Summary
Overview
Work History
Education
Skills
Accomplishments
Selected Papers (Full 21 Publications at http://tiny.cc/biao_yin_publications)
Timeline
Generic

Biao Yin

Worcester,MA

Summary

  • Experienced professionals seeking applied AI / data science, or research engineering roles in cross-domain projects at AI organizations with a Ph.D. from a solid tech school and applied research internships at U.S. and China AI giants
  • Inspiring Team Leader in Collaborative Project Success with leading and mentoring 100+ diverse students and researchers in 4 R&D AI projects for Material Science, resulting in 3 Deep Learning embedded softwares sponsored by US Army DEVCOM US Army Research Lab (ARL) in 5 years
  • 2 Oral Presentations at ICMLA 2023 about AlloyGAN (deepalloy.com) for a novel Domain-Promptable Generation and DeepSC-Edge for a new Edge-Guided Segmentation in Scientific Material Discovery using Deep Learning models
  • Cross-Domain Impact: Authored 21 impactful publications (including 1 patent; 10 first-authored), applying AI models across diverse domains: Materials (ARL), Healthcare (iFLYTEK CO.), Entertainment (Amazon Inc.), and Education

Overview

8
8
years of professional experience

Work History

Research Mentor (Founding Research Assistant)

WPI Data Science & DEVCOM Army Research Lab
Worcester, MA, USA
01.2019 - Current

ARL Mentor: Dr. Robert Jensen (https://www.linkedin.com/in/robert-jensen-phd-adhesives/)

  • Led and mentored over 100 students and researchers, majoring in material science, data science, computer science, business analytics, statistics, etc., handling 4 AI+Material projects on Adhesives, Corrosion, Aviation & Missile Technology, and Cold Spray sponsored by US Army Research Lab (ARL) and PPG over 4 years
  • Focused on real-world deep learning innovation to facilitate material discovery via automating alloy assessment, segmentation and generation on small domain data using GAN and Transformer with expert knowledge
  • Published top papers based on GAN series, Masked Auto-Encoder, and Domain Transfer; Mentored Ph.D. studies in CV (Optimization, Debiasing) and NLP (Rationality, Noisy Labeling) sponsored by ARL, DoE, and NSF.
  • Designed, developed, and under-commercialized production-ready AI platforms embedded with studied deep learning models with ARL, PPG, ASM, and NASA for smart discovery in corrosion and adhesion

Research Scientist II Intern

Alexa Entertainment, Amazon.com, Inc.
Boston, MA
05.2022 - 08.2022
  • Worked on Neural Language Understanding research using real customer data in daily Alexa Device traffic
  • Successfully achieved a prototype moving an offline large personalization model (~hours per customer) to serve dynamic rewrite recommendations in real-time (~microsecond per utterance) with a high impact
  • Proposed self-supervised methods for Dynamic Inference to solve a case in traffic with a paper-ready report
  • Helped mentoring interns with research and engineering position; instructed and organized mid-term presentations

Core Technology Researcher (Intern), AI Research

Core R&D, iFLYTEK CO. (China's AI Giant)
Hefei, China
03.2018 - 08.2018
  • Developed 100+ high-efficient features to automate assessment of early-screening Alzheimer's Disease via patients’ Handwriting, Speech, NLP, and Facial Expressions in cognitive questionnaires with medical doctors
  • Engined the clinical diagnoses using Machine Learning models such as CART, RF, SVM, CNN with 90%+ accuracy; built a software embedding the models now utilized in USTC-Associated Hospitals and issued AI-related patents
  • Collaborated with research teams from MIT CISCIL and USTC on multi-modal human cognition projects

Big Data Engineer (Intern)

Consumer Business Group, iFLYTEK CO.
Hefei, China
02.2018 - 03.2018
  • Created 10 effective features to assess consumers’ credits from Mobile App behaviors in 200 million daily users’ devices using Hive in Xshell on a distributed database storing real user records on real products
  • Completed a feasibility study of Online Financial Risk Control after analyzing DMP data from Consumers’ Contexts, App behaviors, and Typed Texts using Genism Word2Vec, LDA, Scikit-learn and Seaborn

Research Assistant

ASSISTments
Worcester, MA, USA
05.2016 - 12.2017
  • Proved significant business impact of an online educational platform via statistical learning on 200+ Randomized Control Trials such as: human involvement, video or text hints, handwriting or typing, etc.
  • Published papers finding Heterogeneous Treatment Effects for personalized learning on the platform using causal trees; Narrowed down confidence interval with massive human-related features
  • Built 24 essential features based on 3 kinds of students on 8 types of assignments from 200+ SQL tables; reconstructed the lab database for data mining use; provided contributive statistical analysis in NSF projects

