School of Engineering
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Mauricio Valencia
Corp and Fdn Dev Offcr 3, School of Engineering - External Relations
Current Role at StanfordDirector of Corporate Relations, School of Engineering
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Melissa Valentine
Assistant Professor of Management Science and Engineering
Current Research and Scholarly InterestsMelissa Valentine is an Assistant Professor at Stanford University in the Management Science and Engineering Department, and co-director of the Center for Work, Technology, and Organization (WTO). WTO is a world leader in producing field research that uses direct observation of social phenomena to develop new understanding about the changing nature of work.
Prof Valentine's research focuses on understanding work groups and teams in organizations, particularly how they are changing in response to new industry trends and new technologies. She conducts in-depth observational studies to develop new understanding about new forms of work groups and teams. Her work makes contributions to understanding classic and longstanding challenges in designing groups and organizations (e.g., the role of hierarchy, how to implement change, team stability vs. flexibility) but also brings in deep knowledge of how the rise of information technology has made possible new and different team and organizational forms.
Prof. Valentine has won awards for both research and teaching. She and collaborators won a Best Paper Award at the CHI Conference on Human Factors in Computing Systems and the Outstanding Paper with Practical Implications award from the Organizational Behavior division of the Academy of Management. In 2013, she won the Organization Science/INFORMS dissertation proposal competition and received her PhD from Harvard University. -
Gregory Valiant
Assistant Professor of Computer Science
Current Research and Scholarly InterestsMy primary research interests lie at the intersection of algorithms, learning, applied probability, and statistics. I am particularly interested in understanding the algorithmic and information theoretic possibilities and limitations for many fundamental information extraction tasks that underly real-world machine learning and data-centric applications.
