Computational Science Research Center
Date of the Event: February 21, 2020
TITLE: CSRC Colloquium An Unconventional View of Deep Neural Networks Design and Training DATE: Friday, February 21, 2020 TIME: 3:00 PM LOCATION: GMCS-314 SPEAKER: Dr. Victor Pereyra, Consulting Professor, Stanford University ABSTRACT: Under the premise that less is better we consider the design of Deep Neural Networks with several twists to reduce the number of […]
Date of the Event: February 14, 2020
TITLE: CSRC Colloquium Advanced Technology Trends and Computational Tools in Construction: A Case Study of Deep Learning for Activity Recognition DATE: Friday, February 14, 2020 TIME: 3:00 PM LOCATION: GMCS-314 SPEAKER: Dr. Reza Akhavian, Assistant Professor, Department of Civil, Construction, and Environmental Engineering, SDSU ABSTRACT: Technological advancements focusing on effective and efficient information modeling, visualization, […]
Date of the Event: February 7, 2020
TITLE: CSRC Colloquium What We Talk About When We Talk About Quantum Computing DATE: Friday, February 07, 2020 TIME: 3:00 PM LOCATION: GMCS-314 SPEAKER: Dr. Calvin W. Johnson, Professor, Department of Physics, SDSU ABSTRACT: The U.S., China, and other nations are pouring money into quantum computing, terrified of being left behind–especially as quantum mechanics is […]
Date of the Event: January 31, 2020
TITLE: CSRC Colloquium Towards Increase Resilience, Durability, and Sustainability: Computational Modeling and Visualization of Civil Infrastructure Materials DATE: Friday, January 31, 2020 TIME: 3:00 PM LOCATION: GMCS-314 SPEAKER: Dr. Marta Mileti√Ѭá, Assistant Professor, Department of Civil, Construction, and Environmental Engineering, SDSU ABSTRACT: A low ratio between the compressive strength of concrete and its cost makes […]
Date of the Event: January 24, 2020
TITLE: CSRC Colloquium Low Resolution Quantization in Massive MIMO Communications Systems DATE: Friday, January 24, 2020 TIME: 3:00 PM LOCATION: GMCS-314 SPEAKER: Dr. A. Lee Swindlehurst, Professor, Department of Electrical Engineering & Computer Science, University of California-Irvine ABSTRACT: Low-resolution sampling hasbeen extensively studied as a method for reducing the hardware complexity ofcommunications systems with large […]