Alejandro F. Villaverde is a Ramón y Cajal research fellow at the Department of Systems & Control Engineering, Universidade de Vigo. His research focuses on structural properties of nonlinear systems, particularly structural identifiability and observability analysis. His primary research interests include: Structural identifiability of dynamic models Observability analysis of nonlinear systems Control theory and systems engineering Development of computational tools for model analysis Applications in systems biology and pharmacokinetics Villaverde's work centers on developing mathematical methods and software tools to analyze structural properties of nonlinear models. His most notable contributions include the STRIKE-GOLDD MATLAB toolbox and its Python counterpart StrikePy, which implement the FISPO algorithm for simultaneous assessment of state observability and parameter identifiability. His research has practical applications in systems biology, biochemical network analysis, and pharmacokinetic modeling where model validation and parameter estimation are critical. Among his scientific recognitions, he holds the prestigious Ramón y Cajal research fellowship, a competitive program in Spain supporting experienced researchers. Villaverde actively supervises students, with David Rey Rostro having developed the StrikePy toolbox under his supervision. His work demonstrates strong connections between theoretical control engineering and practical computational implementations, making structural analysis methods more accessible to researchers across various disciplines.
Hans Ekkehard Plesser is a Professor at the Department of Data Science, Norwegian University of Life Sciences (NMBU). His work focuses on computational neuroscience, large-scale neural network simulations, and advancing reproducibility in computational research. He is a key contributor to the NEST simulator, a widely used open-source tool for modeling spiking neural networks. His research spans theoretical and applied aspects of neuroscience, including neuron-astrocyte interactions, connectivity patterns in neural networks, and high-performance computing strategies for brain-scale simulations. He emphasizes reproducible computational practices through initiatives like the ReScience project, promoting transparency and validation in scientific software. His publications highlight advancements in simulation methodologies, algorithm optimization, and the integration of supercomputing resources for neuroscience. He has contributed to foundational work on spiking neuron dynamics, firing-rate models, and the theoretical underpinnings of neural network behavior.
Robert J. Doerksen is Professor of Medicinal Chemistry in the Department of BioMolecular Sciences at the University of Mississippi School of Pharmacy , Associate Dean of the Graduate School , and Research Professor in the Research Institute of Pharmaceutical Sciences . Since 2004 he has combined computational chemistry with experimental collaborations to advance drug discovery, particularly in glycoscience and cannabinoid research. Education: B.S. (Double First Class Honours) in Mathematics & Physics, University of New Brunswick, 1986 Graduate Diploma in Christian Studies, Regent College, Vancouver, 1996 Ph.D. in Chemistry, University of New Brunswick, 1998 (Advisor: Prof. Ajit Thakkar) Postdoctoral Fellow, UC Berkeley (with Prof. Martin Head-Gordon) Postdoctoral Fellow, University of Pennsylvania (with Prof. Michael Klein) Research Interests: Dr. Doerksen’s laboratory develops and applies computational medicinal chemistry approaches spanning chemoinformatics , molecular dynamics , virtual screening , and machine learning to understand how small molecules interact with proteins. Central themes include: Glycoscience : lectin–glycan interactions, glycosyltransferase regulation, glycomimetic design. Cannabinoids : CB1/CB2 receptor allosteric modulation, cannabidiol pharmacology, synthetic cannabinoid SAR. Neglected & Infectious Diseases : malaria, hepatitis B, tuberculosis, SARS-CoV-2, urinary-tract infections. Drug Delivery & Formulation : nanoparticle coatings, pharmacokinetic optimization, bioavailability enhancement. Publications Trend: Over 2023–2025 his 15 most recent papers reveal intense activity at the intersection of AI-driven discovery , glycobiology , and cannabinoid pharmacology , with emphasis on anti-infective, anticancer, and CNS-active agents. Key contributions include first-in-class MraY inhibitors for TB, cannabinoid-inspired antivirals against SARS-CoV-2, and glycomimetic antagonists of bacterial adhesins for UTI prevention. Scientific Awards & Honors: UM School of Pharmacy Faculty Service Award (2015–2016) UM School of Pharmacy Faculty Service Award (2010–2011) Editorial Boards: Molecules , AIMS Biophysics , Pharmaceutical Sciences , Perspectives in Medicinal Chemistry Repeated NIH, DoD, NSF, Wellcome Trust, and international grant-review panels (2010–present) Guest Editor for multiple special issues in Molecules and Frontiers journals Advising & Mentoring: As Associate Dean, Dr. Doerksen oversees University-wide graduate programs, chairs the Graduate Recruiting Fellowship and Scholarship Committee, and mentors students across disciplines. Faculty advisor for the UM chapters of the Christian Pharmacists Fellowship International (since 2005) and Taiwanese Student Association (2022–2025). He actively participates in PhD and MS thesis committees worldwide and has delivered NSF GRFP information sessions to support trainee funding. Laboratories & Teams: He directs research within the Computational Chemistry and Bioinformatics Research CORE (CCBRC) , fostering collaborative projects involving medicinal chemists, structural biologists, pharmacologists, and data scientists. The group leverages high-performance computing resources at the University of Mississippi to perform large-scale virtual screening, AI/ML model development, and integrative structural biology studies.
