SAMSI Wraps up 2018 Fall Semester

Ernest Fokoué, a m associate professor of statistics at the Rochester Institute of Technology presents a mini-course at the 2018 Modern Math Workshop in Oct. 10, 2018. He presented an “Overview of Data Science: Regression and Classification” during the two-day workshop.

As 2018 closed, SAMSI reflects on a very productive fall semester.

Since opening programs in PMED and MUMS, SAMSI continues to bring together some of the best minds in applied mathematics and statistics to address modern issues across a broad spectrum of subjects. The institute has either hosted or co-sponsored five different events this past fall that discussed topics such as machine learning, climate, computer-based statistical modeling, using statistics in precision medicine and developing mathematical algorithms to identify gerrymandering in state voter maps.

2018 Modern Math Workshop
SAMSI was a major sponsor for the 2018 Modern Math Workshop (MMW) this past October in San Antonio, TX. The two-day workshop, held annually, focused on encouraging undergraduates, graduate students and recent PhDs from underrepresented minority groups to pursue careers in the mathematical sciences and help them to build mentoring networks.

The MMW featured Javier Rojo, a professor of statistics at Oregon State University as keynote speaker. Mini-courses were also taught by Ernest Fokoué, an associate professor of statistics at Rochester Institute of Technology and Katie Newhall, an assistant professor of mathematics at the University of North Carolina at Chapel Hill. These events preceded the 2018 SACNAS National Diversity in STEM Conference.

Lisa Lebovici, a master’s student in statistics at Duke University, presents findings on gerrymandering data from a case involving the state of Maryland, during the Quantitative Redistricting Workshop held at Duke Oct. 8-9, 2018. Valuable information about the elections was presented at this workshop from social and political scientists, lawyers and mathematicians and statisticians.

Education & Outreach and Research Workshops
SAMSI also hosted a workshop for nearly 40 undergraduates in October that supported this year’s PMED program. Students were treated to talks on how statistics and applied mathematics are used in precision medicine. The students were also treated to a panel consisting of postdoctoral and graduate researchers who discussed their journey in academics and how they are preparing for future careers in the mathematical sciences.

October also included a workshop at Duke University called Quantitative Redistricting, that studied how to use statistical data, census research, sociology and computer-based algorithms to identify “gerrymandering,” an issue that affects the election process in states across the nation. Gerrymandering is the practice of manipulating the boundaries of (an electoral constituency) so as to favor one party or class.

The workshop helped to raise awareness of this important issue. The workshop, led by Jonathan Mattingly, Chair of the Department of Mathematics at Duke University, brought together professionals in sociology, political science, law and statistics and computer science to address this issue.

SAMSI also co-sponsored multiple events this fall: The Nexus of Climate Data, Insurance and Adaptive Capacity and the Workshop on R & Spark – Tools for Data Science Workflows.

The R & Spark Course was a two-day workshop, instructed by E. James Harner, a Professor Emeritus of Statistics at West Virginia University. The event was co-sponsored with the National Institute of Statistical Sciences (NISS) that helped to educate students and researchers about R and Spark software.

R and Spark are computing environments used in statistical science to extract data, compartmentalize it and help to develop algorithms to conduct research with the data provided.

Harner provided instruction to a class of more than 35 students and researchers combined. The instruction helped them to understand how to work within the environment and apply what they had learned to further their own personal research.

Finally, the Nexus of Climate Data, Insurance and Adaptive Capacity workshop was another co-sponsored event, made possible by a collaboration of SAMSI, the Reinsurance Association of America (RAA), Renaissance Computing Institute (RENCI) and the Wake Forest University Center for Energy, Environment and Sustainability.

The workshop, which took place in Asheville, NC, was a national, interdisciplinary scientific research discussion on statistical modeling and climate science.

All of these events help achieve SAMSI’s vision to conduct programs that help to connect young researchers from academia, industry, national laboratories and government to address a wide variety of research using applied math and statistical and computer sciences.

Whether working with other entities or hosting events by themselves, SAMSI continues their dedication to serving the science and math community.

