January 25th, 2024

Andrew is currently a post-doc at Arizona State University in the School of Engineering where he near completing his training as part of an NIH F32 grant. His research focus is in creating digital health technology that can aid in the early detection of neurodegenerative disease.

Tell us about your background.

My early academic career focused on human movement and the theoretical and applied aspects of human motor skill learning. During my PhD, I continued this theme with special emphasis investigating why a significant number of individuals demonstrated a type of practice intolerance (i.e. non-learning) even after repeated practice. When I then transitioned into my post-doc I transitioned my in-lab research into an online digital platform where the investigation of this interesting behavioral phenotype could be more broadly investigated among a large, diverse cohort.

What sparked your interest in learning data science?

In my view, data science and statistics, allow us to generate the best inferences given the data available. During my master's thesis I had zero skill in programming and relied heavily on excel to merge, clean, and process my date. It took forever. Then I analyzed my data using SPSS, which was only available within my school's library. What would probably take me a couple of weeks today took me months then. Thankfully, as I progressed through my studies, I was fortunate to have mentors and resources made available to me to learn.

What are you most excited about ReproRehab?

I am most excited about seeing others who participate in ReproRehab, either as TAs or Learners, grow their skills in becoming more reproducibility conscious scientists. Opportunities to develop skills like the one's taught at ReproRehab can be hard to find dedicated time and community to develop around. It has been awesome to see the many directions and applications people of being running with as part of this program.

What advice would you give to those who wish to follow a similar path?

Don't let coding intimidate you. Is it tough? Yes, at first. It's like learning a second language, but it only takes a little experience to really get hooked. If I can learn to do it, anyone can. Dive in, have fun, and don't hesitate to ask others for help.

Anything you are passionate about outside of reprorehab?

I am a relatively new dad, and as my daughter has gotten older we enjoy going to our local comic book store and finding the next month's issue. I highly recommend the series 'Paper Girls' which is about a group of young women who work a paper route in the 1980's but get caught up in a battle between different groups of time travelers.

Anything else you would like to share? (website/github/podcast)

Concurrent with my role as a postdoc I also work as an instructor at the University of Nevada, Las Vegas (UNLV). Several weeks before I wrought this piece there was a mass shooting on the campus of UNLV. This deeply impacted all members of the university community, the effects of which will be felt for awhile. Only recently has there been increased federal funding for NIH sponsored research in gun violence, so there is still much to learn and do. Therefore, I'd like to take the opportunity to raise awareness about an organization called everytown.org, which is a non-profit that uses data science to help educate on the current state of gun violence in the U.S.

October 27th, 2023

Johanna is a Post-Doc at the Donders Institute for Brain, Cognition and behaviour in Nijmegen, the Netherlands and a recurring TA to Repro-Rehab. Her focus is on Git and Open Science.

Tell us about your background.

Johanna has a background in psychology, computer science and an interest in neuroscience and neuroimaging. Born in Germany, she obtained her PhD as part of a 6 year stint in Melbourne, Australia. She is also currently obtaining a bachelor degree in Computer Science from the University of Hagen, Germany.

What sparked your interest in learning data science?

I like the creativity associated with data science, and the story that data can tell. In addition, I like programming and making nice plots. In sum, data science requires both analytical and creative thinking, and a combination of problem solving and design, which makes it great fun.

What are you most excited about ReproRehab?

The community and the enthusiasm! I am also excited about seeing people progress in their skills and figuring things out. Plus, the focus of ReproRehab is a focus on better science – a topic that is very dear to my heart.

What advice would you give to those who wish to follow a similar path?

1. Teaching forces and helps you to understand a topic better – so sign up to teach for what you want to learn or understand better.

2. You are never too old to learn something new.

3. Success if often a the result of either money, time, or effort. If you don't have the first one, you can still focus on the latter two.

Anything you are passionate about outside of ReproRehab?

Neuroimaging, computer science and noise. I am really fascinated by how the brain works, how computers work, and how we can overcome the "noise" that interferes with figuring things out (noise is a broad term here that can range from bad code to heart rate effects in a neuroimage). I also help organizing a data science and machine learning podcast and I spend a lot of time with my cat and books.

Anything else you would to share? (website/github/podcast)

https://github.com/likeajumprope

September 7th, 2023

Allison is a rehabilitation clinician-researcher. She received her Doctorate in Physical Therapy from the University of Connecticut, Neurologic Clinical Specialist certification through completing the Kessler Institute for Rehabilitation Residency in Neurologic Physical Therapy, and her PhD in Biomechanics and Movement Sciences from the University of Delaware. She is currently a postdoctoral researcher at Washington University in St. Louis working with Drs. Keith Lohse and Catherine Lang. Her research investigates the application of digital health tools, in particular wearable sensor technology, to improve measurement, clinical decision-making, and patient outcomes in rehabilitation care.

Tell us about your background.

