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.

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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 from the Undergraduate Workshop on Data-Driven Decisions in Healthcare

big group of students outside SAMSI

February 2013 Undergraduate Workshop participants.

SAMSI recently held the Undergraduate Workshop on Data-Driven Decisions in Healthcare for about 30 students. Visiting professors, postdoctoral fellows and graduate fellows who are participating in this SAMSI program led the sessions providing cutting-edge research into the lectures. Students had a chance to work with data from the SEElab at Technion in Israel, got an overview of personalized medicine and a tutorial in R and a demonstration of the ARENA software.  Here are a few of the students’ impressions from the workshop.

Eric Laber instructing students

Eric Laber, NCSU, giving lecture at the workshop.

Eric Kernfeld, Tufts University Class of 2014, Applied Mathematics

“I had a great time at the workshop on Data Driven Decisions in Health Care this past weekend. It was a nice opportunity to meet statisticians, something I don’t get the chance to do back at Tufts. I also met a lot of undergraduates majoring in statistics and mathematics. The food was good, the staff were welcoming, the accommodations were convenient, and the talks were well-pitched. I recommend SAMSI workshops to anyone who’s interested in the topics, especially to people considering graduate education down the road.”

Danielle Llanos, Georgetown University

“I thought the SAMSI workshop was wonderful. It was a great opportunity to learn from talented individuals, and a chance to expand my network. The lecture topics were incredibly interesting and were very relevant to my career goals. Probably the best part of the workshop was the graduate student panel. The ability to ask those burning questions and learn from the experiences of others was great. I would recommend any SAMSI workshop to students looking to learn more about opportunities in the sciences, and expanding their educational experiences.”

three students at table

Students networking at lunch.

Brittany Boribong, sophomore, biomathematics major at University of Scranton

“As a student with no background in statistics and programming, I found the workshop a bit overwhelming but no less interesting. Coming into this with no experience just allowed me to take that much more out of the workshop.  I was able to explore new fields of math that I never considered before and learn about topics that I had no idea even existed. As a Biomathematics major, I found the topic of using data to derive decisions in healthcare intriguing since it is an application of my major that I was not aware of. Another wonderful aspect of the workshop was the chance to speak to people in different fields. During lunch, I had the opportunity to speak to a post-doc fellow and during dinner, I spoke to one of the professors that gave a lecture earlier in the day; these opportunities don’t come along every day. It was enjoyable hearing their stories and being able to have a casual conversation with them. The panel made up of current graduate students and post-docs was also helpful in that they were able to share their experiences about graduate school and offer along any advice. I found it particularly helpful since one of the speakers was currently in a biomathematics program and I was able to ask questions I had about my major.

However, the best part of the workshop, in my opinion, was being to meet other students. Coming from a university with a smaller math department, I really enjoyed meeting students from around the country with interests similar to my own. It was great being able to make connections with students in different fields and from universities from all over. Overall, I had a wonderful time meeting new people and exploring different fields of mathematics during the workshop and found this to be a great experience.”

The Undergraduate Workshop Focusing on SAMSI Computational Methodology for Massive Datasets

This blog entry was written by James Anderson, undergraduate student double majoring in statistics-mathematics and economics from the University of Connecticut.

The undergraduate workshop attendees

Attendees and some presenters from the SAMSI undergraduate workshop held October 26-27, 2012.

This undergraduate workshop was notably different from my previous experience, though in no way inferior.  In fact, I would argue the content of this workshop was better for my current position. Massive datasets are surprisingly common and the topics covered included astronomy, high dimension regression, climate change, and image rescaling. In these contexts, we mainly discussed how to manage large datasets without crashing an individual computer.

The other aspect of the workshop, which I really enjoyed, was discussion panels. The students got a chance to talk to people working in academia and industry, as well as graduate students and postdocs. The professionals talked about their respective occupations and how they got to where they are, which was very interesting. On the other hand, the younger group talked about their transitions out of their respective undergraduate programs. This was particularly useful as I will be going through this phase over the next few months. One thing I was once more impressed with was SAMSI’s concern for the attendees. The presenters were happy to go into great detail about their presentations and field any general discipline related questions they could with interested attendees (the presentations had to be kept pretty short). This really impressed me; it didn’t matter if it was in the context of a presentation or not, the mentality seemed to be that the workshop was happening all the time. There was a great opportunity during panels or breaks to ask questions and get information that was quite personalized and would have been hard to find in another way. The workshop gave me a lot of information and resources that will be valuable going forward.