About

Carlos Lopez is an Assistant Professor of Materials Science and Engineering at Penn State, joining the department in 2024. His research focuses on the structure and rheology of various soft matter systems, including polyelectrolyte solutions, poly(ionic liquid) gels, and colloidal dispersions. Lopez's academic background includes a Bachelor of Science and a Master of Science in experimental and theoretical physics from the University of Cambridge, completed in 2010, and a Ph.D. in chemical engineering from Imperial College London, supervised by Prof. Joao T. Cabral. His graduate research concentrated on understanding the properties of polyelectrolytes in solution through rheological and neutron scattering techniques. Lopez has worked as a postdoctoral researcher in Germany at the University of Paderborn and RWTH Aachen University, where he became a group leader in 2020. His work during this period combined fundamental research into the self-assembly of intermediate filament proteins, microgel thermodynamics, and the rheology of polymer solutions with industrial projects related to dye transfer inhibition and perfume deposition in laundry detergents. At Penn State, he is associated with the Intercollege Graduate Degree Program in Materials Science and Engineering, emphasizing cross-disciplinary collaboration. His research aims to advance understanding of soft matter systems, contributing to both fundamental science and industrial applications.

Research topics

• Computer Science
• Artificial Intelligence
• World Wide Web
• Data science
• Computer Security
• Information Retrieval
• Machine Learning
• Engineering
• Human–computer interaction
• Psychology

Selected publications

• An augmented multilingual Twitter dataset for studying the COVID-19 infodemic

  Social Network Analysis and Mining · 2021 · 62 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Information Retrieval

  This work presents an openly available dataset to facilitate researchers' exploration and hypothesis testing about the social discourse of the COVID-19 pandemic. The dataset currently consists of over 2.2 billions tweets (count as of September, 2021), from all over the world, in multiple languages. Tweets start from January 22, 2020, when the total cases of reported COVID-19 were below 600 worldwide. The dataset was collected using the Twitter API and by rehydrating tweets from other available datasets, data collection is ongoing as of the time of writing. To facilitate hypothesis testing and exploration of social discourse, the English and Spanish tweets have been augmented with state-of-the-art Twitter Sentiment and Named Entity Recognition algorithms. The dataset and the summary files provided allow researchers to avoid some computationally intensive analyses, facilitating more widespread use of social media data to gain insights on issues such as (mis)information diffusion, semantic networks, sentiments, and the evolution of COVID-19 discussions. In addition, the dataset provides an archive for researchers in the social sciences wishing to have access to a dataset covering the entire duration of the pandemic.

  DOI

• Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments

  Journal of Computing and Information Science in Engineering · 2020 · 21 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Abstract This work presents a deep reinforcement learning (DRL) approach for procedural content generation (PCG) to automatically generate three-dimensional (3D) virtual environments that users can interact with. The primary objective of PCG methods is to algorithmically generate new content in order to improve user experience. Researchers have started exploring the use of machine learning (ML) methods to generate content. However, these approaches frequently implement supervised ML algorithms that require initial datasets to train their generative models. In contrast, RL algorithms do not require training data to be collected a priori since they take advantage of simulation to train their models. Considering the advantages of RL algorithms, this work presents a method that generates new 3D virtual environments by training an RL agent using a 3D simulation platform. This work extends the authors’ previous work and presents the results of a case study that supports the capability of the proposed method to generate new 3D virtual environments. The ability to automatically generate new content has the potential to maintain users’ engagement in a wide variety of applications such as virtual reality applications for education and training, and engineering conceptual design.

  DOI

• Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset

  arXiv (Cornell University) · 2020 · 106 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Data science

  The objective of this work is to explore popular discourse about the COVID-19 pandemic and policies implemented to manage it. Using Natural Language Processing, Text Mining, and Network Analysis to analyze corpus of tweets that relate to the COVID-19 pandemic, we identify common responses to the pandemic and how these responses differ across time. Moreover, insights as to how information and misinformation were transmitted via Twitter, starting at the early stages of this pandemic, are presented. Finally, this work introduces a dataset of tweets collected from all over the world, in multiple languages, dating back to January 22nd, when the total cases of reported COVID-19 were below 600 worldwide. The insights presented in this work could help inform decision makers in the face of future pandemics, and the dataset introduced can be used to acquire valuable knowledge to help mitigate the COVID-19 pandemic.

  DOI

Frequent coauthors

• Conrad S. Tucker

  Carnegie Mellon University

  37 shared

• Omar Ashour

  Wake Forest University

  14 shared

• James Cunningham

  5 shared

• Fréderic Heymes

  IMT Mines Alès

  4 shared

• Thomas Stranick

  Lafayette College

  3 shared

• Caleb Gallemore

  Lafayette College

  3 shared

• Philippe Martin

  Sciences pour l’action et le développement - Activités, produits, territoires

  3 shared

• Pierre-Alain Ayral

  Étude des Structures, des Processus d’Adaptation et des Changements de l’Espace

  3 shared

Education

• MSci, Physics

  University of Cambridge

  2010

Awards & honors

• Julia Higgins Centenary Prize for PhD work at Imperial Colle…

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