What Does It Mean to be a Woman in Mathematics?

The following blog post was written by Jessica Matthews, Cooperative Institute for Climate and Satellites (CICS-NC).

room full of women watching presentation

Workshop for Women in Mathematics held April 6-8, 2016.

When offered the invitation to speak at SAMSI’s Opportunities Workshop for Women in Math Sciences, I gladly accepted. When it came time to actually prepare the presentation, I realized that I had never attended, let alone presented, at this type of workshop ever before. I am well versed in putting together a scientific presentation, but this was different. So I myself was faced with the opportunity to consider what it meant to be a woman in mathematics. I had the opening talk time slot, which inherently carries with it the pressure of setting the tone for the entire event. I chose to draw from my personal experiences and to discuss career possibilities beyond the classroom, skill sets I have found necessary (beyond math), and a few key challenges faced by women in our field. A spirited discussion regarding the pay gap and the importance of negotiation entailed. I enjoyed the free-flowing discussion, and felt like this open and welcoming atmosphere was present for the rest of our gathered time.

Throughout the two and half days of the workshop, we had the privilege of hearing from a number of women who have successful careers in academia, industry, and government. They shared their lessons learned, fielded questions, and led discussions about career opportunities and challenges experienced. I cannot possibly capture a comprehensive account of all the great talks and conversations that took place in this workshop, so I provide merely a few personal highlights.

two ladies talking in the hallway

Amanda Goldbeck (R) talking to a participant of the workshop.

Amanda Golbeck introduced the concept of viewing one’s career path as a jungle-gym rather than a ladder. We tend to have the ingrained view of the traditional (and linear) career path, while in reality, to maintain a healthy life–work balance, flexibility is required.  Another grain of wisdom she offered is that being a strong leader is important, but being a valuable team member is paramount. I think this is often forgotten in our power-hungry society, but the truth is that more can be accomplished via cooperation and we should value the cultivation of teamwork skills.

Panel at the women in math workshop

L-R: Ulrica Wilson, Lea Jenkins and Amanda Goldbeck.

Drawing on her experiences at a historically black university, Ulrica Wilson offered a great explanation as to why having workshops such as this one is not only relevant, but important for increasing and maintaining diversity. When we take the time to create this space, we are able to stop focusing on what makes us different and just focus on the math—which is really what we were all drawn to when we chose this pursuit in the first place!

Marie Davidian gave a fascinating overview of notable women in the mathematical sciences, both in the past and the present. I was captivated with the story of the trailblazer Gertrude Cox, founding head of the (then-named) Department of Experimental Statistics at NCSU in 1941. Her recommendation for the position came in the way of a footnote appended to a letter containing a list of recommended male peers: “Of course if you would consider a woman for this position, I would recommend Gertrude Cox of my staff.” This truly puts into perspective how far the community has come with regard to gender equality.

The workshop attendees were energetic and engaged, which made the panel-led discussions and breakout sessions (not to mention breaks) both stimulating and fun. The participants were largely graduate students and early career scientists, who had plenty of thoughtful questions for the expert representatives from academia, industry, and government. Even though I may have been cast as one of the experts, I found that I learned a lot and left the workshop with a to-do list of actions I am interested in taking. In particular: joining a mentor network, engaging more in professional society events, and advocating for family leave benefits.

I am glad to have had this opportunity to consider the challenges, and solutions to those challenges, faced by women and minorities in the mathematical sciences. I’d like to thank SAMSI for hosting this event and allowing us to gather and reflect on both the progress that has been made, and the issues that remain. It is only through this type of directed intention that we may continue to move towards equality.

Advertisements

Statistical Methods and Analysis of Environmental Health Data

The following was written by SukhDev Mishra,Ph.D., Division of Bio-Statistics, National Institute of Occupational Health, Indian Council of Medical Research, Ahmedabad(India)

group shot

Statistical Methods and Analysis of Environmental Health Data Workshop group.

I was fortunate to attend the SAMSI workshop on Statistical Methods and Analysis of Environmental Health Data last week in Mumbai. It focused on various topics related to the statistical analysis of environmental health data, some of which discussed latest methodological development in this field, particularly during the first day’s opening lecture from Professor Joel Schwartz.

