About

Dr. Jen-Yi Huang is the Dale A. Seiberling Endowed Professor of Food Engineering with a joint appointment in the Department of Food, Agricultural and Biological Engineering at The Ohio State University. He holds a B.S. in bio-industrial mechatronics engineering and an M.S. in food science and technology from National Taiwan University, and earned his doctorate in chemical engineering and biotechnology from the University of Cambridge. Following his doctoral studies, he worked as a research fellow at the National University of Singapore. He began his faculty career at Purdue University in 2016, where he was promoted to full professor in 2025. Dr. Huang’s research focuses on developing novel food processing technologies aimed at reducing energy and water use and minimizing food waste, while ensuring the production of high-quality, nutritious, and safe foods. He is also interested in applying life cycle assessment approaches to transform food systems and promote long-term sustainability. His research program has received nearly $13 million in funding from federal agencies and industry, including a notable $10 million USDA project called When Blue is Green (BiG), which aims to sustainably increase seafood production and consumption in the Midwest through aquaponics innovation. In 2023, he received the Purdue Researcher Recognition Award.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Data Mining
• Distributed computing
• Information Retrieval
• Algorithm
• Computer network
• Telecommunications
• World Wide Web
• Real-time computing

Selected publications

• Geographical POI recommendation for Internet of Things: A federated learning approach using matrix factorization

  International Journal of Communication Systems · 2022 · 84 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Data Mining

  Summary With the popularity of Internet of Things (IoT), Point‐of‐Interest (POI) recommendation has become an important application for location‐based services (LBS). Meanwhile, there is an increasing requirement from IoT devices on the privacy of user sensitive data via wireless communications. In order to provide preferable POI recommendations while protecting user privacy of data communication in a distributed collaborative environment, this paper proposes a federated learning (FL) approach of geographical POI recommendation. The POI recommendation is formulated by an optimization problem of matrix factorization, and singular value decomposition (SVD) technique is applied for matrix decomposition. After proving the nonconvex property of the optimization problem, we further introduce stochastic gradient descent (SGD) into SVD and design an FL framework for solving the POI recommendation problem in a parallel manner. In our FL scheme, only calculated gradient information is uploaded from users to the FL server while all the users manage their rating and geographic preference data on their own devices for privacy protection during communications. Finally, real‐world dataset from large‐scale LBS enterprise is adopted for conducting extensive experiments, whose experimental results validate the efficacy of our approach.

  DOI

• AoI-aware energy control and computation offloading for industrial IoT

  Future Generation Computer Systems · 2022 · 105 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Distributed computing
  DOI

• Dynamic Admission Control and Resource Allocation for Mobile Edge Computing Enabled Small Cell Network

  IEEE Transactions on Vehicular Technology · 2021 · 79 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Distributed computing

  Mobile edge computing (MEC) has recently risen as a promising paradigm to meet the increasing resource requirements of the terminal devices. Meanwhile, small cell network (SCN) with MEC has been emerging to handle the exponentially increasing data traffic and improve the network coverage, and is recognized as one key component of the next generation wireless networks. However, with the growing number of terminal devices requiring computation offloading to the edge servers, the network would be heavily congested and thus the performance would be degraded and unbalanced among multiple devices. In this paper, we propose the joint admission control and computation resource allocation in the MEC enabled SCN, and formulate it as a stochastic optimization problem. The goal is to maximize the system utility combining the throughput and fairness while bounding the queue. We decouple the original problem into three independent subproblems, which can be solved in a distributed manner without requiring the system statistical information. An admission control and computation resource allocation (ACCRA) algorithm is designed to obtain the optimal solutions of the subproblems. Theoretical analysis proves that the ACCRA algorithm can achieve the close-to-optimal system utility and reach the arbitrary tradeoff between the utility and the queue length. Experiments are conducted to validate the derived analytical results and evaluate the performance of the ACCRA algorithm.

  DOI

Frequent coauthors

• Ying Chen

  Beijing Information Science & Technology University

  36 shared

• Chuang Lin

  INTI International University

  26 shared

• Ronald X. Xu

  University of Science and Technology of China

  22 shared

• Sikandar Ali

  14 shared

• Bo Cheng

  14 shared

• Yuan Wu

  University of Macau

  13 shared

• Leye He

  Third Xiangya Hospital

  12 shared

• Wei‐Jie Song

  Central South University

  12 shared

Education

• Ph.D., Food Science and Technology

  The Ohio State University

  2002

• M.S., Food Science and Technology

  National Taiwan University

  1997

• B.S., Food Science and Technology

  National Taiwan University

  1995

Awards & honors

• Purdue Researcher Recognition Award (2023)

Similar researchers at Ohio State University

• Guo-Liang Wangh-115
• Brad J. Bushmanh-101
• Christopher Hillh-97
• David Bromwichh-93
• K. Luan Phanh-92