 IEEE Conference on Computational Science & Computational Intelligence”, 2021 Best Journal Paper Award from “Springer Nature Operations Research Forum Journal”, 2024 “FIU Top Scholar Award, Research and Creative Activities, Junior Faculty with Significant Grants (Sciences)”, and 2023 FIU “Faculty Senate Excellence in Teaching Award”, among others. He is a Senior Member of IEEE. His publications in top-tier journals and conferences have received multiple best journal/conference paper awards, and extensively cited with an h-index of 40. He also co-authored three books on security, privacy, and distributed sensing, edited multiple books on learning and optimization for interdependent networks, and serves as an Associate Editor of IEEE Transactions on Information Forensics and Security. He has advised 12 PhD students, 4 Master students, and 16 undergraduate students (NSF REU, DHS ADMIRE Fellow, NSH CAHSI, and research assistants).

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Florida International University: Miami, FL, US

2019-08 to present | Assistant Professor (Knight Foundation School of Computing and Information)

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M. Hadi Amini

https://orcid.org/0000-0002-2768-3601

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Florida International University: Miami, FL, US

Education and qualifications (1)

Carnegie Mellon University: Pittsburgh, PA, US

Works (50 of 141)

A Survey on Optimal Transport for Machine Learning: Theory and Applications

Security and Privacy Challenges of Large Language Models: A Survey

Quantum Encryption for Secure Federated Learning Against Generative Adversarial Network Attacks

A Comprehensive Review of Artificial Intelligence and Machine Learning Methods for Modern Healthcare Systems

A Secure Object Detection Technique for Intelligent Transportation Systems

Distributed Machine Learning and Computing

Distributed Machine Learning and Computing: An Overview

Vertical Federated Learning: Principles, Applications, and Future Frontiers

Coordinated restoration of interdependent critical infrastructures: A novel distributed decision-making mechanism integrating optimization and reinforcement learning

Forecasting and optimization for minimizing combined sewer overflows using Machine learning frameworks and its inversion techniques

Fast high-fidelity flood inundation map generation by super-resolution techniques

A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-Constrained Computing

Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images

Subset Sensor Selection Optimization: A Genetic Algorithm Approach With Innovative Set Encoding Methods

FLID: Intrusion Attack and Defense Mechanism for Federated Learning Empowered Connected Autonomous Vehicles (CAVs) Application

"Towards Real-time House Detection in Aerial Imagery Using Faster Region-based Convolutional Neural Network"

Optimal Control of Combined Sewer Systems to Minimize Sewer Overflows by Using Reinforcement Learning

Scenic routing navigation using property valuation

An integrative framework for coordination of damage assessment, road restoration, and relief distribution in disasters

A Novel Scalable Reconfiguration Model for the Postdisaster Network Connectivity of Resilient Power Distribution Systems

Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art

A Federated Learning Framework for Automated Decision Making with Microscopic Traffic Simulation

A Machine Learning Framework for Overflow Prediction in Combined Sewer Systems

A2BCF: An Automated ABC-Based Feature Selection Algorithm for Classification Models in an Education Application

Cyberphysical Smart Cities Infrastructures

Leveraging Asynchronous Federated Learning to Predict Customers Financial Distress

A Survey on Federated Learning for Resource-Constrained IoT Devices

Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications

Data Analytics for Smart Cities: Challenges and Promises

DeepCOVIDNet: Deep Convolutional Neural Network for COVID-19 Detection from Chest Radiographic Images

Embodied AI‐Driven Operation of Smart Cities: A Concise Review

Flexible Resources for Smart Cities

Data-driven inferences of agency-level risk and response communication on COVID-19 through social media-based interactions

Data analytics to evaluate the impact of infectious disease on economy: Case study of COVID-19 pandemic

FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures

Introduction to Blockchain Technology

Protection of large-scale smart grids against false data injection cyberattacks leading to blackouts

Topological Data Analysis for Network Resilience Quantification

Preclinical Western Blot in the Era of Digital Transformation and Reproducible Research, an Eastern Perspective

Blockchain Applicability in Internet-of-Things (IoT) Environment

Blockchain Interoperability from the Perspective of Interdependent Networks

Coupling Blockchain with Emerging Real-Life Applications

FedPARL: Client Activity and Resource-Oriented Lightweight Federated Learning Model for Resource-Constrained Heterogeneous IoT Environment

Leveraging Blockchain Technology for Artificial Intelligence

Toward Smart Contract and Consensus Mechanisms of Blockchain

Evolutionary Algorithms and Efficient Data Analytics for Image Processing

Distributed Outage Detection in Power Distribution Networks

FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots

Fundamentals of Brooks–Iyengar Distributed Sensing Algorithm

Optimization, Learning, and Control for Interdependent Complex Networks

Peer review (3 reviews for 3 publications/grants)