Mustafa Onur is the McMan Professor and Chair of Petroleum Engineering at The University of Tulsa, where he directs the TU Petroleum Reservoir Exploitation Projects (TUPREP). He holds a Ph.D. and M.S. in Petroleum Engineering from The University of Tulsa and a B.S. from Middle East Technical University. Previously, he held professorships at Istanbul Technical University and Universiti Teknologi Petronas (Malaysia), including a Schlumberger Chair position. Research Focus: Dr. Onur specializes in inverse problem theory, mathematical optimization, and data science applied to reservoir management, geothermal systems, and uncertainty quantification. His work integrates machine learning with traditional reservoir engineering to solve complex problems in energy extraction and carbon sequestration. Publication Trends (2024-2025): His 15 most recent articles emphasize deep learning-based reservoir surrogates, CO₂ storage optimization, geothermal energy extraction, and constrained production optimization. Key innovations include Embed-to-Control frameworks, physics-driven interwell simulators, and stochastic optimization algorithms for uncertainty management in subsurface systems. Awards & Recognition: 2010 SPE Formation Evaluation Award 2014 SPE Distinguished Member 2018 SPE Reservoir Description and Dynamics Award Leadership: As TUPREP director, he leads advanced research in reservoir exploitation, focusing on practical applications of AI and optimization in petroleum and geothermal engineering. He serves as Associate Editor for SPE Journal and Journal of Petroleum Science and Engineering .
Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.
Brian Ziebart is a Professor in the Department of Computer Science at the University of Illinois at Chicago. He earned his Ph.D. in Machine Learning from Carnegie Mellon University in 2010. Research Interests: Machine Learning, Robotics, Assistive Technologies, Human-Computer Interaction, Adversarial Prediction, Inverse Optimal Control, Structured Prediction. Key Grants: NSF CAREER (RI)-1652530, NSF EAGER (SCH)-1650900, NSF IIS-1526379, NSF III-1514126, Future of Life Institute grant, NSF NRI-1227495. Notable Awards: Best Paper Runner-Up (ECCV, 2012), Best Paper Award (ICML, 2011), CMU School of Computer Science Dissertation Honorable Mention (2011). Teaching & Leadership: Senior Lecturer at CMU, actively involved in mentoring students and leading research teams.
Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science
Martha Tsigkari serves as an Associate Professor at The Bartlett School of Architecture, University College London (UCL), where she bridges architectural practice with cutting-edge computational research. Her position situates her at the forefront of digital transformation in the built environment, with institutional affiliations spanning UCL's Faculty of the Built Environment and direct contributions to UN Sustainable Development Goals 4 (Quality Education), 11 (Sustainable Cities), and 13 (Climate Action). Her research program critically examines the integration of artificial intelligence, machine learning, and cognitive psychology into architectural design processes. Key investigations include spatial and visual connectivity analysis, XR-enhanced collaborative design environments, and AI-driven optimization of building performance. She explores how digital tools reshape creativity, professional identity, and sustainability outcomes in architecture, with particular focus on data commoditization, skills evolution, and human-AI collaboration in design workflows. Her interdisciplinary approach connects architectural theory with computational neuroscience and industrial digitalization trends. Tsigkari's publication trajectory reveals a clear evolution from computational structural analysis (2012-2017) toward AI ethics and professional transformation (2022-2024). Early work established foundations in performance-driven facades and material systems, while recent output confronts existential questions about architectural practice in the AI era. Her scholarship consistently addresses the tension between technological capability and human-centered design values, with growing emphasis on sustainable development frameworks and educational implications. Scientific Awards: No major awards are documented in the available records. Advising and Grants: While specific supervisees and funding mechanisms aren't detailed in current sources, her extensive collaborative network across 30+ publications indicates active mentorship and research leadership. Co-authorship patterns suggest involvement in multi-institutional projects addressing AECO industry digitalization, with potential ties to UK research councils and industry partnerships like RIBA. Labs and Teams: Tsigkari operates within The Bartlett's digital research ecosystem through recurring collaborations with Kosicki, Tarabishy, and Psarras. Her work manifests in experimental toolsets including Glaucon (XR design environment), HYDRA (optimization framework), and SandBOX (conceptual design system), indicating leadership in UCL's computational design labs focused on human-AI interaction and sustainable building technologies.
