SAMSI 2016-2017 ASTRO Year-long Program Begins with Bang


This composite image shows an exoplanet (the red spot on the lower left), orbiting the brown dwarf 2M1207 (centre). 2M1207b is the first exoplanet directly imaged and the first discovered orbiting a brown dwarf. It was imaged the first time by the VLT in 2004. Its planetary identity and characteristics were confirmed after one year of observations in 2005. 2M1207b is a Jupiter-like planet, 5 times more massive than Jupiter. It orbits the brown dwarf at a distance 55 times larger than the Earth to the Sun, nearly twice as far as Neptune is from the Sun. The system 2M1207 lies at a distance of 230 light-years, in the constellation of Hydra. The photo is based on three near-infrared exposures (in the H, K and L wavebands) with the NACO adaptive-optics facility at the 8.2-m VLT Yepun telescope at the ESO Paranal Observatory.
This composite image shows an exoplanet (the red spot on the lower left), orbiting the brown dwarf 2M1207 (centre). 2M1207b is the first exoplanet directly imaged and the first discovered orbiting a brown dwarf. It was imaged the first time by the VLT in 2004.  The photo is based on three near-infrared exposures (in the H, K and L wavebands) with the NACO adaptive-optics facility at the 8.2-m VLT Yepun telescope at the European Southern Observatory (ESO) Paranal Observatory.

Since the dawn of time we all have often looked at the night sky and wondered WHAT, if anything, is out there?

In this ongoing 2016-2017 yearlong SAMSI Program on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO), astrophysicists, mathematicians and statisticians are working together among many other things, to explore better ways to find the existence of other planets, in particular the ones which have habitable conditions as our own planet Earth.

This year’s opening workshop for the ASTRO program was held at the NC Biotech Center on August 22-26 which brought together some of the most brilliant minds in the field to discuss among other research topics, the possibility and existence of other worlds or “exoplanets”. The workshop featured a multitude of talks and panel discussions on the various research topics that includes Astrophysical Emulation, Astrophysical Populations (exoplanets), Gravitational Waves, Synoptic Time Domain Surveys and Cosmology.

Over 90 participants from around the nation and also a from other countries (Canada, Spain, UK), specializing in astronomy and astrostatistics were present for the five day workshop that featured speakers from NASA, Caltech, Harvard, Penn State and Yale just to name a few. The year-long program will allow astrophysicists, mathematicians and statisticians to collaborate via virtual media (e.g., weekly webex meetings) and they will be working together for approximately the next nine or so months to analyze huge size data and explore better ways to improve current methodologies based stellar observations produced by spectrographs and other ground-based and space-based astronomical surveys.

Currently, one of the emphasis in the astronomy field is to find the existence of exoplanets and/or other worlds that have the potential to support life. 20160826_105800As this is a hot topic in the astronomy field, scientists and mathematicians are focusing their efforts on finding these exoplanets right here in our own galaxy. By trying to locate exoplanets, the potential exists for probes to be sent to explore these regions for earth-like planets.

Since 1988, survey based analyses have identified the discovery of more than 3,500 exoplanets. The data provided for these discoveries came from the High Accuracy Radial Velocity Planet Searchers (HARPS), beginning in 2004, and later by the Kepler Space Telescope launched in 2009.

Eric Fiegelson, a Distinguished Senior Scholar and Professor from Penn State’s Astronomy and Astrophysics Department, was one of the attendees and was one of the speakers for the opening workshop. Fiegelson was extremely excited about this opportunity to work with other researchers in order to learn how both astrophysicists and astrostatisticians can bring their collective experience and knowledge to the table in order to potentially lead to the discovery of other exoplanets.

Over the past 25 years Fiegelson has been involved in astronomy and teaching, he said, “This event was the first time I have ever seen a room filled with nearly 50% astronomers and 50% statisticians…SAMSI made this possible!” Fiegelson explained why this was significant because until now, the two disciplines in the science of astronomy rarely worked together on a grand level research endeavor like this. Fiegelson is also one of the many visiting fellows at SAMSI for this program whom are charged with supporting the research and collaboration of this program from this consortium of brilliant minds in the field of astronomy and statistics.

20160824_171428It was only fitting that while this workshop was in session, astronomers announced that they may have found a planet 1.3 times more massive than Earth. The exoplanet is known as Proxima B and current analysis suggests that it resides near the star Proxima Centauri, our sun’s nearest neighbor. Proxima B is within the habitable zone to Proxima Centauri, which means that the exoplanet can support liquid water given sufficient atmospheric pressure and therefore has the potential to sustain life. Proxima B is approximately 4.7 million miles away and would take almost 20 years to reach with our current technology of space exploration. Still the existence of Proxima B is our most hopeful prospect yet of finding other life out there in the cosmos.

The news of this exciting discovery was well received by those attending the opening workshop. The existence of the very source of their research further supplanted the need to explore this topic even more. Many of the researchers are excited for the chance to work together and learn each other’s capabilities. Overall, the hope for the ASTRO program is that it provides a wealth of opportunities by promoting the sharing of data and ideas and by allowing scientists to collaborate almost on a daily basis for nine months that could potentially have huge ramifications into the research of five focused research topics.

