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PhD in Computer Science

MS in Computer Science

BTech in Electronics and Communication Engineering

Machine Learning Engineer

• Developed an end-to-end AI-powered resume writing system, transforming the manual resume writing process into an automated solution.
• Collaborated with human resume writers to understand and map the resume creation process, and gathered extensive datasets for model training.
• Leveraged advanced techniques such as RoPE scaling, flash attention, quantized low-rank adaptation, and ORPO (combines instruction-tuning with preference alignnment) to train state-of-the-art open-source LLMs.
• Built an LLM with capabilities comparable to a professional resume writer, resulting in a significant reduction in workload for human resume writers.
• Deployed the model on an A100 server, allowing users to generate tailored resumes.

Graduate Teaching Assistant

• Assisted with and reviewed research proposals for the course Privacy in the Electronic Society

Data Scientist

• MLOps- Containerized and deployed applications using GCP and Kubernetes, leveraging KServe for model inferencing, MLflow for tracking, and Kubeflow for managing ML workflows.
• Entity Extraction- Applied a multi-modal deep learning architecture to extract data from unstructured PDFs.
• Constrained Optimization- Worked on a constrained optimization problem using mixed integer programming.
• Retail Video Analytics- Transfer-learned a Faster R-CNN model for object detection and implemented a deep learning-based tracker.
• Conversational AI Chatbot- Developed a chatbot with the Rasa Machine Learning Framework and integrated external APIs, including Google Maps API.
• PII Detection- Fine-tuned a pre-trained model for custom named entity recognition to detect and mask sensitive data in text files, using Apigee for API management and testing.
• Designed an algorithm to match medical service providers with members, utilizing multiprocessing to save 40 hours of execution time. Managed GCP compute instances, used Cloud SQL for PostgreSQL, and developed an interactive web application with Plotly and Dash.

Graduate Teaching Assistant

• I worked as a Graduate Teaching Assistant for the course Principles of International Business at W. P. Carey School of Business.
• As part of my work at the Behavioral Research Lab, I worked on an algorithm that categorized open-ended survey responses into two categories using NLP algorithms for topic extraction.

Research Intern - Software Engineering

• Research on developing a new dimensionality reduction technique using tree-indexing.

Research Intern - Software Engineering

• Developed a Multi-Lingual Information Retrieval System that retrieves Wikipedia Articles across languages (French, Arabic and English).
• Used pre-trained Arabic, French and English word embeddings on monolingual vector spaces.
• Aligned these monolingual vector spaces using Orthogonal Procrustes Alignment Technique to get a multilingual vector space.
• Used t-SNE technique for dimensionality reduction and Bokeh for visualization purposes.

Research Intern - Software Engineering

• Analyzed datasets to understand urban dynamics in rapidly growing cities. Developed a platform for integrating data from physical and social sensors, resulting in two applications- one visualizing social demographics in Qatar and another, QEvents, displaying various events in Qatar through a map-centered dashboard.

PhD Research

• Exploring scalable LLMs to analyze linguistic techniques and patterns for combating online propaganda and disinformation.
• Research on detecting AI-generated content.
• User Research using Quantitative Methods.
• Implementing semantic search and RAG (Retrieval Augmented Generation) with high-dimensional vectors using Hadoop, Spark, and Milvus, a scalable vector database.

MS Research

• Continuous Glucose Monitoring & Prediction- Engineered features from CGM time-series data, trained SVM and K-NN models to analyze meal intake patterns, and forecasted hypoglycemic events using RNN LSTM.
• Botnet detection on social media- Applied the CoorNet algorithm to identify coordinated inauthentic behavior.
• Pattern recognition- Utilized Bayesian decision theory for classification in various use cases.

Undergraduate Research

• Project 1 - Developed a face-authenticated and voice-controlled robot on Raspberry Pi that detects objects and recognizes emotions using deep learning algorithms.
• Project 2 - Created a Sign Language Recognition System with two approaches.
• Utilized a glove equipped with flex sensors, contact sensors, accelerometers, and gyroscopes to convert sign language into speech.
• Implemented machine learning and computer vision algorithms to recognize sign language from images.

• GREPSEC VI Grant Recipient
• USENIX Security '23 Student Grant Recipient

• NYU School of Engineering Fellowship

• Machine Learning Engineering for Production (MLOps)

• Rasa Developer Certification

• AWS Fundamentals- Addressing Security Risk

• AWS Fundamentals- Going Cloud-Native

• Huawei Certified Network Associate

• Member of AI Ethics Committee - Atos zData

• Under-Secretary-General, Executive Board Affairs - NITD Model United Nations

• Human Rights Delegate - BITSMUN

• Student Volunteer - TEDxBirlaPublicSchool