Ph.D. in Computer Science | Researcher & Software Engineer
🔬 Distributed Systems • Federated Learning • Blockchain • Consensus Protocols
📍 San Antonio, Texas

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I'm a distributed systems researcher and software engineer with a Ph.D. from SUNY Buffalo.
My work spans scalable Byzantine Fault Tolerant (BFT) protocols, federated learning, and distributed databases.
I've held research positions at University of Utah, SUNY Buffalo, and taught graduate courses in Distributed Systems.
Currently, I’m open to full-time, part-time, or internship opportunities in software engineering, research, or cloud infrastructure.

• Ph.D. in Computer Science — SUNY Buffalo (2017 – 2022)
  Thesis: Analyzing and Improving Performance of Byzantine Fault Tolerant Consensus Protocols
• M.S. in Computer Science — University of Connecticut (2014 – 2016)
• B.S. in Computer Science — King Khalid University (2005 – 2010)
  Dean’s List, Honor Award Recipient

Postdoctoral Researcher – SUNY Buffalo
Sept 2022 – May 2024

• Optimized distributed transaction processing in CockroachDB
• Reduced abort rates with concurrency control strategies
• Benchmarked YCSB & TPC-C on AWS/CloudLab

Research Intern – University of Utah
May 2024 – Sept 2024

• Developed a framework for uncertainty quantification in decentralized FL
• Integrated the Flower framework for evaluation
• Analyzed update and message loss rates in FL systems

Course Instructor (Graduate) – SUNY Buffalo
June 2024 – Aug 2024

• Designed and taught Distributed Systems
• Delivered lectures, assignments, and assessments

Teaching Assistant – King Khalid University
2011 – 2013

• Taught Java Programming and Intro to Computer Science
• Assisted in curriculum design and grading

A framework for implementing and benchmarking BFT protocols. Published in IEEE COINS 2021.
Tech: Go, Distributed Systems, Consensus, Docker

A multi-leader BFT protocol to increase throughput and reduce latency.
Published in IEEE IPCCC 2021.

Cluster-based BFT protocol for geo-distributed systems.
Preprint on arXiv: BunchBFT: Across-Cluster Consensus Protocol

• BigBFT: A Multileader Byzantine Fault Tolerance Protocol – IEEE IPCCC 2021
  DOI

• PaxiBFT: Bottlenecks in Blockchain Consensus Protocols – IEEE COINS 2021
  DOI

• Performance Analysis of Distributed ML Systems – ICCCN 2019
  arXiv

• Comparison of Distributed ML Platforms – ICCCN 2017
  DOI

• Distributed Systems & Consensus Protocols
• Federated Learning & Edge Computing
• Blockchain Scalability & Fault Tolerance
• NoSQL / NewSQL Databases
• System Benchmarking and Performance Analysis

• Languages: Go, Python, Java, C/C++, Bash
• Distributed Systems: gRPC, Docker, Kafka
• Cloud Platforms: AWS, Google Cloud, CloudLab
• Databases: CockroachDB, MongoDB, SQLite, PostgreSQL
• Machine Learning: TensorFlow, PyTorch, Keras, Flower
• Tools: Git, GitHub, VS Code, Android Studio, LaTeX
• OS: Linux, macOS

• Languages: English (Fluent), Arabic (Native)
• Residency: U.S. based in Texas, USA

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