10 Minutes With: Hyungsuk Tak, SAMSI Postdoctoral Fellow

We recently took a moment to   connect with one of our busy   Postdoctoral Fellows, Hyungsuk Tak. We took ten minutes and asked him ten questions…This is what he had to say:

1. What made you decide to pursue a future in mathematics? Who has inspired you the most in your career thus far?

Tak: I have loved mathematics since my college days! But I did not like doing math for the sake of mathematics. I know that it is exciting for some people, but to me, it was boring. Instead, I wanted to use mathematics to solve real-world problems. In this sense, Statistics was PERFECT FOR ME. However, I had no intention to pursue a Ph.D. until I met Professor Carl N. Morris in the Harvard Statistics Department. He helped me experience statistical research and this motivated me to transfer into the Ph.D. program. During my Ph.D., I met Professors Xiao-Li Meng and David van Dyk who introduced Astro-Statistics to me. This later became my career path and it is why I am working in the ASTRO Program here at SAMSI.

2. Where did you grow up and where did you go to school?

Tak: I grew up in Seoul, South Korea. I also received my undergrad degree there. I then moved to the USA for my masters and Ph.D. in Statistics at Harvard.


“I personally believe that mathematical ability comes from the power of thinking, the power of thinking comes from imagining something, and imagining something comes from reading books.”


3. When you aren’t tackling complex math equations or doing research, what do you enjoy doing in your spare time?

Tak: I mainly do three things in my spare time: 1) reading; 2) exercise; and 3) surf the internet. For instance, in the morning I always read a book (written in Korean) while I have breakfast, for about 30 minutes – I like historical and classical novels more than contemporary ones. If I find the book really interesting, then I often read the book after I get home until I go to bed. I also do exercise for about two hours on Tuesday, Thursday, and Saturday. When I was a child I suffered from tuberculosis, so health is the most important thing in my life. When I get home, I often spend most of my time surfing the internet and get caught up with current events by reading Korean news articles. I really enjoy seeing how our new Korean president, Moon Jae-in, is doing? He is the person I voted for in the recent Korean Presidential Election.

4. What were some of the reasons you decided to apply for a SAMSI Postdoctoral fellowship?

Tak: There were three reasons: 1) SAMSI post-docs have a great amount of freedom in doing research because SAMSI allows post-docs to work with any professors or researchers at Duke, UNC, NCSU, and/or any other universities in the USA; 2) SAMSI is the place where domain scientists visit (physically or remotely) for collaborations, which means there are plenty of opportunities to learn new things. Finally, SAMSI post-doc fellows receive a generous salary for a post-doc position in Statistics.

5. What are some of the things that have intrigued you about the SAMSI program you are supporting this academic year?

Tak: I currently serve in the ASTRO program. The most intriguing thing is that I have been fortunate enough to have the opportunity to meet and work with astronomers who have brought interesting and realistic problems to SAMSI during the workshops or weekly meetings. All my current research, that was initiated after I came to SAMSI, is based on solving these realistic problems.

6. What program or workshops will you be supporting in the 2017-2018 academic year? Are you looking forward to any new research coming up?

Tak: I am continuing my research in Astro-Statistics rather than start new research in other fields unless there is a program closely related to my current research in terms of methodology.

7. How are you enjoying living and working in North Carolina?

Tak: When I landed at the RDU airport (from Boston), I saw, from the airplane, that N.C. is full of trees. Everything I saw through the window in the airplane was green with almost no buildings – I immediately loved this nature-friendly environment. I saw a fox (or coyote) and I have seen many deer around SAMSI; one day three deer were standing next to the entrance! I really enjoy N.C. for the nature-friendly lifestyle. I also enjoy sometimes hiking and walking trails.

8. When your time is over at SAMSI, what will you miss the most and why?

Tak: I will miss the people at SAMSI the most. For example, post-docs, administrative officers, directors, graduate and faculty fellows, visitors, and custodians. Since I spend most of my time on the SAMSI campus, plus the fact that the institute is a little isolated (surrounded by woods), even a short and small interaction with people at SAMSI has been invaluable and memorable to me.

9. What are your plans for the future? Do you see yourself working in academics or business/industry and why?

Tak: I am going to apply for a tenure-track position at an academic institution in the US this winter. If it does not work out however, then I will start looking for industry jobs early next year. I may not do a second post-doc.

10. What advice and/or guidance would you give to other undergraduate/graduate students interested in working in mathematics?

Tak: I recommend reading as many books as possible. I personally believe that mathematical ability comes from the power of thinking, the power of thinking comes from imagining something, and imagining something comes from reading books. Again, this is not based on a causal inference but based on my personal belief (prior information that can be biased!).


Undergrad Workshop Helps Student See Bright Future in Applied Math and Statistics

My name is Victoria Sabo and I am a mathematics and Spanish double major at Georgetown University. I am very interested in applying math to problem solving in the real world, such as using programing and data in security, population modeling, analyzing businesses, or even tracking supermarket inventory to minimize product waste.

My research interests are why I applied to the SAMSI undergraduate workshop from May 14-19. The workshop gave me the opportunity to apply mathematics and computer science to realms usually isolated from the sciences. Based on the description, I imagined being exposed to new applications of math, stats, and computing while having the opportunity to harness my mathematics knowledge to solve an actual problem that I may not have known could be solved using the skills of a mathematics major. In the end, I gained ample skills, both academic and professional, and I was able to test them out while working on my own group research project.

David Jones, SAMSI Postdoctoral Fellow, presents information on the Light Curve Project to students at the Institute for Advanced Analytics on the campus of North Carolina State University. The instruction was part of SAMSI’s week-long Interdisciplinary Workshop for Undergraduate Students, May 14-19, 2017.

After dedicated postdocs presented the overviews of six projects, we were allowed to rank our top choices:

  1. Lightcurve Classification for Periodically Varying Stars (Light Curves Project)
  2. Distributionally Robust Stochastic Programming for Financial Applications (Finance)
  3. Finding Exoplanets Using Radial Velocity Data (Exoplanets)
  4. Automatic Genre Classification of Music Pieces (Music)
  5. Time Delay Estimation for Gravitationally Lensed Light Curves (Time Delay)
  6. Data Assimilation for Numerical Weather Prediction (NWP)

I was fortunate enough to receive my first choice which was the Automatic Music Genre Classification project. That meant for the entire week, I would work on a team to investigate algorithms used for supervised learning, where training data taken from a music dataset, to be used to create a system for predicting the genres of unlabeled songs.

When we first met in groups, we discussed how to read the data and began thinking of probability techniques common to machine learning that would be useful for the task. We read scholarly articles about previous approaches to the problem, then met the following day to begin coding programs based on our dataset.

“I was pleasantly surprised at the diversity of the attendees at the workshop. The backgrounds of the students ranged from civil engineering, to a double major in math and piano…This variety in background facilitates the sharing and cross-pollination of ideas from different fields, which I deeply appreciated.”                                                                                                                – Kevin Multani,  Applied Science, Department of Engineering Physics, University of British Columbia – Vancouver, Canada

An undergraduate student presents the findings of her group’s project during SAMSI’s Interdisciplinary Workshop for Undergraduate Students held on the campus of North Carolina State University May 14-19, 2017.

As the week went by, we experimented with different combinations of song features, such as loudness, danceability, and song_hotttnesss (no, not a typo), and various techniques. The techniques, used for coding the data, aimed at achieving the highest accuracy in song genre classification. The techniques included: k-means clustering; k nearest neighborhood; Gaussian classifiers; PCA; and t-SNE. Through this process it was very interesting to note the limitations on our research and how the attributes, such as the data set qualities or the time constraint, affected what we could accomplish. Overall, this research project introduced me to what it was like to work on a team to conduct formal research. I also enjoyed spending the week bouncing ideas off of my other group members as we worked to solve a problem found at the intersection of two distinct subjects: math and music.

Besides just the experience of working in a research group, I created lifelong memories from this workshop thanks to the incredibly intelligent people I had the pleasure of meeting. I was introduced to undergraduates from across the United States and Canada, many of whom had international backgrounds as well. Everyone possessed a unique skill set, from their university, when it came to computer programing. The diverse backgrounds of every participant contributed to the success of the research project because of the various courses taken by the undergraduate students. I loved hearing about everyone’s majors and their career goals. I found it was invaluable to be able to exchange advice with people who were as interested in the sciences as myself. During meals and breaks, we would discuss our intended graduate study goals as well as past research we had conducted thus far. I was given advice on what conferences to attend and which schools were best for certain master’s degree or Ph.D. programs. I definitely have reevaluated my future plans since conversing with and listening to such a wide range of science and math students.

One of my peers in the workshop, Kevin Multani, an undergraduate student from the University of British Columbia – Vancouver, Canada had similar points to share:
I was pleasantly surprised at the diversity of the attendees at the workshop. The backgrounds of the students ranged from civil engineering, to a double major in math and piano — there was even a student who was double majoring in Philosophy and History (of Mathematics)! This variety in background facilitates the sharing and cross-pollination of ideas from different fields, which I deeply appreciated. Most of my learning came from discussion and conversation with the students and mentors. In fact, through conversation with my mentor, David Jones, I’ve gained a solid understanding on what to expect for graduate school. Overall, the SAMSI Undergraduate Workshop was a refreshing experience, both personally and academically.

Even though the friends that I made during the week were an enriching part of this SAMSI undergraduate workshop experience, the panels and talks organized for us also made an impact on me academically. We received information on North Carolina State University’s master’s program for science in analytics, since the Institute for Applied Analytics, where the event was hosted, is located on the university’s campus. I came out of this workshop with a broader understanding of the great career opportunities in data analytics. Thanks to the talk from Michael Rappa on opportunities in data analytics and his program within the institute, my eyes were opened as to how many different applications of data analytics there are for people with those skills. For instance, I had never considered that someone with a math background was needed to calculate the appropriate amount of supermarket inventory to prevent over and under stocking? Likewise, I did not know that companies hired analysts to evaluate their businesses in order to maximize the efficiency of their hiring efforts. Due to my interests in applying math to real world problems, I am now going to focus my efforts on exploring this area as a possible career path. I am also looking forward to augmenting my computer programming skills because I recognize now, that for these types of jobs, coding and programming, in addition to a solid linear algebra and classical mathematics background, are essential skills for the type of work in which I am interested.

A group of students prepares for their project presentation during SAMSI’s Interdisciplinary Workshop for Undergraduate Students, May 14-19, 2017. The workshop required students to work in multiple groups and present findings on assigned subjects.

I entered this SAMSI workshop as a mathematics major, but I lacked the knowledge of how I could put that degree to good use applying math knowledge to real world problems. After the workshop however, I have now conducted research in an application of math to music; something I never imagined was possible!

I was also introduced to countless other opportunities available for individuals trained in math, computer science, and analyzation techniques. I feel that by taking more courses geared towards applications of math in the real world, I can better prepare myself to succeed in a career in data analytics. Additionally, I am now informed on what it takes to create a successful application to graduate school and which programs I should consider that will best prepare me for a productive and fulfilling future.

Therefore, this undergraduate research workshop not only provided me with research, public speaking, and teamwork experience, but it also educated me on what options exist for my future. Although I have much more to think about, SAMSI was a starting point in helping me determine where I would like to see myself in the coming years and helped to catalyze the best way for me to utilize my mathematics and computing knowledge to benefit others in the future.

Undergraduate students from across the nation pose for a group shot during SAMSI’s Interdisciplinary Workshop for Undergraduate Students, May 14-19, 2017.

