Saksham Bassi

I am a Software Engineer at HSBC, where I work on gaining insights through data and building applications in Global markets team.

Previously, I was a Research Intern at Tata Institute of Fundamental Research, Bangalore where I worked on time-series analysis and modeling, and computer vision with Prof. A. S. Vasudeva Murthy. I have also had the fortunate opportunity to work as a Research Intern during my bachelors at The Inter-University Centre for Astronomy and Astrophysics where I worked on building Deep Learning models for classification of variable stars.

I completed my bachelors at the Pune Institute of Computer Technology, University of Pune in Information Technology, where I was advised by Dr. Shweta Dharmadhikari.

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Research

My research interests lie in computer vision, time-series analysis and machine learning. I had a great time applying machine learning models in interdisciplinary fields like astronomy, medical imaging, finance, industrial IoT.

A learning algorithm for time-series based on statistical features
Saksham Bassi*, Atharva Gomekar*, A. S. Vasudeva Murthy
International Journal of Advances in Engineering Sciences and Applied Mathematics

Machine learning technique for time series which combines statistical features and neural networks to model, factorize and reconstruct data.

Change in structural patterns in the time series of Onion prices in India
Saksham Bassi*, Atharva Gomekar*, A. S. Vasudeva Murthy
In submission

Analyzing the structural patterns in time-series of Onion retail prices using various mathematical techniques to conclude about the patterns and periodicity.

Classification of variable stars using Deep Learning
Saksham Bassi*, Kaushal Sharma*, Atharva Gomekar
In submission

1D and 2D deep learning models to classify variable star classes.

A CNN approach to precision agriculture: An exploratory study
Atharva Gomekar*, Saksham Bassi*, A. S. Vasudeva Murthy
Project report, 2019  

Identifying diseases in farm fields using computer vision techniques on drone images. Transfer learning of pre-trained Convolutional Neural Networks and unsupervised segmentation as a pre-processing for training neural networks from scratch.

Deep Learning Diagnosis of pigmented Skin Lesions
Saksham Bassi*, Atharva Gomekar*
ICCCNT, 2019

Convolutional Neural Networks to predict the kind of skin cancer.





Thanks to this awesome guy.