Research News

Cleveland Clinic and IBM are taking steps towards building the healthcare workforce of the future by offering college students a front-row seat to Discovery Accelerator projects. 

The Discovery Accelerator (DA) is the two organization’s partnership to advance healthcare and life sciences research through high-performance computing. Each summer, the DA invites students to apply for internships to work alongside the top researchers in the field on high-performance computing, preparing these students to incorporate emerging technologies into their future careers. 

This year, 13 interns spent the summer in Cleveland Clinic Center for Computational Life Sciences (CCLS) labs learning how to advance research with high-performance computing techniques. The students were selected through a competitive application process because of their skills and desire to combine their knowledge of research and computer science. 

“Computer scientists are essential for advancing the pace of our research and making breakthroughs in drug discovery, disease prevention and treatment,” says Lara Jehi, Chief Research Information Officer and Director for CCLS. “Through the internship program, we are able to introduce a whole new generation of students to the meaningful work they can do in healthcare.” 

Each student was assigned a research topic to explore alongside their lab’s primary investigator and a mentor. Interns from the summer of 2024 shared their experiences working with researchers who are leading the way in applying high-performance computing to healthcare’s biggest challenges: 

Alex Klaben 

Junior, biomedical engineering major, computer science minor, University of Rochester

Project: Predicting low cardiac output syndrome (LCOS) in children 

Why did you want to intern at Cleveland Clinic?   

As someone who grew up in the Cleveland area, I know that Cleveland Clinic is an excellent hospital system that's always working towards new healthcare innovations. As a biomedical engineering major, it seemed like a good place to get experience with healthcare research and get a better feel for the medical devices and research I’ll work with in my career.  

Can you tell me about the project you worked on?   

My project focused on detecting and predicting low cardiac output syndrome (LCOS) in children with congenital heart disease. LCOS is a common complication in heart surgeries treating congenital heart disease. Over the summer, I used Python to analyze and label data to test if machine learning methods can predict LCOS. Hopefully, in the future, the data I put together can also be used to compare the effectiveness of different definitions for LCOS so that it can be better detected. 

What were you most excited to learn?   

I was most excited to learn how medical research really gets done. Classes don't really show you what something looks like in the real world, they are more theory focused. I was really excited to see how the things I've learned in school are implemented in a practical, real-world situation.  

Anna Anello  

Senior, neuroscience and computer science majors, Kent State University 

Project: Gait changes for neurodegenerative diseases  

Why did you want to intern at Cleveland Clinic?   

I have wanted to work at Cleveland Clinic for ages. My aunt worked at Cleveland Clinic for over 20 years, and she always talked about how much she loved working there. As I continued in college, I knew I wanted to do medical research and Cleveland Clinic was the perfect fit. 

Can you tell me about the project you worked on?   

The project I worked on was a perfect combination of my two degrees. I was developing code in order to get five cameras synchronized in order to record gait changes in those with neurodegenerative diseases. This was really interesting because gait is a huge biomarker for neurodegenerative diseases, and I knew this had the potential to improve treatment. 

What were you most excited to learn?   

I was most excited to learn about technology's impact on the healthcare industry. I had the opportunity to do the IBM Quantum Learning Journey, which taught me a lot about quantum computing. This led to me participating in the Qiskit summer school, and now I'm running an IBM event at my university this fall. 

Maeve Gaffney 

Fourth year biological and biomedical program, Trinity College Dublin  

Project: Applied quantum computing for protein structure prediction 

Why did you want to intern at Cleveland Clinic?   

The Discovery Accelerator Internship at the Cleveland Clinic offered an opportunity to participate in research that is truly at the cutting-edge of healthcare. It was extremely exciting to see the beginnings of the next stages of personalized medicine. 

Can you tell me about the project you worked on?   

During my internship, I worked in Dr. Daniel Blankenberg’s lab on a project that applied quantum computing to protein structure prediction. 

What were you most excited to learn?   

This experience not only allowed me to gain new programming skills, it also allowed me to take a deeper look into the application of quantum computing in biochemical research. It was a privilege to work on this project as it has widened my perspective of the integration of new technologies in healthcare, and I look forward to taking this new understanding into my future career. 

Ryan Peters 

Junior, computer science, Purdue University  

Project: Developing a quantum convolutional neural network model to predict immunogenicity 

Why did you want to intern at Cleveland Clinic?   

I was interested in working at Cleveland Clinic because of its cutting-edge approach to medical research. I wanted to learn from and work with several of the most esteemed researchers in the world. Additionally, the opportunity to contribute to groundbreaking research that could directly impact patient care aligns with my passion to use computer science to improve lives. 

Can you tell me about the project you worked on?   

Over the summer, I was working with the Center for Immunotherapy and Precision Immuno-Oncology, as well as an IBM team, to develop a quantum convolutional neural network model to predict immunogenicity based off an HLA (human leukocyte antigen) peptide's amino acid sequence. Within this project, I learned a lot about quantum computing including key engineering principles, fundamentals and coding packages to communicate and develop the model. Specifically, I was tasked to help encode, configure and optimize data to run on both the quantum computer and a simulator. 

What were you most excited to learn?   

Going into the internship, I was very eager to learn about quantum computing, as well as machine learning methods. I had a background in machine learning, which helped with the project. However, I had never worked with quantum computers or Qiskit and I was interested in learning more about the process. I was also eager to improve my problem solving and communication skills.