Bruce Pitman, a professor of mathematics from the University of Buffalo and a visiting researcher at SAMSI, opens up the MUMS workshop at Gross Hall, on the campus of Duke University in Aug. 2018. Pitman was one of many researchers attending the workshop that focused on the utilization of model uncertainty and uncertainty quantification methods to solve complex problems.

SAMSI Hosts NC ASA Chapter Mentoring and Early Career Development Workshop


By Elizabeth Mannshardt, NC ASA President 2018


**The following is an excerpt from the NC ASA blog. Click HERE to see the full version of this article.**

Sonia (Davis) Thomas, professor of the practice of Biostatistics at the University of North Carolina at Chapel Hill, discusses leadership and “Presentation Best Practices” based on her Leadership in Biostatistics graduate course at that institution.

NC American Statistical Association (ASA) was excited to offer the first Mentoring and Early Career Development Workshop for students and early career professionals. It featured prominent statisticians from our community and included interactive sessions on goal-structuring and professional development. Workshop participants also had the opportunity to meet with ASA President Lisa LaVange in a small group setting prior to the NC ASA “Stories of Significance” Fall Dinner that evening, as well as network with the NC ASA community at the dinner.

The workshop was designed to provide a variety of viewpoints on professional statistical careers through a series of presentations as well as interactive sessions to engage participants. These sessions included professional statisticians from a broad range of backgrounds participating in both a professional panel discussion as well as small group focused mentoring with workshop participants. There were also break-out sessions for young professionals on Leadership and Career Next-Steps as well as Mentor versus Sponsor, and for students on Resumes and Interviews and Peer Mentoring. The workshop also designed a series of modules on topics such as Branding, Networking, Goal-Structuring, and The Imposter Syndrome, which included hands-on activities in their Career Development Workbook. The schedule was purposefully designed to intersperse traditional presentations with the interactive sessions and break-out sessions in order to foster engagement and maximize participation. The NC ASA Fall Dinner that evening also provided an opportunity for networking with the larger statistics community.

The ASA North Carolina Chapter is a professional organization composed of statisticians throughout the state who represents the ASA – the world’s largest community of statisticians. Since the ASA was founded in Boston, MA in 1839, the organization continues to support excellence in the development, application, and dissemination of statistical science. The organization promotes the practice of statistics in a wide variety of ways, such as: meetings, publications, membership services, education, accreditation, and advocacy. Their members are worldwide and serve in industry, government, and academia in more than 90 countries. These members help to advance research and promote the practice of statistics in order to inform public policy and improve human welfare.

To become a member, visit the NC ASA website.

Participants of the NC ASA Chapter workshop on Mentoring and early Career Development gather for a group photo on Nov. 30, 2018.

SAMSI Welcomes NEW Postdoctoral Researchers for 2018-19

Each year SAMSI welcomes a new crop of postdoctoral researchers that support their annual academic programs. This year, SAMSI welcomes five new researchers who are focused on supporting the 2018-19 PMED and MUMS Programs.

Supporting the Program on Statistical, Mathematical and Computational Methods for Precision Medicine (PMED)

Xinyi Li

Xinyi Li received her Ph.D. in Statistics from Iowa State University in 2018, under the supervision of Dr. Lily Wang. Her research interests are in developing statistical methods as well as designing computational algorithms in sparse learning, functional data analysis, and high-dimensional nonparametric regression. Her application areas include neuroimaging, genomics, and public health. In her spare time she enjoys sports (badminton, hiking, skiing, etc), reading and watching sports (tennis, football, badminton, etc).



John Nardini

John Nardini received his Ph.D. from the University of Colorado, Boulder, where his dissertation was on mathematical models of epidermal wound healing. He is interested in the derivation and analysis of partial differential equation models of biological phenomena, as well as inverse problems to fit these models to experimental data. While at SAMSI, he will be working with Professors Kevin Flores from NC State and Greg Forest from UNC Chapel Hill.


