Impressions of the SAMSI Undergraduate Workshop on Computational Neuroscience – Part 1

Several undergraduate students recently attended the SAMSI Undergraduate Workshop focusing on Computational Neuroscience. Here is Suzanna Mostaghim’s impressions of the workshop. Suzanna attends Virginia Tech. She has a wonderful blog you should check out called Inklings and Tea.

The Statistical and Applied Mathematical Sciences Institute (SAMSI) holds a two day undergraduate workshop in the fall and spring as well as a week-long one in the summer.

This year, the fall workshop from October 19-20th focused on computational neuroscience. Over one and a half days there were seven scientific talks, which was incredibly intense. I personally have over six pages of notes.

I got in on Sunday night, a lot of other participants got in in the morning and afternoon due to flights and better planning than myself. As I was the only girl from Virginia Tech I was paired with a random roommate (who is in fact an awesome triple major, check out her blog here).

SAMSI was great and paid for our hotel rooms so we wouldn’t have to worry about waiting to get reimbursement for the room for a few weeks. We were staying only about 10 minutes from NISS and about 25 minutes from Duke.

Our first day started early, we shuttled over to SAMSI at 8:15, registered, and began talks at 9:00.

 Ciprian Crainiceanu from John Hopkins University

Ciprian Crainiceanu from John Hopkins University.

The first talk was by Ciprian Crainiceanu from John Hopkins University; he talked about clinical brain imaging from a statistical view, pointing out that data scientists and electrical engineers would use different techniques and names. Look out for his MOOC on Coursera early next year.

For me, I recognized this area as image deblurring – the data science point of view. This was something I had touched on in a Computer Modeling and Data Analytics course at Virginia Tech taught by Julianne Chung whose research is in image deblurring.

We sped through the first slideshow he showed on Neurohacking and moved onto Data Structures and Operations which was based on classification typing. It was intense to go through so many slides in less than two hours and absorb as much information as I possibly could.

After Dr. Crainiceanu was finished, we had a five minute break and then Ana-Maria Staicu of NC State stepped up to the plate and gave us an impressive technical primer on Tensor Imaging Study of MS.

I don’t think I ever wrote so many notes on a singular lecture outside my classes until that point. She brought down the information to the level that an undergraduate would understand and expanded upon it to show us how to approach the problems she faced while analyzing diffusion anisotropy.

students walking into building

Entering the Center for Neuroimaging at Duke.

When she finished we broke for lunch and traveled to the Center for Neuroimaging at Duke and did an overview of the facilities and once again listened to a talk. I won’t go into detail, but I’ll admit you should definitely visit them for grad school if you’re interested in biostats and psychological disorders.

In full disclosure, I think that it could have been organized a little better for the field trip. However, it was still a wonderful experience and good break in between intense scientific talks

Next we had an R tutorial from Benjamin Risk, who works at SAMSI. He sped through and gave a thorough tutorial. However, due to the faulty internet connection a lot of students weren’t able to follow completely.

After R, we got to one of my favorite talks by Sarah Vallelian. Which was about computed tomography using MATLAB and Python; something I’ve covered at Virginia Tech. But, I won’t expound upon the details. She showed us the equations for attenuation and how to use Radon Transforms when doing compute tomography.

When the talks for the day were over we attended a panel discussion hosted by postdocs and graduate students about graduate school. One in particular was very sarcastic and, while amusing, it got a bit tiring after a while. However, overall they were very helpful and gave a good amount of advice for graduate school considerations.

We then shuttled back to the hotel and groups of us conglomerated together and bonded, exchanging contact information in order to keep in contact after the workshop ended the next day.

The second day started just as early as the first and we started with a talk on models for muscle activation by Lauren Miller from UNC – Chapel Hill. She covered the simplistic models and moved onto more complex ones such as the Three-Element Hill Model, explaining on a level that we understood.

Ezra Miller at SAMSI workshop

Ezra Miller, Duke University.

Our very last talk was on Topology for Statistical Analysis of Brain Artery Images by Ezra Miller. I knew very little about topology, and when he was done I understood a lot more than I had ever learned prior to that talk.

He was incredibly engaging and broke down topology to the point where I believe almost anyone could understand. I honestly am considering applying to Duke just to have him as a professor one day.

With the end of the Ezra’s talk, the workshop came to an end. I had to go back to Blacksburg and part ways with my new found friends. But, it was definitely an experience I will never forget.

Topics in Probability: a VI-MSS Workshop

Science Across Virtual Institutes (SAVI) is a NSF program that facilitates collaborations among researchers from different countries.  One of the awards under this program is the Virtual Institute for Mathematical and Statistical Sciences (VI-MSS).  This award connects two U.S. NSF funded research institutes (SAMSI and ICERM) with several leading research institutes in India (Indian Statistical Institute (ISI), Chennai Mathematical Institute (CMI), the Indian Institute of Science (IIS), the Institute of Mathematical Sciences (IMSc) and the Tata Institute of Fundamental Research (TIFR)).  VI-MSS provides funding to U.S. graduate students, post doctoral fellows and faculty for research visits to partner institutes in India. Another key objective of VI-MSS is to sponsor joint workshops that have the potential to lead to collaborative research between scientists in the two countries.  The workshops are co-sponsored by the Indian federal research funding agency, Department of Science and Technology (DST).

group photo at the CMI workshop

Participants from the SAMSI-SAVI Workshop on Topics in Probability at Chennai Mathematical Institute, Siruseri, Tamilnadu, India

Topics in Probability was one such workshop that was held at CMI on December 18-20, 2012.  The workshop was co-organized by me and the director of CMI, Professor Rajeeva Karandikar.  CMI, located in Siruseri at the outskirts of Chennai was founded in 1989 as part of the SPIC science foundation and is a premier research institute in Mathematical Sciences in India.  The workshop brought together leading researchers in Probability Theory;  from U.S. Universities (Professors Richard Bass, Sandra Cerrai, Tom Kurtz, Ramon Van Handel and Mathukumalli Vidyasagar);  and  from several top Indian research institutes (Professors Siva Athreya, Vivek Borkar, Arup Bose, Manjunath Krishnapur, Krishanu Maulik and Anish Sarkar).  A broad range of topics were represented, including Random Matrices, Diffusion Processes, Percolation Theory, Infinite Dimensional Stochastic Analysis, Stochastic Partial Differential Equations, Random Graphs, Limit Theorems, Information Theory, Discrete Probability Models and Elliptic PDEs with applications in Probability.  Approximately fifteen graduate students, both from U.S. and India, participated in the workshop.  Talks were spread over three days with 3-4 talks each day giving ample opportunities for interaction and discussions.  As a result of a small number of talks per day, the pace of lectures was leisurely and the environment was relaxed and informal with frequent questions and extended discussions.


Nuala’s Impressions from the Astrostatistics Workshop

The following post was written by Nuala McCullagh who is a graduate student in the Physics & Astronomy department at Johns Hopkins.

Nuala McCullagh sitting on a bench

Nuala McCullagh

I was thrilled to have the opportunity to visit SAMSI for the Massive Datasets program for three weeks in September. One of the most positive aspects of my visit was my exposure to several flavors of diversity, the most salient of which was diversity of expertise and discipline. As a graduate student in the Physics & Astronomy department at Johns Hopkins, I have been active in promoting diversity within my department. Physics and astronomy, along with most math and science fields, have traditionally lacked racial and gender diversity, and while the benefits of diversity are well established and generally accepted, it can still be difficult to convince scientists that it is an issue they should care about. The benefits of the diversity I observed at SAMSI were very clear, and my experience there really reinforced my belief that diversity can inspire creativity and productivity.

At the opening workshop, we heard talks from experts in statistics, computer science, applied math, neuroscience, environment & climate science, high energy physics, and astronomy. While the conference covered a wide range of disciplines, there was a common thread of having to deal with massive datasets. I was surprised to learn about the similarities between my work in cosmology and work in other fields such as climate studies and neuroscience. Hearing about the problems and solutions in those fields have helped me think about my own problems in a different way.

At the astrostatistics workshop, we heard about large galaxy surveys, computer simulations, multi-dimensional datasets, time-domain astronomy, and more. It was helpful to hear about the different statistical problems with massive datasets in the context of astronomy, and interesting to see the similarities and differences between them. For example, just within cosmology, the statistical problems that arise when working with large dark matter simulations are different from those that arise in detecting weak lensing in galaxy surveys. Meanwhile, people who study exoplanets work with large simulations with many parameters, much like the simulations in cosmology. Hearing about the various statistical problems astronomers have encountered allowed me to make connections between different areas in astronomy that I would not have noticed otherwise.

I appreciated the opportunity to learn about a wide variety of problems concerning massive data. It was interesting to note the statistical similarities in seemingly disparate scientific problems. It was also reaffirming to see the positive impact that diversity can have in inspiring creativity and productivity in science.