AWS AI - Amazon Bedrock Inference

▪ Led 3rd party payment platform integration and multi-factor authentication projects for Amazon payments in Mexico, serving as the subject matter expert and primary technical point ofcontact for these projects between product, business operations, and 10+ engineering teams▪ Mitigated high-severity operational issues for directly customer-facing payment services in real-time▪ Mentored and guided junior/incoming engineers in a rapidly expanding team that saw a growth of20+ new engineers in one year

▪ Enhanced and maintained 10+ backend/full-stack applications that provided data-driven intelligence for empowering high volume trading executions▪ Developed robust, production-ready code/frameworks to deploy VWAP-based ML trading models▪ Prototyped software libraries to streamline integration testing for services using Pub-Sub frameworks in CI/CD pipelines

Vision & Eye Movements Lab (http://www.eizenman.ca/)▪ Developed end-to-end framework to diagnose neuropsychiatric disorders with visual scanning data▪ Designed and implemented techniques in feature selection, applied statistics, computer vision and deep learning with libraries in Python (Tensorflow, Keras, Scikit-Learn, Pandas, OpenCV)▪ Analyzed sequential gaze-tracking data with a range of state-of-the-art ML models (MLPs, RNNs, CNNs, UNets, Denoising Autoencoders, etc.)

▪ Assisted inverted-lecture classrooms of 400+ students & leading tutorials/labs of 30+ students to facilitate understanding of program design principles, efficiencies, & data structures in Python (CSC148)

▪ Developed microcontroller firmware for remote data sampling of proprietary 6-axis optical force sensor on Intuitive Surgical’s Da Vinci Robot▪ Technology: STM32, Arduino, ESP8266 WiFi, BNO080 IMUSupervisor: Dr. Septimiu Salcudean

▪ Developed HCI framework for the CommU humanoid robot, allowing it to use visual context and physical gestures to engage in interactive conversations with humans▪ Enhanced AIML-based chatbot pipeline to integrate remote, real-time image capture & processing (OpenCV), image-to-text deep learning (pre-trained Inception V3 w/ LSTM), and Google Cloud speech synthesis/recognitionSupervisor: Dr. Hiroshi Ishiguro*code accessible at: https://github.com/Reinaesaya/OUIRL-ChatBot

▪ Developed critical in-team web applications (full-stack) used for QoR data visualization and rapid IP design (Django, Bootstrap, AngularJS, AmCharts)▪ Enhanced hardware validation tests for Ethernet IP on Altera’s Arria 10 and Stratix 10 FPGAs using the Quartus software and JDSU Optical Network Testers (Tcl, Perl)▪ Analyzed weekly regression test runs for accurate failure reporting, maintenance, and debugging

▪ Improved & maintained UBC Cuddlebot firmware to enhance remote control server and actuator control system (C++, Javascript, Go, ChibiOS, Linux Yocto)▪ Analyzed haptic fabric sensor data using random forest model to classify correlations between human touch behaviour and individual recognition (see 2015 ICMI paper)Supervisor: Dr. Karon Maclean

▪ Developed first working prototype of a custom eye-gaze tracking system for Intuitive Surgical’s da Vinci Robot (Python)▪ Created hardware components using SolidWorks and 3D printing▪ Incorporated Haar feature detection for pupil recognition and robust estimation for calibration (OpenCV)▪ Designed calibration GUI (Python TkInter)Supervisor: Dr. Septimiu Salcudean*Reference paper: Tong, I. et al. A Retrofit Eye Tracker for the da Vinci and its Integration in Task Execution using the da Vinci Research Kit, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, 2015