Brian Wu is an R&D engineer with 11+ years of experience designing and deploying computer vision and deep learning algorithms on embedded platforms such as FPGA and ARM. He specializes in FPGA/ZYNQ SoC design using Verilog HDL and GPU acceleration with CUDA and OpenGL. His expertise spans SLAM/Visual SLAM, ROS, VR/AR, stereo vision, 3D sensing, object detection, and motor control. He is proficient in C/C++, Python, JavaScript, Golang, and Verilog, and experienced with TensorFlow and Darknet. He also builds full-stack systems using Django, React/Vue/Next.js, MySQL, Node.js, and Go.

Experience leading the machine learning competency, which involves scoping machine learning problems and driving project adoption, deploying and managing fraud detection and NLP models in our production machine learning infrastructure on AWS, and building a new infrastructure for production machine learning using Spark and MLflow.

Laura Halacheva is a data scientist with experience in data modeling, statistics, and machine learning, both theoretical and applied. She has wide experience with applications in various domains, including medical data analysis, natural language processing, conversational models, and e-commerce. At Ontotext, she led the data science of the R&D department. Her clients include MobiBiz, Canadian Heritage Information Network (CHIN), and more.

As a data scientist, Peter Faase has expertise in solving complicated data problems and working with Big Data to generate useful insights, intelligence, and applications. Combining his strong communication, presentation, and analytical skills, he has created data-driven solutions for organizations by designing and running complex data analyses trajectories using machine learning, predictive analyses, Artificial Intelligence (AI), and other statistical and mathematical techniques. He is skilled with tools such as Python, MySQL, SPSS, and Excel and also familiar with Agile Scrum techniques.

Nicholas Lu is a goal-oriented engineer focused on ML systems and distributed architecture. He works across the MLOps lifecycle, building and maintaining data and model pipelines using Airflow, Spark, Kafka, and Kubernetes. His experience includes deploying containerised workloads with Docker, managing infrastructure via Terraform, and implementing CI/CD with Jenkins, GitLab, and Bitbucket Pipelines. He supports site reliability through monitoring, automation, and incident response in cloud environments (AWS, GCP, Azure, Alibaba Cloud). He codes in Python, Java, SQL, and Bash, and works extensively with Linux and open-source tools.

Gabriel is a software engineer with 15 years of experience, specializing in front-end development with JavaScript, React, React Native, Vue.js, Angular, Node.js, and Express.js. He has developed applications and websites for many industries, including HR and staffing, fashion and social networking, healthcare, and eCommerce (including Walmart). Gabriel is passionate about simplifying processes, inventing solutions, and following best practices.