Sean C. Rife is Professor of Psychology at Murray State University, co-founder of scite.ai, and Head of Academic Relations at Research Solutions, Inc. His work focuses on moral/political psychology, metascience, and AI's role in research evaluation. PhD in Psychology (Kent State University, 2014) MA in Sociology (East Tennessee State University, 2009) MA in Experimental Psychology (East Tennessee State University, 2008) BS in Psychology (North Georgia College and State University, 2005) His research explores ideology-personality connections, social media's influence on moral expression, and AI-driven tools to quantify scientific progress. He advocates for open science practices and has led large-scale replication efforts across multiple countries. Key projects include scite (AI-enhanced citation analysis), statcheck implementation, and TAPAS text similarity analysis. He has developed tools like psyLex and liwcR for linguistic analysis and research evaluation. Recent publications focus on terror-management theory validation, political orientation's impact on pandemic perception, and AI applications for citation classification. His work intersects psychology, data science, and academic technology development. Contact: Office: 209 Wells Hall, Murray State University Email: srife1@murraystate.edu, srife@researchsolutions.com Phone: 270-809-4404
Tahsin Reza is an Assistant Professor at the University of Waterloo, affiliated with the Faculty as a full-time member. His research focuses on high-performance computing, distributed systems, and large-scale graph processing. His work emphasizes algorithmic optimization for irregular parallelism, distributed approximation algorithms, and efficient handling of massive graphs with billions of edges. Key research interests include developing frameworks like YGM for HPC, HyGN for NUMA architectures, and tools such as PruneJuice for graph pruning. His contributions span graph algorithms for Steiner trees, temporal graphs, and metadata-driven pattern matching. He has extensively explored GPU and hybrid CPU-GPU systems to accelerate graph processing tasks in domains like InSAR data analysis and VANET tracking. No scientific awards or grants are explicitly mentioned in the provided materials. His work has been published in top venues, consistently addressing challenges in scalability, efficiency, and real-world applicability of graph-based solutions.
Lukas Grasmann is a Researcher at the Faculty of Informatics, Vienna University of Technology (TU Wien), based in the Databases and Artificial Intelligence research group (Institute E192-02, Room HA0302). His contact details include email lukas.grasmann@tuwien.ac.at and phone +43-1-58801-192216, with professional activities centered on cutting-edge database technologies and AI applications. His educational qualifications comprise: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) in Engineering Grasmann's research spans database systems and artificial intelligence with emphasis on big data analytics, distributed query processing, and skyline query optimization. His work focuses on integrating specialized query paradigms into Apache Spark SQL for efficient large-scale data analysis, particularly applied to perishable food supply chain optimization for waste reduction. This intersects computer science fundamentals with sustainability-driven practical implementations. His 2022-2023 publications reveal a concentrated research trajectory in enhancing Spark SQL's capabilities for skyline queries, addressing both theoretical optimization challenges and real-world deployment in distributed environments. The work demonstrates consistent innovation in bridging database theory with big data infrastructure, advancing scalable solutions for multi-criteria decision problems. Scientific recognition: No formal awards or fellowships documented in source materials Research funding and collaborations include: Lead researcher on FFG-funded project "AI-driven collaborative supply and demand matching platform for food waste reduction" (2022-2025) Contributor to HyperTrac project (2018-2022) focusing on traceability systems Active participant in KnowledgeGraph initiative (2020-2028) developing semantic knowledge networks He operates within TU Wien's Databases and Artificial Intelligence research ecosystem (Institute E192), collaborating with specialists like Pichler and Selzer on database scalability challenges. The group maintains strong industry connections through applied projects targeting supply chain intelligence and food waste analytics.