Supporting the Program on Model Uncertainty: Mathematical and Statistical (MUMS)

Pulong Ma

Pulong Ma received his Ph.D. in Statistics from University of Cincinnati. He has worked on spatial and spatio-temporal statistics with applications in remote-sensing science and climate science in his dissertation. In particular, he developed flexible (e.g., nonstationary and non-separable) covariance function models for massive spatial and spatio-temporal datasets. He also proposed a statistical downscaling framework to simulate high-resolution geophysical fields in global observing system simulation experiments (OSSEs). While at SAMSI, he will be working with Professor Jim Berger, from Duke University, in the area of uncertainty quantification (UQ). He is currently exploring interesting applications with focus on building statistical emulators for expensive computer models.  


Wenjia Wang

Wenjia Wang  received his Ph.D. in Operations Research from Industrial and Systems Engineering, Georgia Tech. His research interests are focused on statistical modeling, statistical design and theoretical analysis of Gaussian process as well as Kriging. He is also interested in their applications in computer experiments, machine learning and uncertainty quantification.



Lei Yang

Lei Yang earned her Ph.D. in Statistics from Colorado State University in 2018. During her Ph.D. study, she developed theoretical framework and computational methods for stochastic inverse problem, which turns out to be related to generalized fiducial inference. She is also interested in bayesian projected normal time series model applied to protein sequence data as well. At her spare time, she enjoys swimming and getting hands dirty on predictive modeling projects.

MUMS Opening Workshop Brings Together Leading Minds in Uncertainty Quantification

Bruce Pitman, a professor of mathematics from the University of Buffalo and a visiting researcher at SAMSI, opens up the MUMS workshop at the Ahmadieh Family Auditorium, 107,  Gross Hall, on the campus of Duke University in Aug. 2018. Pitman was one of many researchers attending the workshop that focused on the utilization of model uncertainty and uncertainty quantification methods to solve complex problems.

More than 100 researchers from across the country attended the opening workshop for the Program on Model Uncertainty: Mathematical and Statistical (MUMS) on the campus of Duke University in late August.

The MUMS program brings together statisticians and applied mathematicians with disciplinary scientists from a wide-range of fields to understand the effects of modeling and uncertainty on predictions. The focus of the workshop was to layout the foundations for the MUMS program by examining the theoretical basis for statistical uncertainty, the strengths and weaknesses of models of real world processes and the uncertainty of those processes.

The workshop featured statisticians, mathematicians and data science researchers who presented their talks on how model uncertainty and uncertainty quantification methodologies can be used across a broad spectrum of subjects.

Bani Mallick, a distinguished professor, and also serves as Director for the Center for Statistical Bioinformatics and for the Bayesian Bioinformatics Laboratory at Texas A&M University, gives a talk on the Hierarchical Bayesian Models for Inverse Problems and Uncertainty Quantification at the MUMS workshop.

“The kickoff meeting brought together the world leaders in uncertainty quantification, many of

whom continue to work and interact at SAMSI as long-term visitors,” said David Banks, Director of SAMSI and the program’s directorate liaison. “Additionally, the participants are teaching a graduate course in model uncertainty,

which has drawn in students from all over the Triangle.”

In addition to the working groups, a fall course is being presented for graduate students that introduces statistical and mathematical sensitivity analysis and uncertainty quantification techniques for large-scale models arising in current applications. The course is ongoing through December.

“The MUMS program is a classic melding of applied mathematics and statistics,” said Banks.

“Uncertainty quantification is an exciting new field, with important applications in weather forecasting, modeling of pyroclastic flows, and materials science,

among others.”

The opening workshop produced six working groups:

  1. Uncertainty Quantification in Materials
  2. Reduced Order Models (ROMS) Theory and Application
  3. Prediction Uncertainty and Extrapolation
  4. Data Fusion
  5. Foundations of Model Uncertainty


  1. Storm Surge Hazard and Risk

The working groups will meet throughout the academic year and discuss ways to use these uncertainty principles in a diverse variety of disciplines, such as: engineering, probability, operations research and machine learning, just to name a few.

“One of the grand challenges scientists face is how to make probability statements when the model is so complex and intractable that it cannot be studied with standard tools from mathematical statistics,” said Banks. “The MUMS program provides a general solution strategy, by approximating the complex model with Gaussian processes. Bayesian methods enable the scientist to not only fit the most accurate Gaussian process, but also to estimate a discrepancy function, which indicates where the approximation is poor.”

