Professional Summary
Dr. Arvin Tashakori is a Senior Applied Research Scientist in Computer Vision and Robotics whose work sits at the frontier of AI-powered medical and surgical systems. With a PhD from the University of British Columbia, postdoctoral research experience at Stanford University, and over a decade of industry experience, he has built a career transforming cutting-edge research into scalable, real-world technology. His work has earned recognition among the very best in the field, with publications in Nature Machine Intelligence, recognition by Science Robotics as an Editor’s Choice, and features across Nature, Physics World, CTV News, and dozens of other international outlets. He was named a Finalist for the Apple Scholars in AI/ML program, placing in the top 3 of roughly 5,000 applicants globally, and his research has been recognized at premier venues including NeurIPS, ICLR, and CVPR. Dr. Tashakori’s expertise spans Computer Vision, Robotics, Wearable Electronics, Deep Learning, Natural Language Processing, Large Language Models, Vision-Language Models, Generative AI, and Secure and Trustworthy AI. He brings rare end-to-end mastery across Software, Firmware, and Hardware development, with deep specialization in Wearables, Edge Computing, Consumer Electronics, and the Internet of Things, particularly within digital health and biomedical applications. Currently leading the development of advanced machine vision algorithms for autonomous neurosurgical robotic systems at Koh Young Research Canada, Dr. Tashakori continues to push the boundaries of what intelligent systems can achieve in high-stakes clinical environments, pairing theoretical depth with a relentless focus on building technology that delivers measurable impact.
Education

Postdoctural Scholar, Bioengineering - Machine Learning — Stanford University, Stanford CA, United States

Jan.24 to Dec.24

Research: Work on Personalized Multimodal AI-powered E-skin Device For Tracking Musculoskeletal Health Under Supervision of Prof. Zhenan Bao and Prof. Scott Delpt @ Stanford Wearable Electronics Initiative (eWEAR)

PhD, Electrical and Computer Engineering — University of British Columbia, Vancouver BC, Canada

Jan.19 to Dec.23

Thesis: Privacy-aware and Personalized Human Motion Understanding Using Wearable Sensing Textile

MSc, Electrical and Computer Engineering — Sharif University of Technology, Tehran, Iran

Sep.16 to Dec.18

Thesis: Boosting-based Transfer Learning for Brain-Computer Interfaces (Best M.Sc. Thesis Award)

BSc, Computer Science (Second Major) — Sharif University of Technology, Tehran, Iran

Sep.14 to May.16

Thesis: Security Analysis of a Decentralized Electronic Voting Protocol in the Universal Composability Framework

BSc, Electrical Engineering (First Major) — Sharif University of Technology, Tehran, Iran

Sep.10 to May.16

Thesis: Efficient FPGA and CUDA Implementation of Generalized Sparse FFT (Best B.Sc. Thesis Award)

Experience

Senior Applied Research Scientist, Computer Vision & Robotics — Koh Young Research Canada, Burnaby BC, Canada

From Jan.25 to Present

  • - Research and development of state-of-the-art computer vision and image processing algorithms tailored for medical robotic systems, significantly enhancing diagnostic accuracy and operational efficiency.
  • - Engineering scalable computer vision and deep learning solutions to address complex 3D vision challenges, improving spatial recognition and autonomy in robotic applications.
  • - Investigating and resolving a wide range of challenges across computer vision, robotics, and image processing domains, driving innovation and advancing technological capabilities within the team.
  • - Designing, implementing, and deploying comprehensive full-stack computer vision and deep learning pipelines, ensuring seamless integration and robust performance in deployed medical robotic platforms.
  • - Collaborating with cross-functional teams to translate cutting-edge research into practical solutions, fostering a multidisciplinary approach to problem-solving and product development.

