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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
Published:
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portfolio
Perception Machine Learning Engineer
Autonomous Robots
DEKA Research and Development Corporation
Duration: January 2021 - Present
Perception Machine Learning Engineer
Medical Devices
DEKA Research and Development Corporation
Duration: January 2021 - Present
Machine Learning and Statistics Research Assistant
Nano-medicine Center (NEU)
Duration: January 2020 - May 2020
Risk Assurance Intern
Deloitte Touché Tohmatsu Limited
Duration: Summer 2018
Nuclear & Heat Transfer Equipment Systems Intern
Larsen & Toubro Limited
Duration: Summer 2017
publications
talks
Automated Tetrahedron Meshing
Overcoming Numerical Challenges in Volumetric Data Processing
This paper explores the intricacies of tetrahedral mesh generation from medical volumetric data, highlighting the challenges posed by numerical errors and presenting a Delaunay triangulation-based approach for creating high-quality and subject-specific meshes.
Sinkhorn Balancing in Sudoku: A Probabilistic Approach to Constraint Satisfaction
This study explores the application of Sinkhorn balancing, a probabilistic algorithm with lower computational complexity, robustness, and ability to handle diverse problems, as an effective technique in solving Sudoku puzzles compared to traditional back propagation methods.
Analyzing the Relationship Between Crime Count, Weather Conditions, and Weekend Outings: Can They Serve as Valid Excuses for Cancellations?
This project employs statistical analysis to explore the correlation between crime count, weather conditions, and weekends, aiming to determine if these factors can be statistically significant reasons for canceling outdoor plans with friends.
YOLO v3 Object Detection Implementation and Evaluation in PyTorch
This project focuses on recreating the YOLO v3 algorithm from scratch using PyTorch, incorporating various data augmentation techniques and evaluating its performance on the MS COCO dataset, revealing challenges and insights into the model’s training and inference capabilities.
Markov Chain Modeling of Wind Speed Time Series: Analysis, Simulation, and Limitations
Explore the application of Markov chains in predicting wind speed behavior using a dataset from Chièvres, Belgium. The project includes data preparation, transition matrix computation, simulation, and autocorrelation analysis, highlighting the model’s strengths and limitations in capturing the complex dynamics of wind speed fluctuations
Modified SIR Modeling on the Spread of Ideas
This project explores the dynamics of idea sharing by introducing a system of differential equations, incorporating SIR-like and population growth models to simulate the spread of shared ideas, analyzing factors such as user flow rates, identifying potential cyclical patterns, and incorporating a capacity limit to simulate a more realistic model within a community.
Data-Driven Prediction of Aircraft Engine Remaining Useful Life for Enhanced Aviation Safety
This project focuses on leveraging machine learning techniques and the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset to preprocess sensor data and develop a sophisticated model for accurately predicting the Remaining Useful Life (RUL) of aircraft engines, thereby enhancing aviation safety and operational efficiency through timely maintenance or replacement of engine components.
Patterns in Capsets
This study tackles the unsolved Capset Problem through an unconventional analysis of attribute distributions, aiming to determine the size of capsets with ‘n’ attributes and proposing that identifying specific distribution patterns could lead to a breakthrough.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.