Jonathan Sarasa, University of Michigan
During my time at BDSI, I researched the association between differentially-methylated regions of the genome and genetic point mutations linked to disease with the Genomics group. I found the research challenging, yet an extremely effective way to improve my skills in R and knowledge of statistics and genomics. The lecture component of BDSI - which typically takes up about half your "work" day - gave me the opportunity to learn both the basics of statistical theory and the many unique research areas in Biostatistics. I came in with a weaker background in stats, but was able to learn quickly from informative lectures, helpful research mentors, and wonderful peers. My time in BDSI was also very social. In our free time, the whole cohort would often get ice cream, go to the arcade, or swim in the Huron River. I made amazing friends and great memories, and wouldn't have spent my summer any other way! Go Blue and apply to the Big Data Summer Institute!
Liz Szymanski, Oberlin College
BDSI was the best way I could have spent my summer. As part of the Genomics group, my peers (now good friends) and I completed research projects in RNA spatial transcriptomics. It was exciting to learn such a new technological approach and be given the freedom to design our own projects, while still receiving support every step of the way. The math, statistics, and coding bootcamp sessions helped me learn skills that I could immediately use in my own research. The guest lectures from public health experts were eye-opening — hearing about their research and journeys gave me the confidence to apply to graduate school in Public Health. Best of all, BDSI gave me the chance to meet fun, brilliant people who share my interests. I really value the memories I made, the research skillset I developed, and the career guidance I received throughout the entire six weeks. I also fell in love with Ann Arbor! My cohort and I definitely enjoyed the music festival, the kayaking, and the restaurant-hopping, and we fit in as much frisbee, karaoke, and Codenames as possible.
Anil Anderson, Georgetown University
I had a wonderful experience in BDSI. I was in the Electronic Health Records group, and my favorite part of the program was working with my research team on our project. Our goal was to build a parsimonious logistic model to predict hospitalizations using demographic, comorbidity, and BMI information about the patients. Working on the project, I not only gained many of the statistical and computing skills needed to build an effective model, but I also learned to think in a creative way about problems that did not have a clear-cut solution. The EHRs we worked with contained many anomalies, from missing data to clearly unfeasible BMI measurements, and it was fun working with the team to come up with a sensible way of addressing these issues. Despite the remote format of the 2021 BDSI program, I was able to spend a lot of time with the other members of my team working on the project, and overall, I really appreciated the emphasis that the program placed on collaboration and recognizing the contributions of others. Everyone in the program was incredibly welcoming and supportive, and I feel lucky to have had the opportunity to work with them. I enjoyed BDSI so much that I have decided to apply to biostatistics and statistics graduate programs, and I am sure the analytical and problem-solving skills I gained in BDSI will help me greatly in graduate school.
Tannistha Mondal, Indian Statistical Institute
BDSI was an enriching experience in every sense. This time it was held online over a period of 8 weeks. Initially we were given lecture videos and we could interact with professors in office hours but later we had online lectures, which were illuminating and which provided a deep insight into various fields of statistics and biostatistics. During many of the lectures, we were divided into breakout rooms and thus there was an interactive aspect to the lectures. In some of the sessions, while working together in the breakout rooms, we got hands on experience in coding in R and Python. There were lectures on machine learning, deep learning, social networks, genetics and what we learned in these lectures was immensely helpful in our group projects. I was in the data mining group and all the project groups in data mining basically worked on classifying and analysing tweets and understanding public sentiment regarding covid. My project group worked on classifying tweets specifically about covid vaccination policies in colleges and universities and analysing public sentiment and attitude towards vaccine acceptance in colleges. For our project, we had to use various classification methods many of which were covered in the machine learning lectures and I really enjoyed working with smart people having diverse academic backgrounds. At one point, we even collaborated with another data mining project group. I think I liked this aspect of BDSI the most - we were always encouraged to come together, collaborate and forgetting our differences in backgrounds, to work together as one and to contribute to data science in our own way. This very spirit of collaboration is crucial in building a career in scientific research, and BDSI helped me get a glimpse of this aspect, apart from helping me learn new things in statistics and data science and explore some interesting fields of research, which in turn has shaped my interests and plans of joining research.
Sabir Meah, University of Michigan
Due to the pandemic and related BDSI cancellation in 2020, I entered BDSI the summer of 2021 in the odd situation of someone who already had earned an undergraduate degree in statistics and was already committed to attending a graduate program in biostatistics (at Michigan). To be perfectly honest, I was not entirely sure about what the program would offer me in such circumstances. However, not only were my doubts alleviated when I actually got the opportunity to experience what BDSI offered, but my expectations were blown away.
