Notably, I was program chair for SIGBOVIK 2019, a very serious, extra prestigious conference for research of a less than serious nature. My current work looks at formalizing machine knitted object equivalence in order to aid with program rewriting. However, I believe that using the correct problem description can result in both computationally tractable and representational methods. Obviously, the real world is a highly complicated place where both the task and the notion of optimal can be ill defined. My research interests lie with better computational tools for optimizing physical fabrication tasks.

Before coming to CMU, I received my bachelors in Computer Science and Molecular Biology (Course 6-7) from the Massachusetts Institute of Technology. His research is mainly focused on designing multimodal (electro-acousto-optical) neural interfaces for better understanding the neural basis of brain function and dysfunction as well as designing next generation brain-machine interfaces.I am a sixth year PhD student in Carnegie Mellon University's Computer Science Department, advised by James McCann.

He is the recipient of a number of awards including the Scaife (DSF) Foundation Award, the HAND Foundation Award, SPIE Research Excellence Award, and the IEEE BRAIN Best Paper Award. He has published more than thirty papers and holds three patent applications. His research interests are at the intersection of optics, nanotechnology, and neuroscience. He is also a faculty member of the Biomedical Engineering Department as well as the Center for the Neural Basis of Cognition (CNBC), which is a joint venture of University of Pittsburgh and CMU. These novel neurophotonic techniques enable high-throughput bi-directional interfacing with the brain to understand the neural basis of brain function and design next generation neural prostheses.īio: Maysam Chamanzar is an assistant professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). I will show that ultrasound waves can sculpt virtual graded-index (GRIN) waveguides in the tissue to define and steer the trajectory of light without physically implanting optical waveguides in the brain. Then, I will discuss our recent work on developing a novel complementary approach to guide and steer light in the brain using non-invasive ultrasound. This photonic platform can be monolithically integrated with implantable neural probes for simultaneous electrophysiological recording and optical stimulation of brain. First, I will introduce a novel compact flexible photonic platform based on biocompatible polymers, Parylene C and PDMS, for optogenetic stimulation of neural circuits with high spatiotemporal resolution. In this talk, I will discuss our research on developing next generation neural interfaces. Existing large and rigid optical waveguides cause damage to the brain tissue and vasculature. To overcome this challenge, implantable photonic waveguides such as optical fibers or graded-index (GRIN) lenses have been used to deliver light into the tissue or collect photons for imaging.

Scattering and absorption prevents deep penetration of light in tissue and limits light-based methods to superficial layers of the tissue. One of the challenges of optical modality is light delivery deep into the brain tissue in a non-invasive or at least minimally invasive way. Optical techniques have been recently developed for structural and functional imaging as well as targeted stimulation of neural circuits. Title: From Flexible Implants to Virtual Acousto-optic Neural InterfacesĪbstract: Understanding the neural basis of brain function and dysfunction may inform the design of effective therapeutic interventions for brain disorders and mental illnesses. Maysam Chamanzar, Carnegie Mellon University

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