New Deputy Director Begins Term at SAMSI

Elvan Ceyhan, SAMSI Deputy Director

After many months SAMSI is proud to welcome their newest Deputy Director, Elvan Ceyhan.

Ceyhan, who was a visiting associate professor at the University of Pittsburgh in 2016, joined the SAMSI directorate in July this year. He joins the SAMSI team and will also serve as a research associate professor of the Department of Statistics at North Carolina State University (NCSU). He replaces former Deputy Director, Sujit Ghosh, who is currently a Professor of Statistics in the same department at NCSU.

Ceyhan, a Turkish native, received his undergraduate education and a Bachelor of Science in Mathematics from Koc University (KU) in Istanbul, Turkey. In 1997, he came to the United States and originally attended Oklahoma State University’s (OSU) Ph.D. Program in Mathematics, before changing his mind and switching to their statistics master’s program. He went on to receive his Master of Science degree in Statistics from OSU in 2000. That same year, Ceyhan began the Ph.D. program in the Applied Mathematics and Statistics Department at Johns Hopkins University – he went on to receive his Ph.D. from Johns Hopkins in 2004.

From 2004 to 2005, Ceyhan worked as a postdoctoral fellow at the Center for Imaging Science at Johns Hopkins. After his time at Johns Hopkins, he returned to Turkey and served as an assistant professor in the Department of Mathematics at KU until 2011, when he was promoted to an associate professorship. Ceyhan served in that capacity until 2016, when he went to the University of Pittsburgh for the visiting associate professor post. Throughout his academic career, he has (co) authored almost 50 journal articles and given numerous talks and presentations.

What Ceyhan enjoys most about applied mathematics and statistics is data analysis, finding hidden patterns and studying trends in data. “I was always good in math in primary school,” he said. “I entered the university as a physics major and a year later, realized I liked math better, so I switched.”

Ceyhan is easy going and enjoys working with members of the SAMSI directorate, the staff and postdoctoral fellows and visitors that attend the institute. After Elvan took the position, he decided the best thing to do was to pick up where his predecessors had left off in order to increase awareness of how SAMSI supports applied math and statistics fields.

“I would like to continue our conventions and contribute more effort towards diversity in our programs,” said Ceyhan. He also believes SAMSI needs to continue to support heavily data science and big data programs, as these topics are major points of interest in the statistics community.

“I would like to continue our conventions and contribute more effort towards diversity in our programs,” said Ceyhan. He also believes SAMSI needs to continue to support heavily data science and big data programs, as these topics are major points of interest in the statistics community.

Among his many goals as deputy director, Ceyhan will work to expand education and outreach initiatives, support undergraduate workshops and programs and serve as an advisor to postdoctoral fellows in order to help them advance their research and academic careers.

Ceyhan resides with his wife, of nearly 10 years and his two children, daughter Gokce and son Melih. His family moved with him in 2016 when he took the visiting associate professor position in Pittsburgh. The family still misses Turkey and they hope to get back to the country next year to visit.

Ceyhan enjoys watching soccer and studying ancient history in his spare time. SAMSI is glad to have him in this new leadership role within the organization.

SAMSI Welcomes Leslie McClure as New Associate Director of Diversity

Leslie McClure, SAMSI Associate Director of Diversity
Leslie McClure, SAMSI Associate Director of Diversity

Since she began her academic career, Leslie McClure has always had a keen interest and respect for others. It is her passion for representing women and people of color in the mathematical sciences that led to her recently being appointed as the SAMSI Associate Director of Diversity in early August of this year.

“Throughout my education, particularly my undergraduate, I was often one of very few women in my classes, and rarely had female professors,” said McClure. “Women and people of color are represented in lower numbers in the professorate, and have even less representation in the higher levels of academics.”