Santiago Badia is a Full Professor of Computational Science and Engineering at Universitat Politècnica de Catalunya (UPC), holding an adjoint researcher position at the International Center for Numerical Methods in Engineering (CIMNE). He leads the Large Scale Scientific Computing (LSSC) group at CIMNE, focusing on finite element methods, numerical analysis, and high-performance computing. His research emphasizes fluid dynamics, multiphysics problems, and scalable solvers for large-scale systems. Previously, he worked at Politecnico di Milano and Sandia National Labs. He developed the FEMPAR software framework, a parallel finite element tool for PDE simulations, achieving landmark scalability (e.g., 60 billion unknowns on 458,672 cores). FEMPAR is recognized in the High-Q Club of European codes. His expertise includes discontinuous Galerkin methods, XFEM, and domain decomposition preconditioners. Research interests span metal additive manufacturing, superconductor devices, and nuclear engineering applications. Awards include FEMPAR's High-Q Club inclusion. He advises PhD and MSc students (e.g., Jesus Bonilla, Eric Neiva, Marc Olm) and has open positions in postdoc/PhD levels. His team includes researchers like Javier Principe and Alberto Martín. Ongoing projects involve advancing parallel algorithms, multiphysics simulations, and software scalability for exascale computing.
Sourav Sahoo is a Research Assistant at the Department of Chemistry and Bioscience, Faculty of Engineering and Science, Aalborg University. His research focuses on materials science and tribology , particularly graphene-based coatings for enhancing glass scratch resistance. Sahoo holds a Ph.D. in Materials Science and Engineering (2019-2024) and a B.Tech in Ceramic Engineering from National Institute of Technology Rourkela (2015-2019). Education: Ph.D. in Materials Science and Engineering (Aalborg University, 2024) B.Tech in Ceramic Engineering (NIT Rourkela, 2019) Research interests span scratch resistance , graphene oxide , surface science , disordered materials , and tribological properties . His work combines experiments and simulations to study 2D material-protected silica glass. Recent publications (2021-2025) include studies on atomistic structural evolution , superlubricity , and oxide deposition techniques . Scientific awards include: Best Poster Award - 2nd place (2023) Research Excellence Travel Award (2023) "Significant Contribution to the Department" Award (2023) Prime Minister's Research Fellowship (2021) Institute Silver Medal (2019) Sahoo's research has gained significant media attention, including coverage by 9 news outlets and 18 Mendeley readers. His collaborations focus on graphene oxide , shear flow , and borosilicate glass studies.
Alexander Hoyle is a Researcher at the ETH Zürich AI Center, concurrently contributing to natural language processing/machine learning and social science groups. He holds a PhD in Computer Science from the University of Maryland (advised by Philip Resnik) and a Master's in Computational Statistics from University College London (advised by Sebastian Riedel and Jeff Mitchell). His research focuses on computational social science, emphasizing methods for latent construct identification (e.g., topic models, ideal point models) and evaluation frameworks grounded in validity. Key areas include bias/fairness in AI, political science applications, and mental health constructs like suicidality. He pioneered frameworks like PairScale (attitude measurement via pairwise comparisons) and TopicGPT (prompt-based topic modeling). Education: PhD in Computer Science, University of Maryland (2020-2023) MS in Computational Statistics & Machine Learning, University College London (2018) Bachelor's degree (pre-PhD details omitted) Research Interests: Combining NLP with social science needs, particularly in evaluation rigor and interpretability. Active in interdisciplinary work between NLP and computational social science (e.g., measuring attitude evolution on Reddit, improving topic model validity). Advocates for human-in-the-loop approaches to address LLM limitations in tasks like document clustering and sentiment analysis. Grants & Projects: Contributed to a landmark $2.2B DOJ settlement on NYC public housing via econometric modeling at The Brattle Group. Active in graduate labor advocacy (Maryland state legislature testimony) and mentorship (Científico Latino's mentorship program). Labs & Teams: Leads initiatives at the ETH Zürich AI Center, collaborating with groups like Microsoft Research (FATE) and AI2's AllenNLP. Involved in multi-university projects (e.g., University of Maryland's Computational Linguistics lab).