For more information about the MUMS program, visit: /mums.

Merlise Clyde, Chair of the Department of Statistical Science at Duke University gives a broad talk on Model Uncertainty and Uncertainty Quantification at the MUMS Opening Workshop, Aug. 2018.

PMED Workshop Opens 2018-19 Academic Year

A group of researchers meet in a working group at the PMED Opening Workshop in Aug. 2018. Working groups meet in order to discuss ways in which statistics and applied mathematics can assist efforts to improve current challenges between patient and treatment in modern precision medicine.

Almost 150 mathematicians, statisticians, data and biomedical scientists participated in the opening workshop for the Program on Statistical, Mathematical, and Computational Methods for Precision Medicine (PMED) on the campus of N.C. State University (NCSU) in mid-August.

The goal of the PMED program is to bring together mathematical, statistical, computational, and biomedical scientists in order to develop innovative advances in data-driven, quantitative methodology for precision medicine.

“Personalized medicine is a recently developed hot area in bio medicine and medicine that is about custom-tailoring treatments, medicines and interventions for the individual person,” said Elvan Ceyhan, Deputy Director of SAMSI and a directorate liaison for the PMED Program. “There are a lot of methodological, statistical and mathematical and practical challenges, so there is a dire need to address these issues.”

Researchers from all over the United States attended the week-long event that opened SAMSI’s 2018-19 academic programs. There were numerous talks from some of the most influential minds in biomedicine, applied mathematics and statistics. The researchers highlighted a wide-range of methodologies and approaches used in precision medicine

“I have been interested in this general area for quite some time…I am a biostatistician, so I work with problems in the health sciences,” said Marie Davidian, professor of statistics in the Department of Statistics at NCSU and a local scientific contributor to the PMED program. “This is the big challenge in how science research can personalize treatment to the patient. My research area involves statistical methodologies directed toward that goal.”

PMED produced eight working groups and one subgroup for this academic program year. The    working groups will focus primarily on medical issues that require more refined and accurate data derived by biostatisticians, applied mathematicians and other researchers.

Sirisha Mushti, a statistician from the U.S. Food and Drug Administration, presents information about precision medicine during a panel discussion at the PMED Opening Workshop.

“In general, in regular medicine, statistics is applied for the average person, but an average person is not really a person as much as it is median data point, there is no ‘average person,’ said Ceyhan. “In personalized medicine, each person is an individual and different. In this field, the treatment would be customized to the individual, which causes a lot of uncertainty from   person to person and unexpected outcomes. Statistics is very useful in predicting bounds or quantifying those uncertainties.”

This SAMSI workshop is helping to bring together a diverse group of professionals who do not ordinarily collaborate. Programs like this one in precision medicine are forging a path towards using more dynamic and broad-based methods to ensure that more effective and efficient medicines and treatments can be used to improve an individual’s healthcare needs.

“What strikes me, just from the talks I’ve seen, is the diversity of the approaches that different disciplines are taking to address these problems,” said Davidian.

Davidian is hopeful that the internal collaborations on the working groups lead to some innovative approaches as to how statisticians can make an impact into improving precision medicine.

The issues being discussed are not only beneficial to the more senior researchers, but also to the next generation of statisticians – Master of Science and Ph.D. students.

“The thought of personalized medicine and tailoring treatments to individual characteristics is exciting to me,” said Yeng Saanchi, a statistics student enrolled in the graduate program at NCSU. “I don’t think giving generic treatments to everybody, irrespective of their individuality is the way to go, especially if we want healthcare to be efficient and effective.”

Saanchi appreciated the opportunity to have access to a large number of experts in her field and in medicine. In many ways it has helped her to realize where her talents in statistics can be valuable.

“Aside from the great exposure to the work that is being done in the field in precision medicine and meeting all of these knowledgeable people and talking to them about their work, I hope to be able to see how my research could possibly tie into the personalized medicine field,” she said.

“SAMSI workshops are great in order to get a conversation started about a particular subject and building networks to address various problem definitions in those subjects,” said Ceyhan. “SAMSI is very experienced in this…We have received positive feedback already from participants that they have met new people from new areas of study and they look forward to further collaborations.”