Education

Ph.D. - Data Science

Worcester Polytechnic Institute
05.2024

Master of Science - Statistics

George Washington University
Washington D.C.
05.2016

Bachelor of Science - Statistics

Anhui University of Finance And Economics
Bengbu City
06.2014

Skills

  • Languages: Python, R, Javascript, HTML, Latex
  • Machine Learning: CV, NLP, Human-in-Loop, XAI, Self-Supervision, Attention, Generalization, Domain Transfer, Causality, Multimodality, GAN, Diffusion, LLaMA2, Transformer, MAE, Regularization
  • Frameworks: PyTorch, MATLAB Toolbox, OpenCV, Hugging Face, Pandas, Numpy, Sklearn, TensorFlow, Keras, Tableau, SAS, D3, ReactJS, VIM, Docker, Turing, Hive, Xshell, Spark, CUDA Toolkit, GitLab, GrantaDB, MySQL, PgAdmin, GPT

Accomplishments

  • Invited Seminar Speaker for Applied AI Research, UMASS@Boston (2024.1)
  • Invited Speaker in 4 times at Materials Recovery Technology for Defense Supply Resiliency, WPI (2022-)
  • Session Chairs at Top AI Conferences: CIKM 2023, UK (2023.10); ICMLA 2023, US (2023.12)
  • Program Committees: CIKM, DLAV5; Other Reviewers: CVPR, SDS, IEEE BigData, IEEE Transactions on AI
  • NLP Teacher in a Top AI summer school (2018.8): students got offers from PricetonU, UCBerkley, CMU, etc.
  • Data Analyst Volunteer on microfinance marketing in developing countries at FINCA International (2015.6-8)

Selected Papers (Full 21 Publications at http://tiny.cc/biao_yin_publications)

1. [In progress] Yin, B., et al., Generative Self-Supervised Learning for Ordinal Regression on Long-tailed Data.
2. [IEEE ICMLA 2023] Yin, B., et al., AlloyGAN: Domain-Promptable Generative Adversarial Network for Generating Aluminum Alloy Microstructures. In 22nd IEEE International Conference on Machine Learning and Applications, 2023. (https://deepalloy.com/)
3. [IEEE ICMLA 2023] Yin, B., et al., DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses. In 22nd IEEE International Conference on Machine Learning and Applications, 2023.
4. [ACM CIKM 2023] Yin, B., et al., MOSS: AI Platform for Discovery of Corrosion-Resistant Materials. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023.
5. [IEEE ICMLA 2022] Josselyn, N., Yin, B., et al., Transferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation, IEEE Intl. Conference on Machine Learning and Applications, 2022.
6. [Issued Patent 2022] Xu F., Yin, B., et al., Automatic discriminant analysis of graphic problems based on tracked points, CN Patent CN201811352610.2 A., issued at 2022.
7. [BMVC 2021] Yin, B., et al., Corrosion image data set for automating scientific assessment of materials. In British Machine Vision Conference (BMVC), 2021.
8. [ACL 2020] Sen, C., Hartvigsen, T., Yin, B., et al., Human attention maps for text classification: Do humans and neural networks focus on the same words?., the Annual Meeting of the Association for Computational Linguistics, 2020.
9. [IEEE Big Data 2020] Yin, B., et al., A. Corrosion assessment: Data mining for quantifying associations between indoor accelerated and outdoor natural tests., IEEE Intl. Conference on Big Data, 2020.
10. [ACM L@S 2017] Yin, B., et al., Observing Personalizations in Learning: Identifying Heterogeneous Treatment Effects Using Causal Trees. In the Fourth, ACM Conference on Learning @ Scale, MIT, MA, et al., Causal Forest vs. Naïve Causal Forest in Detecting Personalization: An Empirical Study in ASSISTments., Intl. Conference on Educational Data Mining, 2017.

Timeline

Research Scientist II Intern

Alexa Entertainment, Amazon.com, Inc.
05.2022 - 08.2022

Research Mentor (Founding Research Assistant)

WPI Data Science & DEVCOM Army Research Lab
01.2019 - Current

Core Technology Researcher (Intern), AI Research

Core R&D, iFLYTEK CO. (China's AI Giant)
03.2018 - 08.2018

Big Data Engineer (Intern)

Consumer Business Group, iFLYTEK CO.
02.2018 - 03.2018

Research Assistant

ASSISTments
05.2016 - 12.2017

Ph.D. - Data Science

Worcester Polytechnic Institute

Master of Science - Statistics

George Washington University

Bachelor of Science - Statistics

Anhui University of Finance And Economics
Biao Yin