McClure, who also currently serves as the Chair of the Department of Epidemiology and Biostatistics at the Dornsife School of Public Health at Drexel University, received her Bachelor’s Degree in Mathematics from the University of Kansas. She went on to receive a Master’s Degree in Preventive Medicine and Environmental Health from the University of Iowa and later, a Ph.D. in Biostatistics from the University of Michigan.

Before working at Drexel, McClure spent 11 years as a faculty member at the University of Alabama at Birmingham in the Department of Biostatistics.

“I gravitated towards biostatistics because it was a good fit with my interests, but I think I was also attracted to the field because it appeared more diverse than math,” said McClure.

McClure is a trained clinical trials statistician and her current research is focused on the methods that drive adaptive design in clinical trials, as well as the practical implications of implementing an adaptive design. She also works diligently trying to understand why racial inequalities exist in disease, particularly cardiovascular disease and stroke, and the role that the environment may play in those differences.

“Without diversity of people, we do not have diversity of ideas. Without diversity of ideas, we lose creativity in science, and fail to continue moving forward,” — Leslie McClure

Much like her research, McClure also works as a champion to find ways to make the field of mathematics more inclusive to women and under-represented minorities. “As I have pursued my own academic goals, I have also worked to increase and maintain diversity in the math sciences,” she said.

McClure is also part of the leadership for the National Alliance for Doctoral Studies in the Mathematical Sciences, where she serves as the Associate Director of Statistics. One of the main goals of the Math Alliance, located on the campus of Purdue University, is to foster the growth of the community of mathematical scientists in order to promote a diverse workforce.

SAMSI is proud to have added such an accomplished professional to the directorate. SAMSI believes whole-heartedly in creating an academic environment of equality and inclusivity for all. As the SAMSI Diversity Director, McClure will work with local universities and through her numerous contacts nationwide to research and implement strategies that will work towards advancing the careers of under-represented groups in the field mathematics.

McClure stays busy and focused. When she is not doing research or working for the betterment of others, she stays active running and spending time with her husband (who is a Chemistry Professor at Drexel) and their two children, Lillian and Preston. McClure also enjoys spending time with her dog, Bosco, watching Law and Order reruns.

“Without diversity of people, we do not have diversity of ideas. Without diversity of ideas, we lose creativity in science, and fail to continue moving forward,” said McClure.

SAMSI Kicks Off 2017-2018 Programs on Environment and High-Dimensional Data Sampling

Richard Smith, Director of SAMSI, opens the CLIM Opening Workshop at the N.C. Biotechnology Center on Aug. 21, 2017. The workshop marks the beginning of the CLIM Program, focused on using data and climate models to analyze environmental changes on our Earth.

Much like universities around the country beginning their fall semesters, SAMSI also kicked off their 2017-2018 year-long programs’ opening workshops: Mathematical and Statistical Methods for Climate and Earth Systems (CLIM) and Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics (QMC).

Late August was indeed a busy time for SAMSI as the opening workshops occurred in succession during the end of that month. The week-long CLIM Opening Workshop ran from Aug. 21-25 and the QMC Opening Workshop (Aug. 28 – Sept. 1) served as the starting point for both programs.

The CLIM Program looks at analyzing data and climate models to potentially predict future changes on our Earth that could directly impact our environment and the human population. The CLIM Opening Workshop featured many esteemed minds in the study environmental science. The opening workshop led to the creation of 13 working groups, whose overall purpose is to study various factors and data analysis in order to understand how our environment is evolving.

“Climate Science is important for many reasons in our society,” said Richard Smith, Director of SAMSI and Leader of the CLIM Program about the opening workshop. “It is not widely appreciated just how critical the role of mathematical and statistical methods play in climate science.”

More than 120 participants from universities around the world attended the popular workshop. Twenty-five speakers presented lectures on various topics about the science of the environment and how to use mathematical and statistical data to find the root to the causality seen in the modern world. The two panel discussions held during the workshop created much discussion and offered many contributions that led to the creation of the CLIM Program working groups.

