Ayman Mahmud Haque

Machine Learning Engineer Data Engineer Python Developer

■ ad1upj@r.postjobfree.com ■ +1-347-***-**** ■ linkedin.com/in/aymanhaque001/

github.com/aymanhaque001

■ 2+ years of Academic and Research experience in Machine Learning and Deep Learning Algorithms, Feature Engineering, Hyperparameter tuning, Analytics, Visualization and Image Processing. Experience with linear and logistic regression, Clustering, Neural networks, and Dimensionality reduction.

■ Demonstrated academic experience with implementing Supervised and Unsupervised ML and DL models for Natural Language Processing, Image recognition, predictive modeling, and Time-series data.

■ Experienced in creating Web APIs following REST-API architecture, containerization, and machine learning operations/deployment in AWS.

■ Creating Data Pipelines on Snowflake and Azure for efficient ETL and Data warehousing solutions. SKILLS

Languages & Frameworks: Python, SQL, C++, TensorFlow, Keras, FastAPI, MLflow, Scikit-learn, React.js, Docker Tools & Libraries: Git, Pandas, NumPy, Matplotlib, Seaborn, Power BI, Alteryx, NLTK, Gensim, OpenCV, Apache-Airflow, Apache-Kafka.

Cloud: Azure, Snowflake, AWS.

EDUCATION

Missouri State University, Springfield, USA August 2019 – May 2021 Master of Science in Computer Science (CGPA: 3.42/4.00) Brac University, Dhaka, Bangladesh January 2013 – December 2017 Bachelor of Science in Electrical and Electronics Engineering WORK EXPERIENCE

Data Engineer July 2022 - Present

Tata Consultancy Services

Graduate Researcher January 2021 - May 2021

Computer Science Department, Missouri State University. ACADEMIC PROJECTS

Fake Reviews Detection using Topic Modelling and Convolutional Neural Networks

● Performed Data cleaning and preprocessing on a large corpus. Lemmatizing, POS-tagging, stop words removal etc.

● Employed Unsupervised machine learning (Latent Dirichlet Allocation) for topic modeling to extract relevant features.

● Generated Word Embeddings with Word2Vec skip-gram from the corpus for training and developed a ConvNet model.

● Achieved 89% accuracy on 5-fold cross-validation and developed other ML models i.e., TF-IDF + Support Vector Machines, Multinomial Naïve-Bayes, and Gradient Boosting classifier for evaluation. Identifying Optimal Subgroups of Traumatic Brain Injury (TBI) Patients

● Data Cleaning encoding categorical features and dropping unnecessary features to improve data integrity.

● Used K-means clustering to cluster dataset into meaningful groups.

● Visualized the results using TSNE and PCA dimensionality reduction methods. Evaluated clusters with a combination of IVMs, i.e., DB, SI, and CH index.

● Employed evolutionary feature selection to reduce dimensionality and increase classification performance. Evaluated accuracy using MLP.

Drowsiness Detection for Drivers Using Deep Learning

● Used OpenCV library to extract images from webcam feed and HAAR cascades to extract regions of interest.

● Performed Preprocessing of Image dataset for training a Deep learning model.

● Developed a convolutional neural network to classify detected images to appropriate classes.

● Designed a system to keep scores and alert the driver when eyes are closed for a particular threshold.

● Evaluated validation accuracy for different CNN architectures, hyperparameter tunings, and environmental and lighting conditions.

Stock market trend Predictor using Recurrent Neural Networks

● Performed data preprocessing and cleaning on Yahoo Financial Time series dataset and arranged it into a recurring dataset for training LSTM model.

● Developed LSTM network to predict prices for each subsequent day; evaluated for several hyperparameter settings; visualization.

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