Women Share Experience at SAMSI at the Women in Statistics Conference

Over 200 female statisticians gathered in Cary, North Carolina May 15-17 to attend the Women in Statistics Conference. Both SAMSI and NISS were sponsors of the event.

four women talking at break

Women networking during a break including former SAMSI postdocs Elizabeth Mannshardt and Jenny Brynjarsdottir (two on the right).

Women shared their experiences working in statistics and gave tips on how to navigate a career path in this field. Women from industry, academia and government all shared their perspectives of how women are impacting the statistics area.

Snehalata Hurzurbazar speaking

Snehalata Hurzurbazar

SAMSI was featured in a session, starting with an overview of SAMSI and at NISS by the SAMSI Deputy Director, Snehalata Huzurbazar. She focused on opportunities to be involved with SAMSI, and shared her experience of first being a visitor and later her experience as the Deputy Director. She noted that it is hard for women who have small children to leave their home institutions for an extended period of time, but that shorter visits and participation in working groups via Webex are completely feasible and the norm for participation in SAMSI research programs. She also noted that during her time as Deputy Director, SAMSI posted a list of local day care facilities that have been used by other visitors.

three women on the panel

Jessi Cisewski, Xia Wang and Bailey Fosdick.

Jessi Cisewski, from Carnegie Mellon, explained what it was like to be a graduate student fellow at SAMSI. She was able to network with lots of different people and learn about many different opportunities by participating in the programs and workshops. Last summer she was involved with the summer Kepler program, which was a more intensive three week workshop that got astronomers together with statisticians to analyze data from the Kepler telescope. This initial work has greatly expanded and Jessi is still working with astronomers in this area. She encouraged audience members to get involved and told them about an upcoming workshop at Carnegie Mellon that would be about this topic.

Bailey Fosdick talked about was it was like to be a SAMSI postdoc this past year. She also commented on the great opportunities she had to meet so many different people during the workshops this year and that she got involved with some of the working groups from the LDHD program in addition to the Computational Methods in Social Science program. She told people she has learned a lot and has been able to greatly expand her research horizons by being a SAMSI postdoc.

Xia Wang from University of Cincinnati shared her experience of being a postdoc at NISS and how she was able to participate in SAMSI programs as part of her perks of being a NISS postdoc. She is still meeting regularly with a working group that was formed five years ago and they are still producing papers and interesting research.

For more information on becoming a visitor, or applying for a postdoctoral position at SAMSI, visit the website at http://www.samsi.info. The entire SAMSI-NISS session was videotaped and will soon be available at https://women-in-stats.org, the website for the conference.

Researchers Receive IJERPH Best Paper Award 2014

What are the human health implications of climate change? There is by now a well established body of evidence about the direct effects of increasing temperature, for example, heat stroke. But is that the full story? It is also possible that air pollution patterns may change as a result of the changing climate, especially ozone, whose production is stimulated by hot weather. In work started at The Statistical and Applied Mathematical Sciences Institute (SAMSI) and later completed with colleagues at North Carolina State University, Howard Chang studied the effect of simultaneous changes in temperature and ozone, using simulations from climate models. Rather than run the model multiple times under different scenarios (a very time consuming process), Chang and his colleagues devised a statistical approach which saves computation time and also allows them to estimate the uncertainty in their projections. As a result, they find significant increases in projected mortality in the southeastern U.S. during the period 2041-2050 compared with 2000 levels.
The resulting paper, written by Chang, Jingwen Zhou, North Carolina State University (NCSU) and Montserrat Fuentes, NCSU, was awarded the International Journal of Environmental Research and Public Health (IJERPH) Best Paper Award 2014. Their paper, “Impact of Climate Change on Ambient Ozone Level and Mortality in Southeastern United States” received the 3rd prize in the category “Articles.”
On an annual basis the IJERPH Best Paper Award recognizes outstanding papers in the area of environmental health sciences and public health that meet the aims, scope and high standards of the IJERPH journal.

Article link: http://www.mdpi.com/1660-4601/7/7/2866
Award link: http://www.mdpi.com/1660-4601/11/1/1192

SAMSI Researchers Amonst Team Helping to Predict 2013 Boston Marathon Completion Times

After experiencing a tragic and truncated end to the 2013 Boston Marathon, race organizers were faced not only with grief but with hundreds of administrative decisions, including plans for the 2014 race – an event beloved by Bostonians and people around the world.

