Member Highlight: Archana Venkataraman

Archana Venkataraman is an Associate Professor of Electrical and Computer Engineering at Boston University, MA. From 2016 to 2022, she was an Assistant Professor at The Johns Hopkins University, Baltimore, MD. Dr. Venkataraman directs the Neural Systems Analysis Laboratory and is affiliated with the Department of Biostatistics, the Department of Biomedical Engineering, the Rafik B. Hariri Institute for Computing, and the Faculty of Computing and Data Science at Boston University. Her research lies at the intersection of biomedical imaging, artificial intelligence, and clinical neuroscience. Her work has yielded novel insights into debilitating neurological disorders, such as autism, schizophrenia, and epilepsy, with the long-term goal of improving patient care. Dr. Venkataraman completed her B.S., M.Eng. and Ph.D. in Electrical Engineering at the Massachusetts Institute of Technology (MIT), Cambridge, in 2006, 2007, and 2012, respectively. She is a recipient of the MIT Provost Presidential Fellowship, the Siebel Scholarship, the National Defense Science and Engineering Graduate Fellowship, the NIH Advanced Multimodal Neuroimaging Training Grant, numerous best paper awards, and the National Science Foundation CAREER award. Dr. Venkataraman was also named by MIT Technology Review as one of 35 Innovators Under 35 in 2019.

1. Why did you choose to become faculty in the field of Signal Processing?

I have always been curious about the world, fascinated by the sights, sounds, and scents around me. My first signal processing class was a transformative experience that taught me how this real-world information can be mathematically represented as a signal and viewed through the complementary lenses of time and frequency. My first probability class gave me a glimpse into how the world is a random process, but we can still model this randomness and make principled decisions. Finally, my dissertation work introduced me to medical imaging and how we can use the basic tools of signal processing and stochastic inference to tackle meaningful challenges that impact people’s daily lives. Pursuing an academic position was a natural extension for me. It has been an incredibly fulfilling journey filled with tough problems, interesting ideas, and wonderful collaborations.  

2. How does your work affect society?

My work focuses on better understanding and treating disorders of the brain. For example, my lab has pioneered new directions in the field of imaging-genetics, and specifically on analyzing human genetics using AI. Our strategy is to embed known biological structure into the neural network architectures to learn from small sample sizes of very complex data. Not only do our models achieve higher prediction accuracy, but they provide an interpretability that is rare in AI. On the translational front, I have done a lot of work on epilepsy, specifically on trying to localize the seizure onset zone using non-invasive data. Our models work on routinely acquired data and can easily be translated into a clinical setting. On the surgical side, we developed models to localize key areas of the brain, known as the eloquent cortex, from non-invasive functional MRI. Our algorithms provide additional insight to neurosurgeons to help them plan safe and effective operations.

3. What challenges have you had to face to get where you are today?

Academia is a bit of a “wild west” and required a mindset adjustment on my part. I had to go from simply creating new algorithms and publishing papers to building the entire infrastructure to sustain a lab (grant writing, mentoring, publicity) while also teaching, contributing to the university through service, and developing professional leadership. I like to say that an academic lab is a bit like a mini-startup, where the PI is the entire C-suite. With that said, starting my lab has been one of the most rewarding experiences, and I am constantly blown away by the energy, creativity, motivation, technical abilities, and collaborations with my students.

4. What advice would you give to scientists/engineers in signal processing?

My advice would be that great work comes from great collaborations and great partners. As engineers, we often get trapped in our own little bubble, where we are surrounded by other engineers with similar skill sets and mind sets. However, moving the needle to address the many urgent challenges of today — from health and medicine to energy and climate to security and privacy — will require interdisciplinary teams across different stakeholders, with everyone bringing different ideas and perspectives to the table.

5. Anything else that you would like to add?

You can visit the Neural Systems Analysis Laboratory website to learn more about my work.