A teaching assistant with 7 years of experience as a software developer. Expertise in data analysis, data mining, machine learning models, and data visualization. Creative thinking, with hands-on experience on Python, ML/DL, PyTorch, Keras, Numpy, Pandas, Matplotlib, ML models like CNN, KNN, RF, and data structures. I come with prior experience in leading a product delivery in Product & Service based industry with Agile. Additionally, I have completed my Master’s and bachelor's in computer science, and currently pursuing master's in data science at Michigan Technological University.
Area of interest:
Machine learning models, robotics, distributed algorithms, neural networks, computer graphics, graph theory, computer graphics, computer vision, AI, python, scala, tableau, mathematics, time series problems.
GPA: 3.5
Coursework: Introduction to Data Science, Regression Analysis, Artificial Intelligence, Big Data, and Advanced Data Mining
GPA: 8.01
Coursework: Design and Analysis of Algorithms, Database Management Systems, Computer Networks, Systems Programming, Degree Project, Science of Programming, Software Engineering, Advanced Computer Architecture, Introduction to Programming, Logical Organization of Computers, Mathematical Foundations, Concrete Math and Graph Theory, Numerical Methods, Data Structures and Algorithms, Computer Architecture, and Operating Systems.
GPA: 3.51
Coursework: engineering mathematics, applied science, fundamentals of programming, basic electrical engineering, basic electronics engineering, basic civil and mechanical engineering, computer graphics, discrete structures, data structures, programming and problem solving, microprocessors and interfacing,digital signal processing, theory of computation, RDBMS, computer networks, design and analysis of algorithms, object oriented programming, advanced databases, and distributed operating systems.
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Formal Models of Computation
Publications:
Research Projects
American Sign Language Classification:
The project aimed to use SVM, CNN among other machine learning models to create an image classification system for ASL. The ASL dataset was first gathered, and the photos were then preprocessed using scaling, normalization, and data sampling. Models were then trained and tested using the preprocessed images as input, mainly PCA as a preprocessing technique. For model performance, we looked at the accuracy of each model against the execution time taken by each of them. According to the findings, the CNN model with two layers had an accuracy of 73.76%, while the SVM model with PCA had a maximum accuracy of 86.42%. In comparison to the other models, the SVM model with PCA also had the quickest execution time. The computational complexity of training and testing deep learning models on huge datasets was one of the main difficulties encountered, and hence we performed sampling.
Chest cancer classification
The project aimed at classifying chest X-ray images to detect the type of chest cancer. The images were preprocessed through augmentation and regularization. Models implied were CNN and ResNet50V2. Transfer learning was performed to increase the accuracy of model predictions.
Food Image Classification
Food recognition has become an important task in computer
vision due to the increasing interest in developing intelligent
systems for meal planning, calorie tracking, and dietary analysis
ysis. Accurately identifying food items in images is crucial.
for these applications. To address this, we proposed a ma-
chine learning model that can precisely classify food items
from the images. The dataset contains static images of food
items with 11 classes, and each class has around 100 images.
for the same food item. The models we aimed at was the clas-
sic CNN model for image processing and also transfer learn-
ing by the EfficientNet model. To improve the model’s per-
formance, we applied preprocessing techniques such as sam-
pling, resizing, and Principal Component Analysis (PCA) for
feature extraction. The EfficientNet model with preprocessed
data performed best in terms of accuracy and computation
cost. This efficient, model has the potential to aid proper nu-
trition diet to users.
Awards and Honors:
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