Time series data has proven to be critical in the assessment of systematic impact of environmental factors on human health. Professor Francesca Dominici, a researcher with significant contributions in this area was a very dynamic and enthusiastic co-leader for this workshop. She discussed in length the statistical principles and assumptions of multi-site time series analysis along with careful interpretation of such data. Due to technological advances and regular measurement availability, time series data could be accessed and easily analyzed with the techniques elaborated by Professor Dominici, which will be integral to the success of my future studies.

Working Group 5 - Gene x Environment Interactions

Working Group 5 – Gene x Environment Interactions

The Gene x Environment Analysis & Epigenetics lecture taken by Professor Bhramar Mukherjee provided very useful information on interaction/additive and multiplicative models citing practical applications in area of environmental health that she developed. Her very creative way of teaching, blended with great sense of humor, kept us engaged so much so that we wouldn’t blink for a second.

Spatial statistics is a critical part for environmental health data, so it was helpful to have the basics covered by Dr. Safraj Shahul Hameed and Dr. Brian Reich well. Professor Donna Spiegelman presented a wonderful talk on measurement error starting from statistical notations to complete logit function (being a statistician ….I always love this part J ). She put great effort explaining Regression calibration method for MS/EVS and algorithms. Interesting talk!

Working groups were engaged in different exercises that included working on different problems/real data sets generated through various participants and coming up with new analysis and interpretation of data. I worked on Exposure Modelling of Ambient and Household Air Pollution for Acute and Chronic Health Effects. I enjoyed working with my fellow WG colleagues- Kalpana Balakrishnan, Santu Ghosh, Donna Spiegelman, Kevin Lane, Joel Schwartz, Sourangsu Chowdhury , and Poonam Rathi. Fine scientific arguments during the process of analysis were the crux of our exercise; thanks to Joel, Kalpana, Donna and Kevin especially.

This is no way a comprehensive description of this workshop, just my thoughts. I would also like to record here that I learned from each and every speaker and fellow participant. It was a gathering of great scientific minds and very inquisitive researchers. My understanding is that one of SAMSI’s objectives is to foster a culture of collaborative research among Indo-US researcher in area of public health; and I could see that coming true as we collectively discussed ideas on how to continue our work in mutual scientific engagement. I hope these efforts result in great scientific endeavors in coming time for environmental health priorities.

People drinking tea during a break

Enjoying afternoon tea.

One of the unique features of this workshop was meticulous planning by the team of organizers, be it scientific contents or overall execution by Professor Richard Smith, Professor Sujit Ghosh, Professor Francesca Dominici, and Ms. Krista Coleman whose scientific management and interaction with participants was very encouraging.

My working experience mainly includes working in pharmaceutical industry earlier, as biostatistician, and I consider myself a beginner in environmental health. This workshop has helped me to gain more scientific perspectives in this area by leaps and bounds.

This kind of knowledge sharing exercises may prove very helpful for researchers in the area of statistics and epidemiology to address India’s most pressing public health needs. Thank you SAMSI, Harvard, ISI-Kolkata and all of the other participating organizations for such a wonderful experience!

 

 

Postdoctoral Fellow Profile – Lucas Mentch

Mentch-photoweb

Lucas Mentch, SAMSI Postdoctoral Fellow.

Lucas Mentch was born in Indiana Pennsylvania, a town in Western Pennsylvania just east of Pittsburgh, and grew up in central Pennsylvania near Harrisburg. While he was attending high school, he took a statistics course and decided to pursue the subject at Bucknell University in Lewisburg, PA where he majored in mathematics. He asked his professors about how to pursue a career in statistics and many of them told him it was good to have a background in math first.

Lucas attended Cornell University for his graduate studies, where he obtained a Masters and PhD in statistics. He got interested in machine learning and has been looking at developing new statistical inference techniques in this context. He looks at big, messy datasets that are difficult to apply traditional statistical models to, and applies a learning algorithm to pick out large-scale patterns. Those algorithms are good for making predictions, but are difficult to use to assess the uncertainty of a prediction or where it comes from. Lucas’ research is trying to bridge the gap between machine learning and traditional statistical analysis.

