Crowd Modeling and Simulation Professors and Supervisors

Johannes Schönebergمشاهده پروفایل

استادیار

University of California, San DiegoUnited States

Johannes Schöneberg serves as Assistant Professor of Pharmacology at the University of California San Diego's School of Medicine, holding joint appointments in Chemistry & Biochemistry. His research integrates cutting-edge microscopy, computational biology, and stem cell technologies to quantitatively analyze cellular behavior in native tissue environments. His laboratory focuses on mitochondrial dynamics and their role in neurological disorders including Parkinson's Disease and epilepsy. Key methodologies include stem cell-derived organoids, 4D adaptive optics lattice light-sheet microscopy, high-performance computing, and machine learning applications. Research spans computational modeling of cellular processes through tools like ReaDDy software to advanced image analysis techniques for tracking subcellular structures. The group's publication record demonstrates expertise across biophysics, computational biology, and neuropharmacology, with recent work emphasizing mitochondrial network tracking and 4D cellular imaging. Though newly established at UCSD, the lab shows strong interdisciplinary connections through collaborations with microscopy pioneers like Eric Betzig. No scientific awards are currently listed in available sources. The laboratory maintains active research in neurological disease mechanisms with emphasis on mitochondrial dysfunction pathways. As a newly formed research group, current activities focus on establishing organoid models and advanced imaging pipelines. The lab leverages UCSD's strong infrastructure in computational biology and neuroscience research, with potential for future grant development in mitochondrial disease mechanisms and imaging technology.

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Schekotihin Konstantinمشاهده پروفایل

دانشیار

University of KlagenfurtAustria

Konstantin Schekotihin is an Associate Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria University of Klagenfurt. His research focuses on artificial intelligence, machine learning, and semantic technologies with applications in industrial systems and semiconductor manufacturing. Reinforcement learning for industrial scheduling Answer Set Programming (ASP) and stream reasoning Failure analysis automation and ontology engineering Neuro-symbolic AI integration Knowledge-based systems in manufacturing Recent publications emphasize AI-driven optimization in semiconductor production, decomposition strategies for scheduling problems, and multi-agent systems for workflow management. His work combines symbolic reasoning with machine learning to address complex industrial challenges. Contact: Konstantin.Schekotihin@aau.at

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Akash Vaniمشاهده پروفایل

پژوهشگر

Autonomous University of MadridSpain

Akash Vani is a PhD Researcher at the Max Planck Institute for Astrophysics (MPA) in Garching, Germany, affiliated with the Ludwig Maximilian University (LMU) Munich through the International Max Planck Research School (IMPRS). His research bridges observational astrophysics and computational modeling. Education: MSc in Physics (2022), Heidelberg University BSc in Physics (2019), Savitribai Phule Pune University Research Interests: Akash investigates galaxy evolution from z=0 to z=10 using semi-analytical models (L-Galaxies) and observational data (Gaia, JWST). He also explores stellar populations, white dwarfs, and quantum computing applications in astronomy. Key Projects: His work includes Debugging Galaxy Evolution Models , constructing the Fifth Catalogue of Nearby Stars (CNS5) , and Deep Learning for Galaxy Morphology Classification . He contributed to photometric calibration of the UVIT telescope and studies on quantum key distribution protocols. Scientific Awards: Summer Fellow, IISER Kolkata (2019) Publications: He has published in leading journals like Monthly Notices of the Royal Astronomical Society (MNRAS) and Astronomy & Astrophysics (A&A), focusing on galaxy scaling relations, white dwarf identification, and Milky Way modeling. His work highlights discrepancies in quenching processes in simulations and improvements in stellar census completeness. Labs & Teams: Akash collaborates with the Astronomisches Rechen-Institut (ZAH, Heidelberg), Ludwig Maximilian University, and international teams leveraging Gaia data and quantum computing resources.

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Esteban Garcia Mirallesمشاهده پروفایل

