Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.
Mirco Musolesi is a Full Professor of Computer Science at both University College London (UCL) and the University of Bologna. He leads the Machine Intelligence Lab at UCL, part of the UCL Centre for Artificial Intelligence. His research focuses on Machine Learning, Generative AI, and computational models of human behavior, with applications in ubiquitous systems and societal impacts of AI. Education: PhD in Computer Science from UCL (2007) and Laurea in Electronic Engineering from the University of Bologna (2002). Previous roles include positions at the University of Birmingham, Dartmouth College, and the Alan Turing Institute. Research spans multi-agent systems, reinforcement learning, and AI ethics. Notable awards include ACM UbiComp 10-Year Impact Award (2020/2024) and the NetExplorateur/UNESCO Top 100 Innovations (2011). His work on EmotionSense and CenceMe applications has been recognized with Test-of-Time awards. Recent publications (2024-2025) address moral alignment in AI agents, multi-agent environmental policy simulations, and creativity in LLMs. His labs explore AI-driven solutions for urban systems and ethical decision-making frameworks.
Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.
Bart Somers is an Associate Professor at Eindhoven University of Technology , affiliated with the Department of Mechanical Engineering . His primary affiliations include the Power & Flow Group and his own research group, Group Somers , alongside cross-cutting roles in EAISI (Eindhoven Artificial Intelligence Systems Institute) and EIRES (Eindhoven Research on Innovation and Sustainability in Energy Systems). He focuses on advancing combustion science , sustainable fuels , and engine efficiency , leveraging computational fluid dynamics (CFD) and experimental methods. His research interests span alternative fuels (hydrogen, bio-oils, biofuels), high-pressure spray combustion , and low-emission engine design . He investigates combustion optimization through CFD tools like large-eddy simulation (LES) and flamelet-generated manifolds (FGM), emphasizing fuel stratification , ignition dynamics , and emission control . His work bridges experimental diagnostics (e.g., spray visualization, OH* chemiluminescence) and numerical modeling. Academically, he teaches courses such as Thermodynamics , Clean Engines and Future Fuels , and Sustainable Vehicles , integrating practical projects into curricula. His educational activities emphasize interdisciplinary sustainability and innovation, including honors programs focused on professional development. Recent publications highlight his contributions to hydrogen injection strategies, biofuel applications in genset engines, and optimization of diesel-biofuel blends. His work aligns with global sustainability goals, addressing energy transition challenges through advanced combustion technologies.
Jerelle A. Joseph is an Assistant Professor at Princeton University , affiliated with the Department of Chemical and Biological Engineering and the Omenn-Darling Bioengineering Institute . They also hold associated faculty roles in the Department of Chemistry , Andlinger Center for Energy and the Environment , Princeton Institute for Computational Science and Engineering , and the Biophysics Graduate Program . Research Interests : The Joseph Group investigates the physicochemical principles governing biomolecular condensate formation, dissolution, and misregulation . Their work focuses on phase separation mechanisms , computational modeling of protein-RNA interactions , and engineering condensates for biomedical and sustainability applications , including therapeutic targeting of neurodegenerative diseases and design of synthetic microreactors . Scientific Awards : NIGMS MIRA (R35) Award (2024) Biophysical Society Award Lecture (2024) Chan Zuckerberg Initiative Investigator (2023) Postdoctoral Award, Biophysical Society IDP Subgroup (2022) Rising Star in Soft and Biological Matter (University of Chicago, 2020) Advising : Dr. Joseph advises graduate students including Ananya Chakravarti , Dominic Curtis , and Pablo Garcia . The group develops chemically-specific coarse-grained models using molecular dynamics , Monte Carlo sampling , and machine learning to study condensate microstructure , aging dynamics , and surface electrostatics .
James Malazita is Associate Professor of Science & Technology Studies and Associate Director of the Games and Simulation Arts and Sciences (GSAS) program at Rensselaer Polytechnic Institute (RPI). He is affiliated with the Department of Science and Technology Studies in the School of Humanities, Arts, and Social Sciences, and contributes to the GEM (Games and Experiential Media) research group. Education: Ph.D. in Communication, Culture, and Media, Drexel University (2014) BS/MS in Digital Media, Drexel University (2009) Malazita's research lies at the intersection of game studies, platform studies, feminist technoscience, and social justice in computing. He critically investigates how game engines like Unreal shape technological and cultural infrastructures, particularly through the lenses of gender, race, and power. His forthcoming book, Enacting Platforms: Feminist Technoscience and the Unreal Engine (MIT Press, July 2024), offers a groundbreaking feminist and critical analysis of one of the most influential game development platforms. His work also extends to digital humanities, critical design, and the politics of computer science education. His recent publications reveal a strong thematic focus on the co-production of technology and society, with recurring interests in epistemic cultures, disciplinary formation, localization, and critical pedagogy. These works span journals in game studies, design, digital humanities, and STS, reflecting an interdisciplinary approach that bridges technical systems and humanistic critique. Scientific Support and Affiliations: Supported by NEH Office of Digital Humanities Supported by NEH Division of Educational Programs Supported by Popular Culture Association Supported by New Jersey Historical Commission Supported by Red Hat Inc. Supported by Rensselaer’s Teaching and Learning Collaboratory Member of GEM (Games and Experiential Media) ORCID: 0000-0003-3206-3682 Malazita advises graduate students in STS and Games, with former advisees placed at Yale, NYU, and UT Austin. He teaches courses in game studies, human-centered design, interpretive methods, and critical technology studies. His current course includes STSO 2610: DSIS Studio II (Spring 2024). His research raises fundamental questions about the material and cultural limits of platforms, the analyst-platform relationship, and the future of the web as shaped by game technologies.
Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.
Michael Knap is an Associate Professor of Collective Quantum Dynamics at the Technical University of Munich (TUM), within the Department of Physics at the TUM School of Natural Sciences. His research group focuses on condensed matter theory, quantum many-body systems, and quantum simulation. Knap holds office in room 5101.01.037 at James-Franck-Str. 1, 85748 Garching b. München, and can be reached at michael.knap@ph.tum.de or +49 (89) 289 - 53777. Prof. Knap's research delves into the rich physics of quantum many-body systems, particularly exploring non-equilibrium dynamics and transport phenomena in ultracold quantum gases, interacting light-matter systems, and correlated quantum materials. His work spans multiple subfields including topological phases of matter, quantum simulation with trapped ions, fracton physics, and quantum computation. He develops novel numerical approaches based on quantum information theory and utilizes artificial intelligence and machine learning to tackle challenging problems in condensed matter physics. His group's research connects fundamental theoretical questions with experimental implementations in quantum simulators. The analysis of Prof. Knap's recent publications (2023-2025) reveals a strong focus on topological quantum matter, quantum simulation, and emergent phenomena in constrained quantum systems. His work frequently bridges condensed matter theory with quantum information science, as evidenced by publications on fracton hydrodynamics, higher-form symmetries, and quantum error correction. There's a clear progression toward increasingly complex quantum systems and connections to experimental implementations on quantum processors. His research shows significant interdisciplinary reach, connecting condensed matter physics with quantum computing and quantum information theory. ERC Consolidator Grant (2025) ERC Starting Grant (2019) Supervisory Award, TUM Department of Physics (2018) Promotio sub auspiciis Praesidentis rei publicae, Austria (2013) Prof. Knap has established a robust research program supported by prestigious European Research Council grants. His group actively collaborates with both theoretical and experimental groups worldwide, particularly in the quantum simulation community. He has supervised numerous students through Master's Seminars on Collective Quantum Dynamics covering topics like quantum simulation with trapped ions and theoretical quantum computation. His research has received significant attention, with several publications featured as Editors' suggestions and Research Highlights in leading journals. The Collective Quantum Dynamics group maintains strong connections with experimental quantum simulation efforts, particularly in the areas of ultracold atoms and trapped ion systems. Knap's theoretical work often provides frameworks for interpreting experimental results in quantum simulators, creating a productive feedback loop between theory and experiment. His group participates in collaborative research networks focused on advancing quantum simulation capabilities and understanding fundamental aspects of quantum many-body physics.
Prof. Dr. Mirko Meboldt serves as a Full Professor at ETH Zurich's Department of Mechanical and Process Engineering, where he holds dual leadership roles as Head of Lecturers' Conference and Deputy Head of the Institute of Machine Tools and Manufacturing. His office is located at Leonhardstrasse 21 in Zürich, Switzerland. Professor Meboldt's research spans multiple engineering domains with particular emphasis on: User-oriented product innovations New production technologies Mechanical engineering applications Biomedical device development CAD/PDM systems standardization Engineering education methodologies His recent publication portfolio reveals a distinctive interdisciplinary approach that bridges traditional mechanical engineering with cutting-edge medical applications. Key research trends include human-robot collaboration systems, intelligent medical devices for neurosurgery, augmented reality training platforms for medical procedures, advanced manufacturing processes, and AI-assisted healthcare communication analysis. This diverse research portfolio demonstrates his commitment to solving complex real-world engineering challenges through cross-disciplinary innovation. Professor Meboldt places significant emphasis on the educational impact of his work, explicitly stating that he 'regards the impact on the education of young engineers and its relevance for industry as a key motivation and benchmark for his research.' His industrial background at Hilti AG informs his practical approach to academic research, ensuring strong industry relevance across all his projects.
Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.
Dr. Chenming Zhang is an Advanced Queensland Industry Research Fellow at the School of Civil Engineering, The University of Queensland. His research focuses on hydrological processes in coastal and terrestrial groundwater systems, with particular emphasis on evaporation-driven mass and heat transport in soils and tailings, and hydrogeochemical dynamics in aquifers and mine waste systems. Specializes in IoT-based environmental monitoring Develops numerical models for coastal aquifer dynamics Conducts field and laboratory experiments on tailings behavior Research interests span coastal hydrology, groundwater modeling, mine waste management, and environmental monitoring. He works on contamination transport, aquifer protection, and climate impacts on water systems. Recent publications analyze: Iron curtain formation in subterranean estuaries Sea water intrusion mechanisms Salinity dynamics in tidal wetlands Smart sewer monitoring systems Scientific awards include the prestigious Advanced Queensland Industry Research Fellowship. He supervises multiple PhD projects on mine waste hydrology and coastal aquifer management, with notable collaboration on: Evolution Mining's gold tailings projects ARC Discovery Projects on coastal processes Grange Resources' PAF cell instrumentation His work combines field measurements, laboratory testing, and computational modeling to address critical environmental challenges in mining and coastal zones.
