Himalaya Sharma Email & Phone Number

Waterloo, On, Ca

London, Ontario, Canada

Waterloo, Ontario, Canada

• 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.Worked under the supervision of Prof. Plinio Morita (Google Scholar: http://bitly.ws/xr72).

Waterloo, Ontario, Canada

• 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.Worked under the supervision of Prof. Zhou Wang (Google Scholar: http://bitly.ws/xr5G).

Abu Dhabi, United Arab Emirates

In continuation to the work at Vienna University of Technology:• 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.

Vienna, Austria

• 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.Worked under the supervision of Prof. Muhammad Shafique (Google Scholar: http://bitly.ws/xr7b).

Master Of Engineering - Meng, Electrical & Computer Engineering, 88%

Specialization: Artificial Intelligence & Machine Learning

Bachelor Of Engineering, Electrical, Electronics And Communications Engineering, 8.30 Cgpa

Master Of Science - Ms, Biology/Biological Sciences, General, 8.30 Cgpa

Indian School Certificate (Isc), Science, 88.4%