پژوهشگر

University of CopenhagenDenmark

Esteban Garcia Miralles is an Economist at the Bank of Spain's Research Department and External Researcher at the University of Copenhagen's Department of Economics. His work bridges central banking research with academic economics, focusing on critical policy issues affecting European economies. His educational background includes a PhD in Economics from the University of Copenhagen, followed by a Postdoctoral Fellowship at the Institute for Fiscal Studies. This strong academic foundation supports his research in public finance and labor economics. Garcia Miralles' research interests center on the intersection of public policy and individual behavior, particularly regarding retirement decisions, taxation systems, household financial behavior, and health economics. His work often employs rigorous microeconometric methods to analyze how policy changes affect economic decisions across different demographic groups, with special attention to pension systems, inheritance taxation, and fiscal responses to economic shocks. His publication record shows a consistent focus on European fiscal policy challenges, with recent work examining post-pandemic inflation effects, energy crisis responses, and retirement behavior. The articles demonstrate methodological sophistication combining theoretical modeling with empirical analysis of large administrative datasets, particularly from Spanish tax records. Winner of the Alexandre Pedrós Prize for research on inheritance taxation Finalist for the AldE Young Researchers' Prize Regular contributor to high-impact economics journals including American Economic Journal: Economic Policy and Journal of Public Economics As a researcher at the Bank of Spain, Garcia Miralles contributes to the institution's analytical work on fiscal policy, including the development of microsimulation tools for policy evaluation. His collaborations span multiple European institutions, reflecting the transnational nature of contemporary fiscal challenges. His work frequently appears in policy discussions, with features in major Spanish media outlets including El País and El Mundo, demonstrating its relevance to current economic debates.

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Jiali Gaoمشاهده پروفایل

استاد

University of Minnesota Twin CitiesUnited States

Professor Jiali Gao is a faculty member in the Department of Chemistry at the University of Minnesota, where he holds the position of Professor. His research program focuses on developing computational methods to study complex biological systems through the integration of quantum mechanics and molecular mechanics (QM/MM) approaches. Professor Gao's research interests span multiple interconnected areas: Development of novel combined QM/MM methods for studying chemical and biological reactions Understanding the physical origins of enzyme catalysis Modeling diffusion and interactions of macromolecular particles in cellular environments Application of computational methods to energy-related problems including photosynthesis and combustion His recent publications demonstrate a consistent focus on advancing computational methodologies while applying them to significant biological and chemical problems. Professor Gao's work bridges theoretical development with practical applications across multiple domains including protein dynamics, spectroscopy, and reaction mechanisms. Notable contributions include the development of the CARNOT simulation package for reactive systems and significant advances in multistate density functional theory. Professor Gao maintains an active research program with numerous publications each year in high-impact journals such as Nature Communications, Journal of Chemical Physics, and Journal of Chemical Theory and Computation. His interdisciplinary approach, connecting computational and experimental work, strengthens the impact of his research by providing molecular-level interpretations of experimental observations.

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Phil Birchمشاهده پروفایل

دانشیار

University of SussexUnited Kingdom

Computer Vision
Optical Physics
Artificial Intelligence and Image Processing
+۵ مورد دیگر

Dr Phil Birch serves as Associate Professor in the Department of Engineering and Design within the School of Engineering and Informatics at the University of Sussex. He holds dual leadership roles as Head of Research for his department and Impact Lead for REF Unit of Assessment 12. His academic foundation includes a BSc and PhD from Durham University (awarded 1999), along with professional certifications as Chartered Physicist (CPhys) and Chartered Scientist (CSci). Dr Birch's research spans three interconnected domains: Computer Vision & Deep Learning: Specializing in object tracking (drones, humans), medical image analysis, and low-light enhancement Optical Physics: Focusing on liquid crystal devices, computer-generated holograms, and optical metrology Applied AI Systems: Developing practical implementations for UAV navigation, medical diagnostics, and security systems His recent publications (2024-2025) demonstrate strong activity in transformer-based tracking, diffusion model augmentation for medical imaging, and drone-based agricultural monitoring, with 15+ papers in high-impact venues including IEEE Transactions and SPIE conferences. Current funding includes an Innovate UK grant (2020-2024) for multi-modal security systems combining camera and sensor data. He leads the Industrial Informatics and Signal Processing research group, maintaining active industry collaborations while supervising PhD projects in optical AI systems. His lab resources include optical instrumentation, UAV testbeds, and access to high-performance computing for deep learning development.

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Erik Schaffernichtمشاهده پروفایل

مدرس ارشد

Örebro UniversitySweden

Erik Schaffernicht serves as a Senior Lecturer in the Department of Natural Sciences and Technology at Örebro University's School of Science and Technology. His research is primarily conducted through the Center for Applied Autonomous Sensor Systems (AASS) where he leads work in the Adaptive and Interpretable Learning Systems and Robot Navigation and Perception research groups. Dr. Schaffernicht's research spans multiple areas in robotics and artificial intelligence, with particular expertise in sensor systems, behavior trees, and gas distribution mapping. His work bridges theoretical computer science with practical applications in autonomous systems, environmental monitoring, and human-robot interaction. His research often involves developing novel algorithms for robot perception, control, and decision-making in complex environments. His recent publications demonstrate a strong focus on behavior trees for robot control, gas distribution mapping techniques, and applications of machine learning in robotics. The research shows increasing sophistication in using deep learning approaches for environmental sensing and robot navigation, with applications ranging from industrial safety to healthcare monitoring. Dr. Schaffernicht maintains an active research agenda with numerous publications in top robotics and AI venues, including IEEE Robotics and Automation Letters, Robotics and Autonomous Systems, and various IEEE conference proceedings. His work shows consistent collaboration with researchers across Europe, particularly with the AASS research center at Örebro University. His research projects include both ongoing work on automatic cognitive screening tests using eye-tracking technology and completed projects such as AIR (Action and Intention Recognition), RAISE (Robotic System for Air Quality Assessment), and SmokeBot (Mobile Robots for Disaster Site Inspection).