While Lucas was at Cornell, he started thinking that criminology or forensics was a good area in which to try his new methods. “You’ve got either data from specific crimes or a crime database where you are trying to pick out raw patterns. You might be looking for other specific things, such as a specific time when crimes are committed, or certain areas in a city where crime occurs more often. I wanted to use machine learning to find those larger patterns, but also trying to see which variables are actually making a difference,” Lucas explained.

Lucas was alerted to the program at SAMSI by Len Stefanski, a professor at NC State and also by Benjamin Risk, another postdoc who is at SAMSI this year after finishing his degree at Cornell.  Ben is involved with the Challenges in Computational  Neuroscience program.

Lucas is involved with two working groups. He is participating in the Bias group. He remarked “There is not been a lot of attention in the area of bias in the past. It has to do with how much of a forensic examiner’s case-specific knowledge is influencing what they conclude. So, for example, if they know a lot of details about a murder, is that influencing what they say? ” The group is working with the Houston Crime Lab to set up blinding procedures where a case manager acts as an intermediary to police and the analysts. The case manager screens the information before it gets to the analysts to ensure the tests are carried out in an unbiased fashion.

The other working group that Lucas is in is trying to assess the quality of latent pattern evidence. Fingerprints are taken from a crime scene using whatever means are available and then scan it into a system to be imported as an image. But the quality of each scanner can be different, just as any piece of computer equipment or camera taking a photo can be different.  Different kinds of scanners distort the fingerprints in different kinds of ways and some scanners can produce a crisp image, even when the fingerprint itself is very smudged.

“There’s been a lot of work on quality metrics for fingerprints. So you have a fingerprint and someone puts a number on how good the fingerprint is compared to others. One of the things our group is trying to do is to say ‘does it matter what type of scanner you use with the original fingerprint?’  Our group recently got some data and can already see that fingerprints scanned with one type of scanner are almost universally better than those taken with another type of scanner according to most existing quality metrics,” Lucas said. He explained that a good scan of a bad fingerprint can often get a higher score than a good fingerprint scanned with a bad scanner. The group is well into completing this project.

“One great thing about the SAMSI program is that I have been able to meet and interact with people in forensics. Most universities don’t have a Department of Forensics, so it would have been difficult to develop these relationships in a purely academic setting,” noted Lucas.

Lucas on his motorcycle

One of Lucas’ hobbies is to ride motorcycles.

When Lucas has time to himself, he loves to ride motorcycles watch movies of all genres.

Next year Lucas will be back at the University of Pittsburgh. He took a year of leave to be able to participate in the SAMSI program.  He will continue to collaborate with people from his working groups on the projects they have started.

Op-Ed in Post-Gazette: Why Forensic Analysis of Crime Scenes is not as Reliable as you Think

SAMSI was featured in an op-ed piece in the Post-Gazette that was written by Lucas Mentch, Maria Cuellar, William C. Thompson and Clifford Spiegelman, all whom are participating in the SAMSI program on forensics this year.

The piece focuses on the Netflix mini-series, “Making a Murderer,” that raised questions about the actions and motives of law enforcement. Read their piece here.

My Experience at the Undergraduate Workshop Focusing on Forensics

The following was written by Briahnna Austin, and undergraduate student from University of California Riverside.

Briahnna Austin

Briahnna Austin

Statistics is the interchange and communication of everyday information.

This past February of 2016, I was fortunate enough to attend my first SAMSI workshop. The topic was forensic science and I was completely overjoyed and anxious, not only for the material I was going to engage in, but also excited for the interesting people I was going to interact and converse with. Coming from an undergraduate biology background, and aspiring to go into graduate level biostatistics, I have a particular fondness for interdisciplinary fields. This interdisciplinary material I was able to find during SAMSI’s Forensic Science Workshop; the purpose of this workshop was to give insight about how statistics, mathematics, data, and scientific principles amalgamate to form what we call forensic science.

Upon my arrival I was able to meet a professor from Duke at the airport; this was one of the most amazing coincidences since SAMSI has ties with Duke; I took it as a sign the workshop has something important in store for me, which it did. On the first day of the workshop, I was able to learn about comparative bullet analysis, retail sampling, and latent fingerprinting. The speakers highlighted the importance of decision-making and techniques choices. In forensic science, there is a large toolkit of information to pull from, and this toolkit gets larger as technology grows so it is our job as the statistician, investigator, or forensic scientist to make responsible and informed selections. During the first day, I was also able to see a forensics science lab; this is where movies and TV shows portray a lot of action going on, but it is different in the real world. Going to the forensic lab, gave a great opportunity to clear up assumptions and see what the real “CSI” does on a daily basis. The director of the crime lab showed my group around the facilities, and I kept hoping to see something scary or something crazy pop out of the wall, but no luck.

two lab workers

Lab workers at the Wake County Crime Lab.