SAMSI Brings Astronomers and Statisticians Together to Study Universe

Contributed by:

Jim Barrett,
Ph.D. student, School of Physics & Astronomy, University of Birmingham, UK
Maya Fishbach,
Ph.D. student, Department of Astronomy and Astrophysics, University of Chicago, USA
Bo Ning,
Ph.D. candidate, Department of Statistics, North Carolina State University, USA
Daniel Wysocki,
Ph.D. student, School of Physics & Astronomy’s Astrophysical Sciences & Technology program, Rochester Institute of Technology, USA

The four of us are graduate students who have come together from different universities and a variety of disciplines: Jim Barrett studies astrophysics in the University of Birmingham’s School of Physics & Astronomy, Maya Fishbach studies astrophysics at the University of Chicago, Bo Ning studies statistics at North Carolina State University, and Daniel Wysocki studies astronomy at Rochester Institute of Technology. We came to know each other by attending the astrophysical population emulation and uncertainty quantification workshop held by SAMSI. This workshop is one of a series workshops in a one-year long program on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO). We enjoyed the experience we had in this workshop titled Astrophysical Population Emulation and Uncertainty Quantification which was held at SAMSI.

This was a very hands-on workshop which provided all of us with wonderful opportunities to sit down and have face-to-face discussions with fellow researchers from a variety of university backgrounds. Given the fact that we are from different disciplines (3 of us in Astrophysics and Astronomy and 1 in Statistics) and different universities (including one from overseas), it wouldn’t have been possible for us to meet and collaborate until we got to SAMSI at this workshop. So, thanks to SAMSI and National Science Foundation (NSF) for supporting us and bringing this exciting opportunity for us to collaborate and meet with so many eminent researchers in Astrophysics, Mathematics and Statistics, clearly, an opportunity which would perhaps last lifelong in our career.

As we share our experiences in this workshop, please allow us to first explain the context of this workshop. The workshop lasted for one week, held from April 4 -7, 2017. The theme of this workshop was to discuss using fast emulators to generate population models from various fields in astrophysics, including exoplanets, gravitational waves and extragalactic astronomy. Ilya Mandel, a professor at the University of Birmingham, and Derek Bingham, a professor at Simon Fraser University, organized this workshop.

During the first day of the workshop, researchers gave short presentations about their working projects in the morning, and four working groups were formed in the afternoon. The first group’s purpose was to discuss “the population emulation of massive binary stars,” and “the population of exoplanets.” The second group focused on “estimating statistical density functions for the population of gravitational wave sources.” The third and fourth groups focused on “Gaussian process (GP) model inference.” One of these groups focused on setting up GP models and coding them into python notebooks, while the other focused on building a GP emulator into a Hierarchical Bayesian model.

After the working groups were formed, the group members spent the majority of their time discussing new ideas and working on preliminary results throughout the remaining days of the workshop. Besides these group discussions, three tutorial lectures were given by Derek Bingham and Earl Lawrence on the second and third days of the workshop. These tutorials introduced computer model emulators, especially the GP model, discussed model calibration, and gave an overview of how to choose different strategies for the design of computer experiments.

During the workshop, four of us attended different groups and had different experiences. In the rest of this blog, we would like to share our individual experiences and takeaways from this workshop.

“I have been interested in astronomy since I was young, but I never dreamed about the day that I would be able to work side-by-side with astronomers, using statistics to solve their problems.”Bo Ning

Group Analysis

Jim Barrett:

Jim BarrettWorking Group I

I came to the workshop with my supervisor Ilya Mandel from the University of Birmingham in the UK. We work on modelling the evolution of binary stars, and in particular the kind of systems that could potentially become gravitational wave sources. We are actively developing a rapid population synthesis code, which simulates the entire lifetime of a binary star in a fraction of a second. This allows us to generate vast populations of binaries, so that we can use statistics to study the population as a whole.

In particular we are interested in how we can use gravitational wave observations to challenge the assumptions we make in our simulations. However, this is highly challenging, since gravitational wave systems are so difficult to make, we typically need to simulate tens of thousands of systems to get just one gravitational wave source. We therefore came to the SAMSI workshop to get help and advice on building an emulator for our model.


Ilya Mandel, a professor of Theoretical Astrophysics from the University of Birmingham (UK) discusses data capturing methods during the Astrophysical Population Emulation and Uncertainty Quantification Workshop at SAMSI on April 3-7, 2017.

We spent the week engaged in many stimulating and fruitful discussions with the statistics experts and fellow astrophysicists. We discussed the best approaches to building an emulator and spent many hours talking about experiment design. We left the workshop with a solid plan for how to proceed with or emulation problem and eager to continue to collaborate with the workshop participants in the future.

Maya Fishbach:

fishbach-mayaWorking Group IV

After attending the ASTRO opening workshop in August, I was excited to return to SAMSI. My research interest is to learn about populations of black holes from analyzing gravitational wave data, so I had joined Working Group 4 at the opening workshop. At one of the working group’s weekly telecons, Sujit Ghosh, SAMSI Deputy Director, presented his research with Angie Wolfgang and Bo Ning into using Bernstein polynomials to estimate the joint mass-radius density for a population of exoplanets. After email discussions with Sujit, Angie and Bo, I was inspired to further explore statistical methods for density estimation that could also be applied to populations of black holes. Thus, while my goal for this workshop was to explore density estimation techniques, I knew that I would encounter new ideas along the way that would inspire new and unanticipated projects.

For example, in initial discussions with statisticians Sujit, Bo and Ji Meng Loh, the problem of selection effects kept coming up. In the case of gravitational waves, massive compact binaries are louder than less massive ones, and so we are more likely to detect them. Therefore, when inferring the mass distribution over the population of compact binaries, it is critical to account for this selection effect that prefers massive binaries. Fortunately, Tom Loredo, Ilya and Daniel Wysocki had previously thought a lot about how to incorporate selection effects when analyzing populations of astronomical objects. This led to a large fraction of the astronomers and statisticians spending an afternoon listening to and discussing their results. Open problems remained in the case where the selection effect was not known precisely. For example, Leslie Rogers thought about how to define the selection probability for exoplanet mass and radius measurements – because the mass and radius are measured by different surveys. In addition, Kaisey Mandel, was working on defining selection effects for supernova surveys. The topic of selection effects is fundamental in astrostatistics, and it was very useful to discuss known methods of incorporating selection effects in a population-level analysis as well as challenges that remain.

