Last year, Konrad Kording, PhD, associate professor in physical medicine and rehabilitation and physiology, asked how well the success of professors could be predicted by measuring their future h-index. The h-index captures the quality (citations) and quantity (number) of papers, therefore measuring a scientist’s success. Kording’s most controversial discovery, he found a new metric far better at making predictions than the h-index.
“While most scientists agree that our predictions are better, they often feel that our research—highly visible by being published in Nature — by improving indices, might give administrators an excuse to ignore committees and overly focus on numbers,” he said.
One of many discoveries, Kording’s interests lie in building computational models to allow for better patient rehabilitation and to solve other problems his lab finds interesting.
What are your research interests?
I am interested in how we can make sense of data about people and brains. The brain is making sense of the world given the data it has, which is a deep statistical problem. Focusing on uncertainty and statistical issues my lab runs behavioral experiments and develops new data analysis methods with relevance to cognitive science and rehabilitation. But above all, we search for important data questions that have been overlooked.
To obtain more data about the brain, we work in collaboration with colleagues at various universities on very large scale neural recording techniques that could increase the amount of simultaneously recorded data.
What is the ultimate goal of your research?
The ultimate goal is to develop models that describe vast amounts of data about the brain and human behavior. The results could be used to cure diseases, limit suffering, and build cool things.
What collaborations are you involved in?
We collaborate with Lee Miller, PhD, Edgar C. Stuntz Distinguished Professor in Neuroscience and professor in physiology/physical medicine and rehabilitation and Mark Segraves, PhD, associate professor of neurobiology at the Judd A. and Marjorie Weinberg College of Arts and Sciences, where we ask how brains compute in animal models.
In these collaborations a joint team asks computational questions based on our collaborators' experimental techniques. Team members get training in both animal physiology and data analysis techniques. There are multiple other local collaborations in neuroscience and rehabilitation. Beyond our institution we collaborate with many labs on specialized questions.
We believe that deep collaborations are far more effective than any alternative because each scientist can focus on one thing that they are best at.
How does the Shirley Ryan AbilityLab/Northwestern collaboration foster scientific discoveries?
The Shirley Ryan AbilityLab sees a broad set of patients with rehabilitation needs. Being a data lab in such an environment opens up a broad set of possibilities. Some examples of our projects include using mobile phones to monitor patients with Parkinson’s disease and using Nintendo Wii Fit to train patients and analyze their movement. We are also involved in a broad range of projects related to stroke, aphasia and other neurological diseases.
How did you become interested in this research?
I have been excited about big data biology since I was a high school student. It always seemed like the natural way of thinking.
What are some of your personal hobbies?
I love salsa dancing with my wife; it’s just so relaxing. I also love snowboarding and I take by skateboard to work almost every day. I enjoy going to the Museum of Science and Industry with my three young kids.
Who inspires you?
Peter Dayan of Gatsby Computational in London, is a great inspiration to me. He spearheaded much of the use of Bayesian statistics in computational neuroscience, influenced machine learning and drove a highly successful research program in psychiatry and neuroscience. And yet, he will take the time to give detailed feedback on manuscripts sent to him by young scientists he barely knows. Despite his success, he is one of the most humble people I know.