Dr. Serhat Hosder is the James A. Drallmeier Centennial Professor in the Department of Mechanical and Aerospace Engineering at Missouri S&T. He serves as Director of the Aerospace Simulations Laboratory, focusing on computational aerothermodynamics, hypersonic flow modeling, and uncertainty quantification for planetary entry systems. Professor of Aerospace Engineering (2019–present) Director, Aerospace Simulations Lab Advisor to students receiving NASA Space Technology Research Fellowships and Amelia Earhart Fellowships Research funded by NASA, DoD, and NSF Research Interests: Computational aerothermodynamics, hypersonic flow modeling, uncertainty quantification, multi-fidelity methods, directed energy applications, planetary entry systems, and aerodynamic shape optimization. His work combines numerical methods with robust design principles for high-speed vehicles. Scientific Awards: Missouri S&T Outstanding Faculty for Contributions to Graduate Studies Award (2022) Fellow of the Royal Aeronautical Society (2021) NASA Langley Research Center Henry J. E. Reid Award (2018) Associate Fellow of AIAA (2017) Missouri S&T Faculty Research Awards (2015, 2012) Advising & Grants: His students have secured positions at NASA, Sandia National Labs, and academia. Research funded by DoD Joint Hypersonics Transition Office, NASA (Langley, JPL), Missile Defense Agency, NSF, and industry partners like M4 Engineering, Inc.
Dr. Srikanthan Ramesh serves as an Assistant Professor in the School of Industrial Engineering and Management within Oklahoma State University's College of Engineering, Architecture and Technology. Since establishing the Advanced Materials and Additive Manufacturing Laboratory in August 2022, he has led interdisciplinary research at the intersection of materials science, physical phenomena, and advanced manufacturing technologies, with applications spanning healthcare, aerospace, and electronics sectors. His educational foundation includes a Ph.D. in Mechanical and Industrial Engineering from Rochester Institute of Technology (2022) and an M.S. in Industrial and Manufacturing Systems Engineering from Iowa State University (2017). This academic background enables his innovative approach to manufacturing science. Dr. Ramesh's research program focuses on biological and micro-scale additive manufacturing (bio-AM), specializing in biomaterial development for tissue engineering and regenerative medicine. His work integrates computational fluid dynamics, machine learning, and real-time process monitoring to achieve precise control over mechanical, biological, and electrical properties of manufactured structures. He develops experimental tools and process frameworks for droplet-based and extrusion-based AM systems, with particular emphasis on wound healing applications and space-compatible microelectronics. Analysis of his 14 publications from 2020-2025 reveals a strong trajectory toward AI-driven manufacturing solutions, with increasing emphasis on multi-objective Bayesian optimization for bioink design, aerosol jet printing process refinement, and bioprinted tissue construct development. His recent work demonstrates sophisticated integration of machine learning with physical manufacturing processes to solve complex biomedical challenges. His scientific recognition includes: Doctoral Dissertation Pitch Competition (Runner-up), IISE, 2021 Best Oral Presentation, Graduate Showcase, Rochester Institute of Technology, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Wakonse College Teaching Fellowship, Iowa State University, 2018-2019 Graduate Research Excellence Award, Iowa State University, 2017 Best Overall Oral Presentation, Nano@IAstate, Iowa State University, 2017 Dr. Ramesh currently leads significant research initiatives including as Principal Investigator for an NSF REU Site on Additive Manufacturing and Cybersecurity ($464,606, 2025-2028) and a NASA EPSCoR Travel Grant for aerosol jet printing in space missions (2024-2025). As Co-PI on an NSF grant for Privacy-aware Collaborative Design in additive biofabrication ($599,981, 2025-2028), he develops frameworks for mass personalization in medical applications while addressing data security challenges. These projects support his lab's mission to advance manufacturing science through rigorous experimentation and computational innovation. The Advanced Materials and Additive Manufacturing Laboratory operates as a collaborative hub where Dr. Ramesh directs research teams in developing novel biomaterials, optimizing printing processes, and creating functional prototypes for wound dressings, liver tissue models, and space-rated microelectronics. The lab's interdisciplinary approach combines expertise in materials characterization, computational modeling, and machine learning to push the boundaries of what's possible in additive manufacturing for critical applications.
Laurens Bliek is an Assistant Professor at the Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology (TU/e). He specializes in combining artificial intelligence (AI) with optimization techniques for computationally intensive problems, focusing on sustainable applications such as public transport, electric vehicles, and CO 2 reduction. Education: MSc in Applied Mathematics (2014), PhD in Systems & Control (2019) from Delft University of Technology. Prior Role: Postdoctoral researcher at the Algorithmics group, Delft University of Technology. Research Interests: His work addresses AI-driven optimization of expensive cost functions, particularly in logistics, communications, and healthcare. He develops methods to handle computationally intensive simulators and digital twins, emphasizing real-time decision-making and sustainability. Recent Publications: His research spans predictive maintenance using Fourier graph neural networks, real-time container yard allocation, and 5G network optimization. Articles appear in journals like IEEE Transactions on Neural Networks and Learning Systems and Computer Networks . Collaborations: Laurens collaborates with industry partners (LioniX, Dutch Railways) and organizations (European Supply Chain Forum, Logistics Community Brabant). He co-leads the 12-PhD program AI Planner of the Future and participates in AI sustainability working groups. Supervision: Co-promotor of PhD students Ya Song and Abdo Abouelrous, focusing on AI applications in routing and maintenance logistics.
