Late Addition to Chem Seminar Schedule!

Marcus Trygstad, Ole class of 1978, will be speaking this afternoon on

From Beakers to Barrels and Power Points to Patents: Applying Chemistry to Automate Industry

Trygstad is a Business Development Manager at Advanced Analytical Solutions in Houston, Texas. RNS 310, 3:00 treats, 3:15 seminar

"Big Data" Science Symposium Schedule

Chemistry majors can earn seminar credits for each talk on Friday.  Look for a faculty member with seminar cards as you enter the room.  

Thursday, April 10

7 p.m. Panel Discussion

Getting Started With Big Data: A Conversation with Four Innovators

Many of us don’t have a clue what “Big Data” really means, but fortunately we have four innovators in the field of Big Data who can help us learn more about it. The panel discussion will be moderated by Professor of Computer Science Dick Brown.

Panelists

• Katherine Yelick, Professor of Electrical Engineering and Computer Science at the University of California–Berkeley and Associate Laboratory Director for Computing Sciences at Lawrence Berkeley National Laboratory
• George Djorgovski, Professor of Astronomy at the California Institute of Technology
• Stephanie Hampton, Director of the Washington State University Center for Environmental Research, Education, and Outreach (CEREO) and Professor, School of the Environment, Washington State University
• Francis Harvey, Associate Professor of Geography at the University of Minnesota

Friday, April 11

2:45 p.m. Music by Musika Nova

3 p.m. Introduction and Welcome

Anne Walter, Professor of Biology, and the Paul and Mildred Hardy Distinguished Professor of Science at St. Olaf

3:15 p.m. “Big Data and the Future for Ecology”       

(Chemistry majors can earn 1 seminar credit for this talk)

Stephanie Hampton, Director of the Washington State University Center for Environmental Research, Education & Outreach, Professor, School of the Environment, Washington State University

The need for sound ecological science has escalated alongside the rise of the information age and “big data” across all sectors of society. Big data generally refers to massive volumes of data not readily handled by the usual data tools, and presents unprecedented opportunities for both advancing science and informing resource management through data-intensive approaches. The “Big V’s” of big data are Volume, Velocity, and Variety. There is no question that ecology is a poster child for the “Variety” that presents both challenges and opportunities in data-intensive science. Ecological data are highly heterogeneous and widely dispersed. These scattered data capture many of the details of natural history and the ecological process that are not represented in the higher volume data streams more commonly included in big data discussions. Our collective ability to successfully overcome these diverse data challenges is critical to providing sound scientific advice that promotes sustainable human societies.

4:30 p.m. “Evolving Science in Cyberspace”

(Chemistry majors can earn 1 seminar credit for this talk)

George Djorgovski, Professor of Astronomy at Caltech

Science, scholarship, and education are being profoundly transformed by advances in computation and information technology. Much of the scholarly work, including gathering of data, tools for exploration and theoretical modeling, literature, and collaboration tools, are now moving to virtual environments. The exponential growth of data volumes and the simultaneous increase in data complexity offer new scientific opportunities as well as new challenges for knowledge discovery in massive and complex data sets and data streams that are common to all sciences. These challenges are not simply technological: many aspects of this shift are deeply intellectual, striking at the core of how we discover and understand natural phenomena. We are thus now developing new methodologies for the scientific research in the 21st century. At the same time, we see an accelerated co-evolution of science, technology, and society.

7:15 p.m. Music by Musika Nova

7:30 p.m. “More Data, More Science, and … Moore’s Law?”

(Chemistry majors can earn 1 seminar credit for this talk)

Katherine Yelick, Professor of Electrical Engineering and Computer Science at the University of California–Berkeley and Associate Laboratory Director for Computing Sciences at Lawrence Berkeley National Laboratory

The terms “high-performance computing” and “computational science” have become nearly synonymous with modeling and simulation, and yet computing itself is as important to analyzing experimental data as it is to evaluating theoretical models. Due to the exponential growth rates in detectors, sequencers, and other observational technology, data sets are outstripping the storage, computing, and algorithmic techniques available to individual scientists. Along with simulation, experimental analytics problems will drive the need for increased computing performance, although the types of computing systems and software configurations may be quite different. I will describe some of the opportunities and challenges in extreme data science and its relationship to high performance modeling and simulation. My favorite challenge is the development of high performance, high productivity programming models. In both simulation and analytics, programming models are the “sandwich topic,” squeezed between application needs and hardware disruptions, yet often treated with some suspicion, if not outright disdain. But programming model research is an exemplar of interdisciplinary science, requiring a deep understanding of applications, algorithms, and computer architecture in order to map the former to the latter. I will use this thread to talk about my own research interests, how I selected various research topics and the importance of teams and even complete communities of researchers when addressing one of these problems.

8:30 p.m. Closing Remarks

Anne Walter

Friday, May 2

Formal Poster Session

Tomson Hall Atrium
Students will present project results from work covering a variety of subjects in the natural sciences and mathematics.