The ASTRO program, which started in August of this year will be ongoing through May, 2017. To see what other interesting topics and workshops will be discussed in this program, visit:


SAMSI Deputy Director to Deliver Helen Barton Lecture Series at UNC-G

Dr. Sujit Ghosh, Deputy Director of the Statistical and Applied Mathematical Sciences Institute (SAMSI), has been invited by the University of North Carolina-Greensboro’s Department of Mathematics and Statistics to present a series of three lectures this fall as part of the Helen Barton Lecture Series in Mathematical Sciences.

The lecture series has been a fixture in the academic community since 2012 and the target audience for these talks are graduate and upper level undergraduate students and faculty members. Dr. Ghosh is one of many distinguished mathematicians/statisticians who have been invited to speak for the series.

Ghosh’s three-part series, entitled, “Statistical Inference Subject to Shape Constraint,” will take place on the UNC-G campus from, Monday, November 14 thru Wednesday, November 16.

The focus of Dr. Ghosh’s talk will be to present an introductory overview of lectures on statistical inference for density and regression function estimations that are known to preserve a set of shape constraints. Some popular applications include the study of:

  • utility functions, cost functions, and profit functions in economics
  • the analysis of growth rates as a function of various environmental factors
  • the study of dose response curve in the phase I clinical trials
  • the estimation of the monotone hazard rates and the mean residual life functions in reliability and survival analysis and many more

In addition to theoretical results and applications, the lectures will also feature demos of R software packages that can be used to compute various statistical data and graphics.

Ghosh has served as the Deputy Director at SAMSI since 2014. He has served as the Co-Director of Graduate Programs in Statistics at North Carolina State University, where he managed over 150 students annually from 2010 – 2013. Before serving in his current role at SAMSI, Ghosh served as the Program Director in the Division of Mathematical Sciences within the Directorate of Mathematical and Physical Sciences at the National Science Foundation from 2013 – 2014.

Prof. Ghosh has more than 20 years’ experience in conducting, researching and applying statistical analysis of biomedical and environmental information in a wide variety of capacities and subjects. On top of these accomplishments professionally, he has a lengthy and extensive academic record which includes: giving over 125 invited lectures at seminars and national/international meetings; serving as a statistical investigator and consultant for over 40 different research projects funded by numerous private industry leaders and federal agencies and publishing over 95 referred journal articles in the area of biomedical, econometrics and environmental sciences just to name a few. Dr. Ghosh has also co-edited a popular book entitled “Generalized Linear Models: A Bayesian Perspective.”

To see more information on Dr. Ghosh’s lecture or other upcoming events visit the web page for the Helen Barton Lecture series.

IMSM 2016 Prepares Graduate Students for ‘Real World’ Research

The sun set on a hot July day across the street from North Carolina State University, signaling the end of another positive experience in research.

Nearly 40 Graduate Students, of various science, applied mathematics backgrounds and statistics celebrated their accomplishments and experiences after attending the 2016 Industrial Modeling Workshop (IMSM) for Graduate Students in Raleigh, N.C., July 18-27.

This year marked the 22nd anniversary of the IMSM workshop, a major educational outreach component of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Each year, SAMSI invites graduate students from across the country to attend a 10-day workshop, where various industrial and government agencies partner with academia to solve “real world” problems that impact our lives.

This year, SAMSI was pleased to have representatives from: Sandia National Laboratories; Rho, Inc.; the US Army Corps of Engineers (USACE); Environmental Protection Agency (EPA), Pfizer and the Cooperative Institute for Climate and Satellites (CICS). The IMSM workshop is sponsored by SAMSI as well as the Department of Mathematics and the Center for Research in Scientific Computation (CRSC) at N.C. State University.

Graduate students were split into six teams and presented with six different projects from the various industry and lab partners. Subjects of these problems ranged from climate and health to environmental issues. Each team was guided by at least one Industry and one faculty mentor who offered support and helpful hints to make sure the team could develop workable solutions within the allotted time frame.

The IMSM workshop introduces graduate students to the effective application of academic knowledge towards solving “real world” problems. Students also learned valuable skills about time management and team-based research in a time-constrained environment – a practice that is key to achieving results in industry and government labs. The group of students was dynamic, representing such disciplines as Geophysics, Engineering, Biology and of course Applied Mathematics and Statistics. The diversity of students played a pivotal role in helping the teams to develop synergy through their collective strengths and experience in order to reach a common goal. Most students were excited about the opportunity to attend and collectively looked forward to the challenges presented in the IMSM workshop. In the end, industry and lab partners as well as the students benefitted from the experience of producing research results that have the potential to advance “real world” applications.


One highlight of this year’s projects was a problem set directed at ways to identify elements of various allergens in order to develop therapies against food allergies. This important issue was posed by Rho, Inc.

