Advisor: Martin Yarmush
Alvin Chen conducted his undergraduate studies at Rutgers University, where he received his B.S. in Biomedical Engineering and B.A. in Biological Sciences with a concentration in Neuroscience. Early in his undergraduate studies, Alvin developed an interest in the use of computational approaches to solve challenging biomedical problems. He spent several years in the laboratory of Dr. Li Cai, where he applied bioinformatics and image analysis tools to study the neuroinflammatory and antioxidative activities and differential regulation of Nrf2-mediated genes in mice. Alvin later joined the laboratory of Dr. Martin Yarmush, where he worked on developing in silico and in vitro models for the high-throughput prediction of metabolic and dose responses in drug candidates. In addition to his main project, Alvin was able to apply his computational background to assist in multiple aspects of the ongoing tissue engineering and cell/molecular biology research in the lab, including developing: algorithms to recognize and track MSC migration through a multi-channel microdevice; methods to quantify fluorescence-binding in flow cytometry experiments; techniques for multifactorial analysis of skin sensitizers in allergic contact dermatitis; segmentation approaches to delineate nuclei morphology in liver and brain scaffolds; image analysis approaches to quantify hepatic micro and macrosteatosis in histopathology slides; and machine learning methodologies to analyze complex and large-scale neurocognitive and neuroimaging data in heterogeneous patient populations. Over time, Alvin’s research direction transitioned from a purely computational basis toward the integration of software and algorithms with imaging systems, robotic devices, and other forms of hardware. In his senior year, Alvin began working on the design of a compact and low-cost robotic system that uses image-guidance to perform vascular access, the purpose of which is to minimize vascular access-related complications and adverse events by drawing blood, delivering fluids, and potentially introducing intravascular catheters and devices in an autonomous manner. Utilizing his prior experience in image analysis, biostatistics, and machine learning, Alvin implemented algorithmic approaches to identify vascular structure patterns in real-time from medical images acquired on the system. He was then able to validate his work through a large clinical study.
The success of Alvin’s undergraduate work led him quickly transition into his PhD studies in the Yarmush lab, where Alvin focused on the continued development and commercial translation of the automated vascular access technology. The system that Alvin developed for his dissertation research combines 3D near-infrared and ultrasound imaging, computer vision software, and a miniaturized dexterous robot that inserts the needle based on real-time image guidance. To enable device autonomy, Alvin introduced methods to robustly segment, localize, and track the pose of the vessels, and he built multiple functional robotic prototypes capable of aligning and steering the needle under real-time image guidance. These systems were systematically validated in a number of in vitro, in vivo, and human clinical studies. In parallel with his dissertation research, Alvin also led collaborations with other institutions, including the Massachusetts’s General Hospital, to couple the vascular access device with microfluidics-based blood analysis techniques. The outcome of these collaborations was an integrated platform that enables blood draws and tests to be completed rapidly and in a single automated step. One translated, these technologies may receive adoption in a wide range of clinical settings and may be extended to a host of diagnostic and interventional applications.
During his graduate studies, Alvin received a number of prestigious fellowships, including the NIH/Rutgers Biotechnology Training Program Fellowship and the NIH F31 Individual Predoctoral Fellowship. Together, these training opportunities provided him with a strong pathway toward scientific independence. Using the resources provided through these programs, Alvin was able to augment the mentorship from the lab with guidance from outside experts in different areas of science, engineering and medicine. Alvin worked closely with academic scientists and engineers, industrial teams, clinical experts, and business development groups to carry forward his research aims while overcoming scientific, technical, and translational challenges. Alvin’s research led him to publish 10 peer-reviewed papers in leading science and engineering journals and to present at a number of national and international conferences. Alvin was also able to contribute to the preparation and writing of several funded research grant proposals centered on his thesis work, including an NIH R01 Research Grant and an NSF Small Business Research Grant. His role on these applications provided valuable experience in developing focused, ambitious, and goal-oriented research programs, and in work with funding institutions throughout the application, review, and reporting process. Finally, in addition to carrying forward his own research, Alvin also mentored undergraduate and graduate students across multiple departments within the School of Engineering, and a number of his students have been selected for university or national awards as a result of the research they conducted under his guidance.