Hjalmar Alexander Bang Carlsen is an Associate Professor at the Copenhagen Center for Social Data Science (SODAS), Faculty of Social Sciences, University of Copenhagen. He specializes in mixed digital methods and is deeply engaged in research and teaching related to digital data analysis, particularly in the context of political and civic participation on social media. He is a key contributor to the Social Data Science master's degree program. University: University of Copenhagen School: Faculty of Social Sciences Department: Copenhagen Center for Social Data Science (SODAS) Position: Associate Professor in Mixed Digital Methods Email: hc@soc.ku.dk ORCID: https://orcid.org/0000-0002-2638-0932 His research centers on mixed methods strategies for digital data, with a substantive focus on civic and political engagement via social media. Key areas include informal volunteering during crises, gender inequality in online political participation, and the use of large language models (LLMs) for qualitative interviewing. He leads three major projects: SoMeVolunteer (on crisis volunteering), public participation on Facebook, and AInterviewer (an LLM-based interviewing tool). The recent publications reflect a strong trend in digital sociology, crisis response, and methodological innovation. His work combines large-scale social media data with surveys, interviews, and textual analysis, emphasizing ethical and epistemological considerations in computational social science. Topics span from refugee solidarity and pandemic volunteering to framing contests among climate NGOs and gender disparities in digital political engagement. While no formal scientific awards are listed in the provided text, Carlsen is actively funded by the Velux Foundation and UCPH Data+, and his work is widely disseminated through media and academic outlets. He collaborates closely with researchers like Jonas Toubøl and Snorre Ralund, and his projects often involve interdisciplinary teams. He has secured seed funding for innovative methodological development, indicating strong grant-writing capacity. He is involved in public engagement, with multiple media appearances discussing Danish civic response during the pandemic and refugee crises. His research outputs include journal articles, book chapters, and a co-authored textbook on mixed methods. He also participates in workshops and public lectures, contributing to both academic and public discourse on digital society. Carlsen is affiliated with SODAS and the Social Sciences Datalab, indicating active involvement in data-intensive research infrastructure. His work on AInterviewer suggests leadership in emerging AI-driven qualitative methods, positioning him at the forefront of digital social research innovation.
Keir K. Rogers is an STFC Ernest Rutherford Fellow at Imperial College London (starting 2025). Previously, he held the Dunlap Fellowship at the University of Toronto and a postdoctoral position at the Oskar Klein Centre for Cosmoparticle Physics, Stockholm University. He earned his PhD from University College London and a Master’s from the University of Oxford. His research focuses on cosmological model physics, leveraging statistical and machine learning techniques to analyze cosmological data. Key areas include testing ultra-light axion models of dark matter, analyzing cosmic microwave background (CMB) and large-scale structure data, and developing computational frameworks like cosmological emulators and Bayesian optimization. He contributed to bounds on dark matter models using Lyman-alpha forest data and pioneered methods for CMB component separation using wavelets. Scientific awards include the STFC Ernest Rutherford Fellowship and Dunlap Fellowship. His work involves collaborations on next-generation surveys like the Dark Energy Spectroscopic Instrument (DESI) and CMB-S4. He leads efforts to improve parameter inference through simulation-based methods and has developed tools for mitigating high-density absorber contamination in Lyman-alpha forest analyses.
Carmel Majidi is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads the Soft Machines Lab, where his research focuses on soft robotics, wearable computing, and advanced materials that mimic biological systems. His work enables robots and machines to safely interact with humans through soft, deformable technologies. Education: Ph.D. in EECS, University of California, Berkeley (2007) B.S. in Civil and Environmental Engineering, Cornell University (2001) His research interests span soft robotics, stretchable electronics, liquid metal materials, and bio-inspired engineering. He develops soft electromechanical materials for applications in haptics, medical devices, and human-machine interfaces. His group pioneers fabrication methods for soft composites and microfluidic systems that function as artificial skin, nervous tissue, and muscle. The recent trend in his publications highlights a strong focus on soft actuators, self-healing materials, liquid metal composites, and haptic interfaces for virtual and augmented reality. His work integrates materials science, robotics, and advanced manufacturing to enable scalable, robust, and practical soft systems. Scientific Awards: 2023 Inno Fire Award for Trailblazing Innovators, Pittsburgh Business Times Majidi leads a major research thrust in a new NSF Engineering Research Center (ERC) dedicated to improving robot dexterity, funded up to $52 million. He has been instrumental in developing soft robotic systems inspired by prehistoric organisms, such as the pleurocystitid, and in creating healthcare devices powered by body heat using liquid metal technologies. His research has been featured in Popular Science , Communications of the ACM , and Ars Technica . He is actively involved in advancing scalable manufacturing of soft electronics and enabling robust integration between soft materials and conventional microelectronics. His lab collaborates across disciplines to push the boundaries of physical AI and wearable robotics.
