Salaar Liaqat

40 St. George St ยท Toronto, ON, Canada, M5S 2E4

I am a PhD student at University of Toronto, studying under the supervision of Eyal de Lara. My research interests are wearable sensing, with applications to healthcare and assistive technologies, as well as machine learning.


University of Toronto

PhD in Computer Science
Superviser: Eyal de Lara
Jan 2020 - Present
Masters in Computer Science
Superviser: Eyal de Lara

GPA: 3.83/4

June 2018 - Dec 2019

Simon Fraser University

Bachelors of Science
School of Computer Science: Software Systems

GPA: 3.71/4.33

September 2015 - May 2018


Programming Languages
  • Proficient in C, C++, and Java
  • Proficient in Python and libraries such as NumPy, Pandas, SkLearn, Tensorflow, Keras and Spark
  • Experienced with MatLab, Go Lang, Scheme, Haskell and Ruby
  • Proficient in Android development
  • Proficient in embedded systems and hardware development on Linux and bare metal
  • Strong understanding of database concepts, SQL and NoSQL
  • Experienced in web development, including Flask, VueJS, AngularJS and ASP.NET
  • Strong knowledge in machine learning, natural languge processing and signal processing
  • Solid understanding of computer architecture, multi-threaded programming and distributed systems
  • Experienced in working with teams (2-7) members, scrum and taking leadership roles


Sharing is Caring: Exploring machine learning-enabled methods for regional medical imaging exchange using procedure metadata

Salaar Liaqat, Joanna Pineda, Jeevaa Velayutham, Allen Lee, Joshua Reicher, Jason Nagels, Marzyeh Ghassemi, Benjamin Fine


Regional Medical Imagaing Mapping for Procedure Exchange
  • Developed models to enable hospitals with different terminology to map procedures to a single standardized ontology
  • Explored various NLP representations and ML models and achieved over 95% accuracy on a 1500+ class dataset
  • Work featured on SIIMcast podcast and presented on Canada Health Infoway as a webinar to healthcare professionals across Canada
  • Work is being presented at Women in Machine Learning (WiML) at NeurIPS 2019 in Vancouver
  • Work published in AMIA Informatics Summit 2020 in Houston
  • Project was completed as a collaboration between University of Toronto, Trillium Health Partners, Vector Institute, and the Hospital Diagnostic Imaging Repository Services
  • SIIMCast Podcast
  • Canada Health Infoway Webinar
Feb - Dec 2019
Real-Time Audio Captioning with Augmented Reality Smart-Glasses
  • Developed assistive technology to enable people who are deaf or hard of hearing to understand speech
  • Used Moverio BT-300 Smart Glasses to record sound and display captions
  • Developed custom speech detection algorithm to filter sound on glasses
  • Used the Kaldi speech recognition toolkit running on edge server to transcribe speech audio to text in real-time
  • Achieved latency of 1.8s for sentence transcription using the edge system
  • Ran preliminary study and participants found the latency to be usable
  • Report
July 2018 - Nov 2018
Elevator Monitor
  • Developed an elevator monitoring system using two BeagleBone computers
  • BeagleBone would record elevator location using distance sensors
  • Developed and used protocol for radio frequency communication between BeagleBones
  • Second BeagleBone would communicate with first beaglebone and display elevator information on NodeJS site over local network
  • Developed sensing and communication code in C
Sept - Dec 2017
  • Developing a camera and gallery Android application which categorizes photos using machine learning based on image recognition
  • Used image recognition web service and built in image recognition model to allow online and offline functionality
  • Developed search functionality to find pictures using keywords, color, date and logical expressions
Sept - Dec 2017
Gait Analysis
  • Used Android phone to collect accelerometer and gyroscope traces of participants walking
  • Used Python to analyze traces and calculate step count and walking distance
  • Used traces to identify walking patterns of different users using machine learning (Naive Baye) and step detection
  • Completed analyses in Python, using NumPy, Pandas, SkLearn
July - August 2017
Smart Terrarium
  • Created a smart terrarium, in a team of 4, which presented controls to maintain an environment to support plant life
  • Used a temperature, humidity and brightness sensor to record the environment
  • Used a BeagleBone computer to control lights, humidifier and fans to optimize the environment
  • Designed a web application using NodeJS to remotely control and monitor terrarium
  • Programmed the embedded system in C and JavaScript
Sept - Dec 2016
Vending Machine
  • Worked in a team of six to create an embedded system to control a vending machine
  • Allowed users to use NFC to login to accounts and a touch screen interface to dispense snacks from the machine
  • Created using Arduino and Raspberry Pi
Sept - Dec 2015

Work Experience

Teaching Assistant

University of Toronto

CSC 148 (Introduction to Computer Science): Responsibilities inlcuded leading lab activies, assisting in lectures and marking exams

Winter 2019, Summer 2019, Fall 2019

Awards & Certifications

  • Qualified as Golden Key International Honor Society Member
  • Awarded SFU Alumni Scholarship ($500) in February 2016
  • Awarded Passport to Education Scholarship ($500) in September 2015
  • Distinguished as a National AP Scholar in July 2015