During the next day of the workshop, I was able to learn about the uniqueness fallacy, statistical reliability, contextual/confirmation bias as well as a Bayesian model for fingerprint statistics. This gave insight into how important reproducibility of work as well as professionalism comes into play. In this field of work, it is essential to keep out biases and ensuring statistical reliability can assist with the types of bias we went over. The take away from both days was the idea of accountability of your work and passion for the field. Every speaker enjoyed his or her line of work. Their commitment to the field was inspiring, and shows first hand how forensic science is a collaborative effort, and when working open dialogue and communication is key to success.

Students listening to a lecture.

Students listening to a lecture.

The last large take away I acquired from this workshop was regarding networking. One of my most vivid memories during the SAMSI workshop, beside the awesome food, was communicating with the post-doc student, and undergraduate students. At the end of the first day I was able to talk to post-doc students, which help steer me in the right direction for my educational future. I am glad SAMSI provided the time to network with post-doc students; they were very friendly and funny. Not only did I network with the post-doc students, but the students attending the workshop as well. The SAMSI workshop gave me the opportunity to make new friends. Moving forward in education and career aspiration, I will be calling upon others for different aspects in STEM. Looking around the conference room and realizing these students will be the next set of forensic scientists, investigators, statisticians, and researchers, it is important we are able to network with one another. I would definitely recommend this workshop to other students and I encourage student to seek out other SAMSI opportunities as well. Lastly, do not forget to take many pictures; looking back, I realized how scenic Durham is and wish I had more pictures.

Reaching into an Abyss – Challenges in Computational Neuroscience and Graduate School

group shot of students at SAMSI

Students attending the Undergraduate Workshop at SAMSI.

The following was written by Praveen Suthaharan, an undergraduate student from North Carolina State University who recently attended the SAMSI Undergraduate Workshop on Computational Neuroscience.

Continually baffling researchers across the globe, the 3 pounds of matter that sits in our skull holds many mysteries that have yet to be discovered. Brain research, or Neuroscience, is on the verge of revolutionizing our world. In the past few years, by taking advantage of the advancements made in the computing world, several neuroscientists have delved into the brain trying to unfold many of its hidden intricacies. I, too, aspire to be part of this rising era of computational neuroscience research.

I’m an undergrad, majoring in Statistics and Neurobiology, at North Carolina State University. I plan to pursue a PhD in Computational Neuroscience. My exposure to the coursework in Statistics and Neurobiology has made me curious about the areas of study that lie at the intersection of the two fields. This curiosity has led me to steadfastly chase the inevitable question of, what IS computational neuroscience? This year’s SAMSI undergraduate workshop has served as a portal for me to explore this question that stemmed from my curiosity.

It was a Saturday morning and I could see new prospects for my future as I stepped into SAMSI and grabbed my official name tag. My pulse rate started beating fast, with a sense of excitement, as I walked into the conference room to a group of other dedicated and driven prospective scientists. The series of presentations started with a high note as Dr. Ciprian Crainiceanu began his talk with a tutorial on clinical brain imaging. Given the time, he provided a fast-paced, yet comprehensive lecture on ‘neurohacking’ and on the process of how brain images are coded into computable values for the purpose of monitoring/detecting changes in the brain. His presentation set the tone for our next presenter, Dr. Ana-Maria Staicu, who provided deep insight on the applications of an interesting image processing technique (anisotropic diffusion) on a well-known neurological disorder known as Multiple Sclerosis. At this very moment, as the momentum of wanting to think began to fade, I got distracted.

As the aroma of freshly baked bread hit my olfactory senses with a blast of pleasant sensation, I glanced at the time knowing it was lunch time. Immediately as we vacated the conference room, an announcement about taking a group photo was broadcasted to the students. We all congregated outside of SAMSI like any group of young, excited individuals – confused, yet composed.

people on the shuttle going to Duke University

Riding the bus to Duke.