In addition to the opening workshop and working group research, the PMED program will also host a spring cou­­rse for graduate students in 2019. It will be interesting to see what new and innovative ideas come out of this experience.

To see more information about the PMED program, visit: /pmed.

Participants present research posters during a poster session at the PMED Opening Workshop.

Grad Students Enjoy 2018 IMSM

A research group presents their findings to peers and industry mentors on the last day of the 24th annual IMSM. The week-long workshop teaches students the value of collaborative research and opens their minds to current opportunities in data science research.

It was another productive year for SAMSI’s 24th annual Industrial Math and Statistics Modeling (IMSM) Workshop for Graduate Students, presented in July, on the campus of N.C. State University in Raleigh, NC.

Almost 40 students from more than 30 universities nationwide attended the annual workshop that began in 1993. The workshop offered students something they don’t ordinarily get in a classroom and gave them a snapshot of life as a data science researcher.

“Students worked in groups of six or seven on problems that came from industry or national labs,” said Mansoor Haider, who is an Associate Director at SAMSI, a professor in the math department at N.C. State University and also Chair of the IMSM workshop. “It’s often their first experience working on problem solving, using mathematics and statistics that come from folks outside of academia.”

From day one of the workshop, the students in attendance relied, not just on their personal knowledge of mathematics, but also on developing relationships with their peers so that they could work together as a team to present their findings. The collaborative environment is intentionally setup each year in order to mirror current industry trends.

“The thing that I think is really unique about IMSM is that it’s a short period of time where you really have to work collaboratively to get something done,” said Grant Weller, a Vice President for Research at Savvysherpa, a division of the United Healthcare Group. “The projects we [Savvysherpa] tend to work on are very large and require collaborative work and people of different backgrounds and disciplines working with each other. It’s been impressive to see how this group has come together.”

Weller is very familiar with SAMSI. He supported the 2011-2012 Uncertainty Quantification Program as a graduate student visitor. Like Weller, many former SAMSI undergraduate and graduate students and postdocs come back to attend SAMSI programs and workshops later in their professional and academic careers. They share their knowledge and their experience with the next crop of talented applied mathematicians, statisticians and computer scientists.

This year’s supporting organizations included:

  • Saavysherpa (a division of the United Healthcare group)
  • MIT – Lincoln Laboratories
  • Sandia National Laboratories
  • Rho – a national drug development company
  • The U.S. Environmental Protection Agency (EPA)


  • The US Army Corps of Engineers (USACE)

“This was the first workshop I have been to of this kind and I wanted to get a feel what a job in industry would be like and this workshop was a really good experience for me in terms of that,” said Michael Byrne, an attending graduate student enrolled in the applied math Ph.D. program at Arizona State University. “It was a great opportunity for me to work with other people that are industry-minded and to get an idea of what people do in industry on a daily basis.”

In addition, to their group research, students also participated in a short career fair where they got an opportunity to connect and network with this year’s partner organizations. Companies and organizations often comment that there is a shortage of math and statistical science students to fill the critical roles they need to address their current research. IMSM helps to potentially fill that void.

“We really viewed this as an opportunity to connect and develop relationships with SAMSI, which is a great research institution in statistics and applied mathematical sciences, and is the primary focus of what we [Savvysherpa] do,” said Weller.

At the end of week-long event, each group presented their findings to their peers and faculty and industry mentors. The students walked away from the experience with a new perspective on how research is conducted and they also walked away with new contacts and friendships from the experience.

To see what the groups presented and to find out more about IMSM, visit: /imsm-18.

Participants, faculty and industry mentors at the 24th annual IMSM on the campus of N.C. State University.

2017-2018 CLIM Program Transitions

Emily Kang gives a talk about Statistical Emulation with Dimension Reduction for Complex Physical Forward Models at the CLIM Transition workshop in May 2018. Kang and other researchers convened to present research from their working groups and to talk about conducting future research regarding climate science.

There is a proverb that states, “March comes in like a lion and out like a lamb.” It is a proverb that truly explains the extreme swings of the weather effects on our environment.

Much like this proverb, the Program on Mathematical and Statistical Methods for Climate and the Earth System (CLIM ) also came in like a lion and then transitioned quietly this May to complete another successful SAMSI year-long program.

The CLIM Program began in spectacular fashion with a solar eclipse, followed by the first of three devastating hurricanes to hit the United States in the summer of 2017 – and that was just the opening workshop!

During the academic year, the CLIM program hosted a distinguished atmospheric scientist from MIT, Kerry Emanuel. Widely regarded as the world’s leading hurricane expert, Emanuel gave a fascinating talk the future of hurricanes and how changes in our environment are causing these storms to become more and more intense.

In all, the program produced 13 working groups that addressed issues such as: remote sensing, data assimilation, analytics, climate prediction, environmental health and climate extremes, just to name a few.

A research course for graduate students was also hosted in the fall semester, called “Statistics for Climate Research.” The course was taught by seven different instructors who were part of the nearly 15 academic visitors for the CLIM program. Over 20 students registered for the course (plus several auditors in regular attendance) with students from departments such as Marine Sciences at UNC and Economics at NCSU, in addition to the local mathematics and statistics departments. The fall course was followed up by a spring course that covered statistical and computational methods for the analysis of data arising from climate research.

The program was a huge success and it attracted some of the most talented minds in environmental research today. The Transition Workshop met in May and each working group presented their findings to their peers that addressed their particular research topic.

Though the CLIM Program has transitioned, the research captured during the program is still ongoing and the environmental and applied math and statistical scientists will continue to collaborate for further study. To see an overview of the CLIM program, visit: /clim.

Richard Smith, a professor of statistics from the University of North Carolina at Chapel Hill and a former member of the SAMSI directorate, does a Q & A with researchers at the CLIM Transition Workshop in May 2018.

Undergraduate Modeling Workshop Shows Students Connection between Math and Environmental Research

Ingrid Tchkaoua, an undergraduate student from Jackson State University, presents a group project at the Undergraduate Modeling Workshop, May 25. The group was researching how ocean temperature data is captured and how it is used to predict environmental changes in the future.

SAMSI completed their Undergraduate Modeling Workshop in May. The week-long workshop brought more than 30 undergraduate students from across the country together to understand how applied mathematics, statistics and computer science technologies can be used to interpret and predict ongoing changes in our environment.

“In this workshop, we wanted undergraduates from mostly quantitative fields to experience the mathematical/statistical applications in climate research,” said Elvan Ceyhan, SAMSI’s Deputy Director. Ceyhan was also an organizer of the workshop.

The workshop gave students valuable experience in how modern research is conducted, while also encouraging them to enter careers in the math sciences. Because of this workshop, many students became aware of how much applied mathematical, computer science-based and statistical concepts are used environmental research.

Students listened to researchers in the fields of applied mathematics, statistics and environmental research. The researchers lectured on how applied math and statistics work in concert with scientific data to help build models for study of the factors that affect various elements of our environment, such as ocean temperature, air quality and seasonal variations in vegetation in a given region.

Students also learned some basics about R Software, which is widely used to crunch data for the purpose of building simulations, plotting variances and finding trends to predict change in a given subject.

A research group, made up of undergraduate students, a graduate student, and postdoctoral fellow mentors, look at data collected during a break out session at the Undergraduate Modeling Workshop, May 23.

“The ‘SAMSI 2018 May UG Workshop’ was very helpful in broadening my knowledge about not just Statistical Modeling but Machine Learning as well – I learned new things which I would not have otherwise,” said Chandini Malhorta, a statistics major at NC State University (NCSU). “The multiple R-sessions, especially the ones on Spatial Statistics by Doug [Nychka] and Andrew [Finley] were quite helpful, this was something which I would have never learned in my entire Undergraduate had I not attended their session.”

Students worked in various groups and were guided and mentored by SAMSI postdoctoral fellows and workshop leaders. The student groups presented their findings on the following topics:

The students wrapped up the week by presenting their findings to their peers and fellow mentors.

“The lecturers and post-docs really have broadened my horizon and expanded my network,” said Rice University Junior, Hongyu Mao. “This workshop connected us to many friends of the same interests and we learned from each other.”

