Adapting or solving problems using massive computational resources is a challenge in a wide field of applications. We focus on understanding new and emerging computing technologies to extract their power and use solving state-of-the-art problems in different fields. For example, LIO is a QM library/tool which can be used with Amber (potentially with any other similar tool). As the workload is not homogeneous, using GPU and CPU probed to be more powerful than using each separately.
Our aim is to determine the feasibility of large-scale distributed systems, with a focus on cryptocurrencies, using especially-designed network emulation and simulation tools which are best suitable for each problem. This allows to empirically evaluate properties and features of this type of systems, comparing against an increasing knowledge base of theoretical results.
Efficiency on parallelizing code depends, among other factors, on the problem size. Smaller problems are very difficult to parallelize using standard parallelization techniques and hardware. Our aim is to propose new solutions that allow smaller problem instances to be parallelized using specialized hardware support.
In the area of Natural Systems of Distributed Information Processing we study the computational properties of social, neuronal or biological systems. They all share: 1. memory and learning are distributed processes, 2. they are robust to death and failures of their individuals, 3. their properties (adaptive, cognitive or cultural intelligence) emerge from the interaction of system’s agents. One open line of work focus on cultural evolution process and social learning in communities of human beings. Culture is distributed information, transmitted through imitation and other forms of social learning. Humans learn things from others, improve and transmit them to the next generation. We are interested in social learning factors that alter the learning expected by the individual experience.
Selected works on Journals and Conferences
M. Geier, D. González Márquez, E. Mocskos
SherlockFog: a New Tool to Support Application Analysis in Fog and Edge Computing
Cluster Computing (2019) - In press,
G. Landfried, D. Fernández Slezak, E. Mocskos
Faithfulness-boost effect: loyal teammate selection improves skill acquisition in on-line games
PLOS One 14(3): e0211014, March 2019
M. White, J. Angiolini, Y. Alvarez, G. Kaur, Z. Zhao, E. Mocskos, L. Bruno, S. Bissiere, V. Levi, N. Plachta
Long-Lived Binding of Sox2 to DNA Predicts Cell Fate in the Four-Cell Mouse Embryo
Cell, 165(1), 75–87. Selected as issue cover image, March 2016
D. González Márquez, A. Cristal Kestelman, E. Mocskos
Mth: Codesigned Hardware/Software Support for Fine Grain Threads
IEEE Computer Architecture Letters (2016), September, 2016
He is a full time professor at UBA and researcher at CSC-CONICET. He received his Ph.D. in Computer Science from UBA in 2008. His research interests are in the areas of distributed systems, computer networks and protocols, parallel programming and applications.
He is a postdoctoral fellow at National Research Council of Argentina (CONICET) and teacher assistant at UBA. He received his Ph.D. in Computer Science from UBA in 2017. His research interests are in the areas of numerical modelling and parallel computing.
He received his Ph.D. in Computer Science from UBA in 2017. He is an assistant professor at the same university and he has a Postdoctoral position at CSC-CONICET. His research interests are in the areas of processor architecture, parallel programming, computer networks and protocols.
He received his Ph.D. in Computer Science from UBA in 2018. His research interests are in the areas of distributed systems, computer networks and protocols, IoT and Fog/Edge Computing.
He is full time professor at UTN FRBA, and Associate professor at UBA. He is pursuing his PhD Thesis focused on Computer Architecture. His research interest is improving the power / performance ratio in computer systems.
He is a full time professor and researcher at UNGS and head teaching assistant at DC - UBA. He received his Major in Computer Science from UBA in 2012. His research interests are in the areas of mobile ad hoc networks, computer networks and its integration with machine learning.
Pursuing a PhD in Computer Science (UBA-CONCIET), with a MSc in Anthropological Sciences as background (sociocultural orientation). His research interest is the novel area of Computational Social Sciences.
I’m a systems engineer, graduated at Los Andes University, Venezuela, I completed a master in Computer Science working on topics related to distributed systems, and issues of privacy and anonymity. I’m currently an UBA doctoral student and have a CONICET scholarship.
Suggestions, ideas, and comments are most welcome.
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