I decided to pursue a career in physical therapy (PT) after sustaining a running injury and having a really positive experience (and outcome!) from PT intervention. I became particularly interested in neurologic rehabilitation during my DPT training and went onto to pursue residency training in neurologic rehabilitation shortly after achieving my DPT. During my time as a resident, I became involved in the development of a clinical practice guideline that aimed to synthesize the evidence for improving walking capacity in individuals with chronic stroke, traumatic brain injury, and incomplete spinal cord injury. The result of this work was a set of recommendations for how physical therapists can improve their patient's capacity to walk, measured by standardized tests rendered in the clinic. I wondered, however, how these interventions impact a person's daily walking activity outside the clinic or lab. This experience fueled my desire to pursue a career in rehabilitation research investigating the use of digital health tools, such as wearable sensors, to better understand the impact of PT interventions on the daily lives of patients who seek these services. In my postdoctoral work, I am leveraging my data science, clinical and research training skillsets to explore how wearable sensor technology can be integrated into routine rehabilitation clinical practice.

What sparked your interest in learning data science?

My interest in data science stems from the vast amounts of data generated by wearable sensors. This enormous amount of data can be manipulated to compute a laundry list of variables that reflect different aspects of movement. During my PhD training, I was really interested in exploring which sensor-derived variables might be most important/meaningful in people with chronic stroke in the context of walking. Almost all of the variables I was interested in investigating were variables not readily computed by the sensor I was using. The initially daunting task of figuring out how to manage and wrangle all of this data to compute the variables I was interested in ultimately led me to really enjoy data science and appreciate the value of having good science skills.

What are you most excited about ReproRehab?

I am most excited about my transition from being a ReproRehab Learner to being a ReproRehab TA and being able to give back to the program the skills I've learned to help others develop their own data science skills. Participating in ReproRehab has- and continues to be- a great networking experience. I have gained a lot of great insights for my own work from learning about how others are using data science to answer important questions in their areas of research.

What advice would you give to those who wish to follow a similar path?

I think my best advice would be to stick with it. Any of the PhD students who sat near my cubicle while I was trying to figure out how to code will vouch for me when I say that I had a a really hard time acclimating to coding (I actually attempted to sneak out of a coding class my first day as a PhD student, to no avail). I'm so glad I stuck with it. Having data science and coding skills are so valuable both within and beyond the research realm. Learning these skills in the context of questions/topics you are passionate also makes it easier. Having confidence in yourself that you can do this is also key (and easier said than done at times)!

Anything you are passionate about outside of ReproRehab?

I love all animals, especially dogs and horses, and volunteering at animal rescues, hiking, and spending time with my husband, Tucker and Olivia (my two pups), and my family.

Anything else you would to share? (website/github/podcast)

I'm always in the market for new connections on LinkedIn!

April 24, 2023

Bethany Lo is currently a lab manager at the Neural Plasticity and Neurorehabilitation Laboratory at the University of Southern California and was a learner in the 2022 ReproRehab cohort. She is interested in studying cognition and learning in older adults using neuroimaging analyses.

Tell us about your background.

I graduated with a B.S. in Neuroscience from Baylor University in 2019 after initially becoming interested in brain research during high school. Since then, I’ve mainly worked with Dr. Sook-Lei Liew at USC on stroke rehabilitation research, helping develop reproducible pipelines and manage large multi-site neuroimaging databases. I’ll be starting my PhD in Cognitive Neuroscience at UC-Riverside this fall with Dr. Rachel Wu.

What sparked your interest in learning data science?

When I first learned that writing a few lines of code could save hours and hours of manual work! I took a statistics course during my undergraduate studies and truthfully didn’t enjoy it very much (perhaps because of the instructor). When I came to USC and starting using code on the actual data I was working on, it took on a whole new meaning. Data science is so cool!

What are you most excited about ReproRehab?

I learned so much as a learner in the 2022 cohort. It’s incredible to have connections to this network of researchers, especially at the beginning of my career. I’m also really excited to see ReproRehab answer the need for more reproducible practices and believe that this group is making an impact in the community.

Is there anything that you’re especially looking forward to doing with your newfound programming and data science skills?

I’m very thankful to be starting my graduate career with some programming skills and knowledge of reproducible science principles. I’m also looking forward to hopefully be able to share a bit of this knowledge with others in the future.

Anything you are passionate about outside of ReproRehab?

I consider myself an artist as well as a scientist, and really believe that art/creativity and science are mutually beneficial and not at all exclusive to particular “types” of people. I am currently experimenting in projects surrounding woodworking, wheel-throwing, and crocheting!

Anything else you would to share? (website/github/podcast)

I’d love to connect with you, please add me on LinkedIn!

March 3, 2023

Duncan Tulimieri is a graduate student at the University of Delaware and a teaching assistant for ReproRehab. He has recently been improving his ability to write with clarity and conciseness. He enjoys problem-solving (especially programming problems), experimental visualization, mentoring, writing, and collaborating with his peers.

Tell us about your background.

I graduated from Dension University, a small liberal arts school in Granville OH, in 2019. There I majored, and was a departmental fellow, in the Health, Exercise, and Sports Studies program with a minor in Biology. I played four years of club ice hockey and ultimate frisbee. After graduating from Dension University, I came to the University of Delaware to pursue a PhD in Biomechanics and Movement Science in the Sensorimotor Control and Robotic Rehabilitation laboratory.

