Crossing over, branching out: Meet the NCSA Genomics team

by Katherine Kendig

Making the impossible possible

The Genomics team at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign is a student-centered team that participates in academic research, industry work, and the education of campus faculty, students, and postdocs in the use of high-performance computing for genomic biology. “Every single project is collaborative with faculty or partners,” says Dr. Liudmila Mainzer, who heads the group. “Someone else brings in their research problem and we…make the impossible possible.”

Mainzer believes in the power of a heterogeneous team. “Science has been moving away from silos into nuclear problem thinking,” she notes. “Multiple disciplines have to work together.” That’s why NCSA Genomics brings together biologists, computer scientists, physicists, bioinformaticians, and engineers: team members with expertise in different fields who can share their knowledge, enabling the team to handle disparate challenges. “We tackle diverse and complex projects,” Dr. Mainzer notes. “We need all these people.”

Meet the team

Matt Weber

When Matt Weber started working with NCSA Genomics during his senior year at Illinois, he held a special distinction: he was the newly defined team’s first hire. As a junior, he’d taken a class that combined genetics and computing and immediately realized “this is what I want to do.” Now a Master’s candidate in bioinformatics, Weber appreciates his ongoing tenure with the fast-growing Genomics team—it’s taught him “the importance of collaboration…the value of working with other people on the same project.” Collaboration, he says, is particularly valuable in the Genomics group because the team’s different backgrounds provide a larger pool of knowledge: “We cross over in a lot of areas, but we also branch out in a lot of different directions.”

Ryan Chui

After Ryan Chui admitted he had never worked with Linux before joining the NCSA Genomics team, there was a collective gasp from his colleagues—probably because in the office, Chui is known as the CS guru, with roughly ten different languages under his command. Chui started programming in high school, when his parents gave him the “OG” book on C programming (The C Programming Language by Brian Kernighan and Dennis Richie) over the summer. He is now the Genomics team’s in-house expert for software support, profiling, benchmarking, and optimization, suggesting that his early introduction to computing paid off. There’s always more to learn, however: Chui says the most useful knowledge he’s picked up since joining the team is, in fact, how to work with Linux.

Weihao Ge

Weihao Ge believes that everything can be explained by physics—but that the bigger, messier world of biology is more fun. So she works to combine the two in her PhD research into statistical models for genomic analysis, which she’s adapting for use by the Genomics team. Becoming a part of NCSA has been valuable for Ge because it gives her the opportunity to work with computing clusters and large datasets, scaling up the research she’s able to do. “Joining this group helped me to get faster onto the right track,” she says. “By the way, everybody is so nice here!”

Jacob Heldenbrand

Jacob Heldenbrand says he stumbled into “the bioinformatics side of things” from a background in biochemistry without meaning to, but he eventually started to like computational work more than the scientific research he’d planned on doing. Here, he says, the primary task is to support people solving scientific problems, providing scaffolding for research. Working with NCSA’s tremendous computing capacity has taught him to anticipate the implementation details of each specific project he works on, particularly “when you’re trying to scale up to the size of the problems we’re dealing with at this institution.” Balancing multiple languages and projects across the team can be challenging, he says, but experimenting leads to better solutions.

Tiffany Li

Tiffany Li says the most valuable aspect of her job with the Genomics group is the way it involves direct applications of technology, instead of just theory: “You can learn about cluster environments in class, but to actually use them is different. I wouldn’t have gotten that without NCSA.” Her work on alternative file aggregation tools, necessary for large datasets, is an example of that difference. NCSA has also taught Li to ask for help. “I used to think of it as a sign of weakness,” she says, but she learned to “grit [her] teeth” and do it. Li studied biology in school, but she says it wasn’t a great fit. Working with the multidisciplinary team at NCSA has been “a step in the right direction.”

Aishwarya Raj

When she started in science, Aishwarya Raj says, she was interested in specific topics—but as she shifted her focus to thinking about the components of data gathering, she realized her interest is centered in analysis, at “the intersection of lots of different types of information.” More and more, she adds, “research is becoming interdisciplinary… you can’t just study a science and expect to understand everything.” Studying biomolecular network architecture at NCSA has given her a computational foundation for the kinds of scientific analysis she wants to do in the future. But the most valuable skill she’s developed here is the ability to filter huge amounts of information for what’s useful. “I have a better searching algorithm in my mind now,” she says.

Matt Kendzior

An interest in the “sheer power of computing” brought Matt Kendzior into the NCSA Genomics fold. His expertise in genetics comes from his background in crop sciences, but programming and computational methods form the backbone of his research into transposable elements in soybean genomes. Whereas much of the team works with human data, Kendzior’s work is ultimately more applicable to agriculture than medicine. Does that make collaboration difficult? Not really. “Each person has their own expertise,” he says, “but we all know enough in a broad sense to ask [each other] the right questions and make the whole more than the sum of its parts.”

Ellen Nie

Ellen Nie’s education in computer science gave her the mindset for writing code, but the two primary languages she uses at NCSA Genomics, she says, she’s mostly learning “from scratch” on the job. That’s not unusual when it comes to her work: she also learned about cloud computing at the same time as she rewrote one of the team’s software prototypes for cloud implementation, and although her background isn’t in statistics, she’s creating an implementation of LASSO, a statistical analysis method, that can be used for very large datasets. Despite being one of the youngest members of the team, Nie is a go-to resource for others. For her part, she says the team is easy to work with. “Everyone is friendly, everyone is responsible.”

Jennie Zermeno

Amateur entomologist Jennie Zermeno likes to take things apart and see how they work “and hopefully put them back together” when she’s done. On her own time, she installed Linux on an old computer, played around with R, and rewired the headlights on her car (with eventual success). Learning to use Linux, she says, “was like learning magic spells… I discovered all these things I didn’t know I could do with my computer.” That exploration helped her land a position in the Genomics group, where she now uses Linux to optimize containerization on Amazon’s cloud computing interface. At heart, her work aligns with her passion: “It’s still sticking things together and seeing if they run.”

Pushing forward

NCSA Genomics has growing expertise in genomics workflows, software, and statistics. Now Mainzer is looking to expand the group’s knowledge of machine learning and hardware. Recent September additions to the team are a good first step: Ramshankar Venkatakrishnan is a hardware expert with training in electrical and computer engineering; Sijia Huo specializes in math, economics, and computer science and is helping parallelize R code for a faculty partner’s project; and Cynthia Liu, who will be helping the team benchmark competing workflows, is a student in bioengineering.

Mainzer is committed to hiring graduate and undergraduate students as the team grows and evolves, noting that students are essential to the group’s current and future success because they have the bandwidth and curiosity to explore cutting-edge tools and methodologies and push the team forward. “We must never lose the student element,” Mainzer says. “They keep us alive.”

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