The video depicts the late 1960’s and early 1970’s, an era marked by muscle cars, heavy metal music and social rebellion. The psychedelic images in the video were constructed entirely using mathematics. Numerical simulations of fluid in and around the car were developed using numerical methods to solve the Navier-Stokes partial differential equations that govern fluid flow.

By being creative with the mathematical parameters and solution techniques, Tapia and the student created some highly striking and interesting images and patterns. Using video, they were able to demonstrate that mathematics can take us places where physics can’t.

Dr. Tapia is the 2011 awardee of the National Medal of Science, the highest honor bestowed by the U.S. government on scientists and engineers. He has received the National Science Board’s Vannevar Bush Award, has been elected to the National Academy of Engineering--the first Hispanic to receive these honors, and has served on the National Science Board from 1996-2002.

In 1990, the National Research Council named Tapia one of the 20 most influential leaders in minority mathematics education in the country. Later that year, he received the Hispanic Engineer National Achievement Award for Education from Hispanic Engineer.

Internationally recognized for his research in the computational and mathematical sciences, Dr. Tapia is also Director of Rice University’s Center for Excellence and Equity in Education. He has also been featured in a video promoting the Society for the Advancement of Chicanos and Native Americans in Science (SACNAS).

"Professor Tapia and late professor Blackwell are two folks whom I admire, first for their excellence in mathematics and statistics, and second for their leadership in enhancing diversity--two of our College's core values," said Sastry G. Pantula, dean of the College of Science.

"The Blackwell-Tapia conferences, named after these two leaders, have a great impact on building diverse leaders in science. The OSU community is in for a treat. We can all learn a lot from Dr. Tapia's experiences."

The renowned mathematician holds honorary doctorates from Carnegie Mellon University, Colorado School of Mines, University of Nevada, and Claremont Graduate University. He also has two professional conferences named in his honor: the Richard Tapia Celebration of Diversity in Computing Conference and the Blackwell-Tapia Mathematics Conference.

Dr. Tapia presents the Mathematics Department’s 31^st annual Lonseth Lecture May 10 at 4:00 pm in LaSells Stewart Center, Construction & Engineering Hall, following the annual Mathematics Awards ceremony at 3:30 pm. This scientific talk, “The Remarkable Journey of Isoperimetric Problem: From Euler to Steiner to Weierstrass,” will offer an overview of the history of the impactful isoperimetric problem. A reception will follow the events.

This figure shows that the biosensors can enter a cell’s cytoplasm and nucleus to find where microRNA expression occurs within the cell. Green shows the cytoplasm and the dark green spot is the nucleus. Red indicates the biosensor. The circle and the red nucleus indicate where the biosensor has entered the cytoplasm and nucleus, respectively.

“The object of visualization is very often not raw data. Particularly in the era of big data, summarization or modeling is an essential precursor to making sense of the data. Visualization becomes crucial to understanding how decisions at this stage propagate to conclusions and good visualization tools encourage experimentation with alternate approaches. We have methods for propagating statistical uncertainty through a data pipeline, but we are still learning how to best communicate uncertainty visually.

“There are interesting technical challenges along the way. For example, where should the data live? Can analyses be run on the fly, or do they require lengthy distributed computing? Can an approximate answer be achieved in a quicker manner? Is an approximate answer good enough for visualization purposes? Answering these questions requires close collaboration between computer scientists, statisticians and domain experts.”

Bringing data science to the non-data scientist. Wickham recently won first place in an international competition sponsored by EMC2 and hosted by Crowdanalytix . The contest was designed to visually reveal insights into the differences between a professional and amateur motorcycle rider based on data collected at the millisecond level from sensors on the bike, engine and rider during six laps of racing. Simply separating the data into laps posed a data exploration challenge. The iteration between data preparation and visualization was the key to separating the interesting from the uninteresting data.

Duo Jiang, Statistics

“My research aims at developing statistical and computational methods to address challenges posed by the growing amount, dimensionality and complexity of data in biological and biomedical research. A recent focus has been on correlated data methods in genetic association studies, functional enrichment analysis and biological network inference.

