Our Faculty, Staff & Students

Contact Us

Krenicki Professor of Biomedical Engineering and Department Chair

Dr. Ki Chon
Phone: (860) 486-4767
Email: kchon@engr.uconn.edu


Harley Erickson
Program Assistant II & Graduate Admissions Assistant
Phone: (860) 486-5838
E-mail: harley@engr.uconn.edu

Lisa Ephraim
Undergraduate Academic Advisor
Phone: (860) 486-0163
E-mail: lisae@engr.uconn.edu

Jennifer Desrosiers
Financial Assistant II
Phone: (860) 486-0116
E-mail: jennd@engr.uconn.edu

Amanda Sierpinski
Financial Assistant II (Grants and Contracts) 
Phone: (860) 486-3869
E-mail: ams13020@engr.uconn.edu


Main Office Address

Biomedical Engineering Department
A.B. Bronwell Building, Room 217
260 Glenbrook Road, Unit 3247
University of Connecticut
Storrs, CT 06269-3247
Phone: (860) 486-5838
Fax: (860) 486-2500

Shin, Yong-Jun

Yong-Jun Shin, M.D., Ph.D.
Assistant Professor
Biomedical Engineering
Research Interests:
Modeling intelligent features (adaptation and robustness) of biological systems, Cloud-enabled smart bioMEMS, Internet of Cells (IoC), Internet of Microscopes (IoM)
Research Group:
UCONN Computational and Systems Medicine Lab (CSMLab)  http://csml.uconn.edu
260 Glenbrook Road, Unit 3247 
University of Connecticut
Storrs, CT 06269-3247
Office Phone: (860) 486-7153  
MD, Seoul National University College of Medicine
MS PhD (Electrical Engineering), University of Texas at Dallas
PostDoc (Electrical and Computer Engineering), Cornell University

Research Summary:

All living systems, including microscopic cells, live in environments that are uncertain, dynamically-changing, and even hostile. However, it is remarkable that these systems survive and achieve their goals by exhibiting intelligent features such as adaptation and robustness. Using engineering principles and tools (e.g., control and estimation theory), which can simulate “smart” behaviors of individual cells coupled at the molecular level, my lab is developing Engineering-as-a-Service (EaaS) for studying intelligent cell population dynamics. EaaS is a community-based service-oriented architecture (SOA) that provides modular, interoperable, and reusable computational components as web services for the study of complex intelligent systems. Experimental validation is critical for any modeling and my lab is also developing a new experimental platform for in silico feedback control of protein levels in multiple cells, which can be integrated with digital microfluidics/bioMEMS (MicroElectroMechanical System) for massively-parallel high-throughput experimentation. It is hoped that eventually new medical diagnosis and treatment methods can be designed and tested using this platform.

Information on current projects can be found on my website.

Selected Publications:
  • Yong-Jun Shin (2016), “Digital Signal Processing and Control for the Study of Gene Networks,” Scientifc Reports, in press.
  • Yong-Jun Shin and Bahareh Mahrou (2014), “Modeling Collective & Intelligent Decision Making of Multi-cellular Populations,” The Conference Proceedings of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 334-337.
  • Yong-Jun Shin (2013), “Parallel Computing for Adaptive Multi-Cellular Gene Network Modeling”, Proceedings of the 1st IEEE Global Conference on Signal and Information Processing, pp.103-104 (invited article).
  • Yong-Jun ShinAli H. Sayed, Brandon Hencey, Steven M. Lipkin, Xiling Shen (2013), “Post-translational regulation enables robust p53 regulation“, BMC System Biology, 7:83.
  • Yong-Jun Shin, Ali H. Sayed, Xiling Shen (2012), “Adaptive Models for Gene Networks“, PLoS ONE 7(2): e31657.
  • Yong-Jun Shin, Brandon Hencey, Steven M. Lipkin, Xiling Shen (2011), “Frequency Domain Analysis Reveals External Periodic Fluctuations Can Generate Sustained p53 Oscillation, PLoS ONE 6(7): e22852.
  • Yong-Jun Shin, Leonidas Bleris (2010), “Linear Control Theory for Gene Network Modeling“, PLoS ONE 5(9): e12785.
  • Yong-Jun Shin, Mehrdad Nourani (2010), “Statecharts for Gene Network Modeling”, PLoS ONE 5(2): e9376.
  • Yong-Jun Shin, Jeong-Bong Lee (2010), “Machine Vision for Digital Microfluidics” , American Institute of Physics (API) Review of Scientific Instruments, 81:014302 (selected for the February 1, 2010 issue of Virtual Journal of Biological Physics Research as one of the Top 20 most downloaded articles in Feb. 2010).