With a blink of an eye, we were all set to board the shuttle to the Center of Neuroimaging at Duke. Here, we visited Dzirasa’s lab. We were all given an overview of the research lab and a tour of the facility. This visit has strengthened my interest in computational neuroscience research, and will be looking forward to applying to Duke for grad school.

Person talking at the Duke Lab

Stephen Mague talks to the students about Dzirasa’s Lab at Duke.

On our way back to SAMSI, the desire to acquire more knowledge grew inside of me as I was eager to learn about the applications of Fourier Transform (FT) within neuroscience, to interactively work with brain data using various programming languages, and to attend the graduate school panel discussion. Benjamin Risk, a postdoc who works at SAMSI, engaged us with a tutorial on image reconstruction using Discrete Fourier Transformation (DFT). The ability to manipulate images through mathematical approaches was mind-blowing, especially knowing that these approaches have been invaluable to neuroscience research. Following Benjamin’s talk, Sarah Vallélian introduced her presentation with a tutorial on Computed Tomography (CT). She discussed about several useful signal processing techniques, including back-projection, filtered back-projection, and Hilbert Transform, and gave us the opportunity to work with CT data using some of these techniques. As much as the other students enjoyed these presentations, I believe these interactive activities (i.e., using R, Matlab, and python) served as the best part of this workshop, allowing us to fiddle with the data and providing us with the initial steps to computational neuroscience research.

As the panel discussion about graduate studies commenced, my ears were engaged in the conversation as I was absorbing various useful information coming from insightful graduate students. I have come to realize that research mirrors an abyss – it’s a never ending path of glory. This appreciation of mine for research has now become my driving force to pursue graduate school. With that, the first day came to a close with an enticing dinner. The food formed this perfect taste combination that left my mouth revitalized and extremely satisfied. SAMSI definitely knows how to treat prospective scientists!

Ezra Miller, Duke, giving a lecture at the workshop.

Ezra Miller, Duke, giving a lecture at the workshop.

The next day ended with some more fascinating mathematical/statistical approaches to neuroscience as Dr. Laura Miller and Dr. Ezra miller took the floor. Particularly, Dr. Ezra Miller’s presentation on Topology for Statistical Analysis of Brain Artery Images provided me with a deeper insight on an interesting mathematical approach towards neuroscience. As a matter of fact, his presentation motivated me to immerse myself in Topology and its various applications to neuroscience.

With the end of my undergrad years, just around the corner, new doors to success have emerged with this amazing workshop. Not only did this workshop provide me with a new perspective on my research interest and grad school, but it has also given me the appreciation and audacity to reach into the abyss, knowing that it will lead me on a never ending path of glory. After all, research, in particular, computational neuroscience research, is an abyss – a bottomless pit filled with incessantly approaching questions that permeate your mind with curiosity of the mysteries of the brain.

SAMSI has organized an incredible workshop that I would not think twice about attending in the future.

Postdoc Profile – Benjamin Risk

Ben on top of a mountain in the Galapagos

Benjamin Risk traveling in the Galapagos Islands.

As Ben Risk was growing up in Northbrook, Illinois, (a suburb of Chicago) he always liked to look out in the yard and see the birds soaring above. “As a kid, I was really interested in biology and ecology and birds,” said Ben.

He went to Dartmouth for his undergraduate work where he decided to major in environmental and evolutionary biology. He collected data on the breeding demography of a songbird called the black-throated blue warbler, which got him interested in statistics. “As an undergraduate I had to do a lot of statistical analysis and I had to get help on that because I didn’t have the training. That was the first exposure I had to the importance of statistics,” Ben commented.

Ben writing notes in a notebook on a mountain

Ben in Wrangell St. Elias National Park in Alaska.

After graduating, he moved to Oakland, California, and worked for a few years for an economic consulting firm, Charles River Associates, where he applied statistical methods to anti-trust litigation and environmental economics. “We would come up with a dollar value for how much companies had increased prices through collusion and things like that,” Ben explained, “I was following the instructions of the statisticians, and that also motivated me to consider becoming one of those statisticians.”