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Nicolas Verstaevelمشاهده پروفایل

دانشیار

Toulouse Capitole UniversityFrance

Nicolas Verstaevel is an Associate Professor at the University of Toulouse Capitole and researcher at the Toulouse Institute of Computer Science Research (IRIT), where he contributes to the SMAC (Cooperative Multi-Agent Systems) team. He holds a PhD in Artificial Intelligence and an HDR (Habilitation à Diriger des Recherches). His career includes international experience as an Associate Research Fellow at the University of Wollongong, Australia, and industrial R&D roles at Capgemini. His research explores complex systems through: Agent-based modeling and simulation frameworks Machine learning applications in urban environments IoT-enabled smart city infrastructure Pedestrian dynamics and crowd behavior analysis Real-time traffic monitoring systems He leads projects funded by ANR and EU programs, focusing on high-density crowd simulations and sustainable urban mobility. His publications demonstrate strong emphasis on multi-agent systems, sensor data fusion, and scalable simulation architectures. Key lab affiliations: Cooperative Multi-Agent Systems (SMAC) at IRIT: https://www.irit.fr/-Equipe-SMAC-

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Ariane Nunes Alvesمشاهده پروفایل

استادیار

Leibniz Institute for Crystal GrowthGermany

Dr. Ariane Nunes Alves is a Junior Group Leader at Technische Universität Berlin's Institute of Chemistry, leading the Theoretical Structural Biology group . Her research bridges computational chemistry and biophysics, specializing in protein-ligand binding kinetics, enzyme catalysis in crowded cellular environments, and machine learning applications in drug design. Research Focus: Her lab develops methods to predict binding/unbinding pathways using molecular dynamics (e.g., tauRAMD) and AI models. Key areas include: Kinetics of protein-ligand interactions Effects of macromolecular crowding on enzymes Structure-based drug design with QSKR models Machine learning for hydrogenase pathway identification Awards & Fellowships: HITS Award for Women in Science (2020) Capes-Humboldt Research Fellowship (2019) CellNetworks Postdoctoral Program (2018) Fapesp Ph.D. Fellowship (2014) Teaching: She instructs graduate courses on applied machine learning in chemistry and computational drug design at TU Berlin. Her lab investigates substrate dissociation in hydrogenases, cellular environmental impacts on catalysis, and ML-based kinetic predictions.

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Fabrice Lamarcheمشاهده پروفایل

دانشیار

École Normale Supérieure de RennesFrance

Fabrice Lamarche serves as an Associate Professor at Université de Rennes 1 within the ESIR School of Engineering, while maintaining dual affiliation with the MimeTIC research team at IRISA / INRIA Rennes. His academic career spans uninterrupted service since 2004, evolving from Assistant Professor roles at IFSIC (2003-2009) to current positions at ESIR. As co-founder of Golaem (2009), he bridges academic research with commercial application in crowd simulation technology. His institutional journey includes sequential membership in SIAMES (2004-2006), Bunraku (2007-2011), and MimeTIC (2011-present) research teams at INRIA. Lamarche earned his PhD in Computer Sciences from Université de Rennes 1 in 2003 with thesis work on virtual human autonomy. His educational foundation includes a Master of Computer Sciences specializing in Computer Graphics and AI (1999-2000), a Master of Engineering from INSA de Rennes (1997-2000), and a Technical degree from IUT de Limoges (1995-1997). His research program centers on virtual human behavior modeling with emphasis on decision-making systems, path planning under environmental constraints, and crowd simulation architectures. Key innovations include TopoPlan for human-scale navigation and frameworks integrating high-level task scheduling with low-level motion planning. This work addresses fundamental challenges in creating autonomous virtual characters capable of navigating complex 3D environments while exhibiting realistic behaviors, with applications spanning virtual reality, gaming, and simulation-based training systems. Publication analysis reveals consistent output from 2001-2014, evolving from foundational behavioral animation (2001-2004) to sophisticated crowd simulation systems (2013-2014). A notable trajectory shows increasing integration of cognitive modeling with motion planning, alongside exploration of Brain-Computer Interfaces for virtual navigation. Recent work demonstrates particular strength in semantic decomposition of urban environments and time-space constrained task scheduling. His scientific contributions have earned significant recognition: Rennes city medal (2009) for research excellence Second prize at Deutsch Telekom Awards (FMX 2008) for TopoPlan/MKM integration As an active researcher and educator, Lamarche advises students through Université de Rennes 1 while leveraging INRIA resources and Golaem industry partnerships. His publication record indicates sustained grant funding, particularly through INRIA channels, with collaborative projects extending to neuroscience applications via Brain-Computer Interface research. The MimeTIC team affiliation provides infrastructure for multimodal interaction research in complex virtual environments. Lamarche's laboratory work through MimeTIC focuses on developing practical implementations of virtual human autonomy systems. His research group maintains strong industry connections via Golaem, which commercializes crowd simulation technology. Current efforts emphasize semantic understanding of virtual urban spaces and robust path planning under dynamic constraints, building on foundational work in topological navigation and behavioral decision systems.