Juha Järvelä is a Staff Scientist at the Department of Built Environment , Aalto University, and a member of the Aalto Environmental Hydraulics Lab . His research focuses on environmental hydraulics, particularly on Hydraulics of environmental channels Modeling of vegetated flows Environmentally sound hydraulic engineering Järvelä's research spans fluid dynamics in river systems, with an emphasis on vegetation-flow-sediment interactions. Key areas include flow resistance in flexible and rigid vegetation, hydrodynamic modeling of floodplains, and nature-based solutions for water management. His recent publications investigate Blockage effects of woody vegetation Longitudinal dispersion in patchy vegetation Two-stage channel performance in agricultural settings Hydrodynamic characterization of natural-like plants Järvelä has received notable scientific recognition, including 2014 : Outstanding Reviewer Award from the American Geophysical Union for Water Resources Research 2005 : Doctoral Dissertation Award from the Oskari Vilamo Foundation Contact: juha.jarvela@aalto.fi | Phone: +358505626727 | Postal: Tietotie 1 E, 02150 Espoo, Finland
Camillo J. Taylor is the Raymond S. Markowitz President’s Distinguished Professor in the Department of Computer and Information Science at the University of Pennsylvania , where he has been a faculty member since 1997. He also serves as Associate Dean for Diversity, Equity, and Inclusion at the School of Engineering and Applied Science. His research focuses on Computer Vision and Robotics , particularly in 3D reconstruction, semantic mapping, and autonomous navigation. Education: A.B. in Electrical Computer and Systems Engineering, Harvard College (1988) M.S. and Ph.D. in Computer Science, Yale University (1990, 1994) Research Interests: Dr. Taylor’s work bridges Computer Vision and Robotics to enable autonomous systems to perceive and navigate complex environments. Key themes include semantic SLAM, event camera applications, and meta-learning for adaptive controllers. His projects often integrate vision, physics, and multi-agent collaboration, as seen in systems like EvMAPPER and OCCAM . Recent Article Trends: His 2024–2025 publications focus on semantic mapping , event-based vision , and multi-agent LLM systems , reflecting his lab’s emphasis on real-time perception, physics-informed reconstruction, and rational decision-making in robotics. These works span applications from solar eclipse imaging to wildfire analysis and natural hazard resilience. Awards: NSF CAREER Award (1998) Lindback Minority Junior Faculty Award (2001) IEEE WACV Best Paper Award (2012) Lindback Distinguished Teaching Award (2012) Advising and Service: Dr. Taylor has advised numerous PhD students, including Jason Hughes and Bowen Jiang. He has served as a Program Chair for CVPR (2006, 2017) and General Chair for ICCV (2021). His contributions to the GRASP Laboratory have advanced autonomous micro-UAVs and semantic SLAM.
Graeme J. Kennedy is an associate professor in the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology where he leads the Simulation-based Multidisciplinary Design Optimization (SMDO) research group. His research focuses on developing numerical optimization techniques for structural and multidisciplinary design problems, particularly for fixed-wing aircraft analysis and design. Dr. Kennedy received his PhD from the University of Toronto Institute for Aerospace Studies (UTIAS) in 2012, followed by a postdoctoral research fellowship at the University of Michigan in the Department of Aerospace Engineering. His research spans several critical areas in aerospace design optimization: Development of advanced numerical optimization techniques for structural design Large-scale topology optimization for aerospace structures Aeroelastic and aerothermoelastic optimization of flexible aircraft Optimization of composite structures with manufacturing constraints Electric motor optimization for electric vertical take-off and landing (eVTOL) vehicles He has developed multiple open-source research codes including TACS (parallel finite-element solver), ParOpt (optimization toolkit), TMR (mesh generation tool), and FUNtoFEM (aeroelastic coupling framework). Dr. Kennedy is particularly interested in designing structures that manage heat from battery packs in air taxis while achieving optimal aeroelastic performance. His publications reveal a strong focus on computational methods for solving large-scale optimization problems in aerospace design. The research shows progressive development from fundamental optimization algorithms toward increasingly complex multidisciplinary applications, with particular emphasis on making high-fidelity simulation-based optimization practical for industrial design cycles through high-performance computing approaches. Dr. Kennedy actively mentors numerous graduate students, including six current PhD candidates and multiple former PhD and MS students who have completed their degrees under his supervision. His research group maintains strong connections with industry through various grants supporting the development of computational tools for aerospace design. The SMDO group also engages in educational outreach through 'Optimization through Intuition,' providing accessible learning modules about optimization concepts for middle and high school students, demonstrating Dr. Kennedy's commitment to broadening participation in engineering education.
Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation
