Tiancheng Ted He

About Tiancheng Ted He

I sit at the intersection of human physiology, advanced computation, and clinical breakthrough. As a Director of Digital Science, I specialize in transforming complex \"digital noise\"—from wearable sensors and medical imaging—into validated, regulatory-grade clinical endpoints that accelerate the delivery of life-changing medicines.My approach combines deep technical rigor in AI/ML model development with the strategic oversight required to navigate the complexities of global drug development. I don\'t just build models; I architect the ecosystems that allow AI to scale safely and effectively within a clinical environment.Specialties: • AI modeling – Developed artificial intelligence and deep learning models for informatics projects using Tensorflow, Pytorch, Keras, Scikit-learn, Pandas, Numpy, etc.• Python – Implemented developed AI models for real project uses.• SQL – Developed an enterprise informatics data warehouse to support evidence-based uses.• PHP, Javascript, HTML – Developed the web-based application for the artificial intelligence and machine learning models.• Visual C++, MFC – Primary language for object-oriented, robust, and fast algorithm development / implementation.• Objective C - Developed cell phone application tool for improving enterprise operations and service qualities.•.NET, C#- Designed, coded, and maintained several large, multi-threaded applications and DLLs.• ITK/VTK/FLTK – Developed interactive graphical displays for developed software.• Matlab/Simulink – Used extensively for prototyping and analysis in research and coursework.Paper reviewer- Computerized Medical Imaging and Graphics- Journal of Biomedical and Health Informatics- European Journal of Radiology- Computational and Mathematical Methods in Medicine- SCIENCE CHINA Information Sciences- Journal of Medical Imaging and Health Informatics - International Conference on Medical Image Computing and Computer Assisted Intervention(MICCAI)- International Conference of the Engineering in Medicine and Biology Society- ACM Conference on Bioinformatics, Computational Biology and Biomedicine- International Symposium on BIOMEDICAL IMAGING: From Nano to Macro

Experience

1. Director, Digital Science

   AbbVie

   Jun 2025 — Present · North Chicago, IL, US

   Strategic AI Digital Transformation - Architected the enterprise-wide Digital Health roadmap, integrating AI-driven digital endpoints into the medicine development lifecycle to accelerate clinical trial timelines and improve patient-centric outcomes. Spearheaded the Digital Strategy vision, serving as the primary authority for selecting and validating AI-enabled wearable sensors and imaging tools, ensuring alignment with global clinical and commercial goals.AI Model Innovative Development - Directed the end-to-end development of advanced AI/ML models, leveraging Deep Learning and Signal Processing to extract high-fidelity digital biomarkers from complex wearable and imaging datasets. Operationalized AI utilization for clinical validation, applying state-of-the-art predictive analytics to improve the sensitivity and specificity of digital endpoints, directly impacting the success of clinical study designs. Championed the adoption of Generative AI and Foundation Models to streamline data synthesis and automate technical reporting, significantly reducing the \"time-to-insight\" for cross-functional R&D teams.Organizational Scaling Excellence - Built and scaled a world-class multidisciplinary organization of AI researchers, data scientists, and engineers. Fostered a culture of technical rigor in MLOps and signal processing, ensuring the deployment of scalable, production-ready models. Co-managed the Digital Science Lab and established the foundational frameworks for AI model training, validation, and benchmarking used across the global portfolio.Regulatory AI Governance - Acted as the lead technical representative in regulatory agency meetings, defending the scientific integrity and transparency of AI/ML algorithms used in clinical submissions and CSRs. Engineered robust AI governance and risk-mitigation plans, defining global standards for data integrity, model bias detection, and algorithmic validation to ensure the scalability of digital health initiatives.

Education

• Shenzhen University

  Doctor of Philosophy (Ph.D.), Signal processing

  2007 — 2010