The workshop participants were even treated to a rare solar eclipse that occurred over the continental United States during that time. To accommodate this rare event, organizers planned time during the opening day to go out and view the phenomena as it reached the totality phase. Everyone was excited as they used solar eclipse glasses and/or various safe methods to view the eclipse. The last time a solar eclipse could be viewed from the contiguous United States was Feb. 29, 1979. The eclipse was a special occurrence that was a happy coincidence to fall during the workshop and offered a perspective of how much we are shaped by the world around us.

As the opening workshop closed, participants chose the working groups they would be affiliated with for the remainder of the CLIM program. The workshop created valuable network opportunities between the scientists and mathematicians in attendance so that they can continue their research even after the CLIM program ends in May next year.

“This workshop brought together some of the top experts in climate science with the leading researchers in mathematics and statistics,” said Smith. “The lively discussions generated many ideas that will be developed during the rest of this [CLIM] program.”

Participants use multiple safe methods to view a solar eclipse during a break at the CLIM Program Opening Workshop on Aug 21, 2017. The eclipse was a special event that was viewed almost exclusively from the contiguous United States. Organizers planned time during the opening day to go out and view the phenomena as it reached the totality phase. The workshop marks the beginning of the CLIM Program, focused on using data and climate models to analyze environmental changes on our Earth.

The QMC Opening Workshop began the following week, Monday, Aug. 28, and was hosted at the beautiful Penn Pavilion on the campus of Duke University.

More than 110 mathematicians and statisticians check in for the QMC Program Opening Workshop on Aug. 28, 2017, at the Penn Pavilion, on the campus of Duke University. The goal of the QMC Program is to explore the potential of QMC and other deterministic, randomized and hybrid sampling methods for a wide range of applications, including the numerical solution of PDEs; machine learning; computer graphics; Markov chain sampling, like MCMC and MCQMC; sequential Monte Carlo; and uncertainty quantification.

This workshop brought together more than 110 mathematicians and statisticians, who collectively created 10 specific working groups focused on discussing ways in which they would research how to use big data across a wide range of practical applications.

“Kudos to the QMC Program Leaders Art Owen, Frances Kuo, Fred Hickernell and Pierre L’Ecuyer for getting the year-long SAMSI QMC off to a fantastic start,” said Ilse Ipsen, Associate Director of SAMSI and the QMC Program Leader. “Their commitment, combined with spot-on real-time assistance from SAMSI postdocs Cheng Cheng, Matthias Sachs and Whitney Huang, produced this lively Opening Workshop and an unusually large number of 10 promising working groups.”

The goal of the QMC Program is to explore the potential of QMC and other deterministic, randomized and hybrid sampling methods for a wide range of applications, including the numerical solution of PDEs; machine learning; computer graphics; Markov chain sampling, like MCMC and MCQMC; sequential Monte Carlo; and uncertainty quantification.

More than 20 speakers were invited to speak on a wide variety of sampling methods. The talks generated much discussion amongst participants and created the impetus for the working groups that were created.

Overall, the QMC Opening Workshop was well received by the participants and many looked forward to the future meetings in their respective working groups.

“The QMC Program is well on its way to being super-productive,” said Ipsen.

The research that will come from both the CLIM and QMC programs will help to address ways in which we can improve our environment, improve efficiency and productivity through random sampling across various applications, and advance technology. Research and collaboration are how SAMSI works to advance research in statistics and applied mathematics to innovate the future.

To see what was presented at these workshops, visit: www.samsi.info/clim-ow or www.samsi.info/qmc-ow; to view working groups, visit: www.samsi.info/working-groups.

Mathematicians and statisticians pose for a group photo at the conclusion of the QMC Program Opening Workshop on Sep. 1, 2017, at the Penn Pavilion, on the campus of Duke University. The workshop featured more than 20 speakers and led to the creation of 10 specific working groups focused on discussing ways in which they would research how to use big data across a wide range of practical applications. The goal of the QMC Program is to explore the potential of QMC and other deterministic, randomized and hybrid sampling methods for a wide range of applications, including the numerical solution of PDEs; machine learning; computer graphics; Markov chain sampling, like MCMC and MCQMC; sequential Monte Carlo; and uncertainty quantification.

