Speaking June 28, 2022 at 9:00-10:15 EDT

Margaret Martonosi is the US National Science Foundation’s (NSF) Assistant Director for Computer and information Science and Engineering (CISE). With an annual budget of more than $1B, the CISE directorate at NSF has the mission to uphold the Nation’s leadership in scientific discovery and engineering innovation through its support of fundamental research and education in computer and information science and engineering as well as transformative advances in research cyberinfrastructure. While at NSF, Dr. Martonosi is on leave from Princeton University where she is the Hugh Trumbull Adams '35 Professor of Computer Science. Dr. Martonosi's research interests are in computer architecture and hardware-software interface issues in both classical and quantum computing systems. Dr. Martonosi is a member of the National Academy of Engineering and the American Academy of Arts and Sciences. She is a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE).

Speaking June 29, 2022 at 9:00-10:15 EDT

José E. Moreira is a Distinguished Research Staff Member at the IBM Thomas J. Watson Research Center. He received a B.S. degree in physics and B.S. and M.S. degrees in electrical engineering from the University of Sao Paulo. He received a Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign. Since joining IBM in 1995, Dr. Moreira has worked on a variety of high-performance systems, including two ASCI systems (Blue Pacific and White) and the Blue Gene/L supercomputer, for which he was the System Software architect. Dr. Moreira has been responsible for various architectural and micro-architectural innovations in the three most recent generations of POWER processors. He conceived the POWER10 matrix unit, the first of its kind in a commercial processor. Dr. Moreira is a Fellow of the IEEE (Institute of Electrical and Electronics Engineers) and a Distinguished Scientist of the ACM (Association for Computing Machinery). Contact him at jmoreira@us.ibm.com.

Speaking June 30, 2022 at 9:00-10:15 EDT

Chris R. Johnson is a Distinguished Professor of Computer Science and founding director of the Scientific Computing & Imaging (SCI) Institute at the University of Utah. He also holds faculty appointments in the Departments of Physics and Bioengineering. His research interests are in the areas of scientific computing and scientific visualization. In 1992, with Professor Rob MacLeod, Professor Johnson founded the SCI research group, now the SCI Institute, which has grown to employ over 150 faculty, staff and students. Professor Johnson serves on a number of international journal editorial and advisory boards to national and international research centers. He is a Fellow of AIMBE (2004), AAAS (2005), SIAM (2009), and IEEE (2014) and was inducted into the IEEE Visualization Academy (2019). He has received a number of awards including the NSF Presidential Faculty Fellow (PFF) award from President Clinton, a DOE Computational Science Award, the Governor’s Medal for Science and Technology, the Utah Cyber Pioneer Award, the IEEE Visualization Career Award, IEEE CS Charles Babbage Award, the IEEE Sidney Fernbach Award, Rosenblatt Prize and most recently, the 2020 Leonardo Award.

Keynote Talk: Large-Scale Visual Analysis in the Age of Data

We live in the Age of Data. Ninety percent of all data in the world has been created in the past two years alone, at a rate of exabytes per day. New data of all kinds — structured, unstructured, quantitative, qualitative, spatial, and temporal — is growing exponentially and in every way. Given the vast amount of data being produced, one of our greatest scientific challenges is to effectively understand and make use of it. Because visualization both facilitates the reasoning process by supporting the human capacity to perceive, understand, and reason about complex large-scale data and enables researchers to derive knowledge from data, visual data analysis is one of our most important tools for understanding large-scale complex data. In this talk, I will present recent visual analysis research and applications in science, engineering, and medicine from the Scientific Computing and Imaging Institute and discuss current and future visualization research challenges.