About Me
I recently earned my PhD in Electrical Engineering from the University of Rochester, specializing in statistical modeling, machine learning, and deep learning for relational data. My expertise lies in developing machine learning algorithms, statistical analysis, predictive modeling, and optimization. I am particularly passionate about applying these skills to the fields of machine learning and quantitative finance.
Skills & Expertise
Machine Learning & AI: Model-based machine learning, deep learning architectures, graph neural networks (GNNs)
Statistical Modeling: Predictive modeling, optimization, sequential data analysis
Programming & Development: Python, TensorFlow, PyTorch, NumPy, Pandas
Data Analytics & Finance: Certified in Data Analytics and Finance & Quantitative Modeling
Research & Experience
During my doctoral studies, I worked extensively on statistical modeling and deep learning techniques. My research on blind deconvolution for graph signals showcases my ability to design and implement sophisticated mathematical models. Additionally, I developed an efficient and interpretable deep neural network (DNN) architecture that outperformed state-of-the-art GNNs in source localization tasks on real-world datasets.
Beyond academia, I gained industry experience as a summer intern at Amazon in 2020. I contributed to the MAPLE (Marketing Automation Program and Learning Engine) team by enhancing payment product recommendation systems. My work included developing an end-to-end data pipeline to integrate banner features, significantly improving Click-Through Rates (CTR) for AWS banner ads.
Projects & Contributions
I am an active contributor to open-source projects and frequently share my work on GitHub. My projects include implementations of advanced statistical models, deep learning algorithms, and financial data analysis techniques.
Closing Thoughts
I am excited about opportunities where I can apply my technical expertise, analytical mindset, and problem-solving skills. I look forward to collaborating with professionals and contributing to innovative projects in machine learning and quantitative finance.
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