2017-2018 SAMSI Postdoctoral Fellows

SAMSI welcomes the 2017-2018 Program Postdoctoral Fellows. These eight young professionals will spend the next two years working in their assigned programs: Program on Mathematical and Statistical Methods for Climate and the Earth System (CLIM) or the Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics (QMC). This year’s postdoctoral fellows will bring their own unique talents to work for SAMSI’s programs. SAMSI is proud to present this year’s group!

Cheng Cheng

Cheng Cheng
Cheng is in the SAMSI QMC Program. She earned her Ph.D. in Mathematics from the University of Central Florida in 2017. Her research interests include applied and computational harmonic analysis, emphasis on sampling theory in signal processing, and high dimensional data analysis.

Yawen Guan

Yawen Guan
Yawen is in the SAMSI CLIM Program. She received her Ph.D. in statistics from Pennsylvania State University. During her graduate studies, she was fortunate to work with top scientists in the study of the Antarctic Ice Sheet. She was intrigued by the ice sheet physics and developed a statistical method to combine physics and multiple data sets to study ice streams on West Antarctica. Her research interests are spatial statistics, Bayesian modeling and computational methods for large data.

Huang Huang

Huang Huang
Huang is in the SAMSI CLIM Program. He received his Ph.D. in Statistics from King Abdullah University of Science & Technology (KAUST). His research experiences include computational methods for spatio-temporal statistics and functional data analysis. He enjoys using these statistical tools to collaborate with other scientists who have expertise in climate, oceanography, geophysics, etc., in order to explore interesting environmental problems.

Whitney Huang

Whitney Huang
Whitney is in the SAMSI CLIM Program. His research focuses on statistics of extremes and spatial, spatio-temporal data analysis with applications in climate. Ultimately, his research goal, as a statistician, is to bridge the gap between statistics and atmospheric/oceanic sciences. In his spare time he enjoys hiking, travel, and watching sports (basketball, tennis).

Maggie Johnson

Maggie Johnson
Maggie is in the SAMSI CLIM Program. She received her Ph.D. in Statistics from Iowa State University in 2017. Her broad research interests are in developing statistical methods for solving environmental problems. Some of her particular statistical research interests are in temporal and spatiotemporal statistics, Bayesian statistics, hierarchical modeling, and mixture models. She is originally from Minnesota where her family owns a Highland cattle farm, and in her spare time she enjoys cooking, fly-fishing, and woodcarving.

Mikael Kuusela

Mikael Kuusela
Mikael is in the SAMSI CLIM Program. He is a statistician working on data analysis methods for physical science applications. He is currently working on developing spatio-temporal interpolation techniques for analysis of oceanographic data from Argo profiling floats. In his free time, he enjoys traveling to far-away places, hiking in the summer and skiing in the winter.

Matthias Sachs

Matthias Sachs
Matthias is in the SAMSI QMC Program. During his Ph.D. he has have been working on numerical methods for ergodic stochastic differential equations. He has focused his efforts towards working on discretization methods for variants of the Langevin equation with applications in canonical sampling and molecular modelling. He is currently exploring the application of these models in sampling problems in Bayesian statistics and machine learning.

Christian Sampson

Christian Sampson
Christian is in the SAMSI CLIM Program. He just received his Ph.D. in mathematics from the University of Utah this year. His research interests lie at the interface between geophysics and mathematics. He is interested in sea ice and its role in the Earth’s climate system. While at SAMSI, he will be working with Professor Chris Jones at UNC Chapel Hill.

Graduate Students get Practical Experience in Math and Statistics Research at 2017 IMSM

A graduate student and her group present findings on a problem posed by one of the many program partners at the 2017 IMSM Workshop on the campus of North Carolina State University from July 17-26. The workshop exposes graduate students to methods used by industry and national labs to solve real world problems.

