About Me

My name is Farhat Binte Azam,

Ph.D. candidate in Computer Science and Engineering (CSE) at University of South Florida (USF)

M.Sc. in Computer Science (CS) at University of South Florida (USF)

B.Sc. in Electrical & Electronics Engineering (EEE) from Bangladesh University of Engineering & Technology (BUET)

Research Interest – Computer Vision | Deep Learning | Machine Learning

CV - Farhat Binte Azam (public).pdf

Employments

Jan 2021 - Present

We are developing an innovative initiative - MosquitoAI is dedicated to developing AI-powered systems tailored for classifying various aspects of mosquito biology, including genus, species, instar, and gender and it develops valuable tools for mosquito surveillance and control, ultimately benefiting communities at risk of mosquito-borne diseases. With the capacity to enhance mosquito surveillance, MosquitoAI is at the forefront of cutting-edge technology in the fight against mosquito-related health threats.
Collects and analyzes extensive datasets, aiming to automate the identification of mosquito characteristics, which traditionally required expert entomological training.
All the AI models are being hosted through our website, making them accessible for citizen scientists engaged in mosquito monitoring.

Feb 2020 - July 202

Presented a novel approach to detect cardiac abnormalities in auscultation sounds, addressing challenges like additive noise and sensor-dependent distortion. The method combines linear and logarithmic spectrogram-image features and employs a ResNet classifier, achieving impressive results on minimizing the impact of background noise and sensor variability for classifying phonocardiogram (PCG) signals.
Made a promising solution for computer-aided cardiac auscultation using affordable stethoscopes, a project of heart disease classification funded by Kaggle.
We gathered heart sound data from patients under the supervision of medical professionals in hospital settings, and this dataset is now publicly available on Kaggle.

Deep LearningMachine LearningNatural Language Processing

Computer VisionDigital Image Processing
Digital Signal Processing

Python, C, C++, MATLABPytorch, Tensorflow, KerasAssembly Language

PCB design (Eagle/Proteus)Circuit Simulation (Proteus)Arduino, AVR, Raspberry pi

Authorized by Stanford University, offered through Coursera

Neural Network and Deep Learning (96.50%)
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (95.20%)
Structuring Machine Learning Projects (86.70%)
Convolutional Neural Network (95.60%)
Sequence Models (98.50%)

Authorized by Stanford University, offered through Coursera

Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)

Data Extraction Team

Social Network Coordinator

Exploring Nature: I have an unwavering passion for travel and adventure. Whenever I have some free time, you'll likely find me on a journey with friends, discovering the breathtaking beauty of the natural world. From hiking in the mountains to relaxing on pristine beaches, exploring nature is my ultimate escape.
Road Trips: Going for both long and short drives is my way of finding peace and maintaining focus. The open roads, scenic routes, and the freedom of the journey help me unwind and clear my mind.
Musical Meditation: Music is my constant companion. Listening to songs that match my mood is akin to meditation. Whether it's relaxing with soothing tunes or getting energized with rhythmic beats, music is a source of solace and inspiration in my life.

Check out some of my videos!! 👇