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Wylie Strobergمشاهده پروفایل

استادیار

University of AlbertaCanada

Wylie Stroberg is an Assistant Professor in the Department of Mechanical Engineering within the Faculty of Engineering at the University of Alberta. He leads the Computational BioSystems Lab, where he conducts research at the interface between cell biology, physiology and engineering using a range of computational and theoretical techniques. His research interests span multiple areas including: Biomechanics and Biomedical Engineering Biomaterials Mechanics and Materials Composites and Polymers Nano and Micro Materials Computational Mechanics Systems Biology of Proteostasis and the Unfolded Protein Response Reactions in Crowded Cellular Compartments Wetting Phenomena of Nanoscale Structures Dr. Stroberg's research focuses on developing new quantitative techniques for studying complex biological systems across disparate length and time scales. His work has significant implications for understanding cellular physiology, protein homeostasis, and developing new therapeutic approaches for age-associated and protein folding diseases. He employs advanced computational methods including multiscale modeling, machine learning, and theoretical analysis to investigate biological phenomena from the molecular to cellular level. His research group has published extensively in mathematical biology and computational mechanics, with a consistent focus on enzyme kinetics, cellular stress responses, and computational modeling of biological processes. The publications demonstrate a strong interdisciplinary approach combining techniques from mechanical engineering, computational science, and molecular biology. Dr. Stroberg has received recognition for his work in mathematical biology and systems biology, with publications in journals such as the Journal of Theoretical Biology, Biophysical Journal, and Mathematical Biosciences. He currently advises several graduate students working on diverse projects including multiscale modeling of enzymatic nanosensors, protein-based modulation of endoplasmic reticulum shape, development of machine learning-based coarse grained models for high-entropy alloys, and simulation of high-entropy alloys for thermal-spray deposition. His lab has trained undergraduate and master's students who have gone on to further academic pursuits. The Computational BioSystems Lab maintains active collaborations with other researchers at the University of Alberta and beyond, working at the intersection of engineering, biology, and computational science.

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Masud Rahmanمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Software Engineering
Artificial Intelligence for Software Engineering (AI4SE)
Software Maintenance and Evolution
+۱۱ مورد دیگر

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

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Takayuki Itoمشاهده پروفایل

استاد

Ave Maria UniversityUnited States

Dr. Takayuki Ito is Professor at Nagoya Institute of Technology in the Computer Science & Engineering school. He earned his Doctor of Engineering from Nagoya Institute of Technology in 2000. His academic journey includes positions as a JSPS research fellow, associate professor at JAIST, and visiting scholar at prestigious institutions including USC/ISI, Harvard University, and MIT (visited twice). He has served as a board member of IFAAMAS (International Foundation for Autonomous Agents and Multiagent Systems). Dr. Ito's research primarily focuses on multi-agent systems, automated negotiation, argumentation frameworks, and AI-mediated discussion platforms. His work spans theoretical foundations of argumentation semantics to practical applications in sustainable development, urban planning, and online citizen engagement. He has developed the D-Agree platform for facilitating large-scale online discussions, with notable implementations in Afghanistan for municipal policy-making and SDG implementation. His publication record demonstrates significant contributions to understanding how AI can mediate human discussions, with experiments involving thousands of participants in countries like Afghanistan. His research shows how argumentative agents can improve responsiveness in discussions while also potentially polarizing debates by reinforcing initial stances. His work bridges theoretical computer science with practical social applications, particularly in contexts with challenging participation constraints. Board member of IFAAMAS Developer of D-Agree discussion support system Conducted large-scale experiments in Afghanistan with over 1,000 participants Expert in multi-agent negotiation protocols Dr. Ito's research has substantial implications for democratic processes, particularly in contexts where traditional face-to-face meetings are problematic due to security concerns, cultural restrictions, or pandemic conditions. His work demonstrates how AI mediation can overcome barriers to equal participation, especially for women and religious minorities in restrictive societies.

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