I am an aspiring Machine Learning & AI Researcher, passionate about building intelligent systems that drive innovation and solve real-world challenges. My expertise spans Machine Learning, Deep Learning, and Data Science, developed through rigorous self-learning, university coursework, and hands-on projects at M2M Tech, where I work on AI-driven data science solutions.

🎓 Education

• Stanford University – Machine Learning Specialization
• University of Toronto – Data Science & Machine Learning Certification

🛠 Core Skills:

• Data Science & Analytics – Extracting insights from structured & unstructured data
• Machine Learning & Predictive Modeling – Building & fine-tuning ML models
• Deep Learning & Generative AI – Working with CNNs, Transformers, and GANs
• Computer Vision & Reinforcement Learning – Exploring AI for perception & decision-making
• Data Engineering & Cloud AI – Optimizing workflows for scalable ML solutions

💡 Current Focus Areas:

• Real-Time Multilingual Transcription – AI-powered live captioning & speech-to-text
• Deepfake Detection & Prevention – Developing security-focused AI to combat misinformation

I thrive in collaborative environments where I can contribute, learn, and innovate. My mission is to push the boundaries of AI research, continuously exploring new frameworks, methodologies, and applications.

• M2M Data Science and Machine Learning Internship

• Capstone 1: Data Analytics (Speech & Language Trends Analysis)

  • Dataset Selection & Exploration
  • AI vs. Human Transcription Accuracy
  • Data Visualization & Report

• Capstone 2: Machine Learning (Real-Time Speech-to-Text AI)

  • Train a multilingual speech-to-text model.
  • Add a translation step using NMT models (like mBART, NLLB, or fine tine Whsiper)
  • Optimize a real time inference with lightweight models or caching strategies.

• Capstone 3: Deep Learning & Generative AI (Context-Aware translation)

  • Fine-tune Whisper GPT or LLaMA for context-aware translation.
  • Implement code-switching detection to dynamically adjust translation.
  • Future Work: Improve translation quality with prompting & finetuning LLMs for better language understanding.

  Deliverable: A real-time audio translation that accurately detects code-switching with low-latency.

• Passion Project

  Robustness & Explainability in AI-Generated & AI-Detected Content

  I am actively conducting research on AI-generated and AI-detected content, focusing on:

  • Deepfake Detection & Prevention – Developing robust models to detect AI-generated fake videos using CNNs, Transformers & Adversarial Training.

  • Autonomous Vehicle Perception – Using GANs to create synthetic training data, improving AV robustness in challenging scenarios.

  • AI-Generated Art & Style Transfer – Exploring explainability in generative art models to better understand AI creativity.

This work aligns with the latest research by Meta, DARPA, and AI Ethics communities, with a strong focus on security, transparency, and AI robustness. The ultimate goal is to contribute to cutting-edge AI safety research and enhance the interpretability of AI-generated content across multiple domains.

Research Repository: Deepfake-AV-Art-Research

• ML_Projects – End-to-end machine learning models solving real-world problems (classification, regression, NLP, etc.).
• Data_Analytics – Data exploration, visualization, and statistical modeling using Python, Pandas, and Matplotlib.

• Python_Projects – Fun, practical coding projects to enhance Python skills.
• Software_Development – Full-stack applications using Java, Rust, SQL, and cloud technologies.

• Deep_Learning_Experiments – Implementations of CNNs, RNNs, transformers, and generative AI models using PyTorch & TensorFlow.
• Reinforcement_Learning – Experimenting with Q-learning, policy gradient methods, and simulation-based learning.
• Generative AI – Style Transfer, GANs, and multimodal learning experiments.
• Rust_Projects – Exploring Rust for high-performance computing in AI/ML workflows.

Programming: Python, SQL, Rust, Java, Go
Machine Learning & AI: Scikit-Learn, TensorFlow, PyTorch, Keras
Data Engineering & Analysis: Pandas, NumPy, Matplotlib, Plotly
Development Tools: Git, VS Code, Jupyter, IntelliJ IDEA
Cloud & Big Data: AWS, GCP, Hadoop, Spark
Databases: MySQL, PostgreSQL, MongoDB

I am committed to continuous learning and have completed various certifications and university courses to deepen my expertise in Machine Learning, Artificial Intelligence, and Data Science.

• Stanford University – Machine Learning Specialization and Statistics
• University of Toronto – Data Science & Machine Learning Certification
• University of Pennsylvania – AI, ML Essentials & Statistics Certification
• IBM – AI Developer Certification
• Ludwig Maximilian University of Munich (LMU) – Competitive Strategy & Organization Design
• Microsoft – AI/ML Foundations & Algorithms
• NVIDIA – AI Operations & Infrastructure Fundamentals
• Wolfram Research – Machine Learning Statistical Foundations Professional Certificate
• Google – Advanced Data Analytics Professional Certificate
• Canonical – Linux Professional Certification
• OpenEDG Python Institute – Programming with Python Professional Certificate
• AWS – Cloud Technical Essentials

I’m open to collaborations, research discussions, and opportunities to contribute to AI & ML projects.
📩 Feel free to connect with me on LinkedIn or explore my repositories here!