SAMSI completed the 2017 Industrial Math/Stat Modeling Workshop for Graduate Students (IMSM) this past summer. The event was held on the campus of North Carolina State University from July 17-26, and was attended by more than 40 graduate students from across the nation.

The IMSM is an annual educational outreach event that features collaborations with industry, national labs and other governmental organizations. During the workshop graduate students in mathematics, statistics and computational science disciplines are exposed to challenging real-world problems that arise in industrial and government laboratory research.

“This type of summer workshop has been held at N.C. State since 1995,” said Mansoor Haider, a Professor of Mathematics at N.C. State University and the workshop’s organizer.  “The IMSM name has been in place for well over a decade now. This reflects the importance of integrating statistics with mathematics and computation in solving modeling problems arising outside of academia.”

Several prominent leaders in industry and national labs provided first-hand experience and mentorship to the students. This year SAMSI was proud to partner with professionals from: Sandia National Laboratories; Rho, Inc.; U.S. Army Corps of Engineers, PAREXEL and the Environmental Protection Agency (EPA) among others.

This year’s partners presented problems to the attending students. The students were placed into research groups, and then collectively developed and implemented ways to resolve the issues at hand. The various partner representatives and workshop faculty members provided valuable mentorship and direction to the students. The students also received practical experience in problem-solving and first-hand experience in what it is like to work in a research group in a non-academic setting.

“The need for doctorally trained statisticians and mathematicians in industry and national labs is ever increasing,” said Haider.  “By immersing them in an intensive collaborative research experience, we hope to increase students’ awareness of the variety of career options after graduation, and the skills they will need to be successful.”

Graduate students attend a job fair, part of the 2017 IMSM Workshop, on the campus of North Carolina State University from July 17-26. The workshop exposed graduate students to future professional opportunities in the field of mathematics and statistics. It also helped to teach the students various methods used by industry and national labs to solve real world problems.

Some of the problems tackled by this year’s participants included:

  • How to integrate large-scale data from open source Google Earth Engine with air quality monitoring across the country in order to provide real-time air quality information to users.
  • Using coast line bathymetry data to assist in erosion control – to be used in predicting environmental effects after coastal storms or helping humanitarian aid logisticians to identify effective delivery methods by sea to provide critical relief.
  • Determining root causes of allergies in humans by studying the correlation and interactions between microbes in the environment and those inside the nose. Potential applications of this research aim to adapt the design of buildings and control exposure to identified allergens to reduce allergy and asthma among children at risk.

Participants put in long hours, sometimes well into the night. The students worked together and maximized the individual knowledge strengths of group participants to assist in solving their team’s assigned problem. After several days of team research and collaboration with group mentors and faculty, the groups reconvened and presented their findings to their peers and other academic professionals. The partners attending the workshop got valuable responses to the problems they posed, and in some instances, received insight into alternative research avenues or approaches to pursue in the future.

“For many students, this is their first experience tackling mathematical or statistical modeling problems outside of a university research setting,” said Haider.  “The workshop is intensive… It nicely mimics unique challenges in industrial research like identifying, formulating, and solving problems in a team, and then refining, coordinating, presenting and reporting on the results, all in a short time period.”

 The IMSM is one of the many ways SAMSI helps bring new talent together in order to collaborate with relevant applied math, statistics and computational science organizations. These workshops help to prepare and inspire those considering careers in science and math disciplines for the future.

For more on the Industrial Math/Stat Modeling Workshop and to see research presented from previous workshops, visit: www.samsi.info/imsm-history.

A program partner presents a clinical problem for the students to solve, as a group, during the 2017 IMSM Workshop. The workshop was held on the campus of North Carolina State University from July 17-26. The workshop exposes graduate students to methods used by industry and national labs to solve real world problems.