“In a week’s time, the students showed impressive progress and had some tangible results to present at the end,” said Ceyhan.

The subjects presented challenged the students, in a group setting, to give them practical experience working together to solve problems. SAMSI hosts multiple education and outreach opportunities like this annually as a way to raise awareness for the importance of applied math and statistics. To see more of what was presented and discussed at this workshop, visit the web page: /18-ugrad-modeling.

Participants, event organizers and research mentors take a group photo at the Undergraduate Modeling Workshop, May 25. More than 30 undergraduate students from across the U.S. attended the workshop to learn how applied mathematics, statistics and computer science work together to help analyze and predict changes in our environment.

QMC Program Leaders Praise Research Advances at Transition Workshop

Fred Hickernell, professor of applied mathematics at the Illinois Institute of Technology talks about his working groups research at the QMC Transition Workshop, May 7. Hickernell was among many Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics Program participants in attendance who presented their research findings at the workshop.

At the end of May, SAMSI hosted a transition workshop, bringing a close to their Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics (QMC).

The workshop was the culminating event of the year-long QMC program and brought together more than 100 researchers from around the world to discuss a multitude of ways in which QMC methodologies could be used to improve such things as data sampling; process efficiency and troubleshooting systems; and how these concepts can be integrated into machine learning or computer-based technologies.

“The SAMSI QMC program, small but highly productive, set a perfect example of SAMSI’s mission: Mathematicians and statisticians working together to create new foundations and computational methods, with a future view towards solving challenging technological problems: making power grids more reliable by preventing break downs, and making nuclear reactors safer by tightly monitoring the nuclear reactions (criticality),” said Ilse Ipsen, program organizer and former SAMSI associate director. Ipsen is also a mathematics professor at NC State University

The QMC year began with an opening working last August, followed by mid-term workshops at Duke University and the Alan Turing Institute in the UK, and officially ending with this transition workshop.

Program participants from all over the world (Austria, Australia, Canada, Germany, the UK, and the US) reviewed the impressive accomplishments of ten productive working groups over the past year, which included:

  1. fast methods for sampling reliably and efficiently;
  2. validation procedures for guaranteeing that the samples are indeed representative;
  3. robust methods for performing effective sampling under less than ideal (uncertain, noisy) conditions; and
  4. user-friendly software for automating the complex sampling processes, and visualizing the location of the samples.

“QMC has a rich and deep theory that has found a handful of extremely suitable use cases, but I am convinced that there are more to discover,” said Art Owen, a QMC program leader and a statistics professor at Stanford University. “The SAMSI program built bridges to US based researchers, especially in engineering related applications.”

Participants listen attentively as one of their peer’s shares research findings at the QMC Transition Workshop, May 8.

The transition workshop was an opportunity for the program researchers to reconvene and to discuss their findings from the past year in their research. It also gave the program participants a way to reconnect with colleagues and discuss future collaborations about their research.

“During the lively discussion sessions it was often hard to tell who was a mathematician and who a statistician — an indication of the close collaborations and the growing synthesis of mathematics and statistics,” said Ipsen. “The workshop participants are now looking beyond the SAMSI program, by organizing future workshops and continuing their virtual webex meetings — whatever it takes to ensure the thriving of the research community formed during the SAMSI QMC year.

“The QMC Program has fulfilled its main objective: Strengthening the community of researchers who work on Quasi-Monte Carlo methods and the related area of Probabilistic Numerics; and raising the visibility of these vital research areas in the US,” said Ipsen.

To see all of the workshops and what were presented during this program, visit the QMC web page at: /qmc.

SAMSI Welcomes NEW Directorate Members

SAMSI welcomes three new members to their Directorate, effective July 1, 2018.

The new directorate members are:

  • Sudipta Dasmohaptra, Associate Director of Diversity from Duke University.
  • Greg Forest, Associate Director from the University of North Carolina at Chapel Hill (UNC-CH).


“I am personally grateful to all three of these professionals for having taken on these jobs,” said SAMSI Director, David Banks. “It is especially critical during a NSF renewal cycle, and I look forward to working with them as we advance the SAMSI mission.”