Based on research from the Centers from Disease Control (CDC), food allergies are specifically prevalent in children ages 5 and above. This trend has increased by 18% from 1997 to 2007 and effects nearly 5% of adults and 8% of children. Primarily, eight foods account for 90% of all food allergy reactions: milk, eggs, peanuts, tree nuts, wheat, soy, fish and shellfish.

The students’ focus was to look at nut allergies. Nut allergies make up more than 25% of the most common foods associated with severe allergic reactions. In this specific case, the research developed here could easily be replicated towards the study of other food allergies as well. Allergies are caused by a person’s immune system overreacting to harmless proteins in our food or the environment.  One tool for analyzing these proteins is a peptide microarray. These microarrays help to identify parts of certain proteins that trigger allergic reactions. Fragments of allergy-triggering proteins are arranged on small plates or “chips” and exposed to a patient’s blood.  Antibodies from the patient’s immune system found in the blood will react with some of the fragments. These interactions can be detected by microscopes or scanning machines. The data from these experiments, however tend to be “noisy” when researchers try to accurately determine which protein fragments react with the patient’s antibodies. The students’ aim was to try to identify a more effective way to clear up the noise in these samples. Clearing up the noise ensures better predictability by the researchers in their analysis.

Nut Allergies

The data from samples presented by Rho, Inc., had positive markers for a specific nut allergen. The students analyzed these samples and created an algorithm that could identify these patterns more quickly. The students identified the outliers in each sample, which correlated into clearing up the noisy data from these findings. Correctly identifying these outliers made the predictions about this data more reliable and accurate. The result of applying this approach led to identifying 96% of the noise or “bad spots” on a microarray. By identifying these bad spots with a high degree of certainty, one can have a more effective tool to correctly see what protein fragments are triggering allergies.

Though this algorithm was a big break through, still much research needs to be done. The students’ assistance was a positive step forward on this problem.  With these new findings, Rho, Inc., can now go back and apply some of these same techniques to their ongoing research for this problem. It is work like this that further justifies the purpose of bringing great minds together in order to tackle some of life’s puzzles and help us all to live more problem free.

USACE presented two problems: one on habitat quality assessments in the Columbia River and the second on using surface wave properties to predict nearshore bathymetry. Bathymetry is a measurement of submarine topography and can be used to indicate changes in the ocean floor. This near shore analysis could prove vital for predicting damage to coastal environments due to major storms or significant erosion. Storm surge and erosion also negatively impact transportation routes and civil infrastructure. Collectively, these factors would prohibit efforts of support agencies to assist the civilian populace with critical needs in an emergency.

The group used USACE data from Duck, N.C., compiled from various resources to determine coastal depths within 500 m of the coastline. This distance is crucial when it comes to large vessels providing logistical aid support. Support agencies want to ensure adequate water depth, keeping these large vessels from running aground in poor conditions.  The data could also help to understand the various impacts of erosion on coastal structures and transportation routes.  Studies like this have been used in other situations as well, like saving the Historic Lighthouse out at Cape Hatteras.

Accurate measurements of bathymetry in nearshore regions using conventional means are difficult to obtain.  Direct measurements are costly and sparse, and the underlying topography is constantly changing.

Currently, obtaining accurate data related to this research requires many man hours and often costly equipment. The students used USACE data on wave height, wave number and ocean depth to understand how information on the wave mechanics can be used to generate a map of the underlying bathymetry. They used mathematical representations of the connections between measurable wave properties and bathymetry to develop a statistical algorithm for estimating the water depths along a one-dimensional profile.


The students used data provided by remote sensing platforms, compiled from airborne, satellite and onshore sensors. They studied the dispersion relationship connecting water depth to surface properties, including wave length and period, and discovered using these factors as input provided a relatively accurate estimate of the bathymetry.

Using three different inversion methods, the students accurately determined ocean floor topography up to 900m away from shore. The students found that by using these multiple measurement types, it helped to reduce the amount “noise” in a given variable. In addition, the students determined which inversion method was the best algorithm to use when attempting to accurately identify this data.

Though the group was successful in finding a solution, more work is still needed. The researchers suggested more refinement of their selected inversion method in order to account for more parameters such as beach profile and more access to wave number profiles throughout a given year. These factors could help to isolate trends in the shifting of the ocean floor, which could lead to making mitigation efforts to correct these issues easier.

The group’s final recommendation was to apply this information to a higher fidelity model in order to assess bathymetry in multiple dimensions. The USACE industry mentor looked upon the results favorably. The students’ findings have the potential for numerous applications in keeping with the USACE mission at home and abroad.

Dining Out


Overall the consensus of the graduate students was that this workshop was helpful in preparing them for their future contributions in research. The IMSM is a valuable tool for industry as well. Industries actively seek qualified up and coming researchers by being a part of workshops like this and the research gained also has the potential to advance the work in their various research. As the workshop closed, the students spent their last night dining together and reflecting on the experiences they shared over the previous week and a half with peers and faculty and industry mentors in the program.


Planning and scheduling by SAMSI has begun for the 2017 IMSM; applications for the workshop next year will be accepted in January. To find out more and apply, interested graduate students should visit the SAMSI website at: