Welcome to my homepage! My name is Himalaya Sharma and I am a graduate student currently pursing Master of Engineering in the Electrical and Computer Engineering department at University of Waterloo. I finished my undergraduation at Birla Institute of Technology and Science with a double major in Electronics & Communication and Biological Sciences.
At University of Waterloo, I am working as a Data Scientist in the field of health-technology at the Ubiquitous Health Technology Lab (UbiLab), under the supervision of Prof. Plinio Morita. I have also gained valuable experience in the domain of computer vision while working at the Image and Vision Computing Lab under the guidance of Prof. Zhou Wang.
Previously, I was a research intern at both New York University Abu Dhabi and Vienna University of Technology, where I was affiliated with the Research Group for CARE-Tech (Computer Architecture and Robust, Energy-Efficient Technologies) under the supervision of Prof. Muhammad Shafique. During my time there, I had the privilege of working extensively in the field of Deep Learning and time-series modeling.
Download my resumé.
- Artificial Intelligence
- Data Science
- Product Sense
- Statistics
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Master of Engineering in Electrical & Computer Engineering (Specialization in Artificial Intelligence & Machine Learning), 2022
University of Waterloo
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Bachelor of Engineering in Electronics and Communication Engineering, 2016
Birla Institute of Technology and Science, Pilani
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Master of Science in Biological Sciences, 2016
Birla Institute of Technology and Science, Pilani
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Experience
- Performed Human Activity Recognition (HAR) for 20+ scenarios, achieving a mean F1 score of 0.81, by utilizing both classical and ensemble multi-class classification models.
- Automated data loading and annotation for 40+ participants, obtaining a 70% time reduction, by leveraging Python scripts to extract sensor data from Web APIs, JSON & Excel files and SQL dumps.
- Constructed a scikit-learn data pipeline to facilitate sequential loading, wrangling, feature engineering, and modeling, resulting in a 2x faster experiment iteration, followed by deployment on Azure DS Virtual Machine.
- Identified 4 deep learning models for performance evaluation, by conducting a literature survey of state-of-the-art Image Quality Assessment (IQA) algorithms.
- Wrote bash scripts to benchmark PyTorch & TensorFlow models on 5+ authentic & synthetic distortion image databases.
- Deployed batch jobs on multi-GPU compute nodes on High Performance Computing (HPC) clusters, resulting in a 50% reduction in overall training and evaluation time.
- Implemented data generators to facilitate real-time data feeding to models, resulting in a 80% reduction in memory consumption.
- Leveraged Google’s attention-based Temporal Fusion Transformer (TFT) to improve forecasting performance by 37%, compared to established baselines using classical methods from Python’s Statsmodels.
- Performed literature survey of parametric and non-parametric (machine learning/deep learning) time-series modeling techniques.
- Built a data formatter to filter, preprocess, and transform data, resulting in a 30% increase in productivity by utilizing object-oriented programming principles.
- Experimented with 10+ statistical and machine learning based time-series models for pre-emptive arrhythmia forecasting using electrocardiogram data.
