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Deep Learning Researcher at Simon Fraser University
Transitioning from Deep Learning Researcher to graduate student and seeking a research partnership and/or internship in some subset of Machine Learning, Deep Learning, or Artificial Intelligence. Presently performing computer vision tasks on the Cedar advanced research computing (ARC) system. The Cedar ARC represents a collaborative effort between Simon Fraser University, Compute Canada, and WestGrid. At a high level, Cedar is a heterogeneous cluster comprised of 58,146 CPU cores and 584 GPU devices, making it the most powerful academic supercomputer in Canada and placing it in the world’s top 100 supercomputer installations.
University of Windsor
Master's of Applied Science, Electrical and Computer Engineering, Deep Learning
January 1, 2018 – January 1, 2019
University of Windsor
Master of Engineering - MEng, Electrical and Computer Engineering
N/A – Present
Laurentian University/Université Laurentienne
Bachelor of Science - BS, Mathematics and Computer Science
N/A – Present
University of Michigan
Bachelor of Science - BS, Pharmaceutical Sciences
N/A – Present
Simon Fraser University
Visiting Researcher
January 1, 2019 – Present
Vancouver, Canada Area
University of Windsor
Graduate Teaching Assistant
January 1, 2018 – January 1, 2018
Windsor, Canada Area
Simon Fraser University
Deep Learning Researcher
January 1, 2017 – January 1, 2018
Vancouver, Canada Area
SAP
Software Development Engineer In Test
January 1, 2016 – January 1, 2016
Vancouver, Canada Area
École de technologie supérieure (ÉTS)
Consultant in Android Mobile Development
January 1, 2015 – January 1, 2015
Montreal, Canada Area
NeuroTechX
Consultant in Android Mobile Development
January 1, 2015 – January 1, 2015
Montreal, Canada Area
TandemLaunch Inc.
Software Scientist in Speech Recognition for Android Mobile
January 1, 2014 – January 1, 2015
Montreal, Canada Area
Université du Québec - Institut national de la recherche scientifique
Speech Enhancement Researcher
January 1, 2013 – January 1, 2014
Montreal, Canada Area
Université de Montréal
Computational Linguistics Researcher
January 1, 2013 – January 1, 2013
Montreal, Canada Area
McGill University
Control System Engineer
January 1, 2013 – January 1, 2013
Montreal, Canada Area
National Research Council Canada / Conseil national de recherches Canada
Finite Element Analysis Engineer
January 1, 2011 – January 1, 2012
Montreal, Canada Area
CVR Global Inc
Embedded Software and DSP Engineer
January 1, 2009 – January 1, 2011
Raleigh-Durham, North Carolina Area
Health Sciences North Research Institute
Bioinformatics Software Engineer
January 1, 2001 – January 1, 2002
Sudbury, Ontario, Canada
Stanbio Laboratory, an EKF Diagnostics Company
Research Associate in Dry Reagent Chemistry
January 1, 1998 – January 1, 1999
Elkhart, Indiana Area
Enhancement of Reverberant Speech for the UBI® Automatic Speech Recognition Platform
October 1, 2013 – May 1, 2014
Purpose and Objectives: To assess signal corruption by reverberation of the UBI® far-field speech recognition platform under echoic and semi-anechoic conditions Signal Processing and Digital Audio Editing Tools: 1. MATLAB 2. Audacity Automatic Speech Recognition (ASR) Frameworks: 1. CMU Sphinx-4 2. Google Voice Transcription Sound Quality Tool: 1. Perceptual Objective Listening Quality Assessment (POLQA) Programming Languages: 1. Java 2. Python Operating Systems: 1. Android 2. Windows
A Finite-State Machine of Atypical Oral Language Perception
June 1, 2013 – September 1, 2013
Abstract In cognitive neuropsychology, it has long been understood that individuals on the autism spectrum will often present with atypical perception of oral language, a condition explainable under the framework of hyperlexia language disorder. From a first-person perspective this work addresses the fundamental question: Is hyperlexic receptive and expressive language a consequence of atypical-hypersensitive perception in the auditory modality? Previous cognitive science literature has failed to address the causal relation of auditory defensive behaviors to hyperlexia. This paper introduces state-transition and various latent variable probabilistic models, used in automata theory and computational psycholinguistics, respectively, to explain this causal relationship. The finite-state machine and Bayesian network were selected because they allow for highly effective representations of synaesthetic-visual recognition of speech. These automata and probabilistic methodologies will hopefully present a unique viewpoint and better understanding about the auditory and visual aspect of the autism behavioral profile. Keywords: atypical perception, auditory hypersensitivity, autism spectrum, Bayesian network, causal relation, psycholinguistics, finite-state machine, hyperlexia, latent variable, synaesthesia
Industrial Control Systems Engineering
January 1, 2013 – May 1, 2013
Purpose and Objectives: To create the communication protocols for model-based virtual infrared sensors and core temperature observers in industrial plastic thermoforming. Control Theory Methodologies: 1. Real and Virtual Observers: Closed-Loop Luenberger State Observer 2. Heating Phase Model: Closed-Loop State Space System 3. Generic: Proportional-Integral-Derivative (PID) Controller Supervisory Control and Data Acquisition (SCADA) Subsystems: 1. Embedded Controllers: National Instruments (NI) PXI-8184 2. Human-Machine Interface: NI LabVIEW 2011 3. Object Linking and Embedding (OLE) for Process Control (OPC): NI OPC Server 2012 4. OPC Data Access (DA): XML in LabVIEW 5. Programmable Logic Controllers (PLCs): Allen-Bradley Company 6. PLC Ladder Logic: RSLogix 500 from Rockwell Software