Sudipta Dasmohaptra

Sudipta Dasmohaptra will be SAMSI’s new Associate Director of Diversity. She replaces former diversity director, Leslie McClure from Drexel University.

Dasmohaptra joined Duke in September 2017, where she has served as the Director of the Masters in Statistical Science Program. She had previously served at NCSU as an Associate Professor in Marketing Analytics at the Institute in Advanced Analytics. She has a well-established background in working with industry partners, government and non-profit organizations, as well as academia.

“I am very excited to join SAMSI to build and maintain a climate that fosters diversity and inclusiveness on an ongoing basis through collaborative relationships with a broad and diverse constituency,” said Dasmohaptra about beginning her new role at SAMSI.

Her research interests include: customer and marketing analytics, focused primarily on quantitative data analysis; data management; predictive modeling; and web and digital analytics, just to name a few. Sudipta hopes to continue the work in SAMSI’s diversity program that McClure began last year. Her focus as diversity director will be to further develop SAMSI’s diversity initiatives, identifying opportunities for under-represented groups to participate in SAMSI programs, workshops and special events.

Greg Forest

Greg Forest will assume the role of Associate Director vacated by former Associate Director and Director, Richard Smith. Smith, who was the Director of SAMSI since 2010, stepped down after David Banks took over as Director in January 2018. Smith then moved to an associate director position to help facilitate the transition before going back to academic research.

Forest has been at UNC-CH since 1996 and is currently serving as the Grant Dahlstrom Distinguished University Professor of Mathematics and Biomedical Engineering and also as Director of the Carolina Center for Interdisciplinary Applied Mathematics.

Forest’s research is focused primarily on biomedical technologies and how those enhancements can improve modern medicine. He is also heavily involved in nanoparticle drug strategies for human cancer; studying the correlations between mathematics and multiple applied science challenges; gaining understanding virology and immunology and much more.

Forest’s experience in this research will no doubt be vital in SAMSI’s 2018-2019 Program on Statistical, Mathematical, and Computational Methods for Precision Medicine (PMED) starting in August 2018.


Mansoor Haider

Lastly, Mansoor Haider fills out the new list of recent appointees in the directorate at SAMSI.

Haider will be replacing long-time Associate Director, Ilse Ipsen. Ipsen and Haider are both mathematics professors at NCSU. Ipsen has been an Associate Director at SAMSI since 2011.

“I am excited to join the SAMSI directorate and work with leading scientists from the triangle universities in order to advance SAMSI’s mission,” said Haider of his new appointment.

Haider is an applied mathematician who has been a member of the faculty at NCSU since 1999. His focus is on interdisciplinary research, primarily the application of mathematical and computational models to problems in the life sciences. In addition, Haider also served as Director of Graduate Programs for the Department of Mathematics from 2012-2016.

Haider, who is no stranger to SAMSI, has served as an organizer of numerous SAMSI programs and workshops. “I am looking forward to developing creative strategies for integrating SAMSI’s research programs with mathematical sciences training at all levels [postdoc, grad, undergrad],” Haider said.

Haider also serves as the current Chair of the Industrial Math/Statistical Modeling Workshop (IMSM) for Graduate Students (he has previously served as this committee’s chair in 2017 and 2004 and 2005). IMSM is a joint venture between SAMSI and NCSU that serves as an education and outreach opportunity for graduate students.

“I am particularly grateful to Ilse Ipsen and Richard Smith for their leadership in advancing the mission of our Institute,” said Banks of the outgoing directorate members.

“Ilse ensured the prominence of applied mathematics in the SAMSI portfolio through the creation and facilitation of several spectacularly successful programs, and through the recruitment and mentoring of outstanding postdoctoral fellows. Richard contributed hugely to SAMSI as well —he was the former director, and kindly agreed to stay on for an additional six months to facilitate the transition of new directorate members. Richard’s vision and stewardship ensured the survival of SAMSI, and did much to shape its current form. Both Ilse and Richard will be missed,” said Banks.

This new group of leaders will help to bring a fresh perspective on how best to advance SAMSI’s role in applied mathematics, statistics and the computational sciences. SAMSI staff welcomes these new members and looks forward to working together with them well into the future.