I am a student in the Joint Ph.D. Program in Neural Computation and Machine Learning at Carnegie Mellon University’s Neuroscience Institute and Machine Learning Department. Interdisciplinary research at the intersection of Neuroscience, Statistics, Machine Learning and AI is a unique challenge and I am guided by Avniel Ghuman and Robert Kass, whose expertise spans both domains. My research interests, background and experience are described briefly below and in detail in my CV.
My graduate work focuses on visual cognition in the human brain, specifically face processing. Humans are adept at detecting subtle features of a face. For instance, we can perceive a fleeting, quickly supressed expression of anger easily. Our abilities are supported by dedicated face regions in the brain, and brain areas beyond these regions exhibit face related activity as well. Despite advancing rapidly in recent decades, our understanding of information processing principles underlying face perception in the brain remains nascent, and questions such as “what is the neural code for emotions that we see on a face?” abound.
Broadly, I am interested in uncovering information processing principles underlying face perception in the brain. Specifically, I seek to understand the neural code for faces in terms of what (features of a face), where (areas of the brain), when (e.g. how long after we see a face does a particular region of the brain get involved?) and how (e.g. is the neural code for faces modulated by real world social situations?). I develop and use machine learning techniques to analyze intracranial EEG recordings of human brain activity during visual neuroscience experiments to answer these questions.
My journey to neuroscience and machine learning research in graduate school followed an atypical path. I studied Electrical Engineering at the Georgia Institute of Technology as an undergraduate and interned at startups developing embedded systems for video encoding and ASICs. After graduating, I worked on software based communications technology (Lync, Skype) at Microsoft. Cumulatively, I have worked on problems spanning signal processing, computer and wireless networking, ASIC design, embedded systems, video processing and machine learning. A latent interest in neuroscience led me to graduate school where I have worked on theoretical and computational neuroscience problems.
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