Transition Workshop brings SAMSI’s ASTRO Program to Close

SAMSI recently hosted an ASTRO Transition Workshop from May 8-10, 2017. The workshop was the final event of SAMSI’s Program on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO) and was attended by numerous astrophysicists, astronomers and astrostatisticians from across the country.

Nearly 40 participants attended the workshop in order to discuss their findings compiled from multiple working groups formed throughout the past academic year. The organizing committee for the program listened to spokespersons from each group as they presented their findings. The group also discussed continuing future collaborations between these working groups once the program was over.

SAMSI’s ASTRO program liaison and Deputy Director, Sujit Ghosh, noted that the ASTRO program has been successful in creating a cohesive bond between the statistical and mathematical sciences and the disciplinary sciences, like astronomy and astrophysics. According to Ghosh, this coupling is helping to systematically streamline the analysis of huge data sets that are produced from the Laser Interferometer Gravitational-Wave Observatory (LIGO), gravitational wave (GW) research and exoplanet discoveries.

A panel, consisting of ASTRO program leaders, collected feedback from the numerous researchers in attendance. A significant issue that researchers brought up was the challenge of publishing research articles in domain sciences (i.e. core stat or astrophysics journals) versus the disciplinary sciences. This issue was viewed as a significant obstacle when using these research papers as a reference for tenure-based decisions. The panel of program leaders could not determine the best way to address this situation. Instead they agreed that this topic should be readdressed during future interdisciplinary engagements, like transition workshops.

“This [ICTS-SAMSI] workshop helped form several collaborations to enable what will likely prove to be a fruitful collaboration among people from diverse backgrounds that can propel the progress of science”

Overall, the ASTRO Program focused on ways to create solid partnerships between researchers in applied mathematics, astronomy, astrophysics and statistics (professionals who do not ordinarily work together in the field). In fact, the concept of astrostatistics emerged from numerous collaborations, like this one, between researchers during past SAMSI programs. The partnerships created by this program are important because they could potentially advance research in astronomy. In addition, three mid-program workshops (one on Exoplanets in the Fall of 2016 and two on Synoptic Surveys and GW Astronomy and Astrophysical Population Emulation in the Spring of 2017) were organized by the researchers to support the program during the past year.

SAMSI also expanded its international collaboration capability by organizing a joint workshop with the International Center for Theoretical Sciences (ICTS) in

Bengaluru, India. This workshop enabled scientists to share their ideas and work together across two continents in order to explore the grand challenges in gravitational waves time domain astronomy.

“SAMSI workshops and working groups have helped me understand how my thesis work fits into the larger scientific picture and how to gain a better understanding of what our science priorities are as a community of observational astronomers,” said Jackeline Moreno, a graduate student at Drexel University, who was a member of one of the working groups. Moreno said she was impressed with how the joint workshop brought together experts from around the world and from different research backgrounds to come together and share techniques and insights for analyzing time series data.

Kaustabh Vaghmare, a data scientist from the Inter -University Center for Astronomy and Astrophysics (IUCAA) in Pune, India, who also attended the ICTS-SAMSI workshop, agreed. Vaghmare began by saying that time domain astronomy has improved a great deal in the last decade, due in large part, to advances in robotic telescopes, image processing and database technologies. These advances, according to Vaghmare, have given astronomers the ability to organize several systematic surveys of the sky. In addition to those advances though, Vaghmare sited the importance of the human aspect as a valuable way of sharing information. “This [ICTS-SAMSI] workshop helped form several collaborations to enable what will likely prove to be a fruitful collaboration among people from diverse backgrounds that can propel the progress of science,” he said.

Joint workshops, like the ICTS-SAMSI workshop, help SAMSI to emphasize the value of collaborating with other institutions or across fields of study. The results of these collaborations creates more dynamic ways to solve traditional problems using the tools of applied mathematics and statistics as a guide.

