Biao Yin

• 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

• 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)

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.