Sonia Cafieri is a Professor at the École Nationale de l'Aviation Civile (ENAC) in Toulouse, part of the Université de Toulouse. She leads the Mathematics of Optimization and Operations Research (MORO) group within the OPTIM research team. Her roles include membership in the Managing Board of EUROPT and the Nonlinear Mathematical Programming group of GdR-ROD. She chairs the Program Committee of EUROPT 2021 and serves on ENAC's Research and Doctoral Councils. Education: HDR in Applied Mathematics (2012, Université Paul Sabatier), PhD (2006, University of Naples), and Laurea in Mathematics (2001, Second University of Naples). Research focuses on Nonlinear Optimization, Global Optimization, Combinatorial Optimization (network clustering), and Operations Research applications in air transportation and engineering. Recent work addresses air traffic management, trajectory optimization, and stochastic programming for robust scheduling. Publications span optimization methodologies and applications, with a focus on aircraft conflict resolution, drone trajectory planning, and robust cyclic scheduling. Software contributions include ROSE (reformulation engine) and COUENNE (MINLP solver). Teaching includes courses on Numerical Analysis, Combinatorial Optimization, and Operations Research for Air Transportation at ENAC and other institutions.
Cong Wang is an Associate Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT). His research focuses on robotics, control systems, and mechatronics, with a particular emphasis on physical intelligence, robot learning, and human-robot interaction. He has led significant projects funded by the National Science Foundation (NSF), including the CAREER Award for Surrogate Learning in robotics and collaborative work on exoskeleton development for mobility restoration. Wang’s research interests span robotics manipulation, networked systems, and automation technologies. He has published extensively in top-tier conferences and journals, with notable contributions to topics like remote driving systems, video streaming codecs, and UAV-based manufacturing. His work integrates theoretical advancements with practical applications, such as assistive robotics for mobility assistance and teleoperated systems for intelligent transportation. Notable achievements include the NSF CAREER Award and collaborative grants involving interdisciplinary teams. His research has been highlighted in media coverage for innovations in robotic hand skills and exoskeleton technologies. Wang’s lab actively explores adaptive control strategies, crowdsourced learning methodologies, and the ethical implications of automation in labor markets.
Daniel Vázquez Vázquez is an Assistant Professor in the Chemical Engineering and Materials Science Department at IQS School of Engineering (Universitat Ramon Llull). He is an active member of the Grup d'Enginyeria i Simulació de Processos Ambientals (GESPA), where he contributes to research in process engineering and environmental systems. His work spans multiple areas of chemical engineering with a particular focus on sustainable process design and optimization. Research Interests Daniel's research primarily centers on Inherent Safety Engineering and Safety Index development, with significant contributions to Multiobjective Optimization methodologies for chemical process design. His work bridges theoretical developments in optimization techniques with practical applications in environmental engineering and sustainable chemical processes. He has developed novel approaches like the OFISI (Optimizable Fuzzy Inherent Safety Index) that integrate fuzzy logic with process safety assessment. His research portfolio demonstrates a strong trajectory toward addressing complex sustainability challenges, particularly in the areas of carbon management, renewable energy integration, and circular economy principles. Recent work shows an increasing focus on planetary boundaries and the development of methodologies that ensure chemical processes operate within environmental limits while maintaining economic viability. Publication Trends Analysis of Daniel's recent publications reveals a clear evolution in research focus from traditional chemical process optimization toward more comprehensive sustainability frameworks. Early work concentrated on inherent safety metrics and distillation sequence design, while more recent publications integrate machine learning techniques (particularly Bayesian symbolic regression) with process engineering to develop hybrid modeling approaches. There's a marked emphasis on carbon management, with multiple publications addressing carbon dioxide removal technologies, methanol production from renewable sources, and the integration of carbon capture with energy systems. Collaborations and Projects M-AD-NESS Project (2024-2027): As Principal Investigator, Daniel is leading research on Dual Anaerobic Bioelectrochemical Digesters for in-situ biomethane production from wastewater sludge. GESPA Project (2022-2025): As a researcher, he contributed to this group focused on Environmental Process Engineering and Simulation, funded by the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR). Daniel's research has gained significant attention, with his 2021 Nature Communications paper 'Delaying carbon dioxide removal in the European Union puts climate targets at risk' receiving 61 citations, policy references, and coverage in multiple news outlets. His work has been highlighted by 4 news outlets, blogged about, referenced in policy sources, and shared widely on social media platforms. Laboratory and Research Group Daniel is an active member of the Grup d'Enginyeria i Simulació de Processos Ambientals (GESPA), a research group dedicated to environmental process engineering and simulation. This group brings together multiple researchers from IQS to address challenges in sustainable chemical processes, waste management, and environmental protection through advanced modeling and optimization techniques.