What sparked your interest in learning data science?

I became interested in data science when I realized that, if utilized properly, it can be a powerful tool to extract meaning from a seemingly arbitrary set of numbers. I became passionate about data science because I fell in love with the cleanliness, clarity, automation, and other safeguards it provides against making senseless mistakes.

What are you most excited about ReproRehab?

I am most excited to pass down the small amount of wisdom I have accrued throughout my time diving into data science. I want to teach others the lessons, good and bad, I learned along my journey so that they can learn from, and not repeat, the mistakes I have made.

What advice would you give to those who wish to follow a similar path?

Explore and embrace errors. If you hear someone talking about something that you do not know or read something that has a topic that seems like it may help, dive in! Go watch way too many YouTube videos. Go read blog posts that only make a little bit of sense. Go ask your peers if they have heard about the topic. Go get your keyboard keys dirty and implement the idea in a small project. At the end of your exploration, you will find that your knowledge about the topic surpasses any of the sources you have consulted. Beware that during this exploration you will be confused and frustrated, but when has that ever stopped you? This may seem like wasted time, especially when your screen is painted red with errors, but you will remember all the things not to do which is debatably more valuable than remembering the things to do. You will not be restricted by what you have read or watched, but only by your willingness to explore.

Anything you are passionate about outside of ReproRehab?

I am passionate about observing and conserving nature. Some of my favorite times during my undergraduate degree were during my ecology labs in the field where we went out to the biological reserve and saw what we learned in the classroom. Ecosystems are complex and simple all at once, just like the human body. They are fully interconnected and deficits in one area may result in impairments in a completely different space. I derive a lot of inspiration from nature because I believe time is the most powerful optimizer.

Anything else you would to share? (website/github/podcast)

If you would like to connect with me, please do so on LinkedIn. To get in contact with me, you can send me an email. If you want to learn more about me, check out my website. To see some of the programming projects I have done, and am currently doing, check out my GitHub.

November 10, 2022

Keith Lohse is a Clinical Associate Professor of Physical Therapy and Neurology at the Washington University School of Medicine in Saint Louis. He is accredited as a professional statistician (PStat) by the American Statistical Association. He has expertise in multivariate statistics, signal processing, and longitudinal/time-series data. He has recently been learning about machine learning, especially unsupervised and reinforcement learning algorithms. He enjoys problem solving, developing new data visualization skills, and the challenge of translating complex data to a diverse audience.

Tell us about your background.

I started college at Idaho State University very unsure about what I wanted to do with my career. I really liked cartoons and graphic design, but I thought it would be more pragmatic to study science. I looked at the undergraduate course catalogue and saw that psychology was the science with the fewest required classes, so I signed up for psychology in the hope of exploring the most electives. Although I did explore a lot of fun electives in linguistics and philosophy, I really got hooked by taking a psychology-specific statistics course in my first semester. From there I went on to take more advanced statistics in the math department and that set me on a path of wanting to learn as much as I could about measurement, design, and analysis within the world of rehabilitation.

What sparked your interest in learning data science?

From my first class, I was really fascinated by statistics and how people used inferential statistics to test hypotheses in science. As I learned more about just how hard that is to do, I also learned about how trials can give discrepant results, how many people you need to gather data on, and the difficulties of integrating data from different sources. I still know vastly more about statistics than I do about data science proper, but more and more of my work is focused on making rehabilitation data Findable, Accessible, Interoperable, and Reusable (FAIR).

What are you most excited about reprorehab?

I am excited to help build this network of researchers and cultivate data skills in rehabilitation science. I am grateful for this opportunity to give back to my community and create educational materials that are openly available to anyone who is interested all over the world.

What advice would you give to those who wish to follow a similar path?

Students often tell me that they don’t have a “math brain” or just “don’t get programming”. But this seems like the only domain in which students feel this is a reasonable excuse; students never lament their lack of “biology brain” or how they just “don’t get words”. In my experience, students think that I am good at math or programming because of some natural aptitude, when the reality is that I have these skills because I SURVIVED math and programming.

I think it is important to remember that learning is a two-way street. As a learner you need to bring motivation and diligence to a problem, but if you still don’t “get it”, maybe the material is not being presented in the best way for you. Math never really clicks for me until I find the need to apply it to a specific problem and can use it in a context I understand. If you feel like you can’t “get it” despite trying your best, seek out and explore new environments for learning. Try to find a problem that is important to you and commit yourself to mastering the tools you need to solve it.

Anything you are passionate about outside of reprorehab?

I love spending time with my wife, Emma, and our two dogs, Olive and Moose!

I also enjoy hiking, going on runs around Saint Louis, and exploring new and interesting music. There are a lot of great genres of music out there, but I love rock operas - Blind Guardian’s “Nightfall on Middle Earth” being my favorite, narrowly beating Rush’s “2112”!

Anything else you would to share? (website/github/podcast)

People can always checkout my personal website, which includes links to my GitHub repositories and YouTube channel: https://sites.google.com/site/lohsekr/