“Through interdisciplinary research and collaborations, I hope to make statistical innovations that not only provide improved data analysis, but also enable new ways of leveraging data to answer biological questions and transform study design considerations for researchers at OSU and in the broader scientific community.”

Debashis Mondal, Statistics

Mondal focuses on research applications in agriculture, geographical epidemiology and environmental sciences.

"Advances in the field of spatial statistics are important because they can be used to answer scientific questions in agriculture, astronomy, biomedical imaging, computer vision, climate and environmental sciences, epidemiology and geology.

"I seek to enhance scientific understanding of environmental bioassays, arsenic contamination of groundwater and geographic variations in cancer risk. My statistical and computational work addresses questions relevant to environmental or global change and to health studies. I am also interested in Markov chain Monte Carlo computations, time series, ranking and selection and random graphs and trees."

Sharmodeep Bhattacharyya, Statistics

“I work on developing statistical methods for network and high-dimensional data. Large network data sets are currently becoming quite common in several scientific fields from biological to social sciences. My work is focused on networks and high-dimensional data related to large scale -omics studies, neuroscience studies and social interaction studies.

"The development of statistical methods to analyze large-scale data coming from several different experimental sources helps our understanding of complex systems, such as human brain, which has so far remained highly elusive.”

Davide Lazzati, Physics

Lazzati's research is focused on understanding the physics of cosmic dust and gamma-ray bursts—the brightest and most mysterious explosions in the present day universe. He also studies theoretical high-energy astrophysics, quantum chemistry, soft condensed matter and numerical methods. He was among the first to realize the importance of time dependent effects in the interaction of the burst radiation with interstellar material.

Patrick De Leenheer, Mathematics and Integrative Biology - Bioinformatics hire

De Leenheer’s research interests include mathematical biology, differential equations and control theory. He brings extensive experience in developing instructional and scholarly bridges between mathematicians and biologists. Prior to joining OSU, he was on the mathematics faculty at the University of Florida for nearly 10 years.

De Leenheer earned a master of science electro-mechanical engineering and a Ph.D. in applied sciences from Ghent University in Belgium.

David Hendrix, Biochemistry & Biophysics - Bioinformatics hire

Hendrix’s lab focuses on understanding the structure, function and mechanisms of action of non-coding RNAs. Since the discovery of numerous non-coding RNAs in the past decade, their function is still largely unknown. Hendrix uses structure prediction, genome-wide sequence analysis and deep sequencing data to explore the roles these molecules play in gene regulation. His team also develops algorithms to understand different areas of computational biology.

Thomas Sharpton, Microbiology and Statistics - Bioinformatics hire

Sharpton is developing the quantitative biology curricula and is teaching courses in bioinformatics and microbial genomics. His research team focuses on characterizing the ecological, evolutionary, and functional properties of the microbiome—the vast collection of microorganisms that live on our bodies.

The team seeks to better understand how the physiologies of our body and our microbiome interact. Their work is interdisciplinary, relying heavily on microbiology, bioinformatics and systems biology, and borrowing from molecular biology, computer science, and statistics.

David Koslicki, Mathematics - Bioinformatics hire

"My research is mainly data-driven as I primarily develop new mathematical techniques to answer biological questions in genomics. Studying metagenomics in particular, I routinely analyze DNA sequencing data with sizes ranging from 10's of gigabytes to 10's of terabytes. Thankfully, Oregon State is well equipped to facilitate analyzing this sort of data, particularly with the Center for Genomics Research and Biocomputing.

"The recent discoveries regarding the human microbiome make it an exciting time to be at the interface of biology, mathematics, and computer science."

Koslicki’s research focuses on bioinformatics and the application of tools from the mathematical theory of symbolic dynamical systems to problems in genomics. He is currently interested in problems stemming from the field of metagenomics: the study of bacterial communities through their sampled DNA. He uses a variety of big data techniques, including compressed sensing, probabilistic data structures, and high-performance computing.

