| Analog neuromorphic module based on carbon nanotube synapses AM Shen, CL Chen, K Kim, B Cho, A Tudor, Y Chen ACS nano 7 (7), 6117-6122, 2013 | 95 | 2013 |
| Challenges with structural life forecasting using realistic mission profiles B Gockel, A Tudor, M Brandyberry, R Penmetsa, E Tuegel 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials …, 2012 | 95 | 2012 |
| Three-dimensional reconstruction of statistically optimal unit cells of polydisperse particulate composites from microtomography H Lee, M Brandyberry, A Tudor, K Matouš Physical Review E 80 (6), 061301, 2009 | 68 | 2009 |
| Synaptic resistors for concurrent inference and learning with high energy efficiency CD Danesh, CM Shaffer, D Nathan, R Shenoy, A Tudor, M Tadayon, Y Lin, ... Advanced Materials 31 (18), 1808032, 2019 | 40 | 2019 |
| Doping modulated carbon nanotube synapstors for a spike neuromorphic module AM Shen, K Kim, A Tudor, D Lee, Y Chen Small 11 (13), 1571-1579, 2015 | 17 | 2015 |
| Self‐Programming Synaptic Resistor Circuit for Intelligent Systems CM Shaffer, A Deo, A Tudor, R Shenoy, CD Danesh, D Nathan, ... Advanced Intelligent Systems 3 (8), 2100016, 2021 | 6 | 2021 |
| Bioinspired neuromorphic module based on carbon nanotube/C60/polymer composite K Kim, A Tudor, CL Chen, D Lee, AM Shen, Y Chen Journal of Composite Materials 49 (15), 1809-1822, 2015 | 5 | 2015 |
| An Adaptive Intelligent System Based on Energy‐Efficient Synaptic Resistor Circuits with Fast Real‐Time Learning R Shenoy, A Tudor, D Nathan, A Deo, Z Rong, CM Shaffer, CD Danesh, ... Advanced Intelligent Systems 4 (10), 2200105, 2022 | 2 | 2022 |
| Synaptic Resistor Networks for Intelligent Systems with Real-Time Learning AW Tudor University of California, Los Angeles, 2017 | | 2017 |
Kyle (Kyunghyun) KimIntelVerified email at ucla.edu
