I base my teaching approach on practicality. Usually for programming languages, I start teaching practical learning from day one for my students. I build programming assignments on practical tasks. It could be getting the restaurants data for your city from yelp and build it from there.
I have an Electrical Engineering bachelors from McMaster University. I have done a certification in Artificial Intelligence from University of Toronto. I have been doing software development for the past 7-8 years. I have experience in C#, python and C++
Adnan Lanewala (concealed information)
• Languages: C#, Python, Keras, Scikit-learn, C/C++, WPF, Ladder Logic, SQL
• Applications: Visual Studio, Team Foundation Server, Anaconda, Jupyter notebook,
Sciex [(concealed information) March 2018 – To present
Data scientist / Software Developer
• Implemented different noise models to get better analysis on the chromatography using statistical mathematics in C#
• Collaborated with research scientist on implementing different algorithms in C# for peptide mapping and peptide sequencing
• Gathered simulated labelled data to test different machine learning techniques on the dataset in python jupyter notebook
• Extensively worked on the Linq queries for filtering out the data
Relevant Project at Sciex
Peak shape evaluation using Machine Learning
• Using feature engineering, mine different features for machine learning to evaluate different chemicals compounds using peak shapes
• Collected and consolidated data from different department and labelled it for supervised learning
• Tested and evaluated different machine learning and deep learning models
• Evaluated performance based on Receiver operator curve (ROC) and changed threshold to have a proper balance between recall and precision
Thermo Fisher Scientific [(concealed information) Aug 2015 – Feb 2018
• Developed Drivers for various types of medical instruments using C# in .NET Framework
• Worked with medical device manufacturers on how their instrument will be used in lab and how to successfully integrate their software API
• Attended to customer’s project requirements and integrated feedback into engineering design
• Coordinated with various project managers and integrators to successfully install the software components of projects
• Trained customers on lab automation software (Momentum) and assisted in their scientific analytical experiment
Tornado Spectral System [tornado-spectral.com] Jan 2015 – August 2015
• Developed Spectroscopy / Optics software application using C# in .Net Framework
• Integrated external libraries (C/C++) of Charge-Coupled Devices (CCD) into the software
• Created company’s first Relational Database using SQL server 2012 to store data from the existing product and wrote queries using Linq to SQL
Husky Injection Molding Systems [(concealed information) May 2011 – Dec 2014
• Designed the controls software for development of Automated Machine Sequence, Process Controls, and Human Machine Interface
• Developed strong team relations while coordinating with various designers to account for machine safety and cycle time, which had a direct effect on customer’s production
• Implemented in C#, an application software tool for software designers, which searched for reference projects for their currents projects
Course Certificate, Natural Language processing (sequence model) Jan 2020 – Feb 2020
• Learned, built and trained Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
• Applied sequence models to text and audio applications, including speech recognition and music synthesis.
Graduate Certificate, Artificial Intelligence Sept 2018 – Dec 2019
University of Toronto, Toronto, ON
• Explored all modern branches of machine learning and AI, such as deep learning using neural nets and reinforcement learning
Bachelor of Engineering, Electrical Sept 2008 – April 2013
McMaster University, Hamilton, ON
• Specialized in programming and internet communication protocols
Vehicle Speed Detection Using Dashcam Video July 2020 - Present
• Used image processing tools such as OpenCV and NumPy to pre-process video and images for preparing the training set.
• Implemented FlowNet model in Keras using its existing implementation in pytorch as a baseline.
• Use transfer learning to fine tune the model weights with the available training data.
Malaria Detection using Deep learning Jan 2019 – April 2019
[Link to Kaggle notebook]
• Implemented different Convolution Neutral Networks (CNN's) on malaria cell images
• Achieved ~96% accuracy on validation set
Detection Fatalities using Ontario Fire Marshall Dataset Sep 2018 – Dec 2018
[Link to github repo]
• Worked with the Ontario fire marshall dataset from 2011 - 2016 to predict injuries and fatalities
• Gained valuable insights from 911 calls and what could be a good predictor for early injury detection and fatalities
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