Joe Beckman, Biochemistry and Biophysics

“Researchers increasing collaborate across OSU and around the world to better understand what we are exposed to in everyday life, what the cellular actions of these exposures are and how we respond biochemically to these exposures. This involves measuring thousands of chemicals, tens of thousands of genes that are changing, and hundreds of thousands of biochemical molecules.

"The integration and management of these data has become a major challenge as has learning how to make the result comprehensible to the public and to decision makers.”

Juan Restrepo, Mathematics

Restrepo's research is focused on uncertainty quantification, ocean dynamics, climate, oil/pollution transport and acoustics. He has worked on bio-related homeland security work as a visiting professor at Los Alamos National Laboratory, bone dynamics, voting theory as well as climate dynamics research.

"Elucidating whether a present or future extreme event has low probability, and/or is the result of a changing world is fundamental to developing risk analyses. Finding ways to improve the chances of a fast and cheap recovery after a disaster (rather than of avoiding it) is of great social interest. Producing better predictions from complex dynamic models by combining data and models, taking into account their inherent uncertainties, has high practical engineering and scientific impact.

"The two aspects that distinguish our research, which focuses on extremely high-dimensional problems, are 1) we work with time dependent processes, in which classical equilibrium notions are not applicable, and 2) we work with processes that generate outcomes which are not simply characterized by their mean and their variance.

"My group combines data/observations and methods from probability and statistics, statistical physics, machine learning, and dynamics in order to propose new methods for answering questions in climate, ocean processes, disaster recovery and resilience in natural and man-made systems.

Benjamin Dalziel, Mathematics and Integrative Biology

Dalziel is a population biologist working at the interface of theory and data. He uses mathematical models to uncover causal connections among different types of times-series data, including high-resolution data on animal movement patterns, population density, and the incidence of infectious disease.

"I want to know how populations work: Why do epidemics of infectious diseases happen more often in some cities than others? In addition, what leads migratory animals to “flock” over long distances each year, and how does this affect their vulnerability in a changing world?

"To me, data science is about integrating diverse sources of information--such as environmental measurements, behavior and genetic data--to predict how complex adaptive systems like a group of interacting animals will respond. This is part of a systems–based approach to understanding nature, and it’s made possible by recent increases in the volume and quality of data available.

"But big data is noisy, and a challenge now is how to develop rigorous approaches for extracting “signals” from the all the noise. This isn’t the statistics you learned in school – it’s new, and it’s a bit wild. In a way, data science is about approaching wilderness – that which defies the mind’s attempts at appropriation, as the poet Don McKay says."

Baru will present an overview of current programs and activities related to Big Data and Data Science at NSF and highlight inter-agency engagements within this area. He will also discuss future directions for Data Science research, education, and infrastructure.

NSF's BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics, along with innovative applications in domain science, including social and behavioral sciences, geosciences, education, biology, the physical sciences, and engineering that lead towards the further development of the interdisciplinary field of data science. Read more about NSF's BIGDATA program current solicitation. Deadline for proposals is February 9, 2016.

Considering that Data Science is a rapidly emerging, evolving field and discipline, Baru will take questions and allow ample time for discussions about where the field ought to be going given what we know today.

Currently on assignment with NSF, Baru is a Distinguished Scientist and Associate Director of Data Initiatives at the San Diego Supercomputer Center (SDSC), at the University of California, San Diego where he works on applied and applications-oriented research problems related to data management and data analytics. He leads the Advanced Cyberinfrastructure Development (ACID) Group at SDSC and is also Director of the Center for Large-scale Data Systems research (CLDS).

Baru has participated in a number of "data cyberinfrastructure" initiatives, including as Principal Investigator (PI) of the OpenTopography project; Cyberinfrastructure Lead, Tropical Ecology, Assessment and Monitoring network (TEAM); Co-Investigator of the Cyberinfrastructure for Comparative Effectiveness Research project (CYCORE); Member of the founding Senior Management Team of the National Ecological Observatory Network (NEON) and Co-PI of the NEON Cyberinfrastructure Testbed; Co-PI of the CUAHSI Hydrologic Information Systems (CUAHSI-HIS); Director, NEES Cyberinfrastructure Center (NEESit); PI/Project Director, Geosciences Network (GEON); and member of the How Much Information? project.

Hosted by the Department of Statistics, the fall Milne Lecture will be held on Monday, November 23 at 4 pm in the Memorial Union, Horizon room with a reception and cash bar from 5 - 7 pm. The Milne Lecture in Mathematics, Statistics, and Computer Science is a collaborative series of distinguished lectures launched in 1981 to honor founding Mathematics Department Chair and William Edmond Milne, a pioneer in numerical analysis.

Bickel is widely recognized as a leading statistician in any metric: breadth, depth or productivity. He will use illustrative examples to show how statistics is the transfer agent for methodology related to extracting information from aggregates.

In his talk, Bickel will address the new challenges posed by “big” and complex data as well as discuss a significant experience with the ENCODE project, a public research project that aims to identify all functional elements in the human genome and serves as one of the follow-ups to the Human Genome Project.

Credited with a wide range of contributions to the field of statistics, Bickel has conducted pioneering research in statistical sub-disciplines and has made important contributions in many areas of statistics, including robust statistics, decision theory, semiparametric modeling, the bootstrap, nonparametric modeling, machine learning, computational biology, and other areas where statistics and quantitative approaches play an important role.

“The message that statistics is a “transfer science” which enables discoveries in every area of science and most other disciplines is becoming a universal truth,” said Sastry G. Pantula, dean of the College of Science. “I am thrilled to have one of the top minds in statistical and data sciences come to Oregon State to talk to our community about how statisticians convert Big Data to useful knowledge.”

Bickel’s scientific findings have helped reshape aspects of statistical theory and methodological development. His research has influenced developments in other quantitative disciplines, including branches of engineering, economics, finance, computational biology, public health, among others. He is a co-author of the textbook, Mathematical Statistics: Basic Ideas and Selected Topicsv.I and II.

Bickel served as president of The Institute of Mathematical Statistics, The Bernoulli Society and the Board of Trustees of the National Institute of Statistics. Nationally, he has held leading positions within the National Academy of Sciences and the National Research Council.

Bickel has received many awards and honors, including being named the Wald Lecturer and Rietz Lecturer of the Institute of Mathematical Statistics. He was the first to receive the COPSS Presidents’ Award from the Committee of Presidents of Statistical Societies, which includes current and past presidents and presidents-elect of five professional societies of statisticians in Northern America. Three of Bickel’s students have also received the COPSS Presidents’ Award.

His work has also been recognized outside of the statistical profession through the John D. and Catherine T. MacArthur Foundation and Guggenheim Fellowships; and election to the American Academy for Arts and Sciences (AAAS), the National Academy of Sciences and the Royal Netherlands Academy of Arts and Sciences.

Born in Bucharest, Romania, Bickel earned a Ph.D. from the University of California, Berkeley at the age of 22. The Hebrew University, Jerusalem and the ETH (Eidgenossische Technische Hochschule, Zurich) awarded him honorary doctorate degrees. Although he officially retired in 2006, Bickel maintains an active research program in the Department of Statistics at Berkeley.

Support for the Milne Lectures comes from a generous gift from the Milne family as well as support from the College of Science’s Departments of Mathematics and Statistics, the College of Engineering‘s School of Electrical Engineering and Computer Science and from the Center for Genome Research and Biocomputing at OSU.

Among the key hires are David Hendrix, assistant professor of biochemistry/biophysics and computer science; Duo Jiang, assistant professor of statistics; David Koslicki, assistant professor of mathematics; Patrick De Leenheer, professor of mathematics and integrative biology; Thomas Sharpton, assistant professor of microbiology and statistics.

“I am thrilled to welcome this extraordinarily talented cohort to the College,” said Sastry G. Pantula, dean of the College of Science.

“They will strengthen our foundation in fundamental sciences while building bridges to enable discoveries in other sciences, engineering and education.”

While the need for mathematical biosciences has grown rapidly due to massive sets of data in life sciences, computational and mathematical algorithms and new statistical methodology, the current community of mathematical bioscientists remains relatively small. The new faculty will strengthen the College’s efforts to advance research at the intersection of mathematical, statistical and biosciences research and nurture a new generation of scientists in a comprehensive, systematic way.

Mathematical Biology: What is it?

De Leenheer is one of a growing number of researchers worldwide who works in both the mathematical and biological sciences. De Leenheer uses mathematics to better understand how a variety of biological systems behave.

Although mathematical biology evolved throughout the twentieth-century, only in the last couple decades has it become its own branch of applied mathematics, primarily because research in biology and medicine has become more dependent on mathematics and computation. To illustrate, federal agencies such as NSF have initiated programs in Mathematical Biology and Research at the Interface of Biological, Mathematical and Physical Sciences.

De Leenheer uses dynamical mathematical models that describe and illuminate biological processes ranging from the cellular to the ecological scale. Currently, he is developing new modeling approaches for the analysis and design of Marine Protected Areas (MPA) to enhance fisheries as part of an NSF-funded project. This work will be instrumental in better informing policymakers on MPA implementation.

Bioinformatics: The new age of data

Bioinformatics professors David Koslicki and Thomas Sharpton have found Oregon State particularly favorable for their research, thanks to the extremely collaborative culture and the high-quality biological and computing resources at the Center for Genome Research and Biocomputing.

“A transdisciplinary field, bioinformatics requires expertise in biology, computer science, mathematics and statistics. It's rare that one researcher has sufficient expertise in all these areas so collaboration is often needed to solve a problem,” says Sharpton.

“OSU is easily the most collaborative environment that I have been a part of, and the supportive and interactive nature of my colleagues helps produce more impactful bioinformatic discoveries at a faster rate.”

Bioinformatics, which is the creation of software tools, algorithms and databases to analyze biological data, evolved into a discipline in the 1970s with the development of DNA sequencing. The explosive quantities of genomics-related data have spurred the growth of bioinformatics databases and tools for a variety of biological fields: medicine, microbiology, ecology, pharmacology, and many more.

“We have massive data sets that have the ability to transform many different fields,” says Koslicki. “But you need algorithms that are extremely efficient to be able to analyze these things.”

So, what does a bioinformatics project look like?

Koslicki invented a bacterial community reconstruction tool in which he sequenced the DNA of an environmental sample to determine which bacteria were present. Using an optimization technique derived from mathematical theory, Koslicki developed a swifter, more accurate method of classifying bacteria.

“Simultaneously, we were able to develop the algorithm to help the biology as well as learn some new mathematics about these compressed sensing techniques that hadn’t been observed before,” remarked Koslicki.

Spanning microbiology and statistics, Sharpton’s lab researches DNA sequences of microorganisms that live on the human body, known as the human microbiome, to understand how they influence health.

“Bioinformatics is critical to our work,” says Sharpton.

“We develop and apply computational and statistical methods to ascertain which microbes comprise the human microbiome, their biological functions, and their association with human health.”

The College of Science is investing in young, diverse faculty whether it’s to advance OSU’s Marine Studies Initiative or national priorities like precision medicine. Currently we are recruiting a quantitative biologist, two computational biologists and senior leaders in mathematics and statistics.

Teaching the next generation of students

Mathematical biology has a reputation for being one of the most difficult branches of applied mathematics, but that only spurs Leenheer and Koslicki’s determination to mentor and train the next generation of students who will work at the intersection of mathematics and biology.

“The important thing as I train graduate students is that they should have a solid mathematical background,” says Koslicki. “Presently, I am teaching the probability sequence. Probability is key for the kinds of things I do. In addition, students need to be able to program and to work with these big data kind of problems."

De Leenheer adds, "In the next five years, I hope to see biology students who have taken certain math courses in order to go to that next step and start using math as a tool in their own research. That would be fantastic."