The program offered academic courses on Analytical Methods and Applications to Astrophysics and Astronomy in the fall of 2016 and Time Series Methods for Astronomy this past spring. The program also provided numerous opportunities for graduate and undergraduate students to participate and see what future opportunities are available to them in the field of astronomy from a mathematician’s point of view.

As the ASTRO Program transitions, SAMSI sets its sights on the two new 2017-2018 programs: Program on Mathematical and Statistical Methods for Climate and the Earth System (CLIM) and the Program on Quasi-Monte Carlo and High Dimensional Sampling Methods for Applied Mathematics (QMC). Both programs open this August and will end in May 2018.

Transition Workshop Completes Optimization Program for 2016-2017

The Optimization Transition Workshop was hosted by SAMSI from May 1-3, 2017 to effectively close the Program on Optimization for the 2016-2017 research year.

The workshop was attended by nearly 40 participants who discussed findings in research conducted from the program’s 13 different working groups formed last fall. Scholars and researchers from multiple fields of applied math and statistical science not only  explored progress made by their groups throughout this past year, but they also discussed effective ways to collaborate after the program was over in hopes of continuing to tackle some of the complex issues the field of optimization presents.

The program featured six different workshops:

The program also hosted multiple opportunities for graduate and undergraduate students: an undergraduate workshop earlier this year; and a two-part course hosted in Fall 2016 and Spring 2017.

Overall, the program was attended by some 500 participants throughout this past research year. The program offered an opportunity for colleagues to share their knowledge and to network with up and coming researchers in the field. Programs like this one help SAMSI to provide researchers in statistics, applied mathematics and data science fields a forum to meet, discuss and collaborate on a wide range of topics.

SAMSI is one of eight math institutes funded by the National Science Foundation (NSF) whose purpose is to advance research in the mathematical sciences, increase awareness of mathematical sciences and disciplines and directly engage prospective intellectual talent in that effort.

To see what was presented, click on this link: Optimization Transition Workshop. In order to find more information on the workshops for this program for this past year, click here: Program on Optimization.

 

 

SAMSI Educates Undergrads on Mathematics and Optimization

SAMSI hosted an Undergraduate Workshop as part of its Education and Outreach initiative in their Program on Optimization from Feb. 27-28, 2017.

Nearly 40 undergraduate students from universities across the country were treated to lectures on optimization methods used in large-scale statistical analysis and were also introduced to statistical inverse problems. In addition, students received hands on familiarization with software packages that help to determine these complex calculations.

Though the workshop only lasted two days, students stayed busy! They received an overview on who SAMSI is and how they are helping to support and promote those considering the fields of mathematics and statistics. Students also met and networked with SAMSI Postdoctoral Fellows. Post docs mentored the young group on what they should focus on in their academics to get ready for the job market in the fields of applied mathematics and statistics.

On the last day, students took a field trip to SAS, a major internationally known software company, headquartered in N.C.

During their visit, the students received talks from computer scientists, analytical mathematicians and optimization specialists on how SAS develops software in line with client user and business objectives.

To see what was discussed, visit: OPT E&O Undergraduate webpage.

SAMSI Ends 2016 Successfully; Prepares for Upcoming 2017/18 Campaign

As 2017 begins, another year of successful SAMSI workshops has closed. Since the beginning of the 2016 academic year, in late August, SAMSI has hosted at least seven workshops that supported the goals of both the Program on Optimization (OPT) and Program on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO).

Since the 2016 academic year began, the OPT Program offered a two-part course in Numerical Optimization and Applications. These courses, given in fall and spring semesters, teach students how to use various statistical and applied mathematical techniques to solve complex optimization problems. In addition to these courses, OPT has hosted four workshops, most recently, the Workshop on the Interface of Statistics Optimization (WISO).