The lectures explored a wide variety of advanced topics in biostatistics and big data at the depth and rigor of a graduate program. Being today a current Michigan biostatistics student, I especially relish the chance I had to learn about the research of my future professors before even starting graduate school. Similarly, my research group afforded me the opportunity to get hands-on experience constructing a long short-term memory neural network, a topic I did not even broach in my undergraduate machine learning class. My favorite part of the program, however, were the journey lectures, which offered me a glimpse into the personal and career journeys of those in academia, a side of the profession I had hardly seen before.
I can confidently recommend BDSI to any undergraduate student interested in big data and/or biostatistics, especially those with aspirations of attending the biostatistics graduate program at Michigan. While my next steps after my undergraduate degree were already set before I started BDSI, the program nonetheless gave me a valuable step up in preparing for graduate school in biostatistics at Michigan, an advantage I am seeing the benefits of to this day.
Samuel Hawke, Carnegie Mellon University
BDSI was the single most informative experience of my life in terms of figuring out what I want to do after I graduate. During my 6 weeks at BDSI, I dove deep into the field of Biostatistics, focusing on using math and data to improve human health. We were exposed to a wide range of related topics and also had the opportunity to work on research projects. Collaborating with like-minded students to perform research in Machine Learning with medical data was an invaluable experience for me. I learned firsthand how to work on a specific question which nobody else had previously considered. Nothing compares to doing exciting work with smart, hardworking peers in such a supportive environment.
Corriene Sept, Case Western Reserve University
BDSI was an amazing experience. I learned a ton from experts in the fields of biostatistics, statistics, and computer science, met new friends from all around the world, and applied my knowledge in a research project. BDSI was / is a community, and it was incredible to meet all these talented people interested in the same topics and work together to solve problems. The research project my group worked on was using copy number, methylation, and gene expression data for hundreds of cancer cell lines to predict efficacy of chemotherapies while accounting for tissue type. My group ran into similar problems to other groups, and working with members of other groups to solve common problems such as dealing with missing data or combining datasets with millions of data points really speaks to the collaborative community atmosphere of BDSI. Not to mention, it was a lot of fun being in Ann Arbor for the summer! We went to the Art Fair, visited museums, listened to live music at the A2SF (Ann Arbor Summer Festival,) played a ton of cards and beach volleyball, went kayaking, and explored the many amazing restaurants downtown.
Kyla Chasalow, Cornell University
I learned many things from my weeks at BDSI, but the most wonderful aspect of the program was the community. It was really my first time being part of a group of people learning, teaching, and working in statistics together. I appreciated the mix of curiosity, energy, and humor in our group, and I gained a real sense of graduate school and research as a collaborative effort. I was part of the Machine Learning research group. Initially, we planned to have a competitive prediction task among our three subgroups. But in the end, our comradery overpowered that, and the three groups explored complementary questions and challenges for machine learning (in my case model interpretability). I think this reflects the collaborative and supportive ethos that formed in our group.
Monica Iram, University of Minnesota
The time I spent at the University of Michigan was enlightening for me in many ways. While it's true that I walked away with exceptional experience in a field that I am very passionate about and an enhanced understanding of statistical concepts, I ended up learning a lot more than that. Throughout the six weeks, I learned about my personal goals, what sparks my intellectual curiosity, and where I want to see myself in the future. Having the opportunity to fully immerse myself in research and coursework that challenges and excites me provided the perfect environment for me to learn more about myself, for which I am grateful.
Kiril Bangachev, Princeton University
In the six weeks between June 17th and July 26th, 2019, I was extremely lucky to be part of the Big Data Summer Institute (BDSI) at the University of Michigan at Ann Arbor. The program introduces undergraduate students to the intersections of data science and human health with mornings lectures and afternoon research sessions. At the end of the six weeks, we presented our research at the Symposium along guest speakers. The guest speakers shared about their own research in the age of Big Data.
In this website I would like to share about my own academic experience at BDSI.
On the BDSI Materials page, you will find a list of all the materials I interacted with during the program.
These include the morning lectures, research slides, research code, and talks given
at the Symposium. You can find all these materials by clicking on the respective links.
On the Research page, you will find a description of the research project in which I participated. Again, feel free to follow the links.
Note that this website is created to reflect my personal involvement with the program. If you want to learn more about the research projects of other teams or past editions of BDSI, please check-out the program's website and wiki-page.
