About Me

As a coding enthusiast, I am committed to harnessing the power of technology and data to unlock untapped potential in businesses and organizations. Currently, I am pursuing a Master of Science degree in Information Systems at Cornell University, Cornell Tech. Here, not only am I gaining invaluable knowledge from world-class faculty but also collaborating with exceptional peers, which amplifies my technical and practical abilities.

Prior to Cornell, I earned my Bachelor of Management (2019-2023), summa cum laude, at Beijing Foreign Studies University, majoring in Information Systems and Information Management. My dedication to academic rigor and innovative thinking was recognized through multiple scholarships and honors. In particular, I was awarded the Beijing Municipal Outstanding Graduate (Top 5%) and the Beijing Municipal Outstanding Thesis (Top 0.5%), signifying my commitment to excellence.


MethodologyOptimization (Simplex Method, Genetic Algorithm), Queueing Theory
ApplicationBack-End Software Development, Inventory Planning, Production Scheduling, Capacity Management

In addition to academics, I sought to gain practical experience in industries. From July 2022 to October 2022, I interned at the Solution and Service Group of Lenovo, developing back-end algorithms for production-related information systems. These systems include MARS (chipset inventory forecasting system), SMT Optimization system (subsystem for PCBA manufacturing planning), and Advanced Planning System (production scheduling system), which profoundly enriched my understanding of optimization, as well as domain knowledge in the area.

After the internship at Lenovo, I interned at Beijing Daxing International Airport Terminal, developing a queueing-theory-based forecasting system for passenger check-in time to facilitate capacity management of the airport.


MethodologyAgent-Based Modeling, Structural Equation Modeling, Machine Learning (Random Forest, K-means, PCA, Imputation Methods)
ApplicationSupply Chain Management, Innovation Management, Comprehensive Evaluation

During my undergraduate studies, I did several research under Prof. Xiaoyu Ma’s and Prof. Zhou He’s guidance, which cultivated my research interest in applying machine learning, optimization, and simulation methods to supply chain management (e.g., optimize process efficiency, centralize production monitoring, and generate just-in-time insights). In particular, I believe that by simulating supply chain networks and developing machine learning algorithms to optimize for resilience and efficiency, firms can better position themselves to respond to disruptive events and accommodate customer demands.

I have several working papers that employ agent-based modeling and structural equation modeling to identify high-risk nodes in supply chain networks and explore factors (e.g., the reconfigurability of networks) affecting resilience, therefore deriving tactics to alleviate the damage and build resilience.

Beyond supply chains, I also have experience in innovation management, machine learning, and comprehensive evaluation. My academic work in innovation management has been published in Technological Forecasting and Social Change (10.1016/j.techfore.2023.122736). It uses machine learning methods to construct a novel index system and evaluate national innovation performance.

Ethan Y. Hao

September 1, 2023