Jie Ding Biography

Jie Ding

Chief Executive Officer , MorphMind

Jie Ding

Jie Ding is a Chinese-American researcher, educator, and entrepreneur recognized for his work in statistics, machine learning, and data science. He is an Associate Professor in the School of Statistics at the University of Minnesota Twin Cities and the co-founder and CEO of the AI startup MorphMind.     His research focuses on the intersection of mathematical theory and practical, scalable artificial intelligence.

Early life and education

Ding completed his undergraduate studies at Tsinghua University in Beijing. He later moved to the United States to continue his graduate education at Harvard University, where he earned his PhD in Engineering Sciences from the John A. Paulson School of Engineering and Applied Sciences (SEAS).

Academic career

Following his doctoral studies, Ding joined the University of Minnesota. He was appointed as an Assistant Professor in the School of Statistics and later promoted to Associate Professor. In addition to his primary appointment, he holds graduate faculty status in the Department of Electrical and Computer Engineering and the Data Science Program. He has also served as an Amazon Scholar, contributing to research on foundation model training.

Ding is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and serves on the editorial boards for prominent journals in information theory and signal processing.

MorphMind

In 2025, Ding co-founded MorphMind, a technology company focused on "steerable AI." As CEO, Ding has positioned the company to address the gap between the power of large-scale AI models and the need for human-centric control.

MorphMind’s platform is designed to:

Facilitate Collaboration: Enable users to recruit, question, and steer teams of AI specialists rather than relying on a single, opaque model.

Break Down Workflows: Decompose complex research and business processes into manageable, inspectable steps.

Ensure Transparency: Provide a traceable trail of sources and computations, allowing users to guide and correct AI reasoning at every stage.

Research interests

Ding’s academic research aims to uncover the foundational principles of data science to improve the interpretability, reliability, and safety of machine learning systems. Key areas include:

Deep Learning Foundations: Investigating "over-parameterization" to understand why large-scale models generalize effectively to new data.

Agentic AI: Developing frameworks where AI systems can autonomously plan and execute complex tasks while remaining under human supervision.

AI Safety: Researching robust methods for watermarking and preventing model-stealing attacks.

Information Theory: Applying mathematical frameworks to optimize data transmission and processing efficiency.

Awards and recognition

Ding is the recipient of several honors, most notably the National Science Foundation (NSF) CAREER Award, which recognizes early-career faculty who excel as both researchers and educators.    His work has also been supported by awards from organizations such as the U.S. Army, Cisco, AWS, and Meta.

Selected publications

Ding frequently presents his research at major conferences, including NeurIPS (Conference on Neural Information Processing Systems) and ICML (International Conference on Machine Learning). His work is regularly published in leading journals such as:

Journal of Machine Learning Research (JMLR)

IEEE Transactions on Information Theory

IEEE Transactions on Signal Processing