Bo Ning:

Bo NingI have been an active participant in the Astro program at SAMSI since the opening workshop began in August, 2016. Since then, Sujit Ghosh, Angie Wolfgang, and I have been working on a project, using a nonparametric method for estimating the mass and radius relationships of exoplanets. Previous studies focused on making parametric assumptions based on the power-law model. However, these assumptions are somewhat arbitrary and often fail to hold true. As a result, we are using a more flexible model to estimate exoplanets’ mass and radius relationships.

This workshop provided Sujit, Angie and I with the opportunity to meet and to have face-to-face discussions on details of model inference. After the workshop ended, Angie and I spent an extra week working on our project. Our outcomes from the past two weeks were huge. For example, we finished the outline of the paper draft and obtained some preliminary results. We also sorted out our future plans and possible cooperation after the end of this workshop.

In the meantime, during the workshop, I also had discussions with Maya Fishbach and Daniel Wysocki about their project on gravitational waves. Even though this topic is quite different from modeling mass and radius relationships for exoplanets, the nonparametric model Sujit, Angie and I used in exoplanet was also useful to solve some of their problems, which was very exciting.

I would like to thank SAMSI for providing a great opportunity for interdisciplinary cooperation. I have been interested in astronomy since I was young, but I never dreamed about the day that I would be able to work side-by-side with astronomers, using statistics to solve their problems. Through my participation in this program, and by attending this workshop, I learned a lot about how to apply statistical models to solve problems in astronomy.

Daniel Wysocki:

DWysocki1After the incredible learning experience I had during the ASTRO opening workshop last August, I was pleasantly surprised that the Astrophysical Population Emulation and Uncertainty Quantification workshop surpassed it. As a 2nd year astrophysics Ph.D. student, I am working on methods to constrain the properties and origins of the population of compact binary objects responsible for the gravitational waves observed by the Laser Interferometer Gravitational-Wave Observatory (LIGO). By working with astronomers and statisticians working on problems from different domains, but with similar statistical challenges, I gained a much deeper understanding of the fundamental concepts and problems underlying the statistics relevant to my research.

One subject I gained a great deal of insight into was dealing with selection effects. Since I work with gravitational wave observations of individual binaries, all of my inferences on the population have to account for the fact that we’re more likely to detect massive objects due to the resulting increased signal strength, as well as a number of other biases. I came to appreciate how easy I have it after Eric Ford described a problem that depended on the number and types of planets orbiting each star; incredibly challenging considering we may never see some of those planets. Many questions I had on selection effects going into the workshop were cleared up, and I even discovered an error in an essential equation in a paper I’ve been writing.

In addition to the effective mix of people, I also thought the number of people attending the workshop hit a sweet spot. There were enough people to keep good diversity in skill-sets, but it was also a small enough number that I got to meet the majority of people attending, which is a hard balance to meet.

A new collaboration was started as a result of this conference, between Dr. Sujit Ghosh, Bo Ning, Maya Fishbach, and myself, which will come to fruition over the coming months. In describing the related astrophysical problems Maya and I are working on, Sujit came up with an alternative approach utilizing copulas, which I was unaware of beforehand. We will be working on a paper where we apply this type of method, and compare its performance with the approaches we’ve taken in the past.

As the ASTRO program comes to a close, I’m sad to see it go. Since the opening workshop, and the many SAMSI teleconferences I have attended throughout the year, I have learned a great deal about the general field of astrostatistics, and now understand the major statistical challenges being faced across the many branches of astronomy. I hope to find myself back at SAMSI for similar programs in the future.

Drexel PhD Candidate Gains Perspective on Big Data in Astronomy at International Workshop

Contributed by: Jackeline Moreno, Physics Ph.D. Candidate, Drexel University

Contributed by: Jackeline Moreno, Physics Ph.D. Candidate, Drexel University

I am a fourth year Graduate Student at Drexel University. My research area is optical AGN variability and accretion physics.  However, attending workshops like this one and participating in a SAMSI ASTRO working group, has expanded my interest to other types of variable objects and time series signatures.  I enjoy thinking critically about how these characterizations relate to physical properties of objects grouped in the same hyperplane of parameter space.

Our community of astronomers, statisticians and physical scientists are excitedly anticipating the era of time domain astronomy and, our new lens for probing the distant universe, gravitational wave detection.  The SAMSI-ICTS workshop (Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy) made a pioneering effort to bring together experts from seemingly different research fields in order to find common ground to exchange techniques and insights for analyzing time series data.  The workshop was hosted by the International Centre for Theoretical Sciences (ICTS) in Bengaluru, India. ICTS and SAMSI worked together to arrange speakers to present interesting content, coordinate for meals, handle logistics for the workshop and manage transportation for outings to explore the city. Special thanks are owed to ICTS as they went above and beyond assisting with visas, travel, accommodations and in orchestrating the 4-day workshop.

James Long, Asst. Professor of Statistics from Texas A&M University, gives a talk during the Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy in Bengaluru, India. The four day workshop was held at the International Center for Theoretical Sciences (ICTS) and was a co-sponsored workshop with SAMSI.

James Long, Asst. Professor of Statistics from Texas A&M University, gives a talk during the Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy workshop in Bengaluru, India. The four day workshop was held at the International Center for Theoretical Sciences (ICTS) and was a co-sponsored workshop with SAMSI.

The speakers presented on various topics, such as: variability statistics for classification in surveys; domain adaptation; noise modelling; and a whole slew of methodologies used to study the physics of transients, periodic and aperiodic variables and binary candidates for GW detection and localization. Speakers emphasized critical issues that needed improvements or further investigation. These issues were framed in the form of challenges to facilitate possible projects for collaboration. Talks were followed by panel discussions.  Several participants suggested that future similar workshops should provide allotted time for hacking or coding in conjunction with the panel discussions.  There was also an effort to document the challenges in an Authorea document, to serve as a discussion board afterward.

