Hi, I'm Nicholas

Data Scientist / Quantitative Analyst / Software Developer 

Experience

I have over 6 years of experience in the investment industry working as a Quantitative Analyst / Developer and Research Data Scientist. Recently, I have worked at a data scientist and developer at early stage fintech startups, rapidly deploying POCs. I have a passion for tackling challenging problems through data science and research-driven products.

Education

Master of Information and Data Science - UC Berkeley

BA in Economics - SUNY University at Buffalo

Certificate in Machine Learning for Analytics - University of Chicago

Certificates in Deep Learning and C/C++ Programming - UC San Diego

Skills

Python, SQL, Linux, Google Cloud Platform, C/C++, R Programming, Bash, D3.js, NLP, Deep Learning, Machine Learning, Causal Analysis, Research Design  

Selected Works From UC Berkeley MIDS

Neural Information Retrieval

Our Capstone project: https://saniyalakka19.github.io/#home

We researched improvements to the SPLADE-based Information Retrieval System. Through our research we determined reducing the size of BERT-generated query and document embeddings based on the SPLADE generated term-importance score resulted in significantly lower retrieval time while maintaining Recall and Mean Reciprocal Rank of the unadulterated system, improving overall system efficiency 

Comparison of Abstract Summarization Input Reduction Methods.pdf

Natural Language Processing with Deep Learning

I outline a novel approach to document reduction as a preprocessing step for transformer-based model inference and report ROUGE scores for a distill-bart model, trained on the abstractive summarization task.

Podcast Recommendation Systems

Applied Machine Learning

We created podcast recommender based on two architectures - Matrix Factorization and Deep Neural Network Two-Tower Model - using iTunes podcast data and report results.

ESG Impact on Retail Investors

Experiments and Causal Inference

We ran a Randomized Control Trial using Prolific, providing retail investors supplemental ESG information as treatment and measuring the change in their propensity to invest in a company as the treatment effect.

Final Technical Indicators Dashboard

Data Visualization

We created a dashboard, aimed at retail investors, for exploring the relationship between technical indicators and stock performance, using Tableau. Available here

Improving the Popularity of Massive Open Online Courses

Statistics for Data Science

A statistical analysis of the factors contributing to the popularity of Massive Open Online Courses, with the objective of proving guidance to stakeholders to improve the platform.

Understanding NFT Art Investor Motivations

Research Design

A mock research project proposal aimed at the management of Sotheby's. The project focuses on understanding the motivations of NFT art and traditional art investors, to better cross-sell assets to these groups.