Tech Lead – Software Engineering and Machine Learning — Texavie, Vancouver BC, Canada

From Jan.19 to Dec.24

  • - Led a team of three software, three ML engineers, and three applied scientists in developing scalable cloud services, mobile and web applications
  • - Implemented Agile and Scrum methodologies, achieving a 30% increase in development efficiency and enhanced team collaboration
  • - Managed hiring processes and contributed to strategic decision-making and reporting directly to CEO and CTO
  • - Architected cloud services for diverse products using GCP, Firebase, AWS, and Azure, enhancing system scalability and reliability
  • - Enhanced system scalability and performance by optimizing cloud infrastructure with load balancing and auto-scaling techniques
  • - Oversaw the adoption of CI/CD pipelines for streamlined, reliable, and automated code deployment
  • - Developed an iOS tele-physiotherapy app using Swift, incorporating HealthKit, Google Maps, Core ML, SceneKit, Core Bluetooth, In-App Purchase, Local Storage, and Web3. App Store Link
  • - Created an Android tele-physiotherapy app using Flutter, integrating Google Fit, Google Maps, TensorFlow Lite, Android Bluetooth API, Google Play Billing, SQLite, and Web3j. Google Play Link
  • - Engineered a Desktop App for Unity-based games using C#, including Auth, Bluetooth, and Web Sockets
  • - Developed a Web app for therapists using VueJS and NodeJS, integrating Auth, Stripe API, Web3, EC2, and Kubernetes. Dashboard Link
  • - Deployed TexavieCoin on Blockchain using Solidity and smart contract deployment to Etherscan
  • - Created physiotherapy games using Unity and Unreal Engines, employing C++, Auth, and Web Sockets
  • - Developed advanced Hand and Body Pose Estimation, Typing Detection, 3D Drawing, Activity Recognition, and Action Evaluation using smart apparel, EMG sensors, and cameras with Python, Swift, and CoreML. Demo video
  • - Conducted Hand and Body Motion Analysis using Motion Capture Systems like OptiTrack MoCap, Motive, and Opensim
  • - Developed a motion-to-text generator using Transformer-based models (T5+BERT) and Diffusion-based models (MDM+CtrlNet+VQVAE+DreamBooth)
  • - Created an ETL pipeline for motion data integration and preprocessing, enhancing data quality for real-time processing using MLflow and DVC
  • - Incorporated LangChain for handling complex workflows, Retrieval-Augmented Generation (RAG) for enhanced data retrieval, and vector databases for efficient data storage and querying
  • - Deployed models deliver real-time, descriptive physical activity text, enhancing patient feedback and engagement
  • - Implemented CI/CD pipelines for seamless updates and maintenance of LLM operations
  • - Developed tools for monitoring and reporting on model effectiveness, facilitating continuous improvement and strategic decision-making
  • - Conducted advanced text-to-motion generation and motion analysis, securing multiple grants, patents, and publishing technical papers
  • - Led the implementation of DevOps and MLOps practices, significantly improving deployment speed and operational efficiency

Research Assistant — University of British Columbia, Sauder Business School, Vancouver BC, Canada

From Jan.19 to Jan.20

  • - Updating UBC Sauder School of Business admission procedure using NLP, Computer Vision (OpenCV + customized emotion detection algorithm) methods

Machine Learning Engineer — Cafe Bazaar, Tehran, Iran

From Jul.18 to Dec.18

  • - Designing a framework for finding duplicated apps for Cafe Bazaar, an Iranian Android marketplace with more than 40 million users and offers 250,000 downloadable Iranian and international apps

Lead Software Engineer — Parsian Medical, Tehran, Iran

From Oct.17 to Jun.18

  • - Designed a Smart platform for hospitals to reduce nurse faults in emergency cases (currently used in 23 hospitals)
  • - Implemented Pulsoximeter and Capnograph using customized ARM-based board and presented it in MEDICA exhibition in Germany. We reduced the calculation time of SPO2 in Pulsoximeter using Sparse FFT

Software Engineer — AON Impact Forcast, Chicago IL, USA

From Aug.13 to Jan.14

  • - Developed simulated hurricane tracks in Atlantic Ocean using deep learning, based on 80 years of track data
  • - Developed temporal causal models and extensions of this model (group lasso, graph Laplacian, and hidden Markov random fields) that capture the spatial / temporal information in climate data

