HELLO, I’M BRANDON RUFINO.

I am a Masters student within the Institute of Biomaterials and Biomedical Engineering (IBBME) Department at the University of Toronto. My work is done in the Possibility Engineering and Research Lab (PEARL) under Professor Biddiss. At PEARL we conceptualize, develop, and evaluate innovative technologies that create possibilities for young people with disabilities to participate more meaningfully in arts, music, physical activities, and therapies.

With an undergraduate degree in Electrical and Biomedical Engineering at McMaster University I carry a passion to drive health care and medical solutions through software and hardware fundamentals. More specifically, I have a deep interest in machine learning and AI applications.

Experience Diversifying My Skill Set

Beginning in Summer 2019 I have started my work at PEARL as a research student. My research involves developing a mixed reality interface that can accurately detect musical inputs with real-life instruments to support active participation in rehabilitation programs.

My experience thus far has been fantastic! The team spans multiple disciplines and the research being conducted has clinical relevance and need. It is beyond humbling being able to speak with parents about our work and to hear their feedback on how our games impact their child’s life.

Come visit me at the PEARL Lab 🙂

Hard work really does pay off!

I have recently completed my B.Eng in Electrical and Biomedical engineering at McMaster University. During this time I met great people, learned an incredible amount and developed technical and soft skills that I will carry with me for years to come.

Also, I was fortunate to be a Teaching Assistant (TA) for the iBioMed program at McMaster University. I was able to help the first year students with their lab and project work in which they were challenged to tackle medical problems with engineering knowledge.

Some skills I have gained from this degree are: problem solving, teamwork, communication, software development (python, MATLAB, Java, C), signal processing, biomedical and electronic devices, and medical imaging.

In May 1st of 2017 I had the opportunity to take a 16 month internship at Advanced Micro Devices (AMD) as a Mixed Signal Custom Layout Designer.

I was tasked with the layout design of digital and analog circuits using analog transistor level components. Notably, I was able to take ownership of designing a Voltage Controlled Oscillator and brought it up to specifications to toggle at required frequency with appropriate bandwidth.

This job not only gave me a plethora of semiconducting knowledge, but the people I was able to meet and work with were some of the most talented individuals I have met. Truly a blessed 16 months.

Real friendships are made when you are grinding for a project deadline right before the weekend!

Job opportunity? Check out my resume!

Project Work Sometimes I like to step outside the course outline and try something new

mindFRAME

A daily mood tracker where the user can log activities done in a day and their respective mood at the end of the day. Potential in future to include data analytics correlating activities with mood. Website was programmed using HTML and javascript.

Using Microsoft Azure API, Android Studio, and Unity to design a real time audio transcriber. The goal is to transcribe audio into american sign language and closed captions. The transcribed output will be displayed via AR on a mobile app designed in Android Studio.

A visualization tool to help promote users to keep on track with their daily water intake; capable of plotting water intake over period of time. Both back end and front end GUI were programmed using MATLAB.

Thesis: Creating and evaluating an audio detection interface for musical play and learning with low-cost at home musical instruments Bloorview Research Institute, Masters Student

At Holland Bloorview Kids Rehab Hospital I am developing algorithms for an audio detection interface which will contribute to our long term goal of creating an accessible music education platform for children. Specifically, I am focused on creating a classifier to differentiate which instrument are being played in real-time. Also, I am developing steady beat and rhythm scoring algorithms that would score a performance similar to how a music teacher would. The classifier will be deployed in a video game and tested with children. The steady beat and rhythm scoring algorithms will be tested for agreement with music teachers.

See more about my lab here.

MS-BERT - Multiple Sclerosis Severity Classification Using Clinical Notes St. Michael's Hospital LKS-Chart, Research Volunteer

In this work we developed a pre-trained BERT model, MS-BERT, built on top of BlueBERT and a classifier, which extracted multiple sclerosis scores given consult notes. Our classifier is observed to achieve SOTA performance on all prediction tasks and is made publicly available here.

Accepted into Clinical NLP 2020.

See more about my research team NLP4H here.

Pre-print on arxiv.