Angioplasty Balloon Formation, Simulation, and Optimization
January 1, 2011 – January 1, 2012
Purpose and Objectives: To create a virtual automation, by computer simulation and optimization, of the classical prototyping methodology for manufacturing of angioplasty balloons Simulation and Optimization Methodologies: 1. Nonlinear Finite Element Methods (FEM): Implicit Newton-Raphson Method 2. Nonlinear Optimization: Gradient-based Sequential Quadratic Programming (SQP) 3. Heuristic Search: Genetic Algorithm (GA) 4. Material Model: Von-Mises Stress (J2) Elasto-Plastic (EP) Model Simulation and Optimization Tools: 1. Finite Element Method (FEM): MATLAB and BlowView (Parmesh and BlowSim) 2. Numerical Optimization: MATLAB and CONMIN 3. Computer-Aided Design (CAD): SolidWorks BlowView is similar to the ANSYS and ABAQUS software packages Programming Languages and Integrated Development Environment: 1. C++ 2. Microsoft Visual Studio Operating System and Shell Extension: 1. Microsoft Windows 2. TortoiseSVN
A Supervised Learning Algorithm for the Cardiovascular Resonator
January 1, 2009 – January 1, 2011
Purpose and Objectives: To design training classifiers for the Cardiovascular Resonator, from the 2009 University of Michigan Clinical Study 1.0, using the last 6 seconds of signal data obtained from the left carotid artery, right carotid artery, and heart and make test predictions for newly acquired signal data. Digital Signal Processing (DSP) and Machine Learning Methodologies: 1. Discrete-event and stochastic: Daubechies-4 (DB4) Discrete Wavelet Transform (DWT) 2. Multivariate methods: Multidimensional Scaling (MDS) and Principal Component Analysis (PCA) 3. Ensemble classifiers: Random Forests 4. Cluster analysis: Partition Around Medoids (PAM) Algorithm and Statistical Development Tools: 1. MATLAB 2. R Programming Languages: 1. C/C++ 2. Linux Shell Scripting Operating Systems: 1. Linux Debian 2. Microsoft Windows
Spark Fundamentals II
Cognitive Class
June 24, 2026 – Present
Hadoop 101
Cognitive Class
June 24, 2026 – Present
MapReduce and YARN
Cognitive Class
June 24, 2026 – Present
Apache Pig 101
Cognitive Class
June 24, 2026 – Present
Spark Fundamentals I
Cognitive Class
June 24, 2026 – Present
Accessing Hadoop Data Using Hive
Cognitive Class
June 24, 2026 – Present
Simplifying data pipelines with Apache Kafka
Cognitive Class
June 24, 2026 – Present
Hadoop Programming - Level 2
IBM
June 24, 2026 – Present
Using HBase for Real-time Access to your Big Data
Cognitive Class
June 24, 2026 – Present
Controlling Hadoop Jobs using Oozie
Cognitive Class
June 24, 2026 – Present
Learn Big Data: The Hadoop Ecosystem Masterclass
Udemy
June 24, 2026 – Present
Spark MLlib
Cognitive Class
June 24, 2026 – Present
Spark - Level 1
IBM
June 24, 2026 – Present
Hadoop Foundations - Level 1
IBM
June 24, 2026 – Present
Hadoop Programming - Level 1
IBM
June 24, 2026 – Present
Hadoop Data Access - Level 1
IBM
June 24, 2026 – Present
Accelerating Deep Learning with GPU
Cognitive Class
June 24, 2026 – Present
Machine Learning with Python
Cognitive Class
June 24, 2026 – Present
Deep Learning 101
Cognitive Class
June 24, 2026 – Present
Deep Learning with Tensor Flow
Cognitive Class
June 24, 2026 – Present
Tableau 10 A-Z: Hands-On Tableau Training For Data Science!
Udemy
June 24, 2026 – Present
Visualize Data with D3.js
Udemy
June 24, 2026 – Present
The Complete Developers Guide to MongoDB
Udemy
June 24, 2026 – Present
Taming Big Data with MapReduce and Hadoop
Udemy
June 24, 2026 – Present
Taming Big Data with Apache Spark and Python
Udemy
June 24, 2026 – Present
Apache Spark 2.0 with Scala - Hands On with Big Data!
Udemy
June 24, 2026 – Present
Data Science and Machine Learning with Python
Udemy
June 24, 2026 – Present
Docker on Windows 10 and Server 2016
Udemy
June 24, 2026 – Present
Algorithms and Data Structures in Python
Udemy
June 24, 2026 – Present
SQL and Relational Databases 101
Cognitive Class
June 24, 2026 – Present
NoSQL and DBaaS 101
Cognitive Class
June 24, 2026 – Present
Data Science Methodology
Cognitive Class
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, spanning speech recognition, medical device simulation, bioinformatics, and cognitive neuropsychology, demonstrates a broad intellectual curiosity and adaptability. The academic background with multiple master's degrees and research roles at various universities (Simon Fraser, University of Windsor, Université de Montréal, McGill) indicates a strong inclination towards continuous learning and a research-oriented environment. The target role of 'Computer Vision' aligns well with their recent deep learning and image recognition research. The breadth of skills and exposure to different domains suggest a candidate who can integrate into varied technical teams and contribute to innovative projects.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving orientation and an ability to work on complex, multi-disciplinary problems. The roles as a 'Visiting Researcher' and 'Deep Learning Researcher' suggest an aptitude for independent research and innovation. The 'Graduate Teaching Assistant' role implies communication and mentorship skills. However, without direct assessment data, specific soft skills like teamwork, leadership, or stress handling cannot be definitively evaluated.