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Personal Background:

I have been in the AI/ML space since 2015 and have been in data science since 2014, leading all forms of AI-backed solutions including but not limited to Computer Vision, Natural Language Models (NLP), and most recently Large Language Models (LLMs) and Generative AI. I am currently a Principal AI Engineer at FICO, a 🌍 global data analytics company providing credit scoring solutions for financial institutions, consumers, and businesses worldwide.📊🔢 Previously, I was a Tech Lead at Vertex Inc, a global leading provider of tax technologies 📊💻. I have also been a Senior ML Engineer at an S&P 500 company, LabCorp, developing AI-driven solutions 🧠💻 in drug diagnostics, drug development, operations management, and financial decisions for our global leaders in life sciences 🌍🔬 (see Labcorp SEC filings here). I have also held positions such as enterprise-level Data Scientist at Bayer (a EURO STOXX 50 company), Quantitative Researcher (apprenticeship) at AQR (a global hedge fund pioneering in alternative quantitative strategies to portfolio management and factor-based trading), and Equity Trader at T3 Trading on Wall Street (where I was briefly licensed Series 56 by FINRA). I supervise a small fund specializing in algorithmic trading (since 2011, performance is I also run my own monetized YouTube Channel. Feel free to add me on LinkedIn. 🚀📈

Though I started in Finance, my AI career started from academic environment. I was a PhD student in Statistics at Columbia University from September of 2020 to December of 2021 📈🎓. I earned a B.A. in Mathematics, and an M.S. in Finance from University of Rochester 💼📊. My research interests are wide-ranging in representation learning, including Feature Learning, Deep Learning, Computer Vision (CV), and Natural Language Processing (NLP) 🤖👀. Additionally, I have some prior research experience in Financial Economics and Asset Pricing 💹📉.

Passion Project:

At leisure, I run W.Y.N. Associates, LLC, a registered legal entity in the state of New York, to pilot and drive for-profit personal passion projects.

Deployed Apps:

Since diving headfirst into the world of AI/ML back in 2016, I've been cooking up some cool apps to showcase my love for everything AI, ML, NLP, and GenAI! 🚀🤖 If you're keen to give them a whirl, simply hit that dropdown button to expand the app of your choice. Let's explore the future together! 🌟👩‍💻

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Research

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Teaching

Materials and courses related to teaching will be listed here.

Undergraduate/Graduate Teaching Appointments

University of Chicago Booth School of Business

Pace University

Columbia University

  • GU STAT 4203 Probability Theory (Fall 2021, Summer 2021, Fall 2020, Fall 2018) | Updated Notes.
  • GU STAT 5204 Statistical Inference (Fall 2021) | Updated (handwritten) Notes, Recitation here.
  • GU STAT 5241 Statistical Machine Learning (Spring 2019) | Repo here

Pre-college Teaching Appointments

Public Teaching Appointments

AI4ALL: General Machine Learning and Artificial Intelligence FREE Sources

  • The Fundamentals in Machine Learning | Link | This is an introduction course of machine learning: The Fundamentals of Machine Learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program.
  • Random Graphs and Complex Networks | Link
  • An introduction to Optimization on smooth manifolds | Link
  • Computer Age Statistical Inference: Algorithms, Evidence and Data Science | Link
  • Statistical Learning with Sparsity: The Lasso and Generalizations | Link
  • The Shallow and the Deep: A biased introduction to neural networks and old school machine learning | Link
  • User-friendly Introduction to PAC-Bayes Bounds | Link
  • Geometric Mechanics Part I: Dynamics and Symmetry | Link
  • Artificial Intelligence: Foundations of Computational Agents | Link
  • List of curated books | Link
  • AI4ALL | Github: 💻
    • Fundamentals in Neural Networks | Link | Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks, and (3) Recurrent Neural Networks.
    • Basics in Artificial Neural Networks | Link | The course introduces the fundamental building blocks of an Artificial Neural Network (ANN) model. With ANN being the leading milestone, the course lays the ground work for the audience into the field of Representation Learning.
    • Basics in Convolutional Neural Networks | Link | The course expands from ANN and introduces the fundamental building blocks of a Convolutional Neural Network (CNN). Advanced CNN models are also introduced to lead audience to the field of Representation Learning.
    • Image-to-Image Network Models | Link | The course investigates a higher level of network models that learn the intrinsic representation of image data. Such models learn to produce images rather than annotations or labels, which is different from previous courses. The materials lead the audience into the field of unsupervised learning.
    • Natural Language Processing | Link | The course investigates machine intelligence on language interpretations. Moreover, we investivate deep recurrent network models to study and potentially make predictions using language as input.
  • Online Extension Education (with Packt Publisher)
    • Introduction to FinTech Using R | Video, 💻
    • Fundamentals of Machine Learning | Video, 💻
    • Fundamentals of Neural Networks | Video, 💻
    • More to come.