SAMSI workshops and working groups have helped me understand how my thesis work fits into the larger scientific picture and how to gain a better understanding of what our science priorities are as a community of observational astronomers.” 

All of the talks were video recorded, so visitors can view the talks, participants and abstracts of the presentations. In addition, photos and links to the webpage at SAMSI are also provided. SAMSI was a proud co-sponsor of this event and, in the future, they look forward to supporting research events like this in an international community setting. Sessions between panel discussions were organized into the following broad topics:

  1. Outliers and Background
  2. EM follow up of GW events
  3. Science of Transients, and
  4. Techniques for Time Domain Astronomy

A few talks that stood out to me included Rafael Martinez‘s (Associate Scientist at the Harvard-Smithsonian Center for Astrophysics) talk on “Building a Training Set for an Automatic LSST Lightcurve Classifier.” He talked about combining different classifiers, the problems with miscellaneous labels containing the largest number of objects and problems with period finding algorithms.  Hyungsuk Tak, a SAMSI postdoc, also gave a very nice talk, “Robust and accurate inference via a mixture of Gaussian and terrors,” and he asked the question why do astronomers so often and automatically assume Gaussian distributed errors? He presented a very promising method he developed combining Gaussians and heavy tailed (t-distributed) error models and demonstrated that the accuracy of inferred parameters improved significantly.  Another talk I enjoyed was Kuntal Misra‘s (Scientist of the Aryabhatta Research Institute Observational Sciences [ARIES] in Naintal, India). She talked about “Gamma Ray Bursts and Associated Supernovae”.  She provided a comprehensive discussion of lightcurve and spectral features used to classify and characterize these objects.

Participants of the Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy pose for a group shot at the International Center for Theoretical Sciences (ICTS) in Bengaluru, India. The group was composed of astronomers, astrophysicists and statisticians from all over the world.

Participants of the Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy workshop pose for a group shot at the International Center for Theoretical Sciences (ICTS) in Bengaluru, India. The group was composed of astronomers, astrophysicists and statisticians from all over the world.

From my perspective, as a fourth-year graduate student, I found the SAMSI workshops to be very eye-opening because they gave me so much context about sophisticated and efficient methodologies that work well with different data sets.  They provided a briefing on the latest and greatest techniques being applied to astronomical data in a setting conducive to discussion, cross-discipline education, and collaboration.  SAMSI workshops and working groups have helped me to understand how my thesis work fits into the larger scientific picture and to gain a better understanding of what our science priorities are as a community of observational astronomers.

I’m excited to see where these applications of machine learning take us?  In the future, I’d like to see more applications of hierarchical clustering and other techniques that capture continuity between subpopulations within a broader class.  These methods might help us transition into this massive (time series) data era to better understand our observations as dynamic systems but also in an evolutionary context.

This conference was not only great because of the science and stats. The location and the people who attended made it an unforgettable experience for me! Both ICTS locals and people invited through SAMSI were genuinely welcoming and kind folks. In the evenings after the workshop we all had dinner together, went for bike rides and played some ping pong.  After the workshop, I was invited to join a group touring the central part of Bengaluru and the archaeological sites at Hampi.  The days that followed were an adventure, and I sincerely appreciated the moments I shared with the great friends I made through this workshop!

Participants take a break from the Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy workshop to explore Bengaluru, India. The four-day workshop was held at International Centre for Theoretical Sciences (ICTS) and featured speakers in the field of astronomy from around the world.

Participants take a break from the Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy workshop to explore Bengaluru, India. The four-day workshop was held at International Centre for Theoretical Sciences (ICTS) and featured speakers in the field of astronomy from around the world.

Rutgers Mathematician Undergraduate uses Workshop to Plan Future

Francesca Falzon

Contributed by: Francesca Falzon, Mathematics Major, Undergraduate, Rutgers University

As an aspiring mathematician, I was very excited to be participating in my first-ever mathematical conference at the Statistical and Applied Mathematical Sciences Institute (SAMSI). The two-day Optimization Undergraduate Workshop took place from February 27-28 in Durham, N.C. The workshop was part of SAMSI’s education and outreach initiative and included approximately 40 students from across the United States. The students convened to learn more about cutting edge research topics relating to optimization methods for large-scale statistical analysis.

A Busy Day…

After having a hearty breakfast at the hotel, we were whisked away to SAMSI where we began our day with an introduction from the Deputy Director, Sujit Ghosh. This was closely followed by a hands-on R tutorial, led by Paul Brooks, an Associate Professor at Virginia Commonwealth University. He began his talk with an exercise in finding the optimal point in a real coordinate space. He also thoughtfully carved out some time at the end of his lecture to provide insight and guidance to those of us considering graduate school.

My brief time at SAMSI was a whirl-wind experience packed to the brim with fascinating lectures and engaging workshops.”

Fueled up with tea and coffee during the break, we delved back into more optimization, this time as it relates to Bayesian Linear Inverse problems. Professor Alen Alexanderian of North Carolina State University explained how inverse problems, governed by PDE’s, can allow us to determine A-optimal sensor placement when the starting conditions are unknown.

As someone with a budding interest in computer science, but a limited knowledge of programming, I was very excited to partake in a workshop that taught us the Fundamentals of Scientific Python. Ahmed Attia, a SAMSI Postdoc, walked us through the basics of the Python language as well as the implementation of various tools/functions available through the NumPy package. This segued into a lecture series on the subject of Neural Networks and Optimization in Data Analysis given by Peter Diao and Sercan Yildiz, who are both also Postdocs at SAMSI. Machine learning is proving to be a popular research area, so it was great to be exposed to this rapidly growing field in mathematics and computer science. The program coordinators were extremely thoughtful in the inclusion of a career panel to wrap up the day. Applying for graduate school is often a daunting process so I welcomed the panel discussion on career opportunities, as we got to hear about ‘tips and tricks’ from graduate school application veterans – the SAMSI Postdocs and Graduate Fellows helping to run the program.