Samuel Thibault is a Professor at University of Bordeaux, affiliated with Laboratoire Bordelais de Recherche en Informatique (LaBRI) and Inria Bordeaux -- Sud-Ouest. He is a member of the SATANAS team at LaBRI (theme: High Performance Runtime Systems for Parallel Architectures) and the STORM research team at Inria Bordeaux (previously RunTime). His work bridges academic research and practical implementation of high-performance computing systems. Thibault's research focuses on task-based runtime systems, particularly StarPU, for heterogeneous and parallel computing architectures. His work addresses critical challenges in scheduling algorithms, memory management under constraints, data locality optimization, and performance modeling for complex NUMA architectures. He has made significant contributions to the field of parallel computing through the development and analysis of runtime systems that efficiently manage tasks across diverse hardware resources including CPUs, GPUs, and other accelerators. His research has practical applications in scientific computing, deep learning inference, and large-scale simulations requiring extreme computing power. His recent publication trends show a strong emphasis on optimizing task-based runtime systems for heterogeneous architectures with particular attention to memory constraints and data locality. There's a clear progression toward applying these techniques to deep learning inference workloads, as evidenced by his StarONNX project. His work consistently addresses the challenge of balancing throughput and latency in complex computing environments, with increasing focus on recursive task graphs and dynamic adaptation strategies. Thibault is actively involved in European research initiatives including TEXTAROSSA (focusing on exascale technologies) and EXA2PRO (high development productivity on heterogeneous systems). His work has been published consistently in top-tier conferences and journals in parallel and distributed computing. As part of the STORM team at Inria Bordeaux, Thibault contributes to advancing the state of the art in runtime systems for high-performance computing. His team's work on StarPU has become a reference implementation in the field, enabling researchers and practitioners to develop applications that can efficiently utilize heterogeneous computing resources without needing to manage the complexity of different hardware architectures directly.
Dr. Thomas Kalbacher serves as Deputy Head of Department and Workgroup Leader of Hydroinformatics at the Department of Environmental Informatics, Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. With extensive experience in environmental modeling and software development, he leads critical research initiatives focused on groundwater systems and environmental informatics. His research spans Earth Science - Hydrogeology and Computer Science - Simulator & Workflows, with particular emphasis on the development of OpenGeoSys (OGS) software and related analysis tools. Kalbacher's work addresses sustainable use and protection of natural groundwater systems, examining their quantitative and qualitative impact on surface waters. He investigates how digital twins and near real-world models can help tackle water management challenges in the face of climate change, with a focus on creating robust future projections to preserve landscape multifunctionality. Analysis of his recent publications reveals strong trends in hydrogeological modeling, spectral analysis of groundwater level fluctuations, and development of digital tools for environmental monitoring. His work increasingly focuses on large-scale modeling of groundwater systems, particularly in the Danube catchment area, while also advancing data infrastructure through projects like Digital Earth. A significant portion of his recent research examines methodological approaches to hydrologic modeling, particularly comparative assessments of Richards equation versus infiltration capacity approaches. As leader of the Hydroinformatics workgroup, Kalbacher plays a pivotal role in the OpenGeoSys open-source project, which has become a significant platform for computational hydrology and environmental modeling. His team develops software solutions for regional groundwater quantity modeling (LandTrans) in cooperation with the Research Software Engineering and Visualization group. The Hydroinformatics group under his leadership contributes to advancing environmental modeling capabilities through robust software development practices and integration of high-resolution observation data from modern monitoring stations and satellite observations.
W. Kendal Bushe is an Associate Professor at the Faculty of Applied Science , University of British Columbia , within the Department of Mechanical Engineering . His research focuses on turbulent combustion, numerical simulation, and computational fluid dynamics (CFD) with applications to internal combustion engines and thermal power generation. Developed the Conditional Source-term Estimation (CSE) method for turbulent combustion modeling. Conducted experimental studies on methane/natural gas ignition in engines. Expertise in Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) for reacting flows. Current projects involve autoignition modeling in HCCI engines using Stochastic Particle Models . Teaching Activities : Courses on heat transfer, energy conversion systems, computational fluid dynamics, combustion, and turbulent shear flows. Scientific Recognition : Fellow of the Combustion Institute.
