Team

Ralph Etienne-Cummings
PI, Johns Hopkins University
Ralph Etienne-Cummings, an IEEE, AIMBE and ASI Fellow, received his B. Sc. in physics, 1988, from Lincoln University, Pennsylvania. He completed his M.S.E.E ('91). and Ph.D. ('94) in electrical engineering at the University of Pennsylvania. Dr. Etienne-Cummings is the Vice-Provost for Faculty Affairs, previous (7/2014 – 7/2020) Chairman of Department of Electrical and Computer Engineering and the Julian S. Smith Professor of Electrical Engineering at Johns Hopkins University (JHU). He was the founding Director of the Institute of Neuromorphic Engineering. He has served as Chairman of various IEEE Circuits and Systems (CAS) Technical Committees and was elected as a member of CAS Board of Governors. He also serves on numerous editorial boards and was the Deputy Editor in Chief for the IEEE Transactions on Biomedical Circuits and Systems. He is the recipient of the NSF’s Career and Office of Naval Research Young Investigator Program Awards, among many other recognition. He was a Visiting African Fellow at U. Cape Town, Fulbright Fellowship Grantee, Eminent Visiting Scholar at U. Western Sydney and has also won numerous publication and research awards. He was also recognized as a “ScienceMaker”, an African American history archive and for the “Indispensable Roles of African Americans at JHU” exhibit. In 2021 was elected to the Johns Hopkins Homewood Academic Council, which is the highest body responsible for maintain excellence in research, teaching and service at the University. He has published nearly 400 peer reviewed article, books/chapters and patents/applications on his work.

Andreas Andreou,
Co-PI, Johns Hopkins University
Andreas Andreou, a professor of electrical and computer engineering, is the co-founder of the Johns Hopkins University Center for Language and Speech Processing. Research in the Andreou lab is aimed at brain-inspired microsystems for sensory information and human language processing. Notable microsystems achievements over the last 25 years include a contrast sensitive silicon retina, the first CMOS polarization-sensitive imager, silicon rods in standard foundry CMOS for single-photon detection, and a large scale mixed analog/digital associative processor for character recognition. Significant algorithmic research contributions in pattern analysis and machine intelligence include the vocal tract normalization technique for speech recognition and heteroscedastic linear discriminant analysis, a derivation and generalization of Fisher discriminants in the maximum likelihood framework. An IEEE Fellow, Andreou has secondary appointments in the Department of Computer Science and the Whitaker Biomedical Engineering Institute.

Ernst Niebur,
Co-PI, Johns Hopkins University
Dr. Ernst Niebur is a professor of neuroscience at the Johns Hopkins University School of Medicine, and a professor of pscychological and brain sciences at the Johns Hopkins Krieger School of Arts and Sciences. His research examines neuronal function at the system level focusing particularly on a function known as selective attention, which is the capability of humans and higher animals to scan sensory input for the most important information and to discard other non-essential information. He received his BSc and MSc from the University of Dortmund in West Germany. He was awarded his PhD at Universite de Lausanne in Switzerland. He also received a certificate in Artificial Intelligence from the Swiss Federal Institute of Technology (EPFL). Dr. Niebur has authored or co-authored more than 100 peer-reviewed publications and his work has been cited more than 20,000 times. In the Computational Neuroscience Laboratory, Ernst and his team construct quantitative models of biological nervous systems which are firmly based on their neurophysiology, neuroanatomy and behavior, and which are developed in close interaction with experimentalists. The main interest is neuronal function as the system level, reflecting the interaction of subsystems to generate useful behavior. Modeling is particularly important for understanding this and other system level functions since it required the interaction of several pathways and neural functions. One of the functions studied is selective attention, that is the capability of higher animals to scan sensory input for the most important information and to discard all other. Models of the neuronal basis of visual selective attention are constructed by simulating them on digital computers and comparing the results with date obtained from the visual and somatosensory systems of primates. His group pays particular attention to the study of mechanisms involving implementation of neural mechanisms which make use of the temporal structure of neuronal firing, rather than just the average firing rate.

Stefan Mihalas,
Co-PI, The Allen Institute
Stefan Mihalas joined the Allen Institute in 2011 from Johns Hopkins University, where he was a postdoctoral fellow in neuroscience and subsequently an associate research scientist. As a computational neuroscientist, Mihalas has worked on models of both molecular and systems neuroscience including nervous system development, synaptic plasticity, minimalistic spiking neuron models, self-organized criticality, visual attention and figure ground segregation. His current research interests are aimed at building models to elucidate how large networks of interacting neurons produce cognitive behaviors. At the Allen Institute, Mihalas integrates anatomical and physiological connectivity data to generate models of visual perception in the mouse. To this end, he works to build a series of models of increasing complexity for both individual components, i.e., neurons, synapses, and microcircuits, as well as for large portions of the entire system. This series of models will be compared to the simplified theoretical predictions from statistical physics, information theory and computer vision. Mihalas received his Diploma in physics and M.S. in mathematics from West University of Timisoara in Romania. He received his Ph.D. in physics from the California Institute of Technology.

Martin Lohmann,
Post-Doc, Allen Institute

Akwasi Akwaboah
PhD Student, Johns Hopkins University
Akwasi is a Ph.D. student at the Computational Sensory-Motor Systems (CSMS) Lab, Department of Electrical and Computer Engineering, Johns Hopkins University (JHU). His current research spans the spectrum of neuromorphic circuits & systems that promise “greener” Artificial Intelligence and neural interfacing electronics that hold implications for neurorehabilation as well as bidirectional communication between digital and biological (neural tissue) computers. He is advised by Dr. Ralph Etienne-Cummings. He recently participated in the Telluride Neuromorphic Cognition, where he collaborated on an asynchronous VLSI design of a silicon neuron. Prior to joining the CSMS Lab in 2020, he worked as a research intern at the Dr. James Weiland’s BioElectronic Vision Lab, University of Michigan. There, He collaborated on a number of projects in the areas of retinal prostheses, optimization for closed-loop neurostimulation and calcium imaging. In Summer 2022, he received the MSE in Electrical Engineering from the Johns Hopkins University en route the Ph.D. He also received an MS in Electronics Engineering from Norfolk State University(NSU) in 2020. At NSU, his master’s thesis focused on the formulation of heuristically optimized computational model of a stem cell-derived cardiomyocyte with implications in cardiac safety pharmacology. He holds a BSc. Biomedical Engineering (First Class Honors) from the Kwame Nkrumah University of Science and Technology (KNUST), Ghana which he received in 2017.

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