After graduating from Rutgers, Alvin continued pursuing his interests in computer vision, machine intelligence, and robotics at Philips Research North America, where his current work centers on combining medical image computing and computer-assisted navigation to improve clinical workflows and outcomes in the interventional radiology/cath lab. Alvin’s passion is to conduct fundamental and applied research with the potential to impact the medical practice of the future, and his long-term career goal is to lead a productive research group that aligns academic, clinical, and industrial partnerships in a concerted effort to improve patient health. His undergraduate and graduate experiences at Rutgers, in combination with the training and support provided by the Biotechnology Training Program and the NIH F31 Fellowship, have been integral to his scientific training and education as he continues to chase this goal.
PAPERS PUBLISHED, IN PRESS, OR SUBMITTED
Chen A, † Balter M†, Maguire T, Yarmush ML. Adaptive kinematic control of an automated robotic venipuncture device based on stereo vision, ultrasound, and force guidance. IEEE Trans. Ind. Electron. (in press). †Equal contributions.
Chen A, Balter M, Chen M, Gross D, Alam K, Maguire T, Yarmush ML. Multilayered tissue-mimicking phantoms with tunable mechanical, optical, and acoustic properties. Med. Phys. 43, 212–226, 2016.
Balter M, Chen A, Maguire T, Yarmush M. The system design and evaluation of a 7-DOF image-guided venipuncture robot. IEEE Trans. Robot. 2015;31(4).
Chen A, Nikitczuk K, Nikitczuk J, Maguire T, Yarmush ML. Portable robot for autonomous peripheral venous access using 3D near infrared image guidance. Technology 2013; 1(1):72-80.P
Nativ N†, Chen A†, Yarmush G, Henry S, Lefkowitch J, Klein K, Maguire T, Schloss R, Guarrera J, Berthiaume F, Yarmush ML. 2013. Automated image analysis method to detect and quantify macrovesicularsteatosis in human liver hematoxylin and eosin-stained histology images. Liver Transplantation 2013; 32(51):45-53. PMCID: PMC3923430.†Equal author contribution.
Chen A, Maguire T, Yarmush ML. Physiologically based pharmacokinetic models: integration of in silico approaches with micro cell culture analogues. Curr Drug Metab 2012. 13(6): 863-880. PMCID: PMC3966908.
Wu T, Khor T, Saw C, Chen A, Loh S, Lim S, Park J, Cai L, Kong A. Anti-inflammatory/anti-oxidative stress activities and differential regulation of Nrf2-mediated genes by non-polar fractions of tea chrysanthemum zawadskii and licorice glycyrrhizauralensis. AAPS J 2010; 1: 1-13. PMCID: PMC3032091.
Chen A, Balter M, Davidovich A, Maguire T, Yarmush ML. Performance of an autonomous, image-guided robotic vessel cannulation device on multilayered tissue phantoms in comparison to manual cannula insertion. IEEE Trans. Med. Imaging, 2016 (in review).
Balter M, Chen A, A Fromholtz, Gorshkov A, Colinco A, Bixon B, Martin V, Maguire T, Yarmush ML. Methods for differential white blood cell counting via automated image analysis and fluorescent detection on a centrifugal microfluidic platform. Analytical Methods, 2016 (in review).
Chen A, Fromholz A, Balter M, Yarmush G, Lo J, Maguire T, Yarmush ML. Automated lateral tail vein cannulation in rodents using an image-guided, robotic vessel cannulation device. Phys. Med. Biol. 2016 (in preparation).
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. 3D near infrared and ultrasound imaging of peripheral blood vessels for real-time localization and guidance. Med. Imag. Comput. Assisted Interv., 2016.
M. Balter, A. Chen, A. Fromholtz, A. Gorshkov, T. Maguire, M. L. Yarmush. System design and control of an image-guided robotic device for automated venipuncture and diagnostic blood analysis. IROS – IEEE/RSJ 2016 International Conference Intelligent Robots and Systems, 2016.
A. Chen†, M. Balter†, T. Maguire, M. L. Yarmush. Real-time needle steering in response to rolling veins in a portable, image-guided venipuncture robot. IROS – IEEE/RSJ 2016 International Conference Intelligent Robots and Systems, 2633–2638, 2015. †Equal contributions.
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. Image-guided robotics for autonomous venous access. Philips Research North America, Cambridge, MA, 2016 (oral).
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. Portable image-guided, autonomous robot for rapid blood draws and point-of-care blood analysis. NIH National Institute of Health Training Grantees Meeting, Bethesda, MD, 2016 (oral).
A. Chen, T. Maguire, M. L. Yarmush. Bimodal 3D near infrared and Doppler ultrasound imaging system to reduce adverse events in peripheral vascular access. 25th Biomedical Engineering Society Annual Meeting, Tampa, FL, 2015 (oral).
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. Portable robot for autonomous venipuncture using 3D near infrared polarization imaging. 24th Biomedical Engineering Society Annual Meeting, San Antonio, TX, 2014 (oral).
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. Developing a portable vision-guided medical robot using embedded field-programmable gate arrays. National Instruments Conference, Austin, TX, 2014 (oral).
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. Portable robot for autonomous venipuncture using 3D near-infrared and ultrasound guidance. NIH-Rutgers Biotechnology Symposium, Piscataway, NJ, 2014 (Best poster).
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. Vessel structure mapping and reconstruction in 3D near infrared and Doppler ultrasound images. NIH-Rutgers Biotechnology Symposium, Piscataway, NJ, 2013 (Best poster).
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. 3D multispectral near-infrared polarization imaging to detect subcutaneous blood vessels while minimizing effects of skin surface artifacts. 23rd Biomedical Engineering Society Annual Meeting, Seattle, WA, 2013 (oral)
A. Chen, M. Balter, T. Maguire, M. L. Yarmush. Portable image-guided robot for autonomous venipuncture and optofluidics-based blood analysis. Johnson & Johnson Engineering Showcase, Piscataway, NJ, March 2012.
A. Chen, J. Scichilone, T. Maguire, M. L. Yarmush, J. B. Levine. Multi-view unsupervised canonical clustering reveals predictive associations between neuropsychological and behavioral measures of comorbid prefrontal cortex disorders in children. 10th Annual AACN Conference, Seattle, WA, June 2012 (oral).
N. Nativ, G. Yarmush, A. Chen, D. Xudong, T. Maguire, R. Schloss, F. Berthiaume, M. L. Yarmush. Rat hepatocyte culture model of macrosteatosis: effect of macrosteatosis induction and reversal on viability and liver-specific function. AGA/ASGE Clinical Congress of Hepatology, Washington D.C., December 2011.
N. Nativ, G. Yarmush, A. Chen, D. Xudong, T. Maguire, R. Schloss, F. Berthiaume, M. L. Yarmush. Rat hepatocyte culture model of macrosteatosis. Biomethods Boston Conference, Boston, MA, July 2011.
AWARDS AND HONORS
2014 – 2016 NIH F31 Ruth L. Kirschstein Individual Predoctoral Fellowship, F31 EB018191
2012 – 2014 NIH Biotechnology Training Fellowship, T32 GM008339
2011 – 2012 Rutgers University School of Engineering Graduate Fellowship
2010 – 2011 Rutgers Center for Innovations in Emerging Technologies Graduate Fellowship
2008 – 2010 James J. Slade Scholars Undergraduate Research Fellowship
2007 – 2008 Rutgers Aresty Undergraduate Research Fellowship
2006 – 2010 Rutgers University School of Engineering Undergraduate Scholarship
2015 IEEE Entrepreneurship and Startup Award (2015)
2015 IEEE Global Entrepreneurship Summit Travel Award (2015)
2015 IEEE Robotics and Automation Society Conference Travel Award (2015)
2014 National Instruments Engineering Impact Awards (2014)
2011 – 2014 Rutgers Biotechnology Training Program Symposium Poster Awards
1st place (2011, 2013, 2014); 2nd place (2012)