Software Engineer: MLOps | LLMOps | DevOps | Full Stack

  • MLOps Deck | Link | The slide deck walks through some basic points of becoming a good MLOps or LLMOps engineer.
  • Software-as-a-Service (SAAS) Template | Link | The repository is hosted on HuggingFace and it walks you through a front-end User Interface (UI) in Streamlit application and a user authentication plugin.
  • SAAS Chatbot Template | Link | The repository walks through the main components of building a web-based application with a Llama3 chatbot. The app is upgraded with user authentication and supported with a private API key.

List of AI and ML Textbooks

Author Title Link
Richard S. Sutton and Andrew G. Barto Reinforcement Learning: An Introduction Reinforcement Learning: An Introduction (Web page)
A. Lindholm, N. Wahlström, F. Lindsten, and Th. Schön Machine Learning: A First Course for Engineers and Scientists Machine Learning: A First Course for Engineers and Scientists (Web page)
Benjamin Recht and Stephen J. Wright Optimization for Modern Data Analysis Optimization for Modern Data Analysis (Web page)
Wright & Ma High-Dimensional Data Analysis with Low-Dimensional Models High-Dimensional Data Analysis with Low-Dimensional Models (Web page)
D. Barber Bayesian Reasoning and Machine Learning Bayesian Reasoning and Machine Learning (Web page)
Osvaldo Martin, Ravin Kumar, and Junpeng Lao Bayesian Modeling and Computation in Python Bayesian Modeling and Computation in Python (Web page)
- Solution manual to Bayesian Essentials with R Solution manual to Bayesian Essentials with R (Web page, R code package)
Sir MacKay Information theory, inference and learning algorithms Information theory, inference and learning algorithms (Web page)
James, Witten, Hastie, and Tibshirani An Introduction to Statistical Learning with Applications in R An Introduction to Statistical Learning with Applications in R (Web page)
Jure Leskovec, Anand Rajaraman, Jeff Ullman Mining of Massive Datasets Mining of Massive Datasets (Web page)
Trevor Hastie, Robert Tibshirani, and Jerome Friedman The Elements of Statistical Learning: Data Mining, Inference, and Prediction The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Prof. Roman Vershynin High-Dimensional Probability: An Introduction with Applications in Data Science High-Dimensional Probability: An Introduction with Applications in Data Science (Web page)
Ian Goodfellow, Yoshua Bengio, Aaron Courville Deep Learning Deep Learning (Web page)

Collected Notes

Category Title Link
Economics Microeconomics Microeconomics
Macroeconomics Macroeconomics
Probability Theory Introduction to Probability Theory Introduction to Probability Theory
Probability Theory and Statistics Probability Theory and Statistics
Probability Theory Probability Theory
Statistical Inference Introduction to Statistical Reasoning Introduction to Statistical Reasoning
Statistical Inference Statistical Inference
Linear Regression Model Linear Regression Model
Applied Statistical Science, Data Science, and Deep Learning Intro to Scientific Computing and Data Science Intro to Scientific Computing and Data Science
Statistical Machine Learning Statistical Machine Learning
Deep Learning Notes Deep Learning Notes
Mathematics Partial Differential Equation Partial Differential Equation
Real Analysis Real Analysis
Money Management Securities Exchange Act (SEC), 1933 Securities Exchange Act (SEC), 1933
Securities Exchange Act (SEC), 1934 Securities Exchange Act (SEC), 1934
Security Analysis, 6E Security Analysis, 6E
Series 56 Guide Series 56 Guide
Asset Pricing Asset Pricing
Computer Science Principles of Computer Science Principles of Computer Science
Programming Languages and Algorithms Programming Languages and Algorithms
Management of Computer Networks Management of Computer Networks
Distributed Algorithms and Parallel Computing Distributed Algorithms and Parallel Computing