The Wrap Up…

Day two was quite a different change of pace. On Tuesday we got the chance to visit the SAS Institute campus in Cary, N.C. During our time there, we heard various presentations on current research being done in optimization from Manoj Chari, Yan Xu, and the other members of the Numerical Optimization team at SAS. I am not sure whether I would ultimately like to conduct research in an academic setting or an industry setting, so I found the exposure to both work environments very instructive.

My brief time at SAMSI was a whirl-wind experience packed to the brim with fascinating lectures and engaging workshops. It also proved to be a wonderful opportunity for not only learning about optimization methods, but also for networking with individuals at all academic stages – from fellow undergraduates to graduate students to associate professors. Needless to say, the experience at SAMSI exceeded my high-expectations and instilled in me a new-found excitement about my pursuit of mathematics upon returning back to my home institution!


Paul Brooks, Associate Professor at Virginia Commonwealth University begins his lecture by challenging students to find an optimal point in a real coordinate space at the Optimization Undergraduate Workshop.

SAMSI Postdoctoral Fellows Ready for Next Step in Careers

Postdoctoral fellows are a big part of the SAMSI family. This year we would like to recognize previous postdoctoral fellows as they continue on in their given fields of study. Find out what’s on the horizon for these young professionals:



Lucas Mentch
Currently serving as an Assistant Professor, Department of Statistics, University of Pittsburgh
SAMSI Postdoctoral Fellow: 2015




Ben Risk
Accepted upcoming position as an Assistant Professor, Departments of Biostatistics & Bioinformatics, Emory University
SAMSI Postdoctoral Fellow: 2015




Zhengwu Zhang
Accepted tenure-track position in the Department of Biostatistics & Computational Biology,University of Rochester
SAMSI Postdoctoral Fellow: 2015

VA Tech Graduate Student uses Inverse Problems Workshop to Influence Personal Research


Contributed by: Joseph Tanner Slagel, Graduate Student, Dept. of Mathematics, Virginia Polytechnic Institute & State University

As a graduate student at Virginia Tech, my research interests are in large-scale numerical linear algebra. In particular, I have recently been studying stochastic approximation methods for solving very large least square problems.  These are least square problems where the data size is terabytes (or even petabytes!) in size, and thus cannot fit in a computer CPU’s memory all at once.

I looked at the Statistical Inverse Problems Workshop at the Statistical and Applied Mathematical Sciences Institute (SAMSI) as an excellent opportunity to learn about emerging topics and techniques that I could apply to my own personal research.

I attended the Statistical Inverse Problems Workshop from January 26-27, 2017. The friendly and open atmosphere of the SAMSI workshop made it easy for me to make new connections and to discuss topics related to my research. The experience gave me the opportunity to work with a dynamic range of professionals (graduate students, postdocs and junior/senior level faculty), which helped me gain perspective from mathematicians at various stages of their career.

“Attending this SAMSI workshop was a great way for me to connect with other researchers whose interests overlap with my own.”

A distinguishing feature of the workshop was that, in addition to plenary and research talks, there was a lot of time for research discussions and collaborations on current projects – small groups were encouraged to find an open room and work for multiple hours on emerging research questions.

The talks at the workshop gave me an opportunity to learn about important problems in Bayesian inverse problems. Despite the small group, I heard a range of talks that provided an overview of open problems in the field, expounded on main computational and algorithm challenges, and described lots of cool real-life applications. The most beneficial part of the workshop for me was getting to speak to others about my research. I received a lot of helpful input and pointers to resources that have helped me see where my work fits into the larger statistical inversion community.


Ahmed Attia (instructing), a Postdoctoral Fellow at SAMSI, gives a research lecture at the Statistical Inverse Problems Workshop

Attending this SAMSI workshop was a great way for me to connect with other researchers whose interests overlap with my own. I look forward to returning to SAMSI in the future for more collaborations and discussions!

NOTE: To see what subjects were presented at this workshop visit the SAMSI website at: www.samsi.info/opt-inv-prob.

UT Mathematician Discusses Advances & Future of Super-computing

Jack Dongarra

Jack Dongarra, Director of the Center for Information Technology Research and Innovative Computing Laboratory – University of Tennessee (photo courtesy of University of Tennessee)

The Workshop on the Interface of Statistics and Optimization (WISO) at Duke University’s Penn Pavilion wrapped up recently. After we said good bye to all of our participants, I was reminded of an interesting talk given by one of the twelve insightful speakers. The speaker in question was Jack Dongarra.

Jack is the Director of the Center for Information Technology Research and Innovative Computing Laboratory, from the University of Tennessee. He lectured on: “The Road to Exascale and Legacy Software for Dense Linear Algebra (or what we have been doing for the last 43 years).” Jack’s talk at WISO could be appreciated even by those who are not specialists in the esoteric matters of statistics and optimization. True to the title, Jack has been designing high-performance software for over 40 years. His pioneering work has been honored with many awards, including membership in the US National Academy of Engineering.

Twice a year, Jack co-publishes a benchmark of the 500 most powerful supercomputers in the world. The current number one, as of November 2016, is the Sunway TaihuLight machine in Wuxi, China. Built from over 10 million processors, it can execute more than 1016 operations in a single second. The Titan Cray XK7 at Oak Ridge National Lab, located in Oak Ridge, TN, is number three.


The Sunway TaihuLight machine (above) is the number one super computer in the world. Its more than 10 million processors make it possible to execute millions operations in a single second!

For the many young people in the audience, Jack started his talk with a history of how hardware and software have developed over the years, and how he himself got into the business of benchmarking. A few photos from the 1970’s, featuring Jack and his colleagues in bell bottoms and side burns, proudly posing on a Ford Pinto with the license plate “Linpack” (after their groundbreaking public-domain software package) were well received.

Dongarra Friends

– Jack Dongarra and fellow software developers pose with his trusty Ford Pinto in the 1970’s identified with the license plate “LINPACK.” LINPACK was the group’s groundbreaking public-domain software package.

