School of Engineering
Showing 61-80 of 371 Results
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Riitta Katila
W.M. Keck Professor and Professor of Management Science and Engineering
Current Research and Scholarly InterestsThe question that drives Prof. Katila's research is how technology-based firms with significant resources can stay innovative. Her work lies at the intersection of the fields of technology, innovation, and strategy and focuses on strategies that enable organizations to discover, develop and commercialize technologies. She combines theory with longitudinal large-sample data (e.g., robotics, biomedical, platform and multi-industry datasets), background fieldwork, and state-of-the-art quantitative methods. The ultimate objective is to understand what makes technology-based firms successful.
To answer this question, Prof. Katila conducts two interrelated streams of research. She studies (1) strategies that help firms leverage their existing resources (leverage stream), and (2) strategies through which firms can acquire new resources (acquisition stream) to create innovation. Her early contributions were firm centric while recent contributions focus on innovation in the context of competitive interaction and ecosystems.
Professor Katila's work has appeared in the Academy of Management Journal, Administrative Science Quarterly, Organization Science, Strategic Entrepreneurship Journal, Strategy Science, Strategic Management Journal, Research Policy and other outlets. In her work, supported by the National Science Foundation, Katila examines how firms create new products successfully. Focusing on the robotics and medical device industries, she investigates how different search approaches, such as the exploitation of existing knowledge and the exploration for new knowledge, influence the kinds of new products that technology-intensive firms introduce. -
Noa Katz
Research Professional, Chemical Engineering
BioNoa Katz is a Stanford Science Fellow and an EMBO and Fulbright postdoctoral scholar at Stanford University. She implements biomolecular gene circuits to study and manipulate the central nervous system to promote therapeutic applications for neural repair and autism.
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Amit Kaushal
Adjunct Professor, Bioengineering
BioDr. Kaushal is Clinical Associate Professor of Medicine and Adjunct Professor of Bioengineering at Stanford University. He is a respected internal medicine physician with expertise in applications of computer science, artificial intelligence (AI), and machine learning (ML) to medicine and public health. He has worked in roles ranging from deeply technical to deeply clinical, in both academia and industry.
Dr. Kaushal brings over 20 years of research experience at the intersection of computer science and biomedicine. His work focuses on taking AI/ML applications from concept all the way through live clinical deployment, with attention to fair and ethical use of AI. His work has been featured in JAMA, Nature, Lancet Digital Health, NEJM AI, NEJM Catalyst Innovations in Care Delivery, Nature npj Digital Medicine, JAMA Network Open, Health Affairs Blog, and others; and he has been covered in popular media outlets such as Scientific American, Wired, STAT News, The Verge, LA Times, and more.
Dr. Kaushal launched Stanford University School of Engineering's undergraduate degree program in Biomedical Computation over 20 years ago; he serves as co-director of the major, which has graduated over 150 students since its founding. He is a faculty in the Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford Institute for Human-Centered Artificial Intelligence, Stanford Clinical Excellence Research Center, and Stanford Partnership for AI-Assisted Care.
Dr. Kaushal practices hospital medicine at VA Palo Alto, where he also serves as inaugural Director of the Amplified Reach Catalyst (ARC) Program, an embedded research-support infrastructure for VA hospitalist clinicians.
Dr. Kaushal has served in executive, operating, and advisory roles in industry.
Dr. Kaushal is board certified in both internal medicine and clinical informatics. He completed his BS (Biomedical Computation), MD, PhD (Biomedical Informatics) and Internal Medicine residency training all at Stanford University.
