This is Yiqiao Yin, and welcome to my personal site.
Personal Background:
I have been in the AI/ML space since 2015, 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 Lead Developer at Vertex Inc, a global leading provider of tax technologies 📊💻.
Previously, I was 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 here) in equity market, cryptocurrencies, and real estate investment. I also run my own monetized YouTube Channel.
Feel free to add me on LinkedIn. 🚀📈
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! 🌟👩💻
If any of the apps is not showing up, you can always find the source here.
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Detailed Pages:
Feel free to click on any of the following buttons and view the content.
When it comes to filtering stocks using Carhart's 4-factor model (see this paper, an algorithmic trading strategy based on the famous Fama-French 3-factor model), investors can identify potential investment opportunities by examining four key factors that have been shown to explain a significant portion of the variation in stock returns. These factors include market risk, size, value, and momentum. The momentum factor, in simple terms, refers to the tendency of stocks that have recently performed well to continue performing well in the near future, and vice versa for stocks that have recently underperformed. Essentially, momentum is the measure of a stock's recent price trend, with strong upward trends indicating positive momentum and strong downward trends indicating negative momentum.
2024-04 | Vivian Liu, Yiqiao Yin (2024), Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training, arXiv preprint arXiv:2404.01157: [ArXiv]
2024-03 | Keshav Rangan, Yiqiao Yin (2024), A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge, arXiv preprint arXiv:2402.17081: [ArXiv]
2023-02 | Xuan Di, Yiqiao Yin, Yongjie Fu, Zhaobin Mo, Shaw-Hwa Lo, Carolyn DiGuiseppi, David W. Eby, Linda Hill, Thelma J. Mielenz, David Strogatz, Minjae Kim, Guohua Li (2023), Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score (Feb., 2023), Artificial Intelligence in Medicine, 102510, [Print]
2023-01 | Jaiden Shraut, Leon Liu, Jonathan Gong, Yiqiao Yin (2023), A Multi-Output Network with U-net Enhanced Class Activation Map and Robust Classification Performance for Medical Imaging Analysis (Jan., 2023), Discover Artificial Intelligence, 3(1): [Print, Media]
2022-11 | Kieran Pichai, Benjamin Park, Aaron Bao, Yiqiao Yin (2022), Automated Segmentation and Classification of Aerial Forest Imagery, Analytics, 1(2), 135-143: [Print, media]
2022-08 | Yiqiao Yin (2022+), AI4ALL and K12 AI Education: [Preprint]
2022-01 | Shaw-hwa Lo and Yiqiao Yin (2022), An I-score Review Paper - A Novel Approach to Adopt Explainable Artificial Intelligence (Jan., 2022), Adv. Mach. Learn. Art. Inte., 3(1), 01-11: [Print]
2021-12 | Shaw-hwa Lo and Yiqiao Yin (2021), An Interaction-based Recurrent Neural Network (IRNN) (Dec., 2021), Mach. Learn. Knowl. Extr., 3(4), 922-945: [ArXiv, Print]
2021-12 | Shaw-hwa Lo and Yiqiao Yin (2021), An Interaction-based Convolutional Neural Network (ICNN) (Dec., 2021), Algorithms, 14(11), 337: [ArXiv, Print]
2021-12 | Shaw-hwa Lo and Yiqiao Yin (2021), A Novel Interaction-based Method (Dec., 2021), Discover Artificial Intelligence, 1(16): [ArXiv, Print]
Conferences
2024-01 | Xuan Di, Yiqiao Yin, Yongjie Fu, Zhaobin Mo, Shaw-Hwa Lo, Carolyn DiGuiseppi, David W. Eby, Linda Hill, Thelma J. Mielenz, David Strogatz, Minjae Kim, Guohua Li (2024), Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score, The 103rd Transportation Research Board (TRB) Annual Meeting: [Link]
2023-04 | Leon Liu, Yiqiao Yin (2023), Towards Explainable AI on Chest X-Ray Diagnosis Using Image Segmentation and CAM Visualization (Mar, 2023), FICC 2023: Advances in Information and Communication, pp 659-675: [Link, Print]
2022-11 | Leon Liu, Yiqiao Yin (2022), Towards Explainable AI on Chest X-Ray Diagnosis using Image Segmentation and CAM Visualization (Nov, 2022), Third Symposium on Knowledge-Guided ML (KGML-AAAI-22), Held as part of AAAI Fall Symposium Series (FSS) 2022 in November: [Link, scheduled on Day 2 Session 5 at 2PM EST at Westin Arlington Gateway, Room Fitzgerald D, Arlington, VA]
2022-10 | Yiqiao Yin (credit to Edna Williams) (2022), A Machine Learning based Enrollment Forecasting System (Oct, 2022), OHDSI: [OHDSI, Oct. 14 Agenda]
2023-12 | Kieran Pichai Yiqiao Yin as mentor (2023), A Retrieval-Augmented Generation Based Large Language Model Benchmarked On a Novel Dataset, Journal of Student Research, 12(4): [Print]
2023-12 | Yash Bingi, Yiqiao Yin as mentor (2023), Using Machine Learning to Classify Fetal Health and Analyze Feature Importance, 1st Place by US Agency for International Development in the Regeneron International Science and Engineering Competition and the 4th Place in the Massachusetts Science & Engineering Fair (MSEF): [Site]
2023-05 | Jonathan Gong, Yiqiao Yin as mentor (2023), COVID-19 Chest X-ray Image Classification and Improved U-Net Segmentation, Excellence Award - Silver at the Canada-Wide Science Fair (CWSF): [Site]
2023-03 | Aarav Monga, Yiqiao Yin as mentor (2023), A For-Profit Model of Microcredit, Journal of Student Research, 11(1): [Print]
Books
2023-12 | Yiqiao Yin (2023), AI Decoded: Making Sense of Deep Learning and Generative AI (Dec., 2023): [Book sale on Amazon]
2023-06 | Yiqiao Yin (2023), Understand Asset Prices Using Empirical Studies (Jun., 2023): [Book sale on Amazon]
2022-05 | Yiqiao Yin (2022), Towards Explainable Artificial Intelligence Using Interaction-based Representation Learning (May, 2022): [Book sale on Amazon]
2022-04 | Yiqiao Yin (credit to Professor Shaw-hwa Lo) (2022), Fundamentals of Interaction-based Learning (Apr., 2022): [Book sale on Amazon]
Assignment Evaluations: Instead of homework or assignments, please use the following forms to communicate progress report.
Orientation: Please fill this form out by the first session of the program.
Session Survey: Please fill this form out at the end of each session.
Machine Learning General | Please feel free to check out the ML General repo I have been using for undergraduate teaching.
Deep Learning Notebooks | Please feel free to check out the Deep Learning notebooks I have been using for undergraduate and pre-college level students.
Lead Curriculum Design at Veritas AI | Please feel free to check out the company's website. It's founded by Harvard PhD students.
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.
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.