Dr. Paromita Nath is an Assistant Professor in the Department of Mechanical Engineering at Rowan University's Henry M. Rowan College of Engineering. She holds a Ph.D. in Civil Engineering from Vanderbilt University and specializes in uncertainty quantification with applications in additive manufacturing and healthcare systems. Her research employs computational methods to develop probabilistic models for manufacturing process optimization. Her primary research integrates Bayesian inference and computational modeling to study process-structure-property relationships in additive manufacturing. Current projects focus on developing probabilistic digital twins for manufacturing systems and multi-fidelity modeling approaches. Research spans health diagnostics, sustainable materials development, and digital engineering solutions. Dr. Nath's publications demonstrate consistent focus on uncertainty management in manufacturing systems, with recent emphasis on surrogate modeling, probabilistic control systems, and multi-objective optimization. Her work frequently combines computational methods with practical manufacturing challenges.
Mahdi Imani is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. He holds a PhD in Electrical Engineering from Texas A&M University (2019), and MSc and BSc degrees in Electrical and Mechanical Engineering from the University of Tehran (2014 and 2012, respectively). His research focuses on machine learning, control theory, Bayesian statistics, and signal processing, with applications in gene regulatory networks, network security, and human-AI collaboration. Dr. Imani has received prestigious awards, including the NIH NIBIB Trailblazer Award (2022), the NSF CISE Career Award (2020), and the Outstanding Associate Editor Award from IEEE Transactions on Neural Networks and Learning Systems (2023 and 2024). He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Vehicular Technology, and is a Senior Member of IEEE. His research projects include DARPA-funded work on verified probabilistic reasoning in mixed reality systems, NSF-funded statistical inference methods, and ONR-funded studies on human-AI team synergy. He leads a lab focused on developing scalable Bayesian methods and reinforcement learning techniques for complex systems.
Thomas Bäck is a Full Professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, and Chief Scientist at NORCE Norwegian Research Center, Norway. His career spans roles as Director of the Center for Applied Systems Analysis (Germany) and Vice Scientific Director at LIACS (since 2017). He holds additional positions like Adjunct Professor at the University of Calgary and Visiting Professor at Xi'an Jiaotong University. Bäck's research focuses on evolutionary computation, machine learning, and their applications in sustainable smart industry and health. He leads interdisciplinary initiatives like the Society, Artificial Intelligence, and Life Sciences (SAILS) program, with expertise in natural computing, data-driven optimization, and quantum computing challenges. IEEE Fellow (2022) Member of KNAW (2021) IEEE CIS Pioneer Award (2015) Fellow of International Society of Genetic and Evolutionary Computation (2003) Best PhD Thesis Award from German Society of Computer Science (1995) Bäck has graduated 22 PhD students and currently supervises 14, with 62 MSc students mentored. His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and Associate Editor for multiple journals. He has secured grants from the Dutch Research Council and The Research Council of Norway for projects like ECOLE and CIMPLO.
Liang He is an Associate Professor of Biomedical Engineering at the University of Oxford's Department of Engineering Science and an Official Fellow at Kellogg College. His research focuses on soft robotics, wearable robotics, and embodied artificial intelligence solutions for healthcare, including biosensors, haptic and VR technologies. He leads the Healthcare Biorobotics Lab (HBL) and contributes to The Podium Institute for Sports Medicine and Technology, developing AI-driven injury prevention systems in sports. Education: PhD in Medical Robotics (Imperial College London), MSc (University of Liverpool), BSc (Shanghai Jiao Tong University). Research Interests: Soft robotics, wearable robotics, augmented/virtual reality, machine haptics, generative AI for healthcare, and embodied tactile perception. He pioneered projects like the EPSRC Motion/RoboPatient initiatives (2017–2020) for medical training simulators and the Oxford Robotics Institute's soft-sensing skin program (2021). Professional Roles: Co-chair of the MPLS Division's Research Staff Forum (2022), member of Oxford's Research Staff Consultation Group, and recipient of the MPLS Enterprise & Innovation and Ideas2Impact Fellowships. Key Achievements: Developed sensorized laryngoscope training systems, magnetorheological elastomer valves for soft robots, and origami-based orthotic designs. His work bridges robotics, AI, and clinical applications, emphasizing tactile perception and embodied intelligence.
Mark J. Clayton is the William M. Pena Professor of Information Management in the Department of Architecture at Texas A&M University's School of Architecture. A native of New Orleans, Dr. Clayton has been a faculty member at Texas A&M since 1995. He has served in significant administrative roles including Executive Associate Dean of the School of Architecture and Interim Head of the Department of Architecture. Previously, he taught at Cal Poly from 1988 to 1991. Dr. Clayton's educational background demonstrates interdisciplinary expertise spanning architecture and engineering: B.Arch from Virginia Polytechnic Institute and State University (1983) M.Arch from University of California-Los Angeles (1987) Ph.D. in Civil and Environmental Engineering from Stanford University (1998) Dr. Clayton's research focuses on the intersection of digital technologies and architectural design. His scholarly interests include architectural design, computational design, Building Information Modeling (BIM), parametric modeling, digital fabrication, facility management, information technology, and sustainable design. His work bridges theoretical design concepts with practical building performance considerations, particularly in how digital tools can enhance design processes and outcomes. He has been instrumental in developing frameworks for integrating BIM with other simulation tools for energy analysis, thermal comfort, and urban planning applications. Analysis of Dr. Clayton's recent publications (2017-2024) reveals a consistent trajectory toward increasingly sophisticated applications of computational methods in architecture. His work shows evolution from foundational BIM applications toward more complex integrations with artificial intelligence, urban climate modeling, and multi-objective optimization. Key thematic areas include thermal comfort assessment using BIM, AI-assisted spatial layout planning, energy performance evaluation of climate-adaptive building envelopes, and the integration of aesthetic considerations into computational design optimization. His research demonstrates a progression from technical implementation toward addressing broader architectural and urban challenges. Dr. Clayton has been actively involved in architectural education and practice, serving in leadership roles within the School of Architecture. His interdisciplinary background combining architecture and engineering has positioned him to bridge gaps between design theory and technical implementation. While specific grant information isn't provided in the available materials, his extensive publication record suggests sustained research activity and likely external funding support for his work in computational design and building information modeling. Dr. Clayton's work appears to be closely associated with the CRS Center at Texas A&M University, though specific laboratory facilities aren't detailed in the provided information. His research collaborations seem to span architecture, engineering, and computer science domains, reflecting the interdisciplinary nature of his work in computational design and building information modeling.
Dr. Sachin Jog is a researcher at ETH Zürich, affiliated with the Department of Chemistry and Applied Biosciences and working within the Professorship for Chemical Systems Engineering. His research focuses on sustainable process systems engineering, with particular emphasis on developing innovative modeling and optimization techniques for chemical processes. His educational background includes: Doctoral Degree from ETH Zürich (2021-2025) under Prof. Gonzalo Guillén Gosálbez Master of Science in Process Engineering from ETH Zürich (2019-2021) Bachelor of Chemical Engineering from Institute of Chemical Technology (ICT), Mumbai (2015-2019) Dr. Jog's research interests span several critical areas in modern chemical engineering: Hybrid modeling techniques combining first-principles and data-driven approaches Optimization of integrated chemical clusters with heat, mass, and power integration Accounting for renewable energy intermittency in chemical process design Multi-objective optimization considering both economic and environmental metrics Sustainable process design aligned with Sustainable Development Goals (SDGs) His recent publications demonstrate a strong progression from fundamental modeling approaches to their application in specific sustainable chemical processes like methanol production from CO2. His work consistently bridges traditional chemical engineering with modern sustainability requirements, showing how renewable carbon technologies can be designed for true sustainability rather than focusing solely on climate change mitigation. Dr. Jog's scientific contributions include: Development of hybrid modeling methodologies for process optimization Frameworks for sustainable process design incorporating SDGs Optimization approaches for integrated chemical clusters using renewable energy His research is supported by the Swiss National Science Foundation (SNF) through the 'Bayesian symbolic learning to aid in the design of sustainable chemical clusters (LEARN-D)' project. Dr. Jog works within the Chemical Systems Engineering group at ETH Zürich, contributing to research on sustainable chemical processes and systems engineering approaches for the chemical industry's decarbonization.
Prof Kai Qin is a Professor of AI and Data Science at Swinburne University, affiliated with the School of Science, Computing and Emerging Technologies. He holds roles including Director of the Intelligent Data Analytics Lab, Deputy Director of the Swinburne Space Technology and Industry Institute, and Vice President for Education at IEEE Computational Intelligence Society (CIS). Qin earned his B.Eng. from Southeast University (2001) and PhD from Nanyang Technological University (2007). His research focuses on Computational Intelligence (CI), encompassing Neural Networks, Evolutionary Computation, and Fuzzy Systems, with applications in Machine Learning, Remote Sensing, and Pervasive Computing. His work has garnered over 20k Google citations and recognition such as IEEE Fellow (2025). Key achievements include the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and leadership in conferences like IJCNN 2022. He pioneered the Master of Data Science program at Swinburne (2018–2020) and leads initiatives in federated learning, onboard AI for satellite missions, and AI-driven medical diagnostics. Qin’s professional contributions span editorial roles in journals like Swarm and Evolutionary Computation and leadership in IEEE technical committees. His grants include SmartSat CRC projects for satellite AI and ARC-funded research on gravitational lensing and traffic analytics.
Dr Alma Rahat is an Associate Professor of Data Science at Swansea University, affiliated with the School of Mathematics and Computer Science. He specializes in Bayesian search and optimization, evolutionary algorithms, and multi-objective optimization. His work focuses on solving computationally expensive problems with applications in engineering, healthcare, and education. Education: BEng (Hons) in Electronic Engineering from the University of Southampton (UK), PhD in Computer Science from the University of Exeter (UK), and a Postgraduate Certificate in Teaching in Higher Education from Swansea University. He is a Fellow of the Higher Education Academy (FHEA). Research Interests: Dr Rahat’s expertise includes data-driven evolutionary optimization, surrogate-assisted methods, and active learning. He has contributed to pandemic response modeling for the Welsh Government and the UK Health Security Agency, leveraging machine learning and parameter optimization. His work on healthcare decision-making systems and educational assessment tools demonstrates a commitment to interdisciplinary applications of optimization. Key Contributions: He leads the Surrogate-Assisted Evolutionary Optimization (SAEOpt) workshop at GECCO and is a member of the IEEE Computational Intelligence Society Task Force on Data-Driven Optimization. His research bridges academic theory and industry needs, with patents and publications in top journals and conferences like IEEE Transactions and ACM. Grants: £750k from Welsh Government (Co-PI/Co-I), £230k EPSRC grant (EP/W01226X/1 as PI). Awards: Best Paper in Real-World Applications Track at GECCO, Patent for industrial fluid separation technology. Supervision: Currently guiding PhD students across AI, healthcare, education, and environmental modeling. His supervision emphasizes Bayesian methods and human-in-the-loop systems. Lab/Teams: Active in Swansea’s Computational Foundry, collaborating on projects like beach change forecasting and clinical decision support systems.
Sascha Ranftl is an Erwin Schrödinger Fellow at the Courant Institute of Mathematical Sciences, New York University. Their research spans interdisciplinary domains including Bayesian probability theory, uncertainty quantification, computational engineering, and biomedical applications. Affiliation: Courant Institute, NYU Former Affiliation: Graz University of Technology (Institute of Theoretical Physics & Computational Physics, Graz Center of Computational Engineering) Research Interests: Ranftl's work focuses on integrating Bayesian inference with machine learning and physics-informed modeling to address uncertainty quantification in biomedical simulations, particularly for aortic dissection and impedance cardiography . Their approach harmonizes statistical rigor with engineering pragmatism, emphasizing surrogate models and computational fluid dynamics. Recent Publications: Analyze geometric uncertainties in patient-specific blood vessels, develop physics-consistent neural networks, and apply Bayesian frameworks to biomedical diagnostics and elastoplastic material modeling. Scientific Awards: Erwin Schrödinger Fellowship Contact: ranftl@tugraz.at sranf@duck.com
Bo Liu is a Professor of Electronic Design Automation at the University of Glasgow, specializing in AI-driven electronic design. He holds a B.Eng. from Tsinghua University (2008) and a Ph.D. from KU Leuven (2012). Previously, he was a Humboldt Research Fellow (2012–2013), Lecturer at Wrexham Glyndŵr University (2013–2020), and promoted to Reader (Associate Professor) in 2016 before joining Glasgow in 2020. Research focuses on AI-driven methodologies for analog ICs, antennas, and microwave systems. Key contributions include pioneering AI-assisted optimization in RF design and first industrial-use AI tools for mm-wave ICs. His work bridges machine learning and domain knowledge, addressing bottlenecks in electromagnetic simulations and antenna design complexity. He leads the AIDAC lab and collaborates with industry on EDA tools. Scientific awards include Fellow of IET and Senior Member of IEEE. He serves as an associate editor for IEEE Transactions on CAD and Complex and Intelligent Systems. Current research explores AI-driven design tools, including funded PhD projects on microwave filters, analog IC optimization, and 5G antennas.
Mathias Verbeke is an Associate Professor at the Faculty of Engineering Technology , affiliated with the Department of Computer Science at KU Leuven. He serves as Contact Person for the Declarative Languages and Artificial Intelligence (DTAI) group at the Bruges Campus and leads the M-Group Industrial Artificial Intelligence subdivision. His research focuses on Artificial Intelligence, Machine Learning , and their industrial applications, particularly in predictive maintenance, digital twin technology, and process optimization. His recent work explores uncertainty quantification in multi-objective optimization, anomaly detection frameworks, and physics-guided deep learning for thermal modeling. He actively contributes to Leuven.AI and collaborates with groups like Infinity and M-Group . At the institutional level, Verbeke participates in the Faculty of Engineering Technology Council and Computer Science Department Council . Verbeke teaches courses including Machine Learning , Artificial Neural Networks and Deep Learning , and Master’s Thesis Artificial Intelligence in Business and Industry .
Yu Wang is a Lecturer at Bielefeld University's Faculty of Linguistics and Literary Studies, affiliated with the Computational Linguistics department. He is associated with the research project Monitoring the understanding of explanations under Prof. Dr. Hendrik Buschmeier, focusing on digital linguistics and text technology. While his institutional affiliation highlights computational linguistics, his Google Scholar publications reveal interdisciplinary work in optimization algorithms, graph neural networks, and machine learning applications. Research Interests: Yu Wang's work bridges computational linguistics with advanced algorithmic techniques. His publications explore Multi-objective optimization Graph neural network architectures Swarm intelligence applications Evolutionary computation methods Sparse learning systems Biologically-inspired algorithms Labs & Projects: Yu Wang contributes to the Digital Linguistics Group at Bielefeld University, specifically working on explainability monitoring in explanatory processes. His technical publications suggest collaborations with researchers in optimization and machine learning domains.