He then went to graduate school and received a Master’s degree in environmental science from the University of California, Berkeley. His thesis developed a Bayesian formulation of a metapopulation model. “I was realizing that by specializing in statistics I could be involved in many different fields. I also wanted to be involved in research that would have applications to human health, so I decided I wanted to pursue biostatistics research,” said Ben. It was at that point that Ben enrolled in the Ph.D. program in statistics at Cornell University.

A lot of people at Cornell have been involved with SAMSI, and Ben’s advisors, David Ruppert and David Matteson, also mentioned SAMSI as a place to apply to after he finished his degree. The program on computational challenges in cognitive neuroscience was also announced at JSM 2014, which prompted him to email Haipeng Shen.

Ben is currently researching statistical methods for the analysis of MRI. He is working on one project with Hongtu Zhu, where they are developing a spatial model of the heritability of cortical attributes that are correlated with intelligence. “We are looking at cortical thickness and volume to assess the degree of nature versus nurture,” he explained. He is also working with Daniel Rowe to examine how image processing may affect the conclusions people make regarding which parts of the brain are connected.

Ben typing in his computer on a rock in the Galapagos

Taking measurements of a tortoise in the Galapagos Islands.

Ben is involved in three working groups. He is in the Functional Imaging Methods and Functional Connectivity working group with Jon Aston and Hernando Ombao. He is in the Big Data Integration in Neuroimaging working group with Martin Lindquist and Timothy Johnson. He is also in the Acquisition, Reconstruction, and Processing of MRI Data working group with Dan Rowe.

Ben is an NIH trainee, so will continue his research at SAMSI next year. He is also associated with UNC’s biostatistics department, so will spend time there as well.

When Ben has time, he likes to go cycling and to play guitar. Of course, he still loves birds, so he also likes to go birding when he has time.

Taking a Different Road – Being a Statistics Major

The following is written by Sarah Lotspeich, University of Florida who attended the SAMSI Undergraduate Workshop focusing on Computational Neuroscience.

I declared my Statistics major in the eleventh grade, approximately halfway through my AP Statistics course. As everyone around me pondered medical school and the many types of engineering, I knew that my choice seemed unconventional. Now three years into my undergraduate degree, I have met only a handful of fellow Statistics majors to date. During the third week of October, however, this changed forever as I attended the SAMSI Undergraduate Workshop.

Duke Chapel

Duke Chapel.

It was a gorgeous fall day (a pleasant surprise for me, as my typical “fall” in Gainesville, Florida includes a few fallen leaves and a high temperature in the 80s) in Research Triangle Park, North Carolina. Budding statistics and mathematics students from across the country gathered to explore computational neuroscience, and to enjoy fantastic food. Always eager for an adventure, I flew in as early as possible the day before the workshop to get maximum exploring time in Durham. Perhaps a bit TOO eager, I walked over eight miles through Downtown Durham and to both edges of Duke University’s gorgeous gothic campus.

Dame's Chicken and Waffles

Excellent chicken and waffles place!

Fret not, however, as I was well fueled by Dame’s Chicken and Waffles and fondue from the Little Dipper. Needless to say the local area surpassed my every expectation and left me excited to wear scarves and learn more about statistics the following day. The mingling began at approximately 7:30am the next morning, as over thirty of my fellow “numbers people” bonded over bagels and oatmeal. I was so excited to hear from people who care as much about significance tests and p-values as I do!

The presentations commenced with an absolute bang as Dr. Ciprian Crainiceanu of Johns Hopkins University immersed us in “Neurohacking”. He outlined the basic principles of converting MRI images from picture to a system of numbers, and by the end of the hour left us with a data set and the necessary code to explore it independently. One of my favorite components of the workshop, actually, was the interactive nature of each presentation with the integration of R or Matlab code.

Guest lecturers introduced many fascinating facets of computational neuroscience, and I especially enjoyed how my knowledge on the subject compounded with each additional lecture. As the workshop progressed I found that I was relating information from one speaker’s presentation back to material I learned even hours previously, and even today I walked away with a nice basis on the topic. It very much feels as if I went from zero to one hundred with this material, and I appreciate the challenges posed to us by the complicated subject matter.

Beyond the presentations, the field trip to the laboratory for psychiatric neuroengineering at Duke University provided a “behind-the-scenes” glimpse at the processes of data collection that create the massive sets we dealt with during lecture. I was also just happy for any excuse to ogle the beautiful campus once more. Each new speaker and opportunity brought about new questions to ask and facts to learn, so I was happy for the constantly changing environment of the workshop from lecture to lecture, or even breaks for the field trip or panel.

students by SAMSI sign

From left to right: Jordan Zeldin, Eion Blanchard, Sarah Lotspeich, Michelle Zamperlini.

The many bus rides provided unexpectedly pleasant opportunities to meet new people, as well, as I was shuffled into new groups with each trip. I thoroughly enjoyed swapping stories about my university – about the weather, everyday dress code, the statistics department – with people from other schools! And I was even lucky enough to give suggestions about things to do and places to eat in Florida, as one of my new friends is planning a trip to the Sunshine State soon. Perhaps the most unexpected bonus to this experience was the people.

This was honestly one of the most incredible groups of students, and upon learning more about each person and their involvement I am absolutely honored to have been selected among them for the 2015 SAMSI Undergraduate Workshop. Though the workshop lasted only two day, the people I met and research I was immersed in will carry through my entire career. I cannot emphasize enough the importance of this experience and how strongly I recommend it.

There is a 100% probability that I would love to return to SAMSI sometime in the future.

Learning about Clinical Brain Imaging using R at SAMSI’s Computational Neuroscience Workshop

The following was written by Katy Wang, attendee from the University of California, Riverside, of the Undergraduate Workshop focusing on Computational Neuroscience.

Dr. Ciprian Crainceanu

Dr. Ciprian Crainiceanu.

Of the seven presentations at SAMSI’s Computational Neuroscience Workshop from October 19-20th, the one that was most memorable to me was given by Dr. Ciprian Crainiceanu, a professor in the Department of Biostatistics at Johns Hopkins University. Dr. Crainiceanu’s presentation on neurohacking in R really stood out to me because I learned how to preprocess images, read, write, plot, and manipulate neuroimaging data in R.

You may be wondering “what exactly is neurohacking and how is the application of statistics used in clinical brain imaging?” As defined by Dr. Crainiceanu, “neurohacking is the continuous process of using, improving, and designing the simplest open source scripted software that depends on the minimum number of software platforms and is dedicated to improving the correctness, reproducibility, and speed of neuroimage data analysis.” The goal of neurohacking is the “democratization of neuroimaging data analysis,” in other words, to make neuroimaging data analysis possible for all people to understand. Throughout the presentation, we were shown an image of an axial slice of the T1-w image of the brain that contained a multiple sclerosis lesion surrounded by a hyper intense ring, which indicated blood with a higher concentration of gadolinium chelate. After taking the region of interest, a matrix is used with numbers corresponding to that particular section of the brain in order to understand what the dynamics of blood flow are into the lesion. To see if anything has changed (e.g, Did the brain tumor get bigger? Was the cancer eliminated by surgery?), the follow-up T1-w is subtracted from a baseline T1-w volume. A template-based analysis is also used in which an MNI T1 template is used to see which parts of the brain it maps to. The results are later quantified and mapped to neuroimage.

Students interacting with the lecturer

Students interacting with the lecturer.

Although there was not enough time to actually work with the data during the presentation, Dr. Crainiceanu offered a clear explanation on neuroimaging, an impressive tutorial with Powerpoint slides on how to set up the data, information on data structure and operations (working with various file types, visualization and data manipulation) preprocessing (inhomogeneity correction, intensity normalization, tools in R), registration, segmentation, dynamic visualization in R, and many resources in order to work with and become more familiar with working with the data. Furthermore, we were given suggested prerequisites and coursework, such as (1) Linux/Unix; (2) a basic knowledge of programming; (3) a basic knowledge of array data structures (e.g. 2d and 3d arrays), and most importantly (4) an interest in “hacking” with neuroimaging data! You may also find these Coursera Data Science Specialization courses offered on behalf of Johns Hopkins University as a helpful resource.

All in all, SAMSI’s undergraduate workshop was truly a great learning experience! I went into the workshop with very limited knowledge in computational neuroscience but came out of the workshop with several Word documents of notes, many data files/tutorials, and resources to enhance my knowledge of mathematical and statistical methods in neuroscience.

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.