Jack’s advice for how to build good software: Keep the processors busy with arithmetic, make sure they coordinate their work schedules efficiently rather than sitting idle waiting for results from other processors.

Jack ended with a list of the many challenges for software design, including:

  • Efficiency (speed matters),
  • Scalability (keep up the efficiency, even in the face of growing work),
  • Reliability (with 10 million processors in use, some are bound to fail)
  • Portability (the particular hardware platform should not matter).

Jack Dongarra was one of many interesting speakers at the WISO. If you are interested in seeing the other presentations from the 3-day event, visit the WISO Video Page.


ASTRO Workshop Brings Researchers together to Discuss Exoplanet Exploration


Contributed by: Ian Czekala, Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), Postdoctoral Fellow, Stanford University

My research focuses on understanding young stars and their protoplanetary disks during the planet formation epoch. For a number of reasons, I was particularly excited about attending the Statistical and Applied Mathematics Institute (SAMSI) Hierarchical Bayesian Modeling of Exoplanet Populations, October 17-28. The main reason was that my previous experiences at SAMSI have always been so positive. For example, I first learned about many of the topics and techniques that I use on a regular basis in my research through a similar SAMSI workshop in 2013. Three years later, I was again eager to learn new analysis methods from the statistical expertise gathered at SAMSI. Although two weeks may seem like a long time for a workshop, I knew that the close-knit environment would foster collaboration, catalyze many new projects, and make the conference pass by way too quickly. The following is a brief account of the conference with highlights of aspects that I found particularly interesting, by no way is this a complete or unbiased survey of all that transpired!

First, allow me to explain the context for our workshop. In August, 80 researchers from the fields of exoplanets, gravitational waves, and statistics converged upon Research Triangle Park to kick off the year-long SAMSI program on Statistical, Mathematical and Computational Methods for Astronomy. At the Opening Workshop for this program, we explored ideas and statistical techniques common to these fields and brainstormed interesting projects to work on over the next year. We splintered into five “working groups,” each focused on a particular topic or technique. I joined Working Group IV – Astrophysical Populations, which was focused on hierarchical Bayesian inference of exoplanet populations. Each working group has maintained momentum through weekly teleconferences, and most groups will have a workshop at SAMSI at some point during the academic year. The year-long program will be capped by a “transition” workshop in May 2017.

Angie Wolfgang, a National Science Foundation Fellow at Penn State University and Eric Ford, a professor, also at Penn State, were the main organizers of the Astrophysical Populations workshop. We had about 20 participants split equally between astrophysics and statistics. Our first morning was spent discussing our research interests and what we hoped to accomplish over the next two weeks. Two major groups evolved from this discussion. The first was centered on exploring the mass-radius relationship of exoplanets from photometric transit and radial velocity datasets. The second was focused on spectroscopic techniques to characterize stars and measure their radial velocity. Although our workshop was nominally about exoplanets, it turns out that a proper understanding of stars is fundamental to detecting and understanding the exoplanets that orbit them.

Understanding the Planet Mass-Radius Relationship…
In the past decade, astronomers have transitioned from knowing of the existence of only a handful of exoplanets to discovering a vast collection of several thousand. Most planets have been discovered by the Kepler Mission, which finds planets by measuring the dip in light as a planet transits its host star. It is most informative about a planet’s radius. For a select subset of these planets, precise radial velocity monitoring yields the masses of the planets as well. Because we are necessarily operating at the detection limit of our telescopes when studying small planets, it is very important to utilize proper statistical analysis lest our interpretation be biased.  The fundamental unknown that links a planet’s mass and radius is the planets composition, and so with a proper statistical framework we might hope to infer how planet composition varies amongst the thousands of known exoplanets, telling us something deep about the planet formation process in general.


Leslie Rogers, an Assistant Professor in the Department of Astronomy and Astrophysics at the University of Chicago, speaks about planet composition distribution.

Angie Wolfgang, Bo Ning, a Ph.D. candidate in the Department of Statistics at N.C. State University, and Sujit Ghosh, SAMSI Deputy Director, explored using Bernstein Polynomials to model the planet mass-radius relationship non-parametrically, and showed promising results that included measurement uncertainties. Leslie Rogers, an Assistant Professor in the Department of Astronomy and Astrophysics at the University of Chicago, talked about the planet composition distribution. In addition, she also discussed how to link physically motivated models of planet composition to data and determine if this composition changes as a function of planet formation mechanism. Kaisey Mandel, a Postdoctroal Fellow at the Harvard-Smithsonian Center for Astrophysics, worked on understanding selection effects as they apply to exoplanet surveys. This was his focus since he is also interested in selection effects of Type Ia supernovae surveys.

A sizable group of people worked on translating hierarchical sampling code into the new language STAN. In particular, Megan Shabram, a Postdoctoral Fellow with NASA’s Kepler Mission and Joe Catanzarite, a SOC Scientific Programmer with NASA’s Kepler team, produced an open-source Jupyter notebook that implemented planet occurrence rate calculations in PySTAN.

Central to many of our problems discussed at this workshop was the topic of “emulation” or “uncertainty quantification,” which is actually the primary topic of Working Group I – Uncertainty Quantification and Astrophysical Emulation. Bekki Dawson, an Assistant Professor in the Penn State Department of Astronomy and Astrophysics, and Assistant Professor, Anirban Mondal of the Mathematics, Applies Mathematics and Statistics Department at Case Western Reserve University, worked on developing astrophysical emulators for planet formation models, so that more accurate (and computationally expensive) models could be used in hierarchical Bayesian inference to understand the formation of super-Earths and mini-Neptunes. Related to this problem, Jessi Cisewski, Assistant Professor in Yale’s Department of Statistics, made several informative presentations on Approximate Bayesian Computing (ABC) to solve inference problems where it is difficult to write down a likelihood function.


David Stenning, one of two SAMSI Postdoctoral fellows at the workshop, presented talks on techniques using astrostatistics to improve exoplanet analysis.

