Institution

University of Southern California

About University of Southern California

The University of Southern California (USC[a] or SC) is a private not-for-profit and nonsectarian research university founded in 1880 with its main campus in the city area of Los Angeles, California. As California's oldest private research university,[8] USC has historically educated a large number of the region's business leaders and professionals.

For the 2014-2015 academic year, there were 19,000 students enrolled in four-year undergraduate programs. USC is also home to 23,000 graduate and professional students in a number of different programs, including business, law, social work, and medicine.] The university has a "very high" level of research activity and received $646 million in sponsored research from 2014 to 2015.

Reviewers: 250

18th in USA

Reviews: 2,253

19th in USA

Merit: 6,981

19th in USA

Openness: 1.1

128th in USA

Journal Editors at University of Southern California

Reviewers from University of Southern California

  • Reviewer

    Ulrike Gretzel

    Research related to human technology interaction, online communication, persuasion and technology adoption.

  • Reviewer

    Ian Haworth

    Associate Professor, Department of Pharmacology & Pharmaceutical Sciences

  • Reviewer

    Takeshi Saito

    Through my medical education and patient care, I recognized my strong motive to understand the pathophysiology behind clinical manifestation in infectious disease. Therefore, I sought postgraduate education in molecular microbiology; thereafter my research interest has been centered on hepatitis C virus (HCV) virology, pathogenesis, and virus-host interaction including hepatic immune response. During my PhD and Post-Doctoral training, I have investigated viral-host interactions that activate intracellular innate immune response to the suppression of HCV replication. In recent years, I have focused on understanding how host cells sense HCV infection to initiate the innate antiviral immune response which involves type I interferon and proinflammatory cytokine production that govern hepatic immunity. After recent transition to an independent junior faculty position at the University of Southern California (USC), Keck School of Medicine, my laboratory continues to investigate virus-host interactions that determine disease outcome and pathogenesis of HCV. Through this work, I have gained extensive expertise and skills in virology, cell signaling, cytokine biology, and cell biology. My role as a physician specializing in the care of viral hepatitis at an academic medical center offers the unique integration of basic science and clinical/translational science into a laboratory research program, allowing us to set our aim as "research for better healthcare". My chief goal is to become an established physician scientist with the skills and knowledge necessary to pursue a lasting career dedicated to translating discoveries in molecular virology into clinical practice and development of novel therapies. For successful achievement of my career goals, USC provides protected time for molecular research laboratory operation, thus enabling my guidance of multiple graduate students and participation in collaborative research with highly distinguished investigators. In addition, USC is supported by two major centers for the study of liver diseases: 1) USC Research Center for Liver Diseases, and 2) Southern California Research Center for ALPD and Cirrhosis. The center directors serve as consultants for my research program and career development. In summary, I have substantial expertise and track record in the field of innate immunology against viral infection and am efficiently establishing an independent research program in an environment well suited for the study of liver diseases.

  • Reviewer

    George R. Matcuk Jr., M.D.

    Dr. Matcuk received dual B.S. degrees in biology and chemistry at Carnegie Mellon University. Dr. Matcuk subsequently earned his M.D. from Stanford University.

    Dr. Matcuk performed his radiology residency and musculoskeletal radiology fellowship at the University of Southern California. He was awarded Resident of the Year in 2008, both Fellow and Board Reviewer of the Year in 2009, and Teacher of the Year in 2011. Dr. Matcuk has co-authored over twenty (20) journal articles and seventy (70) educational exhibits and is currently working on a number of other projects. He enjoys daily teaching of USC medical students, residents, and fellows.

    Dr. Matcuk enjoys the interpretation of musculoskeletal imaging studies, including x-ray, ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) and communicating these findings with his clinical colleagues to optimize patient care. Dr. Matcuk also enjoys direct patient interaction during image-guided procedures and the relief his is often able to provide with steroid/anesthetic injections or the radiofrequency ablation of tumors, such as osteoid osteomas.

  • Reviewer

    Rafael Ferreira da Silva

    Rafael Ferreira da Silva is a Research Assistant Professor in the Department of Computer Science at University of Southern California, and a Computer Scientist in the Science Automation Technologies group at the USC Information Sciences Institute. His research focuses on the efficient execution of scientific workflows on heterogeneous distributed systems (e.g., clouds, grids, and supercomputers), computational reproducibility, and Data Science—workflow performance analysis, user behavior in HPC/HTC, and citation analysis (for publications). Dr. Ferreira da Silva received his PhD in Computer Science from INSA-Lyon, France, in 2013. In 2010, he received his Master’s degree in Computer Science from Universidade Federal de Campina Grande, Brazil, and his BS degree in Computer Science from Universidade Federal da Paraiba, in 2007. For more information, please visit http://www.rafaelsilva.com.

  • Reviewer

    Stefan Scherer

    Computational Nonverbal Behavior Analytics My research aims to automatically identify, characterize, model, and synthesize individuals' multimodal nonverbal behavior within both human-machine as well as machine-mediated human-human interaction. The emerging technology of this field of research is relevant for a wide range of interaction applications, including the areas of healthcare and education. Exemplarily, the characterization and association of nonverbal behavior with underlying clinical conditions, such as depression or post-traumatic stress, holds transformative potential and could change treatment and the healthcare system’s efficiency significantly. This is recognized by several leading research funding agencies such as DARPA with the Detection and Computational Analysis of Psychological Signals (DCAPS) project and the joint NSF/NIH program on Smart and Connected Health. Within the educational context the assessment of proficiency and expertise of individuals’ social skills, in particular for those with learning disabilities or social anxiety, can help to create individualized and tailor targeted education scenarios. The potential of cyber-learning and machine assisted training for individuals with autism spectrum disorders is reflected in numerous open research programs. For example, the Department of Defense pursues a clear agenda with the CDMRP Autism Research Program. Overall, this vibrant and highly multidisciplinary area of research that integrates the fields of psychology, machine learning, multimodal sensor fusion, and pattern recognition, emerges as an essential field of investigation for computer science.

    In the recent past, my research has evolved in the direction of understanding multimodal nonverbal behavior in the context of clinical disorders and for educational purposes. Even though, we have found exciting results and identified nonverbal indicators of suicidality or proficient public speaking, there is an extensive potential for future research endeavors in the fields of machine learning, data mining, as well as data visualization and interpretation. One of my present research interests is the identification of optimal tools and algorithms for clinicians to make use of the manifold data that is available due to our automatic nonverbal behavior tracking algorithms and machine learning approaches. Further, I am interested in the development of smart data mining and pattern recognition algorithms that will enable clinicians to browse data and identify salient moments of interest in their patients’ history, interviews, and clinical records. Within the educational domain, I envision a platform that allows socially anxious individuals or individuals with learning disabilities to learn social skills in a forgiving and widely available environment that incorporates the use of virtual humans and possibly robots. Such a platform has potential in both training and standardized evaluation aspects of education.