Anne Draelos
Applications:
Behavioral Science, Biological Sciences, Engineering
Methodologies:
Artificial Intelligence, Bayesian Methods, Computing, Machine Learning, Mathematical and Statistical Modeling, Optimization, Statistics

Anne Draelos

Assistant Professor

Biomedical Engineering, Medical School

Assistant Professor of Biomedical Engineering, Medical School and College of Engineering and Assistant Professor of Computational Medicine and Bioinformatics, Medical School

My lab’s research focuses on understanding computation in large-scale neural circuits through adaptive perturbations and real-time inference. We develop statistical machine learning algorithms to adaptively build models of neural and behavioral data online, and use them for understanding the mapping between multidimensional neural stimulations and complex behavioral outcomes. We emphasize data-driven Bayesian approaches suited for real-time prediction of latent neural and behavioral dynamics.

Additional Information

How did you end up where you are today?

My academic training began in both physics and computer science. My masters work in electrical and computer engineering focused on modeling interconnected resistive systems, and I did my doctoral work in experimental condensed matter physics, focused on the interplay between various quantum phenomena in two-dimensional materials. I am most interested in methods for dissecting complex networked systems, and I ultimately decided to make the switch to neuroscience to study arguably the most complex network around: the brain.v

My postdoctoral work focused on automated neural circuit dissection in larval zebrafish, where I designed both mathematical and software tools for experimental integration of machine learning algorithms. As an Assistant Professor, my lab is focused on leveraging increasingly rich behavioral data (in e.g., mice, monkeys) alongside neural data in streaming contexts to causally relate perturbations of neural function and resultant outcomes in latent behavior spaces. My ultimate goal is to bridge the gap between algorithm and implementation, using adaptive methods as a new design language for neuroscience experiments.

What makes you excited about your data science and AI research?

One of the reasons I decided to move into neuroscience as my application domain was the relatively recent data explosion in the field. We can now record from tens of thousands up to a million neurons simultaneously, at quite high-resolution in time. The field is also increasingly focused on naturalistic and effectively non-repeatable events as behavioral metrics. Both of these items make for incredibly complex and rich problems for neuroscience, which is why I think data science and AI have amazingly interesting roles to play there.


Accomplishments and Awards