WISO took place on the campus of Duke University, one of many SAMSI partners. The workshop lasted for three days and featured twelve lectures from some of the most esteemed minds in field of optimization. The scholars spoke to an audience of more than 100 participants from all over the world. An exciting element of LIVE STREAMING was added to this workshop which gave the event the capability to reach a wider audience. For those who were not part of the LIVE audience, SAMSI archived all the lectures on the WISO video webpage for future viewing.

The ASTRO program has helped to bridge the gap between astrostatisticians and astronomers so that, collectively, they can work together to potentially discover the unknown in our universe.

Since the academic year began, ASTRO has also hosted four workshops and two research related courses. The fall course, Analytical Methods and Applications to Astrophysics and Astronomy, was led by James Long of Texas A&M. Long and other visiting academic fellows at SAMSI worked together to provide instruction on the current methods used to capture astronomical data and how this data is being analyzed and interpolated.  The spring course, Time Series Methods of Astronomy, is being led by Eric Feigelson of Penn State University and a team of others. The course focus is how big data sets are being used to accurately categorize and identify astronomical bodies such as “exoplanets” and distant stars in the universe.

In all, SAMSI has hosted approximately 530 academic scholars, undergraduate and graduate, Ph.D. students and postdoctoral fellows since the 2016 academic year began in late August.

SAMSI is also committed to bringing this same level of excellence to the 2017-2018 programs, Mathematical and Statistical Methods for Climate and Earth Systems (CLIM) and Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics (QMC).

CLIM will bring together esteemed academic scholars in the various fields of the study of climate to explore current climate models, while also discussing how modern environmental issues will impact earth systems and the human population in the future. CLIM begins in August 2017 and runs through May 2018.

Finally the QMC program will tackle complex mathematical techniques that are used in advanced technology such as machine learning, computer graphics and PDE solving systems. The program analyzes further how complex mathematical    sampling algorithms are used in these systems.

As 2017 begins, SAMSI takes pride in what was accomplished last year and looks forward to bringing their participants new and challenging programs emphasizing ways in which applied mathematics and statistics impact our world. Going forward, SAMSI will continue to offer the best programs to the best professionals in the field. For attendees and colleagues, that is what the SAMSI experience is all about.

ASTRO Spring Course Prepares Students for Analyzing Astronomical Big Data

Astronomers continue to search the stars in order to archive large amounts of data in hopes of learning the mechanics and wonders of the universe. SAMSI is hosting a Spring Course, “Time Series Methods for Astronomy,” as part of their Program on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO).

The course started in January and runs through April 26th of this year. This is the first time a team of astronomy scholars, is teaching this course. The course is designed by Eric Feigelson, a Distinguished Senior Scholar and Professor from Penn State University’s Department of Astronomy and Astrophysics. Feigelson is accompanied by several SAMSI Astronomy Visiting Scholars from other academic institutions, who made up the remainder of the class instruction.

The class is taught in four phases:

  • Phase I introduces students to a background of astronomical materials, including the introduction to the public domain ‘R’ Statistical Software environment.
  • Phase II reviews classical time series analysis in the time and frequency domain for data analysis.
  • Phase III consists of various expert SAMSI researchers: Ashish Mahabal [Caltech]; Eric Ford [Penn State]; Bekki Dawson [Penn State] and many more, who will be teaching advanced techniques for astronomical time series analysis.
  • Phase IV has students present course projects for class discussion.

Those taking the course will come away with an understanding of how to capture and interpolate data from sometimes problematic astronomical samples. The skills taught during the course are some of the most state-of-the-art techniques in the field. These techniques allow for astrophysicists and astrostatisticians to work together in order to potentially chart the galaxy. Due to the large amount information captured and in some cases, data samples containing uneven dispersion rates, special consideration must be taken in order to ensure all information is measured correctly. Therefore, researchers must know how to interpret the sample information provided and extrapolate accurate data.

For ongoing updates on the course, visit the Time Series Methods for Astronomy page on the SAMSI website. All presentations will be videotaped and posted on the Time Series Methods for Astronomy Video page. For questions please email astro@samsi.info.