Note: For more information, and to see a list of acknowledgements, please visit Kiril's site.
Lucy Shen, Smith College
Not until recently, I find how much BDSI has changed my academic life. I used to be confident and self-satisfied about what I learned, the number of projects that I completed, and the grade that I received for statistics and data science class before this summer. However, the 6-week program gave me a chance to retrospect myself; I eventually saw a bigger world, interacted with people who have same passion about machine learning applications in public health and put my hands on such complicated MIMIC III dataset that I could never imagine before. I should also thank to professor Wiens, professor Koutra, our GSI Shengpu and all my teammates. Working with them definitely made me more productive, organized and helped me develop leadership skills.
When I am back to Smith College this semester, I audit the machine learning class which I took one year ago but forgot lots of conceptions. I found that because the morning lectures of BDSI provided a review briefly, I still remembered 90% of the algorithms and formulas. Compared with one year ago when I took machine learning first time with lots of confusion, this semester I really touch the core of professor's lecture this time. Besides, so many changes occur without notice, I developed a sense of comprehensive view after this summer. Right now, whenever I want to solve a problem, whether a problem in statistics or in other fields, I always start with a general pipeline (what I learned in my group project) and follow the pipeline in the execution. The most important point is that it comes clear to me why I want to be a data scientist. Even though I got upset and annoyed by bugs from time to time and even though I stayed up so late at night, I valued the potential application of statistics and data science researches the most. Hearing so many stories about phenomenal discoveries of data science in public health from journey lectures and from reading cases, I hope one day I can also be one explorer in this field.
And I have to say that your words at the end of this summer almost made me tear down and became one of the motivations for me recently, "What are you doing everyday? Beautifying your CV or doing something meaningful to others."
As a graduating senior, I feel sorry that I did not attend BDSI during the first summer break so that I could decide my major earlier and took more classes and researches. I made up my mind to continue to graduate study in this field and hopefully one day, I can also be a journey lecturer for BDSI.
Dan Hartman, University of Chicago
its been a big big summer we've had fun by the dozens
gonna pack our bags soon, leave this hall called couzens
but in a couple'a weeks, you're gonna get that itch again,
and make a return to the old u michigan
back to pedro and his kid, and sophia and ishpreet,
back to frita batidos and back to hunting for a seat
the days were hot but you guys are hotter
so hit me up unless boni drops my phone in the water
the server was down, but it's just as well
need another 6 weeks just to navigate shell
and our singles had space but i think we need more
cause akshay somehow used up all 32 cores
and i know, i know, the lectures are a bummer
but it's give and take in this big big summer
and if there's one thing to take away from this song
some models are useful but all models are wrong
And I just really wanted to express my appreciation for the conversation we had after the end of the program. It's really helping me to frame a lot of my questions regarding my future. But more than that, I'm just so thankful for BDSI and all of the work that you and the rest of the staff put into making it the experience that it was. I took from it so much more than I thought I would.
Fengling Hu, Amherst College
As I look back at the experiences that have shaped my academic career, I remember you sharing some of the emails you had received from past BDSI participants with all of us during our experience at U-M. I wanted to write a brief (or maybe not so brief) testimonial/update to you in that sentiment, describing how BDSI has adjusted, transformed, and advanced my academic trajectory.
If you remember, I was one of the few pre-med participants in my year. Entering BDSI, I was nearly 100% set on pursuing a career as an MD; I saw statistics only as a tool for my current work in basic science research and future work in clinical research. However, at BDSI, I saw how cool statistics theory could be through morning lectures and learned how much impact biostatistics could have on patient care through journey lectures. BDSI was a turning point in my perspectives on statistics and in my life goals.
One goal of BDSI is to inspire participants to pursue graduate studies in statistics, and it has done just that. From my dream to pursue an MD, BDSI influenced me to pursue an MD-PhD, with the PhD in biostatistics, bioinformatics, or the like.
I've already written my statements, submitted my applications, and begun to receive a couple interview invitations for the coming months! I even applied to U-M's MD-PhD program (with the Department of Biostatistics) and hope to hear back from them soon so I can continue to learn from all of the BDSI faculty.
Thank you so much for opening my eyes to this fascinating and powerful field. I'm really excited at the prospect of doing more work in biostatistics. I hope to have the chance to write back with more good news in the future!