Software Engineer — Lepont Consultant, London, UK

From Jan.16 to April.16

  • - Designed a platform for modeling and predicting the Iranian stocks using Recursive Neural Networks (LSTM)

Research Assistant — EPFL Machine Learning and Optimization Lab, Lausanne, Switzerland

From May.16 to Sep.16

  • - Sparse convex optimization for deep learning

Research Assistant — Chinese University of Hong Kong, Institute of Network Coding, Hong Kong

From May.15 to Sep.15

  • - FPGA Implementation of 100Mbps Optical OFDM-based transceiver module using Virtex7 and FMC110 AD/DA Daughter Card
  • - Simulation of Physical Layer Network Coding for three users

Co-founder and Chief Technology Officer — Alo Doctor Website, Tehran, Iran

From Jan.14 to Jan.16

  • - Designed a Website to connect specialists to patients, advising patients with specific diseases like STDs

Patents
  • Stretchable Multimodal Smart Textile Apparel and Machine Learning for Accurate and Dynamic Movement, Strength, and Health Tracking. US Provincial Patent and PCT Application filed in 22 — US20260041354A1
  • Asynchronous Fragmented OFDM-CDMA Cognitive Radio LAN. Iranian Provincial Patent filed in 17
  • Underwater Optical CDMA LAN. Iranian Provincial Patent filed in 17
Publications
  • Tashakori A, Jiang Z, Servati A, Soltanian S, Narayana H, Le K, Nakayama C, Yang CL, Wang ZJ, Eng JJ, Servati P. Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves. Nature Machine Intelligence 24Paper · Code · Demo
  • Yu X, Tashakori A, McKeown MJ, Wang ZJ. pfedbl: Federated bayesian learning with personalized prior. IEEE Transactions on Artificial Intelligence 25Paper
  • Yu X, Tashakori A, McKeown MJ, Wang ZJ. Heterogeneous federated learning framework for human activity recognition. Knowledge-Based Systems 25Paper
  • Zhang W, Tashakori A, Jiang Z, Servati A, Narayana H, Soltanian S, Yeap RY, Ma M, Toy L, Servati P. Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textile. Conference on Neural Information Processing Systems (NeurIPS/NIPS) 23Paper · Code
  • Tashakori A, Zhang W, Wang ZJ, Servati P. SemiPFL: personalized semi-supervised federated learning framework for edge intelligence. IEEE Internet of Things Journal 23Project · Paper · Code
  • Yang CL, Chui R, Mortenson WB, Servati P, Servati A, Tashakori A, Eng JJ. Perspectives of users for a future interactive wearable system for upper extremity rehabilitation following stroke: a qualitative study. Journal of NeuroEngineering and Rehabilitation 23
  • Yu X, Tashakori A, McKeown MJ, Wang ZJ. Tackling Hybrid Heterogeneity in Federated Learning for Human Activity Recognition. IEEE Transactions on Neural Networks and Learning Systems 23
  • Yu X, Tashakori A, McKeown MJ, Wang ZJ. H2Fed: Dual-Heterogeneity-Considered Federated Human Activity Recognition. Journal of Neurocomputing 24
  • Zhang W, Tashakori A, Jiang Z, Servati A, Kuo C, Servati P. A Flexible Sensor System for Lower Body Locomotion Estimation. Biomedical Engineering Society Annual Meeting (BMES) 22
  • Tashakori A. Survey on Multi-Agent Q-Learning frameworks for resource management in wireless sensor network. arXiv 21
  • Tashakori A, Htun ST, Servati A, Narayana H, Shao Y, Imaizumi A, Le K, Jiang Z, Soltanian S, Ko F, Servati S. Proposing an E-textile apparel for Hand Gesture Recognition and Arm Movement Estimation in Semi/Non-Line of Sight Environment. SPIE Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables 20
  • Jamali MV, Khorramshahi P, Tashakori A, Chizari A, Shahsavari S, AbdollahRamezani S, Fazelian M, Bahrani S, Salehi JA. Statistical distribution of intensity fluctuations for underwater wireless optical channels in the presence of air bubbles. Iran Workshop on Communication and Information Theory (IWCIT) 16
  • Fazelian M, AbdollahRamezani S, Bahrani S, Chizari A, Jamali MV, Khorramshahi P, Tashakori A, Shahsavari S, Salehi JA. Mining DNA sequences based on spatially coded technique using a spatial light modulator. Iran Workshop on Communication and Information Theory (IWCIT) 16
  • Akhoundi F, Salehi JA, Tashakori A. Cellular underwater wireless optical CDMA network: Performance analysis and implementation concepts. IEEE Transactions on Communications (TCOM) 15
Services and Accomplishments
  • Featured in Science Robotics, Nature, Physics World, CTV News, CTV News Morning Live, NS News, UBC News, CHNL, North Shore News, Virgin radio, CKNW Jill Bennett, Science Daily, Interesting Engineering, Tech Times and more for developing smart glove - Nature Machine Intelligence manuscript (24)
  • Selected as a finalist for Apple Scholars in AIML (Health AI) (24)
  • Served as reviewer for CVPR2023 Conference and IEEE TWC, IEEE TNSE, IEEE SPL, IEEE IoT Journals
  • Awarded UBC Presidential Scholars Awards and UBC Top International Student Awards (19, 20, 21, 22, 23, 24)
  • Awarded Viterbi Graduate Student Fellowship from the University of Southern California (Mar.18)
  • Awarded Graduate Research Assistantship from the Columbia University (Mar.18)
  • Ranked 8th in the Graduate-level National University Entrance Examination among 200k participants (Aug.16)
  • Ranked 34th in the National University Entrance Examination with around 500k participants (Aug.10)
  • Ranked 40th in the National Mathematics Olympiad in Iran out of 200k participants (Aug.09)
  • Ranked 1st and 3rd in the National ACM competition in Iran out of 130k participants (Sep.09, Sep.10)
Skills
  • C
  • C#
  • C++
  • Java
  • JavaScript
  • Python
  • Swift
  • Dart
  • Ruby
  • PHP
  • R
  • Solidity
  • Julia
  • TensorFlow
  • PyTorch
  • Keras
  • Scikit-Learn
  • Pandas
  • Numpy
  • OpenCV
  • XGBoost
  • Hugging Face
  • LightGBM
  • CatBoost
  • Prophet
  • PyCaret
  • SpaCy
  • NLTK
  • Gensim
  • React
  • Angular
  • Vue
  • Node.js
  • .NET
  • Django
  • Flask
  • FastAPI
  • Apple services (CoreML, Scenekit, Core Bluetooth, In App Purchase, Local storage, Healthkit)
  • AWS (EC2, S3, SageMaker, Lambda)
  • Azure (ML, Kubernetes, Cosmos DB)
  • Google Cloud (Firebase, Storage, Auth, Functions, AI Platform, GKE, Google Maps)
  • Oracle Cloud
  • Hadoop
  • Spark
  • Kafka
  • Docker
  • Kubernetes
  • CI/CD pipelines
  • Git
  • GitHub Actions
  • AutoCAD
  • Xcode
  • Android Studio
  • Figma
  • VSCode
  • Unity
  • Unreal Engine
  • Opensim
  • Optitrack
  • Jira
  • Trello
  • VMware
  • PyCharm
  • Anaconda
  • MATLAB
  • Simulink
  • SQL
  • NoSQL (MongoDB, DynamoDB, Cosmos DB, Firestore)
  • Project management
  • Agile methodologies
  • Scrum
  • Leadership
  • Team building
  • Strategic planning
  • Communication
  • Problem-solving
  • Time management
  • Model deployment
  • Continuous Integration and Delivery (CI/CD) for ML
  • Monitoring and managing ML models
  • Automated testing and validation of ML models
  • CUDA
  • Assembly
  • Verilog
  • VHDL
  • CAD
  • YOLO
  • SAM
  • VIT
  • GPT
© 2026 Arvin Tashakori