About

I am a data professional with experience in machine learning, data mining, and statistical modeling. My background in physics has endowed me with an analytical approach to problem-solving. The algorithms and numerical methods I studied during my bachelors have equipped me with a foundational understanding of handling data-intensive tasks

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Education

I hold a BSc in Physics from Concordia University, graduating in 2021. My physics background equipped me with strong analytical and computational skills that enable me to handle data-driven problems with ease.

Notable projects and courses included:

• Optimizing a Topological Photonic Crystal (PHYS 498, Specialization): Utilized machine learning techniques to enhance light-trapping efficiency in photonic crystals by 21%. Improved material analysis through advanced ML methods.
• Computational Methods in Physics (PHYS 440): Studied algorithms including numerical differential equations, stochastic methods, signal processing, and computational biology.
• Numerical Methods in Physics (PHYS 236): Introductory computational physics course focused on Python programming, data analysis, and solving physics problems.
• Quantum Mechanics I & II (PHYS 377 & 478): In-depth study of quantum mechanical systems, Schrödinger equation, operators, perturbation theory, and relativistic wave equations.

In 2022, I completed an intensive data science bootcamp through Le Wagon. Through this program, I gained hands-on proficiency in:

• Data analysis tools like Pandas, Statsmodels, and Jupyter Notebook
• Supervised and unsupervised machine learning techniques
• Deep neural networks for classification, regression, and NLP
• Real-world project work emphasizing an end-to-end ML modeling
• Cloud deployment of models on Google Cloud Platform

This bootcamp equipped me with the hands-on skills and experience to immediately contribute as a junior data scientist/analyst or contribute to machine learning projects.

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Career Journey

• As an Equity Research Analyst with Lionchase North America, I crafted research reports influencing investments worth over $1 billion and showcased a nuanced understanding of diverse industries.
• My transition to data science/machine learning culminated in a capstone project focused on energy optimization. As part of a team, I built an intelligent system using machine learning to analyze appliance-level electricity usage, empowering users to identify high-consumption areas and improve efficiency. The project addressed core user needs around identification, savings, and recommendations. We achieved key milestones including an 80% accurate ML model, 5% faster processing, and deployment on GCP with a Streamlit UI.
• Presently, as a Teaching Assistant at Le Wagon, I'm helping the next generation of data enthusiasts, emphasizing real-world applications in probability, statistics, and linear algebra.

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Technical Proficiency

• Languages: Python (NumPy, Pandas, Scikit-Learn), SQL
• Techniques: Statistical modeling (regression, PCA), data mining (clustering, decision trees), machine learning (NLP, computer vision)
• Tools: TensorFlow, Jupyter Notebook, Tableau, Langchain

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Goals

In the near term, I aim to secure a role as a data engineer, data analyst, or machine learning engineer. These opportunities would allow me to apply my skills in statistical modeling, data infrastructure, pipeline optimization, and ML ops.

Longer-term, I envision myself as a leading practitioner shaping best practices in data analytics and engineering. I hope to take on strategic projects, guide architectural decisions, and mentor junior team members. My ultimate goal is to become a distinguished professional known for technical excellence, creative problem-solving, and cross-team collaboration.

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Interests

My curiosity is currently tuned toward generative AI, especially large language models. I'm devoted to comprehending how these models work under the hood and mitigating issues like hallucination through prompt engineering techniques. Context injection to steer outputs is a particular area of exploration.

I'm also enthralled with leveraging diffusion models to create art, such as using Midjourney to render imaginative images. Experimenting with leading-edge AI like this not only produces fascinating results but also strengthens my mental models of the technology.

I'm invested in hands-on tinkering with emergent AI systems. Both understanding their capabilities and creatively applying them provides insight into shaping responsible and innovative applications. My interests align with expanding the boundaries of human-AI collaboration.

Resumé