School of Engineering
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Professor of Computer Science, & by courtesy, of Electrical Engineering
BioDr. Monica Lam is a Professor in the Computer Science Department at Stanford University, and the Faculty Director of the Stanford MobiSocial Laboratory. Dr. Monica Lam obtained her BS degree in computer science from University of British Columbia, and her PhD degree in computer science from Carnegie Mellon University in 1987. She joined Stanford in 1988.
Professor Lam’s research mission is to disrupt the status quo where centralized monopoly platforms are prevalent and consumers privacy is compromised. This challenging problem led her to ten years of research in many disciplines in computer science: natural language processing, machine learning, compilers, distributed systems, and human-computer interaction. She advocates the development of open-source virtual assistants that users can “program” in natural language; these assistants should be federated to give users choice and to support sharing without a centralized third party. Her research prototype demonstrates a viable open-source alternative to the emerging oligopoly of virtual assistants.
Prof. Lam is also an expert in compilers for high-performance machines. Her pioneering work of affine partitioning provides a unifying theory to the field of loop transformations for parallelism and locality. Her software pipelining algorithm is used in commercial systems for instruction level parallelism. Her research team created the first, widely adopted research compiler, SUIF. She is a co-author of the classic compiler textbook, popularly known as the “dragon book”.
Dr. Lam is a Member of the National Academy of Engineering and an Association of Computing Machinery (ACM) Fellow.
Anand Rajaraman and Venky Harinarayan Professor in the School of Engineering
Current Research and Scholarly InterestsLanday's current research interests include Technology to Support Behavior Change (especially for health and sustainability), Crowdsourcing, Demonstrational User Interfaces, Mobile & Ubiquitous Computing, Cross-Cultural Interface Design, and User Interface Design Tools. He has developed tools, techniques, and a top professional book on Web Interface Design.
Dr. Landay is the founder and co-director of the World Lab, a joint research and educational effort with Tsinghua University in Beijing.
Current Research and Scholarly InterestsI have a PhD in Computer Science from the University of California, San Diego, in the area of High-Performance Computing (HPC), specifically market-based scheduling algorithms. My graduate research was done as part of San Diego Supercomputer Center (SDSC)'s Performance Modeling and Characterization Lab (PMaC), where I investigated economic models of scheduling on high performance computing systems. My adviser was Allan Snavely of SDSC.
My dissertation abstract is as follows: Effective management of Grid and HPC resources is essential to maximizing return on the substantial infrastructure investment these resources entail. An important prerequisite to effective resource management is productive interaction between the user and scheduler. My work analyzes several aspects of the user-scheduler relationship and develops solutions to three of the most vexing barriers between the two. First, users' monetary valuation of compute time and schedule turnaround time is examined in terms of a utility function. Second, responsiveness of the scheduler to users' varied valuations is optimized via a genetic algorithm heuristic, creating a controlled market for computation. Finally, the chronic problem of inaccurate user runtime requests, and its implications for scheduler performance, is examined, along with mitigation techniques.
My current research projects are in the area of Computer Science Education, with an emphasis on assessment and the use of Peer Instruction pedagogy in lecture. With colleagues Mark Guzdial, Leo Porter, and Beth Simon, I run the New CS Faculty Teaching Workshop, an annual "bootcamp" on how to teach effectively that draws attendees from dozens of the top CS programs in the country. The short-term goal is to give newly-hired faculty entering their first year of teaching the skills they need to succeed for themselves and their students. The long-term goal is to transform undergraduate education in CS by seeding our best rising stars with best practices so they can create communities of practice as their institutions and mentor their students in active learning strategies, creating a culture where these are the new norm.
Associate Professor of Computer Science
BioLeskovec's research focuses on the analyzing and modeling of large social and information networks as the study of phenomena across the social, technological, and natural worlds. He focuses on statistical modeling of network structure, network evolution, and spread of information, influence and viruses over networks. Problems he investigates are motivated by large scale data, the Web and other on-line media. He also does work on text mining and applications of machine learning.
Associate Professor of Computer Science and of Electrical Engineering
BioProfessor Levis' research focuses on the design and implementation of efficient software systems for embedded wireless sensor networks; embedded network sensor architecture and design; systems programming and software engineering.
Robert W. and Vivian K. Cahill Professor in Cancer Research in the School of Medicine and Professor, by courtesy, of Computer Science
Current Research and Scholarly Interestshaving pioneered, we (a) predict folding of a polypeptide and RNA chains into a unique native-structure, we (b) model protein structure using the well-established paradigms that similar protein sequences imply similar three-dimensional structures, and (c) we are focusing on mesoscale modeling of large macromolecular complexes such as RNA polymerase and the mammalian chaperonin.
VMware Founders Professor in Computer Science and Professor of Electrical Engineering, Emeritus
BioLevoy's current interests include the science and art of photography, computational photography, light field sensing and display, and applications of computer graphics in microscopy and biology.
Sequoia Capital Professor
Current Research and Scholarly InterestsHuman vision, high-level visual recognition, computational neuroscience
Associate Professor of Computer Science and, by courtesy, of Statistics
BioFields: machine learning, natural language processing.
Topics: unsupervised learning, structured prediction, statistical learning theory, grounded language acquisition, compositional semantics, program induction.
Learning semantics: Natural language allows us to express complex ideas using a few words, but the actual semantics are rarely directly observed. We therefore model the expressive semantics of language as programs whose execution produces observed data, and develop algorithms to learn these programs from indirect supervision.