experience
Technical Lead
Netherlands (Remote)
July 2021 - July 2023
Implemented solutions with scalability for JustGo that offers extensive membership management to allow the members to engage easily and handles increase volumes of data.
Analyzed and organized raw data using SAP and SQL, evaluated business needs, prepared data and built data pipelines and marketing mix predictive models.
Developed data modeling and reporting infrastructures to provide insights via APIs, resulting in a 30% increase in data accessibility and a 50% reduction in report generation.
Applied machine learning techniques to implement data-driven solutions by leveraging Python for data analysis, modeling and algorithm development and fine-tuning them.
Monitored the performance of deployed models and updated or retrained them as needed to maintain or improve their accuracy and efficiency.
Developed and optimized 100+ stored procedures, views, functions & style sheets and restructured schemas with 60+ tables.
Led a dynamic team of six skilled professionals and implemented agile methodologies to streamline project workflows, ensuring 100% on-time delivery of high-quality solutions.
Collaborated with clients from the UK, USA, Europe and Australia to identify and prioritize requirements and conduct feasibility studies.
Education
Computer Science and Engineering
University of Oulu, Finland
Sep. 2023 - Present
Publication
ICCS 2021
A Hybrid Secured Approach Combining LSB Steganography and AES using Mosaic Image for Ensuring Data Security
Security and Privacy
Effectively Predicting Cyber-Attacks through Isolation Forest Learning-based Outlier Detection
ICCIT 2021
IRFD: A Feature Engineering based Ensemble Classification for Detecting Electricity Fraud in Traditional Meters
Technical Skills
Coding
C, C++, Python, R, Matlab
Developer Tools
Linux, Git, Docker, SSRS, Django
Big Data Tools
NoSQL (MongoDB), SQL (MySQL, PostgreSQL, SQL Server), Power BI, Azure Data Factory, Azure Data Lake Storage
Machine Learning
Pandas, NumPy, Keras, Scikit-Learn, TensorFlow 2.0, MatPlotlib, PyTorch, Scrapy, NLTK, Gensim, Scipy
Featured Projects
Multi-Modal Emotion Recognition
This project focuses on constructing a multi-modal emotion recognition system with facial and audio features. It employs subspace-based feature fusion methods, including z-score normalization and Canonical Correlation Analysis (CCA), to combine facial expression and audio features. SVM classifiers are trained using spatiotemporal features from 50 videos of 5 participants and evaluated using the remaining data. LOSO cross-validation is implemented for reliable performance estimation.
Transfer Learning with MiniImageNet and ViT
We worked with the miniImageNet focusing on the train.tar file and later split the data as train, val and test datasets. We pre-trained three models - ResNet18, VGG16 and Vision Transformer (ViT) with the best optimizers on the Training Set of MiniImageNet and then evaluated on the Validation Set and tested on the Testing Set. We saved the checkpoints for each of the models used to later use them on EuroSAT-RGB dataset. We took 5 classes from EuroSat-RGB dataset then randomply took samples as instructed in the project instruction. We used this dataset for transfer learning on the model checkpoints we saved for VGG16, ResNet18 and Vision Transformer (ViT).
Data Driven Understanding of Emotions from Bangla Texts
Since there was no corpus accessible for Bangla texts, we had to build our own corpus first, tokenize the data, extract the features from the texts, analyze the sentiment and classify them into six types of emotions - happy, sad, anger, fear, disgust, and surprise. More than 20K sentences were collected associating specific emotional characteristics labelled with six classes and we also developed four classifier models to detect emotion using different machine learning techniques - SVM, Naive Bayes, Logistic Regression and Decision Tree.