I am Dr. Ali Mirzaeian, and I hold a Ph.D. in Computer Engineering from George Mason University (GMU). Following my graduation, I began my career as an assistant professor in the Computer Science department at Virginia State University (VSU). After gaining valuable experience there for nearly a year, I transitioned to a role as a data scientist at REI Systems and also started teaching as an adjunct professor at American University (AU).
Some of my areas of interest include:
- Deep Learning
- Machine Learning
- Computer Vision
- Natural Language Processing
- Generative AI
- Reinforcement Learning
- Time Series Forecasting
- Graph Neural Networks
- DevOps/MLOps
EXPERIENCE
Industry Experience
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REI Systems
Data Scientist
2021 - Present
I am working on several projects including:-
leading summer 2023 interns on information extraction in the resume database (active)
- Resume Auto-Fill Service: Using Generative AI for field extraction
- Resume Search Engine Service: Using Generative AI for querying a database using plan text
- User interface: Front-end Development and providing REST API
- introduction to data analytic using python for summer 2023 interns (workshop)
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Designing an intelligent chatbot for the Food and Drug Administration (FDA) website
- I used Sentence Transformer to extract word embeddings for queries and textual content, and then applied Approximate Nearest Neighbors (ANN) for ranking the contents. For building the infrastructure of my design, I utilized AWS EC2, Lambda, and S3. In addition, I used Django, HTML, CSS, and JavaScript for designing the back-end and front-end of the chatbot project.
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Using time series forecasting for resource allocation
- I have utilized various time series prediction algorithms, including Prophet, ARIMA, and LSTM. Additionally, I have explored different genetic algorithms, such as NSGA-II, to optimize resource allocation based on the predicted resource requirements.
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Designing an intelligent search engine
- Training (fine-tuning) Hugging Face models for Named Entity Recognition (NER) and Relation Extraction (RE) to build a knowledge graph. Using GraphQL language for retrieving results based on the client's query.
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Trademark similarity search for USPTO
- I used self-supervised algorithms, such as Beit and Dit, for embedding extraction of the trademark images. Additionally, I employed Multi-threaded KNN to rank the results based on similarity.
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leading summer 2023 interns on information extraction in the resume database (active)
Academic Experience
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American University
Adjunct Professor
2023 - Present
I taught:- Data Structures and Algorithms
- Python Programming
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Virginia State University
Assistant Professor
2020 - 2021
I taught several courses including:- Java Programming and Lab
- Web Programming (front-end): HTML, CSS, JavaScript
- Web Programming (back-end): Python, Django, MySQL
- Gradulate level Deep Learning (computer vision, NLP)
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George Mason University
Research Assistant
2017 - 2020
I worked on several projects including:- Accelerator design for neural networks
- Approximation design for neural networks
- Neural network adversarial attack and defense
- Anomaly Detection in computer vision topics
- Applications of Deep Neural Networks (including CNN, NLP, MLP, LSTM)
Additionally, I oversaw collaborative projects with industry partners, where I successfully integrated Agile/DevOps practices to optimize software development workflows, foster team cooperation, and expedite the release of top-notch deliverables. Employing Git and Docker automation tools, I streamlined builds, testing, and deployment processes, leading to improved efficiency and quicker project delivery timelines
SKILLS
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Programming Languages
- Web Development: HTML, CSS, JavaScript, Bootstrap, jQuery, Django
- Programing Languages: Python, Java, C, C++, JavaScript, HTML, CSS, SQL
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Frameworks
- Deep Learning: Pytorch, Tensorflow, Keras, Scikit-learn, OpenCV, Pyspark
- Web Development: Django, Flask
- Web Scraping: BeautifulSoup, Selenium
- Web Services: REST API
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Tools
- Containerization tools: Docker, Kubernetes
- Version Control Systems: Git, GitHub
- IDEs: Jupyter Notebook, Pycharm, Visual Studio Code
- Cloud Computing: AWS opensearch, ec2, s3, sage maker, transcribe
- Web Servers: Nginx
- Business Intelligence: Tableau, Power BI
- Monitoring and Logging Tools: ELK Stack (ElasticSearch, Kibana)
PUBLICATIONS
- Ali Mirzaeian, Zhi Tian, Sai Manoj, Banafsheh Saber Latiani, Ioannis Savidis, Houman Homayoun, Avesta Sasan, ”Adaptive-Gravity: A Defense Against Adversarial Samples” in International Symposium on Quality Electronic Design, ISQED 2022.
- Ali Mirzaeian, Jana Kosecka, Houman Homayoun, Tinoosh Mohsenin, Avesta Sasan, ”Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks” in International Symposium on Quality Electronic Design, ISQED 2021.
- Ali Mirzaeian, Sai Manoj, Ashkan Vakil, Houman Homayoun and Avesta Sasan, ”Conditional Classification: A So for Computational Energy Reduction” International Symposium on Quality Electronic Design, ISQED 2021
- Tanmoy Chowdhury, Ashkan Vakil, Banafsheh Saber Latibari, Sayed Aresh Beheshti Shirazi, Ali Mirzaeian, Liang Zhao, Avesta Sasan ”RAPTA: A Hierarchical Representation Learning Architecture toPredict Path-Based Timing Slacks at Design Time” in Design Automation Conference (DAC), 2021
- Ashkan Vakil, Farzad Niknia, Ali Mirzaeian, Avesta Sasan, Naghmeh Karimi, ”Learning Assisted Side Channel Delay Test for Detection of Recycled ICs” ”, 26th Asia and South Pacific Design Automation Conference (ASP-DAC), Tokyo, January 2021
- Ali Mirzaeian, Houman Homayoun and Avesta Sasan, ”Nesta: Hamming weight compression-based neural proc. engine” in Proceedings of the 25th Asia and South Pacific Design Automation Conference (ASPDAC), 2020
- Ali Mirzaeian, Houman Homayoun and Avesta Sasan, ”TCD-NPE: A Re-configurable and Efficient Neural Processing Engine, Powered by Novel Temporal-Carry-deferring MACs” 2020 International Conference on ReConFigurable Computing and FPGAs (ReConFig), 2020.
- Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou, Sai Manoj, Tinoosh Mohsenin, Khaled N Khasawneh, Liang Zhao, Houman Homayoun, Avesta Sasan ”Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks” in International Symposium on Quality Electronic Design, ISQED 2020
- Ashkan Vakil, Farnaz Behnia, Ali Mirzaeian, Houman Homayoun, Naghmeh Karimi, Avesta Sasan ”LASCA: Learning Assisted Side Channel Delay Analysis for Hardware Trojan Detection” in International Symposium on Quality Electronic Design, ISQED 2020.
- Ali Mirzaeian, Mehdi Fazeli, Ahmad Patooghy, ”A novel countermeasure against fault injection attacks for AES-based cryptosystems,” in Iranian Conference on Electrical Engineering (ICEE), 2016.
Extracurriculars
I'm also interested in playing volleyball, wallyball, swimming, hiking, board gaming, and more. In general, I won't say no to anything, lol.