Emre Neftci, Assistant ProfessorE-Mail: firstname.lastname@example.org
Dr. Emre Neftci received his degree in Physics at EPF Lausanne and his PhD at ETH Zurich in neuromorphic computing. He continued his research at UC San Diego as a post-doctoral fellow in the lab of Gert Cauwenberghs to investigate neural models for probabilistic state-dependent sensorimotor learning and processing in large-scale multi-neuron systems. Since 2015, Dr. Neftci is an assistant professor in the department of Cognitive Sciences and Computer Science at UC Irvine. His current research focuses on theoretical and computational modeling of learning in neural systems that exploit the characteristics of neuromorphic hardware.
Georgios Detorakis, Postdoctoral ResearcherE-Mail: email@example.com
Dan Barsever, Graduate StudentE-Mail: firstname.lastname@example.org
Travis Bartley, Graduate Student (EECS)E-Mail: email@example.com
Travis Bartley received his BS degree in Electrical and Computer Engineering from The Ohio State University in 2010. From 2010 to 2013, he worked as a research engineer under Professor Kazusuke Maenaka at the University of Hyogo. He then joined the Shuji Tanaka Laboratory at Tohoku University, where he worked as a researcher from 2013 to 2015. During these two appointments, he conducted research on circuits and algorithms for sensor systems. Since 2015, he has been pursuing his MS and PhD degrees in Electrical Engineering and Computer Science at the University of California, Irvine. His current research focuses on architecture and algorithm co-design for computationally efficient neural networks.
Takashi Nagata, Graduate Student (ICS)E-Mail: firstname.lastname@example.org
Takashi Nagata received his B.S. degrees in Information science from Tokyo University of Science, Japan, in 2008 and M.S. in 2010 respectively. After graduation, he had 7+ years experience in total as a systems engineer in financial industry and a developer support engineer in a Cloud company where he focused on BigData technologies especially Hadoop and its ecosystem. He joined the Neuromorphic Machine Intelligence Lab at 2017 and has been pursuing PhD degrees in Computer Science. His research interests include Robotics and Machine Learning, especially working memory and reasoning.
Andrew Hansen, Graduate StudentE-Mail: email@example.com
Andrew Hansen received an M.S. degree in Neuroscience from Brandeis University and a B.S. degree in Physics - Biophysics from St. Mary’s University in San Antonio, Texas. His primary interest is to reverse-engineer cognitive processes in neuromimetic neural networks and to develop robust cognitive models using mathematically defined boundaries, empirically derived metrics, and statistical methodologies. Bilaterally, Andrew is interested in exploring the computational dynamics native to neural circuitry to produce and implement novel, cognition-inspired machine learning algorithms. Andrew's inspiration lies in the profound philosophical conundrum of reconciling qualia with a scientifically coherent and functional theory of consciousness. Andrew holds a diverse background of research experience spanning the applied to the theoretical: from neurobiochemistry and epigenetics to the development of single- and multi-neuron simulations as well as programmatic interfaces for experimental neuroscience protocols. When Andrew isn't busy sciencing, he can be found creating artwork via artificial neural networks as well as a variety of conventional means. Additionally, he enjoys playing blues guitar and venturing outdoors with his fellow scientist and life partner Evelia.
Yue Yin, Graduate Student (BME)E-Mail: firstname.lastname@example.org
Yue Yin received his B.S. and M.S. degrees from the University of Wisconsin-Madison and Carnegie Mellon University, respectively, both in Biomedical Engineering with a concentration on biomedical signal processing. He worked in industry shortly after undergraduate, some of his projects include automated analyzer to process periodic waveforms (cardiac action potentials, calcium transients, and force contraction) for drug screening on cardiac tissue, 3D engineered heart tissue based high throughput assay system with automatic liquid handling, and computational simulation on iPSC-derived cardiomyocytes to study drug-induced arrhythmia sensitivity. At CMU, he developed instrumented hip implant model to detect loosening using acoustic impedance analysis. Currently, as a member of NMI-lab, his research focuses on artificial neural network and machine learning.