Erfan Khalaji
Erfan Khalaji
AI Scientist
Email:
khalaji[at]ualberta[dot]ca
"To me, AI is an evolution. It is learning from failures, adapting through iterations, and evolving into something smarter, more robust, and endlessly innovative."
Summary
I’m an AI and Data Scientist who values simplicity and practicality in solving real-world challenges. I believe in taking the time to truly understand a problem before working on a solution, focusing on creating effective and meaningful outcomes. With a background in software engineering, two master’s in computer engineering/science degrees, and experience in healthcare and precision agriculture, I’ve been fortunate to contribute to projects that aim to make a difference. My goal is always to learn, grow, and use type of AI that is for good, and to address challenges that matter to all.
Work Experience
April 2024 - Present
September 2022 - March 2024
January 2023 - June 2024
Aprin 2022 - April 2023
AI Scientist
Croptimistic Technology Inc. | Edmonton, Canada
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Focused on creating efficient and scalable machine learning solutions to advance precision agriculture. My contributions include developing ML pipelines for crop density estimation using advanced computer vision techniques and optimizing workflows to operate three times faster, benchmarking against over 3.5 million data points.
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Architected ML models tailored for edge deployment with inference times under 10 seconds, while leading teams of researchers to foster innovation and collaboration. Through extensive research and MLOps practices, I ensure our solutions remain cutting-edge and practical for real-world agricultural challenges. Additionally, I bridge technical concepts to non-technical teams, promoting clarity and alignment across diverse stakeholders.
Tech Stack: Azure, DevOps, Docker, AWS, VertexAI, Python, PyTorch, OpenCV, Scikit-Learn, Numpy, VLMs, Git, MLflow.
Machine Learning Resident
Alberta Machine Intelligence Institute (Amii) | Edmonton, Canada
Designed and implemented a machine learning pipeline for cereal-crop estimation, integrating zero-shot learning semantic segmentation techniques for agricultural applications. I adapted exemplar-free models for accurate crop counting, ensuring scalability and efficiency in real-world scenarios.
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Worked closely with machine learning scientists and software developers to seamlessly integrate models into production workflows. Additionally, I translated complex machine learning concepts into accessible language for Ag-Tech professionals, fostering clear communication and understanding across disciplines.
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Tech Stack: Docker, Python, PyTorch, OpenCV, Scikit-Learn, Numpy, Scipy, Vision-Transformers, Azure, Git.
Machine Learning Intern
Department of Radiology&Dentistry, University of Alberta Hospital | Edmonton, Canada
As a Machine Learning Intern, I applied advanced machine learning techniques to develop models aimed at healthcare innovations. My work focused on analyzing complex medical datasets to identify patterns and deliver actionable insights, supporting better clinical decision-making processes.
Collaborating with interdisciplinary teams, I ensured the implementation of scalable and robust solutions tailored to real-world healthcare challenges. This experience reinforced my commitment to leveraging AI for impactful applications in critical domains like healthcare.
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Tech Stack: 3D-Slicer, PyTorch, nnUNet, Numpy, Scikit-Learn, Pandas, Python
Data Scientist
Journey Education, Concordia University | Montréal, Canada
Collaborated with the Marketing Department to design and implement efficient ETL pipelines, enabling seamless data integration and analysis. Leveraging advanced NLP techniques, I engineered sentiment analysis and topic modeling systems, improving customer feedback insights and enhancing an insight classification model by 15%.
Demonstrated leadership by managing and mentoring data science teams, fostering collaboration and continuous learning. This role allowed me to apply technical expertise in extracting meaningful insights from data while contributing to strategic decision-making processes.
Tech Stack: Python, NLTK, LLMs, Numpy, spaCy, Gensim, SQL/NoSQL databases, Flask, Git.
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