Hierarchical Spectroscopic Inference with Time Series Stellar Spectroscopy…
A large group of astronomers and statisticians worked on techniques to improve radial velocity precision, with the hopes of finding planets with the mass of earth and below. Eric Ford, Jessi Cisewski, David Stenning and David Jones, Postdoctoral Fellows at SAMSI, Robert Wolpert, a Professor of Statistical Science and the Environment at Duke Univesity; Tom Loredo, a Senior Research Associate in Astronomy at Cornell; Ben Montet, a Postdoctoral Researcher from the University of Chicago and I worked on radial velocity fitting using mock spectral datasets with known statistical characteristics. These datasets are comprised of real stellar spectra of the sun to which have been added planets (the signal of interest) and star spots (a confounding signal). We examined interesting principal component analysis with the hope of isolating the orbiting planet from stellar activity. During this period, we were also treated to two presentations by the SAMSI postdocs David Stenning and David Jones about using Gaussian processes to correlate stellar activity indicators with radial velocity jitter and using diffusion mapping to understand stellar variability.

By the end of the workshop, we were all knee-deep in immersive projects that we had started just 10 days prior – we were reluctant to leave!  The collaborative working environment, with daily updates of what we had accomplished certainly fueled an exciting work schedule, since everyone was motivated to complete new ideas to share with the group. By the end of the workshop, several of us remarked that in fact two weeks was not a long enough period for us to get anything done – we were all so dedicated to the research, we wanted to stay! To cap it all off, we were treated to a tasty “special presentation” by Tom Loredo, who shared with us how chocolate is made.

These researchers will collaborate over the next several months on this continued analysis of exoplanet discovery.


Workshop participants were treated to a chocolate tasting from Tom Loredo, a Senior Research Associate in Astronomy at Cornell. Loredo is a hobbyist chocolatier and candymaker and his confections were enjoyed by the group.

E&O Undergraduate Astrostatistics Workshop: A Stellar Learning Experience


Contributed by: Rachel Matheson, Mathematics Undergraduate Student, Vassar College – Poughkeepsie, NY

As a math major at a liberal arts school, choosing my classes for the next semester always feels like a lot is at stake. I want to take physics, neuroscience, astronomy and biology, but I also want to take social sciences and humanities. Dipping in to the Statistical and Applied Mathematical Sciences Institute’s (SAMSI) Undergraduate Workshops gives me a chance to experience the different flavors of applied math and statistics without the commitment of a class. I was therefore extremely delighted to be invited to come back to SAMSI this October for a two-day undergraduate workshop focused on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO).

SAMSI Workshops – Full of Information…


Jessi Cisewski, an Assistant Professor in Yale University’s Department of Statistics, conducts a lecture on Approximate Bayesian Computation in Astrostatistics. This talk was one of several performed during the workshop.

Though the workshop was brief, it was packed with interesting lectures and hands-on activities. After a yummy breakfast at the hotel, we shuttled off to SAMSI’s campus and were greeted by SAMSI Deputy Director, Sujit Ghosh, who delivered his opening remarks to our group. From there, we quickly transitioned into a lecture from Jessi Cisewski, an Assistant Professor in Yale University’s Department of Statistics, on Approximate Bayesian Computation in Astrostatistics. The lecture was very enjoyable and informative – it served as a reflection and an extension of what I have been learning in my probability class, applied to the stellar initial mass function.  Bekki Dawson, an Assistant Professor from Penn State University’s Department of Astronomy and Astrophysics, then dazzled my mind with stellar facts during her lecture titled Time Domain Challenges for Exoplanets. I was surprised to learn that this is an area where technology is good and up-to-date but we still don’t have the statistical methods to interpret noise in the data properly in order to detect exoplanets similar to Earth.

“SAMSI serves largely as a space for me to feel motivated about my pursuit of applied math and connect with people who feel just as excited as I do about it.”

After a short break, we delved back in to a tutorial on R led by SAMSI Post-Doctorate fellows David Jones, David Stenning and Hyungsuk Tak. This was a helpful overview to lead up to the intensive, hands-on workshop of modeling Gaussian processes. Line-by-line comments in the R code kept me from feeling lost as the lecture sped on, deep into the mathematics and emulator needed in order to make this model run. I could easily go back and gain understanding as post-doctorate fellows stood throughout the room ready to help at an arm’s wave. It felt like a really positive learning environment, despite the high speed at which the material was presented.


On Day 2 of the Undergraduate Workshop, students got to visit the Morehead Planetarium on the campus of UNC-Chapel Hill. The students enjoyed two presentations on the universe and the existence of blackholes.

Opportunities and Guidance from those who have done it…

One of my favorite aspects of coming to SAMSI is being able to talk to the post-doctorate fellows, SAMSI faculty, and my peers, about anything from career path choices to, quite literally, the stars in the sky. The panel on career opportunities led by some of the graduate fellows was a wealth of information for nervous undergraduates to seek advice from those who have “made it,” as well as to start conversations to continue later on. I ended up eating dinner with two post-doctorate fellows, who advised me on everything from which classes to take to not worrying too much.

Leaving with a new sense of purpose…

After a visit to the Morehead Planetarium, I felt sad to be leaving almost as quickly as it began. It is always so reassuring to talk to people who are pursuing what I am interested in, not to mention truly inspiring and exciting. SAMSI serves largely as a space for me to feel motivated about my pursuit of applied math and connect with people who feel just as excited as I do about it. It forges what may be a 2-day community, but that community gets to live on through email and LinkedIn. I am too glad to have had the opportunity to experience SAMSI as a community and as a learning space – it excels at being both!

To see more about what happened at this workshop, visit: ww.samsi.info/astro-undergrad. To see past and upcoming workshops in our ASTRO Program, visit: www.samsi.info/astro.


Students from the Undergraduate Workshop pose for a picture at the working sun dial located at the Morehead Planetarium on the campus of UNC-Chapel Hill. The students visited the planetarium as part of their workshop activities. The 2-day undergraduate workshop was part of the Education and Outreach for SAMSI’s Program on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO).