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Yin's Profile:

Brief

Yiqiao 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. He is currently a Tech Lead at Vertex Inc, a global leading provider of tax technologies 📊💻. Previously, he 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). He has 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 he was briefly licensed Series 56 by FINRA). He supervises a small fund specializing in algorithmic trading (since 2011, performance is here) in equity market, cryptocurrencies, and real estate investment. He also runs his own monetized YouTube Channel. Feel free to add me on LinkedIn. 🚀📈

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

When Yiqiao was in PhD program at Columbia University, he investigated heavily in a domain known as dimension reduction and he focused on developing tools for scientists to understand the important features that affect the prediction outcome. He has multiple papers published on the topic known as I-score or Influence Score (Influence Measure) that is a non-parametric dimension reduction technique on supervised learning. The work gave him the foundation of statistical machine learning and experience of conducting independent research.

For more published work by Yiqiao, please check out the site https://www.y-yin.io/ under 'Research'.

View about stock market:

Yiqiao believed that stock market is mostly fairly efficient. Many research groups and companies are doing great things out there with advanced tools. However, market does get "noisy" once in a while and that breeds opportunity. Yiqiao personally trades off a momentum strategy and he has his own market timing algorithm. This app https://huggingface.co/spaces/eagle0504/technical-trader demonstrates how Yiqiao times the entry point should he decides to enter a stock. This app https://huggingface.co/spaces/eagle0504/Momentum-Strategy-Screener demonstrates how Yiqiao weighs the stocks in his portfolio.

View about AI:

Yiqiao has good faith in today's advancement of AI technology and is a big supporter of AI-backed technology to boost business operation and enhance corporate strategy. Many clients and companies Yiqiao worked with in the past led him to conclude that 'AI alone may falter and stray, but built around a corporate strategy, it paves the way.'

Apps

Yiqiao built a series of AI-backed apps:

Yin's Research and Watchlist on SEC Filings:

Representation Learning

Papers

  • 2024-04 | Yiqiao Yin (2024), Vision Augmentation Prediction Autoencoder with Attention Design (VAPAAD), arXiv preprint arXiv:2404.10096: ArXiv
  • 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
  • 2022-02 | Yiqiao Yin (2022), XAI in Healthcare: A Novel XAI Approach Towards Radiology Image Classification: AAAI 22' Workshops, W37 Home, Poster, Presentation | Venue details: AAAI 22' Schedule Home, AAAI 22' Workshop Page (My talk is in W37: Trustworthy AI in Healthcare) | Updated slides

Selected Awards/Paper/Work from My Students

  • 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, see slides here
  • 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

Economics

  • Yin (2017), Art of Money Management: PDF
  • Yin (2016), Trade Dynamics with Endogenous Contact Rate: PDF

Empirical Asset Pricing

  • Yin (2016), Empirical Study on Greed: PDF
  • Yin (2015), Empirical Study on MVBS: PDF
  • Yin (2015), Cross-sectional Study on Stock Returns to Future Expectation Theorem: PDF
  • Yin (2015), Alternative Empirical Study on Market Value Balance Sheet: PDF
  • Yin (2014), How to Understand Future Returns of a Security: PDF

Trading

  • Yin (2020), Buy Signal from Limit Theorem: PDF
  • Yin (2020), Buy Signal from Limit Theorem: PDF
  • Yin (2017), Time Series Analysis on Stock Returns: PDF
  • Yin (2016), Martingale to Optimal Trading: PDF
  • Yin (2016), Anomaly Correction by Optimal Trading Frequency: PDF, Slide
  • Yin (2016), Absolute Alpha with Moving Averages: PDF, Slide
  • Yin (2016), Absolute Alpha with Limited Leverage: PDF
  • Yin (2015), Absolute Alpha by Beta Manipulation: PDF