Deeksha Pai, University of Michigan
So, we had some struggles from the start
Downloading jupyter notebook is not for the faint of heart
I'm just kidding, most of us used Rstudio anyway
There was the occasional python user but anything else was so yesterday
Understanding spatial bias was not an easy task,
We would ask, every day, wait, what's a microarray and what's a plate?
And to be honest, we still never really got that part straight.
8x8 spots are on one well plus 95 more,
16 special wells, control spots, what else could you ask for?
All the heat maps we made.. man, you would think we were on fire!
And we were, so hopefully none of you want to retire.
We learned so much about R and machine learning, thanks to our mentors
Professor Gagnon-Bartsch, all these brilliant designs were yours.
Hopefully we've done your project justice, and all went well.
Professor Tewari, thanks for the great talks, they were swell.
And let's not forget Young and Greg, the best GSIs with the best R code.
We thank you guys for the complicated functions that you graciously bestowed.
Our little machine learning group of 12, man we've come a long way.
Though I'm not sure any of us ever want to look at another bioassay...
While everyone else stayed in SPH, to cc little we all walked,
Sitting in that hot little classroom, but hey, we really rocked!
We've got the best models in the bus-in-ess
Cross validating so hard, we don't even KNOW what over fitting is.
So many decision trees, forget Nichols, call us machine learning Arboretum.
Heres where I say something statistical so I can end with- degrees of freedom...
Obviously I'm running out of rhymes,
So I'm going to end this fast,
Data Mining and Machine Learning and BDSI were a blast!!
Miriam Goldman, Arizona State University
I really enjoyed all the social events and the opportunity to meet so many nice, different, people who were interested in biostats.
Ben Sadis, University of Michigan
I really would like to thank everyone who put effort into making this program happen…" "It was really neat to have the opportunity to interact with and learn from so many professors and researchers who are leaders in their fields.
Marissa Ashner, Illinois Institute of Technology
I learned more about biostatistics and big data than I ever imagined learning in six weeks, and I think all of the knowledge I gained here will be useful for continuing my education.
Tahmeed Tureen, University of Michgian
BDSI 2017 and current U of M Biostatistics MS student
My experience with BDSI was phenomenal. I met incredible faculty members and researchers from several disciplines such as statistics, biostatistics, and computer science. Some of these people are really famous. There was a point during my time with BDSI where I was explaining what 'Survival Analysis' was to the guy that is known as the 'Guru of Survival Analysis'... Me, an undergrad was explaining the rudimentary aspects of a statistical method to the guy that is world famous for that method... Just talking with and being around these people really cemented what I want to pursue later in my life. If I could one day become a part of something as amazing as the University of Michigan School of Public Health and have my work impact people's lives in a positive way, I'd consider all these hours I put into school worth it.
Danielle Demateis, The College of New Jersey
I really appreciated and felt Bhramar's dedication toward the program. Her journey lecture and outreach has inspired me to follow her path of being a female leader in biostatistics.
Gabriel Goulart, Cornell University
The Institute introduced me to lot of different concepts regarding to Big Data and Biostatistics. The multi-disciplinary of the Institute is the major strength of it. Also, the opportunity to work with different people from different fields was unique.
Chinwendu Nwokeabia, Notre Dame of Maryland
The journey lectures are amazing, and very helpful in providing a snapshot of potential careers after graduate school. The sense of camaraderie as the weeks passed was great and that was mostly due to seeing each other outside of the classroom. Many of the lectures, particularly the professional development and programming ones were very helpful; I do not have much experience with programming, so I am grateful for the chance to have a little practice in it.
Jonathan Boss, Michigan State University
BDSI 2015 and current U of M Biostatistics PhD Candidate
The summer program here is unique in that it operates at the intersection of computer science, statistics, and human health. It is crucial to expose undergraduate students to the cutting edge of biostatistical research, in order to invigorate them about the direction the field is going.
Read more of Jonathan's story here.
Pooja Iyer, University of North Carolina
I learned so much — not only about public health — but about my potential future and gained experiences that I would not have received anywhere else.
Rima Das, Massachusetts Institute of Technology
My favorite part was seeing how big data is applicable in so many fields.
Michael Thompson, University of Massachusetts
As an undergraduate, you're trying to figure out where to go and how to get there. One of my favorite parts of the program were the journey lectures where you get to see how faculty started out and how they got to where they are today.
Tianna Burke, Howard University
I learned a lot about different subjects and I started learning about coding online after the institute.
Eli Ben-Michael, Columbia University
Working with students and professors from many backgrounds was very valuable to me. The institute exposed me to many ideas from many fields that I may not have seen otherwise.