Ph.D. in Electrical and Computer Engineering
University of Toronto
2020 – Present
Research focus on Random Representation Learning for Time Series, developing novel deep learning methods for time-series data analysis.
Ph.D. Candidate in Electrical and Computer Engineering at University of Toronto
Data Scientist | Machine Learning Researcher | AI Engineer
2020 – Present
Research focus on Random Representation Learning for Time Series, developing novel deep learning methods for time-series data analysis.
2016 – 2019
Thesis: Human Action Recognition on Distributed Big Data Infrastructure
2014 – 2019
2012 – 2016
June – September 2025
Developed a conditional autoregressive GNN that predicts annotation edges sequentially, conditioning each step on previously accepted graph states.
December 2020 – Present
Design deep learning models for respiratory disease analysis, including the OscilloFusion Attention architecture for multi-channel oscillometry fusion and phenotype classification. Developed an interface enabling clinicians to explore oscillometry signals, attention maps, and predicted mechanical parameters.
May 2020 – October 2020
Developed a recommendation system for food and restaurants, leveraging a model to generate dynamic meta-tags for foods and restaurant listings, increasing click-through rates. Conducted in-depth analysis of conversion rates across application pages, identifying key areas for optimization and improving user engagement.
January 2020 – August 2020
Researching uncertainty measurement and conformal prediction.
November 2016 – April 2020
Team leader and software architect of Smart Home Gateway based on Zigbee protocol. Developer of Atrovan edge layer. Worked on Modbus and MQTT gateways deployed on the fog layer of smart solutions. Led engineering teams for the Atrovan IoT platform.
January 2021 – March 2023
Courses: Probabilistic Machine Learning (CSC412), Computer Networks I (ECE361), Programming Fundamentals (ECE244), Design and Analysis of Data Structures (CSCB63).
Developing deep learning models for medical data analysis, particularly in respiratory disease diagnosis and treatment.
Random representation learning and segmentation-based methods for time-series classification and feature extraction.
Feature selection and dimensionality reduction techniques for complex, high-dimensional datasets.
Developing contrastive learning frameworks for scalable feature selection and representation learning.
A. Keshavarzian, S. Valaee
Submitted to: International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026)
A. Keshavarzian, S. Valaee
Submitted to: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
A. Keshavarzian, S. Valaee
Submitted to: IEEE Transactions on Artificial Intelligence (TAI)
A. Keshavarzian, S. Valaee
Published in: International Workshop on Machine Learning for Signal Processing (MLSP 2024)
A. Keshavarzian, S. Valaee
Published in: International Workshop on Machine Learning for Signal Processing (MLSP 2023)
A. Keshavarzian, H. Salehinejad, S. Valaee
Published in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)
A. Keshavarzian, J. Wue, C. Chow, S. Valaee
Published in: IEEE International Conference on Communication (ICC 2024)
A. Keshavarzian, S. Sharifian, S. Seyedin
Published in: Future Generation Computer Systems Journal
C, C++, Python, Golang, Java
Spark, Hadoop, PyTorch, TensorFlow, Keras, OpenCV, Numba, OpenAI SDK, CrewAI
Pandas, Seaborn, Plotly, Power BI, PySpark
CrewAI, OpenAI SDK, LangChain, LLM fine tuning
Xilinx, Verilog, VHDL, Microprocessors, Raspberry Pi
HTML, CSS, JavaScript, Bootstrap, React.js, MongoDB, CassandraDB, RDBMS
Supervised by: Dr. S. Valaee
Develops random representation learning and segmentation-based deep learning methods for time-series data, including RASTER, ISD, HIERVAR, and T-ROCKET, to identify informative regions and enhance feature discriminability. Extends these models to unsupervised and contrastive learning frameworks for scalable feature selection in high-dimensional settings. Focuses on theoretical analysis of segment-wise informativeness and contrastive objectives to build robust, ML-ready representations.
Technologies: Python, PyTorch, TensorFlow, Keras
Supervised by: Dr. S. Sharifian and Dr. S. Seyedin
Built and optimized deep neural models for smartphone sensor–driven action recognition, integrating distributed training and inference on Apache Spark.
Technologies: Python, Keras, TensorFlow, Spark
Supervised by: Prof. S.A. Motamedi
Designed and implemented GPU-accelerated algorithms for logo detection and inpainting using CUDA.
Technologies: C/C++, CUDA, Matlab (2017)
Architected a distributed microservices IoT platform for high-throughput data ingestion, dashboarding, and low-latency analytics. Implemented a FaaS-based rule engine using OpenWhisk for scalable event processing.
Technologies: Golang, OpenWhisk, Cassandra, Postgres (2019)