Arvin Tashakori
Professional Summary
As an accomplished Software and Machine Learning Engineer and Researcher, I bring over a decade of experience from both the tech industry and academia. Holding a PhD from the University of British Columbia, postdoctoral research experience at Stanford University, my career is marked by a steadfast commitment to solving complex problems and delivering tangible results in high-stakes projects. My expertise spans a wide array of fields, including Computer Vision, Robotics, Wearable Electronics, Deep Learning, Natural Language Processing, large language models, Generative AI, Secure and Trustworthy AI, and emerging technologies like Web3. I am particularly adept in the realms of Software, Firmware, and Hardware Development, with a strong focus on Wearables, Edge Computing, Consumer Electronics, and the Internet of Things, especially in the Health sector.
Education

Postdoctural Studies, Engineering - Machine Learning

May.25 to Present at Stanford University, Stanford CA, United States

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

Jan.20 to May.25 at University of British Columbia, Vancouver BC, Canada

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

MSc, Electrical and Computer Engineering

Sep.16 to Dec.18 at Sharif University of Technology, Tehran, Iran

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

BSc, Computer Science (Second Major)

Sep.14 to May.16 at Sharif University of Technology, Tehran, Iran

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

BSc, Electrical Engineering (First Major)

Sep.10 to May.16 at Sharif University of Technology, Tehran, Iran

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

Experience

Senior Computer Vision R&D Engineer

From Jan.25 to Present at Koh Young Research Canada, Burnaby BC, Canada

  • - 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

From Jan.19 to Dec.24 at Texavie, Vancouver BC, Canada

  • Team Leadership and Project Management:
    • - 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
  • Cloud Services and Infrastructure:
    • - 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
  • Application Development:
    • - 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
  • Blockchain and Game Development:
    • - 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
  • AI and Machine Learning:
    • - 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
  • LLMOps and Motion-to-Text Generation:
    • - 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
  • Research and Development:
    • - 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

From Jan.19 to Jan.20 at University of British Columbia, Sauder Business School, Vancouver BC, Canada

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

Machine Learning Engineer

From Jul.18 to Dec.18 at Cafe Bazaar, Tehran, Iran

  • - 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

From Oct.17 to Jun.18 at Parsian Medical, Tehran, Iran

  • - 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

From Aug.13 to Jan.14 at AON Impact Forcast, Chicago IL, USA

  • - 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

From Jan.16 to April.16 at Lepont Consultant, London, UK

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

Research Assistant

From May.16 to Sep.16 at EPFL Machine Learning and Optimization Lab, Lausanne, Switzerland

  • - Sparse convex optimization for deep learning

Research Assistant

From May.15 to Sep.15 at Chinese University of Hong Kong, Institute of Network Coding, Hong Kong

  • - 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

From Jan.14 to Jan.16 at Alo Doctor Website, Tehran, Iran

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

Patents
Publications
Services and Accomplishments
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
© 2025 Arvin Tashakori