One of the issues they faced was what to do about the nearly 6,000 runners who were unable to complete the 2013 race. The Boston Athletic Association, the event’s organizers, quickly pledged to provide official finish times for these runners. Thinking ahead, they also had to consider how to provide these runners with an opportunity to qualify for the 2014 race.

To seek advice on these issues, they contacted Richard Smith, director of the Statistical and Applied Mathematics Sciences Institute (SAMSI) and professor of statistics at the University of North Carolina at Chapel Hill, who also happens to be an avid marathon runner. They asked Smith to come up with a statistical procedure for predicting each runner’s likely finish time based on their pace up to the last checkpoint before they had to stop.

Smith quickly assembled a team of fellow analysts that included Francesca Dominici and Giovanni Parmigiani at Harvard School of Public Health, and Dorit Hammerling, postdoctoral fellow at SAMSI, who were in the 2013 race and finished uninjured. The team also included Matthew Cefalu, Harvard School of Public Health; Jessi Cisewski, Carnegie Mellon University and Charles Paulson, Puffinware LLC.

The results, and the method the researchers developed, were published in the April 11 edition of PLOS ONE.

With the help of the Boston Athletic Association, the researchers created a dataset consisting of all the runners in the 2013 race who reached the halfway point but failed to finish, and all the runners from the 2010 and 2011 Boston marathons. The data consist of “split times” from each of the 5 km sections of the course (from the start up to 40 km), and the final 2.2 km. The research team was tasked to predict the missing split times for the runners who failed to finish in 2013.

The researchers adapted techniques used in such contexts as computing missing data in DNA microarray experiments and estimating ratings which Netflix subscribers would have given to movies they had not seen. They proposed five prediction methods and created a validation dataset to measure the runners’ performance by mean squared error and other measures. Of the five, the method that worked best used local regression based on a K-nearest-neighbors algorithm (KNN method), though several other methods produced results of similar quality.

The KNN method looks at each of the runners who did not complete the race (DNF) and finds a set of comparison runners who finished the race in 2010 and 2011 whose split times were similar to the DNF runner up to the point where he or she left the race. These runners are called “nearest neighbors.”

“We had to come up with a method to compare the runners based on the split points up to a certain point of the race and then had to decide how many of the nearest neighbors to examine in order to develop a prediction for the DNF runner that would be based on the different finishing times of these nearest neighbors,” said Smith, who has run the Boston Marathon in the past and will run this year’s race. “We decided to choose 200 nearest neighbors. We also tried 100 and 300 nearest neighbors, but the results changed only slightly and didn’t make them better.” A Powerpoint presentation of the work can be found here.

The Boston Athletic Association decided to grant entry to the 2014 race to anyone who was stopped from completing the 2013 event, so they will have a chance to complete the Boston Marathon after all. But in the course of developing the method, Smith and his colleagues realized there were other uses for the technique.

“We have found that using the KNN method looking at a runner’s intermediate split-time will also be useful in predicting the person’s completion time while the race is in progress,” said Smith. “This can be helpful for relatives and friends to be able to meet the person at the finish line.”

The local television station, WRAL, ran a really nice story about the work the team did. You can watch the story here.

Postdoc Profile – Bailey Fosdick

Bailey Fosdick

Bailey Fosdick

“When most people think of social networks, they immediately think of interactive sites on the Internet. They assume I am studying Facebook and Twitter and I have to explain it is like that but on a much smaller scale,” said Bailey Fosdick, SAMSI postdoc. However, the social networks that Bailey is studying have nothing to do with the Internet. She is looking at scenarios such as how substance abuse and obesity of adolescents are related to their social networks and how baboon troops socialize and fission over time.

Bailey was born and raised in the small town of Steamboat Springs, Colorado, which most people identify as a ski resort town. She spent six years ski racing but finally decided to focus on basketball realizing the color of the ribbon she won in races was more important to her than the actual place (She wouldn’t hesitate to trade a brown 8th place ribbon for a pale blue 10th place one!).  She said people are often surprised to find out she grew up there because they only think of it as a ski resort. After high school, she spent a year at the Colorado School of Mines, a small school located in Golden, Colorado, specializing in engineering and science.  She quickly discovered that while she enjoyed the science and problem solving, she hated the labs. It was then that she decided to get into mathematics.

She rounded out her undergraduate time at Colorado State University (CSU) majoring in mathematics with a minor in computer science. During the summer between her junior and senior year, she spent time at North Carolina State University participating in their Applied Mathematics REU program and eating at Cook Out on the weekends.  While she enjoyed her introduction to research through the REU, she really felt statistics was her calling and was urged to pursue graduate school by a great group of faculty mentors in the Department of Statistics at CSU.  One year later she was in the statistics graduate program at the University of Washington in Seattle.

The University of Washington’s Center for Statistics and the Social Sciences gave Bailey a place to learn about important statistical questions in the social sciences. She enjoys working on real-world problems and collaborating with experts in specific areas of science to develop and apply statistical methods to solve pressing problems in their areas. For her dissertation, titled ” Modeling heterogeneity within and between arrays,” Bailey worked closely with a sociologist and she also worked on a large project involving demographers during her time at the University of Washington.

photo of the football team

Football team.

Bailey’s love for sports continued at the University of Washington where she was apart of intramural co-ed volley and coed-flag football championship winning teams multiple years.  She also played co-ed softball but her team was never able to finish the season with a win.

She heard about SAMSI after seeing an advertisement announcing that SAMSI was looking for postdoctoral fellows and several of the faculty told her it would be a great opportunity to pursue.  She is here for one year as a fellow for the Computational Methods in Social Sciences program. “I felt the SAMSI program was a great fit for the work I had been doing at the University of Washington,” said Bailey.

Bailey is involved with several working groups including social networks, censuses and surveys, and the topology working group, which is actually a part of the Low-dimensional Structure in High-dimensional Systems (LDHD) program.

“I have really enjoyed being here. This region is rich in opportunities with the three major universities located so close together. Although I have yet to visit all three statistics departments, the events at SAMSI have allowed me to meet and engage with many of the incredible researchers in the area,” noted Bailey.

Bailey has started a number of projects since arriving.  She is working with David Banks at Duke University on a project involving Baboon social structures and helping to develop models to predict how baboon troops will split. Usually the troops split according to matrilineage, but then a troop split was observed that did not follow either the mother or father’s line.

Bailey talking to someone at a poster session

Bailey presents her work at the Social Networking workshop poster session.

Another project Bailey is working on is to analyze bike sharing data in Washington, D.C. Many big cities have bike sharing programs where a person will check out a bike on one place and then check it back in at another part of the city. The study is looking at how the flow of bikes throughout the city changes by day of the week, time of day, and for regular versus infrequent riders.

She has found social network research to be very interesting and fulfilling. She is also happy to be making new connections at SAMSI with whom she will most likely continue to collaborate with in the coming years.

When she is not at SAMSI, Bailey enjoys running. She says while in Seattle she usually ran when the sun was out and that typically equated to a couple times of week.  However, she has found that to be a nearly daily occurrence in North Carolina.  She and her husband are also playing softball in a softball league on the weekends.

Next year, Bailey will take a job as assistant professor of statistics at Colorado State University. She is happy to be returning to her home state and is very thankful that the university allowed her to take this year to do the postdoctoral fellowship at SAMSI.

Postdoc Profile – Kenny Lopiano

Kenny outside of SAMSI

Kenny Lopiano at SAMSI, Fall 2012.

Kenny Lopiano, postdoctorate fellow at SAMSI in the Data-Driven Decisions in Healthcare program, grew up in Jacksonville, Florida, then moved to Ponte Vedra, just east of Jacksonville, when he was about 12-years-old. Kenny attended Allen D. Nease High School, which actually offered statistics classes. Kenny took an AP statistics course as a junior in high school. Since the statistics classes were new in the curriculum, his teacher, Penny Futch, was learning the material along with the students. As a result, Kenny supplemented the in class work with his own independent study of the material . His calculus teacher, Della Caldwell, was married to Bill Caldwell who taught at University of North Florida. Dr. Caldwell came to the high school and taught a linear algebra class, which is usually considered a 3rd year college course. In his spare time, Kenny was active in many sports, including wrestling and football.

Kenny is a big Gator fan!

Kenny is a big Gator fan!

By the time Kenny graduated high school and applied to the University of Florida in Gainesville, he had placed out of the introductory courses, and decided to double major in math and statistics. His first semester he was already taking regression analysis and calculus 3. One of his professors, Yongsung Joo, mentored Kenny and spent time teaching him SAS, which was incredibly helpful to Kenny.

“I spent my first summer working at the Fresh Market in the produce department,” said Kenny, “That job taught me a lot about work ethic. Many times I worked 50-hour work weeks.” He had actually interviewed for a job at Blue Cross Blue Shield for a summer internship, but had only been on campus for two months when the recruiters came to campus, so the interviewer immediately said to him, “You know you are not going to get this job, right?” and Kenny said, “Yes, I know I only have my high school credentials to go on.” He just wanted to get some experience interviewing.

His second summer, though, he applied for an internship at the Mayo Clinic in Rochester, MN after Googling for statistics internships. He was lucky enough to get a phone interview and ultimately the job. He worked with the biostatistics department and saw how various people with statistics degrees worked at the Mayo Clinic. Those with undergraduate degrees had certain job responsibilities, while those with a Master’s degree had other responsibilities and those with a Ph.D. degree had even more responsibilities and freedoms to do things, so it was during this summer working primarily with Dirk Larson and Nancy Diehl he decided he wanted to get a Ph.D.

When he returned for his junior year, he could have gotten a minor in actuarial science, or start working on his master’s program, so he started working on his Master’s program. He immediately saw the difference in the way statistics was handled in graduate level courses. For example, a class he took in regression and design was completely different from the way he had learned it in his undergraduate class and he quickly found out he had to learn the subject from a very different perspective.

At the end of his junior year, he applied again to Blue Cross Blue Shield and the same recruiter interviewed him, but this time he got the job! He spent the summer working with Ryan Little who taught him how to map pharmacy records to risk groups. This helps underwriters to set rates for different groups.

Kenny and Nate at graduation

Graduation with his friend, Nate Holt.

He successfully completed his first year exam for his Master’s degree and when he came back for his second year (his fourth year at UF), he had a different attitude. He had broken off a serious relationship and was playing rugby and was not dedicating as much time to his studies as he admittedly should have. His professors tried to warn him to get more serious before he took his second qualifying exam, which meant either you passed and moved on to work with your advisory team, or you failed and either left the program or would work hard to try again. Eight people took the exam, half passed and half failed. Kenny was one of the people who had failed the exam. He was perplexed. Dr. Brett Presnell, the department chair, wrote him a letter and said if he worked on certain things, he would probably pass. At first, Kenny was mad and crumbled up the letter, but then he got it back out and read the comments each professor had made, then met with each one to find out what he could do better. It was a wakeup call for him. “That letter is now framed and hanging in my home office,” Kenny remarked. When he retook the exam, he passed with flying colors.

with Igert cohorts

Kenny with his Igert cohorts.

Kenny was accepted as a graduate fellow for the newly formed NSF IGERT, the Quantitative Spatial Ecology, Evolution, and Environment (QSE3). The IGERT was really transformative for him because he got to work in an environment that was very multi-disciplinary, which he really enjoyed. He was also working with Mary Christman (who was involved with the IGERT) on a project working on generalized additive models. Dr. Christman thought that Kenny’s research and experience would be of interest to Linda Young, so she introduced Kenny to Dr. Young. Young told Kenny about an opportunity that would be happening that summer at the National Institute of Statistical Sciences (NISS) in RTP in conjunction with the National Agricultural Statistics Service (NASS) in Washington DC. Kenny thought about it and decided it would be a good experience, so he spent the summer of 2009 at NISS and the summer of 2010 at NASS working on a team that was made of all female statisticians. He noted that all along his career path, he has been fortunate to have many female mentors.

Kenny sitting at desk working

Kenny working at NISS in the summer of 2009

The work Kenny has been doing here at SAMSI involves working with people in operations research, which is again giving him exposure to different perspectives and having a chance to collaborate with people from other disciplines, which he really enjoys. The patient flow working group has also interacted with doctors and nurses that have been working with the University of North Carolina’s triage and trying to identify finer subgroups. The emergency severity index (ESI) currently goes from 1-5, where a “1” is a critical patient needing immediate care, to a “5” which is the least urgent. The problem of subgroup mainly lies in patients with a “3” which means they are not critical, but need many resources to treat the person.   In addition, Kenny is working with another group of statisticians on methods related to identifying effective treatments using large observational databases.

Next year, Kenny will spend the year mainly at Duke University and work with Alan Gelfand. He will also follow up on the work started with the Data-Driven Decisions in Healthcare program.

2012 in review

As most of our readers are statisticians and applied mathematicians, we feel it is only appropriate to share our own statistics with you on how the SAMSI blog did so far in 2012. We started this blog in August, so we are pretty happy to see that we are gaining a following!

We are also always interested to hear what you would like to see on our blog. Send us your ideas!

The WordPress.com stats helper monkeys prepared a 2012 annual report for this blog.

Here’s an excerpt:

600 people reached the top of Mt. Everest in 2012. This blog got about 2,000 views in 2012. If every person who reached the top of Mt. Everest viewed this blog, it would have taken 3 years to get that many views.

Click here to see the complete report.

Postdoc Profile – David Lawlor

David in Berlin around the time he was working at CERN.

David in Berlin around the time he was working at CERN.

David Lawlor always knew he would end up in some kind of STEM career. His mother works for IBM, his father is a lecturer at the University of Vermont, and his brother is a chemical engineer. His older brother, Patrick, is a chemical engineer working for Conoco Phillips near St. Louis. David grew up in Vermont, where he played ice hockey through high school. His mild demeanor would never reveal that he was a two-time state champion defense man! He’s also a big fan of the hockey team from the University of Vermont.

David grew up playing hockey.

David grew up playing hockey.

(L-R) David's father, Jack, his brother, Patrick, David and his grandfather, Bill.

(L-R) David’s father, his brother, David and his grandfather when David was graduating from the University of Chicago.

David went to college at the University of Chicago and double majored in Mathematics and Physics. During his senior year, he made math his primary major with the intention of becoming a math teacher. After applying to several positions and interviewing at a boarding school he realized that the emphasis was more often on discipline than on instruction, which was not where he wanted to spend his energy. Around the same time, one of his physics professors emailed him to advertise a position in the Physics department. The professor was a primary investigator (PI) on a the ATLAS tile calorimeter, a subdetector project for CERN (the European Organization for Nuclear Research), the organization that operates the Large Hadron Collider. At that time they were assembling the detectors underground and needed some people to help commission the detector and write some computer code. David was accepted for the job, and three weeks after graduating he moved to Geneva, where he lived for a year.  When he first arrived, he worked mainly on the detector in the experimental cavern, but after a few weeks he broke his arm in a bicycle accident. As a result, instead of working on the physical part of the project, he taught himself how to write computer code and learned how to use Linux, python, C++, and ROOT (a data analysis package for high-energy physics.) There were massive amounts of data being generated by the computer simulations at CERN which record and visualize explosions of particles that result from the collisions at the accelerator. It was during his time at CERN that David became convinced applied mathematics was really the area he wanted to pursue.


David with his advisor, Andrew Christlieb, at Michigan State University.

After returning to the states, David worked for a small research firm in Michigan funded by SBIR grants while he applied to graduate programs in applied math. He ended up going to Michigan State for his graduate degree, where his thesis dealt with sparse Fourier transform (SFT) algorithms.  The SFT is an algorithm that processes data 10 to 100 times faster than what is possible with the fast Fourier transform (FFT), the previous fastest technique. The SFT searches for an area of the spectrum has significant energy and omits those that are sparse. His research applies not only to this year’s Massive Data program but also the LDHD program next year.

David said he first heard about SAMSI when he was reading a research paper in which SAMSI was acknowledged as hosting a workshop that had initiated the research. He went to the website to find out more about SAMSI and discovered the organization had postdoc positions, to which he applied as he approached graduation. He was excited to move to the area, having access to many experts in the field at nearby universities and at SAMSI itself.

“I really liked going to the opening workshop and the astrostatistics workshop,” said David, “It was great to get to meet so many people from various disciplines and to realize that everyone is interested in the same problems.” David also said he particularly liked hearing Tamas Budavari’s (Johns Hopkins University) presentation on statistical methods in astronomy.

David is involved with two working groups that are currently using data that Budavari provided from the Sloan Digital Sky Survey (SDSS). He said it is really interesting to work with the same data using two very different algorithmic approaches. “I’m very grateful that SAMSI is able to bring together practicing scientists, statisticians, and applied mathematicians to work on problems of mutual interest,” said David.

Next year, David will spend his time in the Math department at Duke, but will also be very involved with the LDHD program at SAMSI.

In addition to being an applied mathematician, David likes to think of himself as a foodie. He is very committed to the farm-to-table concept and is currently getting seafood from Walking Fish, a community supported fishery. He also bought his Thanksgiving turkey from Coon Rock Farm and plans to join a CSA (community supported agriculture program) in the spring.

Ilse Ipsen Speaks at the Science Communicators of North Carolina and the RTP Chapter of Sigma Xi Pizza Lunch

Ilse Ipsen speaking to the SCONC and RTP chapter of Sigma Xi

Ilse Ipsen spoke to the SCONC and RTP chapter of Sigma Xi on October 9.

Associate Director of SAMSI and professor of mathematics at North Carolina State University, Ilse Ipsen, recently spoke at the Sigma Xi pizza lunch. The lunch is a monthly gathering co-sponsored by the Science Communicators of North Carolina (SCONC) and the RTP chapter of Sigma Xi.

Ilse’s talk, “Rolling the Dice on Big Data” focused on how big data is permeating all aspects of our daily lives. From going to the grocery store, where supermarkets are gathering data on our personal buying habits, to analyzing images from space, to the Internet where Google receives 2 million inquiries a minute and 347 blog posts are happening every minute of the day. Facebook processes 500 terrabytes of information each day and 30 billion pieces of information are shared on Facebook each month.

To give her audience an understanding of how applied mathematicians approach this enormous problem of sifting through data, she used an example of trying to match an e-mail that comes from an unknown source to a series of e-mails that were received from known authors. The e-mail from the unknown source has three key words in it. In her example, she looks at the three e-mails and counts the number of times the key words were used. Then, the length of the sentence is measured to see how many words were used in each e-mail and in the query. Each word in each e-mail is counted and multiplied by the query to get a number. The words that are found in each e-mail and the query are squared and then divided by the sum. This method will help determine which of the e-mails is the author of the query.

If one were to look at every e-mail written each day, there would be about 294 billion e-mails to sort through and there is about 250,000 words in the English language, so it would be an enormous task to accomplish, but many mathematicians and statisticians use the Monte Carlo method to sample and narrow down the search.

She explained that using a randomized algorithmic approach was fast, easy to implement and simple to use and is as good as, or perhaps even better, than using a deterministic approach.

Room full of people listening to Ilse Ipsen's talk

The room was at full capacity for Ilse Ipsen’s talk “Rolling the Dice on Big Data.”

Ilse spoke to a packed room, including many science writers from the Triangle region, members of Sigma Xi and a high school physics class from Kestrel Heights, a local charter school.

SAMSI Renewed for Another Five Years!

SAMSI is happy to announce that the National Science Foundation’s Division of Mathematical Sciences (NSF DMS) has again renewed SAMSI’s five-year grant to 2017.

SAMSI one of eight mathematical institutes that are funded by the NSF DMS, but this is the only one that focuses on statistics and applied mathematics. It was originally founded in 2002 and is now celebrating its tenth anniversary.

The grant is a collaboration between Duke University, North Carolina State University (NCSU), the University of North Carolina at Chapel Hill (UNC) and the National Institute of Statistical Sciences (NISS) along  with the William Kenan Jr. Institute for Engineering, Technology and Science.

Each year, SAMSI holds one to two major programs along with a couple of summer programs that focus on cutting-edge research in statistics and applied mathematics, motivated by various disciplinary sciences. It also hosts several workshops for undergraduate and graduate students, some of which are directly linked to the major programs and giving students the opportunity to meet some of the top researchers in the mathematical sciences.

SAMSI’s 2013-14 programs are Computational Methods in Social Sciences and Low Dimensional Structure in High-Dimensional Systems, and the summer 2013 program is Neuroimaging Data Analysis.

Apply Now for the 2-Day Undergraduate Workshop at SAMSI October 26-27

group of undergraduate students from 2011

Last year’s undergraduate workshop group.

SAMSI is accepting applications for the two-day undergraduate workshop that will focus on Statistical and Computational Methodology for Massive Datasets. The workshop will be held October 26-27 at SAMSI in Research Triangle Park, NC. The program begins at 9:30am on Friday, October 26 and ends at noon on Saturday, October 27.

Applications received by Friday, September 28 will receive full consideration. SAMSI will reimburse appropriate travel expenses, including food and lodging. Participants are urged to arrive on Thursday evening.

The Statistical and Computational Methodology for Massive Datasets program focuses on fundamental methodological questions of statistics, mathematics and computer science posed by massive datasets, with applications to astronomy, high energy physics, and the environment. Serious challenges posed by massive datasets have to do with “scalability” and “data streaming.”