Filippo Masseni is a Fixed-term Tenure-Track Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) , Politecnico di Torino. His academic and research activities focus on aerospace propulsion systems, particularly hybrid rocket engines and solid propellant development. Scientific disciplinary sector: IIND-01/G - Aerospace Propulsion ERC sector: PE8_1 - Aerospace Engineering Research Interests include combustion instability modeling, coupled propulsion/trajectory optimization, multidisciplinary design optimization, and robust optimization techniques. His work bridges theoretical modeling with practical applications in hybrid rocket engines and advanced propulsion systems. In teaching , he serves as Course Lecturer for Combustion in Aerospace Engines and supervises courses like Aeronautical Propulsion and Aircraft Engines, spanning academic years 2019-2025. Supervised PhD Students : Vincenzo Madonia, Daniele Tozzi, Leonardo Stumpo, Alessandra Zumbo, Lorenzo Folcarelli, Giovanni Polizzi Research group: Aerospace Propulsion (DIMEAS)
Delibra Giovanni is an Associate Professor at Sapienza University of Rome, specializing in aerodynamics, aeroacoustics, and renewable energy systems. His research focuses on optimizing turbomachinery performance, including axial fans, wind turbines, and hydrogen storage systems. He employs advanced computational fluid dynamics (CFD) and machine learning techniques to address challenges in renewable energy integration, thermal management, and noise reduction. Key research areas include: Wind energy systems and offshore wind farm design Hydrogen storage and safety in green energy applications Aeroacoustic control in industrial fans and turbines CFD-based optimization of heat exchangers and cooling systems Recent work emphasizes the integration of photovoltaic and biomass systems in renewable energy communities, as well as experimental validation of wave energy turbines. His publications highlight innovations in fan blade design, leakage modeling, and multi-objective optimization frameworks for sustainable energy infrastructure. Collaborations involve both academic institutions and industry partners, focusing on real-world applications such as tunnel ventilation systems and Mediterranean island energy solutions. Giovanni's contributions bridge theoretical modeling with practical engineering challenges in the transition to clean energy.
Shengdun Zhao is an active researcher in the fields of Electrical Engineering, Automotive Engineering, and Machine Learning, contributing extensively to optimization techniques and control systems for electric vehicles and motors. His work spans journals like IEEE Transactions on Vehicular Technology and Journal of Intelligent & Fuzzy Systems , focusing on practical applications of deep reinforcement learning, meta-learning, and multi-objective optimization. Key research areas: Electric motor control, energy management systems, and clustering algorithms. Collaborates with researchers such as Yiming Zhang, Wei Du, Chee-Kong Chui, and Chin-Boon Chng. His publications from 2007–2025 address technical challenges in mechatronics, sustainable transportation, and data-driven engineering solutions. Research Trends Recent articles highlight Zhao's emphasis on deep reinforcement learning for motor control, meta-learning in energy systems, and evolutionary algorithms for multi-objective optimization. He integrates machine learning with automotive engineering to improve electric vehicle efficiency and motor performance.
Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.
Nikolaos Paterakis is an Assistant Professor of Power System Optimization and Electricity Markets with the Electrical Energy Systems research group at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). He is the founder and principal investigator of the Electricity Markets & Power System Optimization Laboratory (EMPSOLab) established in 2019, and a member of the Cyber-Physical Systems Center Eindhoven (CPSe). His research focuses on applying optimization and machine learning techniques to power system and electricity market problems, particularly regarding renewable energy integration and smart grid technologies. Dr. Paterakis received his Dipl.Eng. from Aristotle University of Thessaloniki in 2013, followed by a PhD in Industrial Engineering and Management (cum laude) from the University of Beira Interior in 2015. After serving as a post-doctoral fellow at TU/e from 2015-2017 and working as a consultant for the Energy Market Regulatory Authority of Turkey, he was appointed Assistant Professor at TU/e in April 2017. His research spans power system optimization, electricity market design, renewable energy integration, and the application of machine learning techniques to grid management problems. Recent work emphasizes distributed energy resource integration, local electricity markets, congestion management in low-voltage grids, and real-time grid control using advanced optimization techniques. His publications demonstrate a clear trajectory toward increasingly sophisticated methods for managing grid constraints while enabling market participation of distributed energy resources. Dr. Paterakis has received several prestigious awards including IEEE SEGE'15, SEST 2019, and SEST 2020 Best Paper Awards, and recognition as a Best Reviewer for IEEE Transactions on Smart Grid (2015, 2017) and IEEE Transactions on Sustainable Energy (2016). He serves as Associate Editor for multiple journals including IET Renewable Power Generation, IEEE Systems Journal, IEEE Transactions on Intelligent Transportation Systems, and Elsevier's e-Prime. He leads multiple research projects including MEGAMIND (NWO-funded), P2P-TALES (NWO-funded), and the Electricity Markets Game series (TU/e BOOST!-program). His educational contributions include teaching courses on power system analysis and optimization, electricity markets modeling, and developing innovative educational tools for power systems education. In 2021, he was elevated to Senior Member of the IEEE Power & Energy Society.
Kevin Hughes is a Senior Lecturer in the Energy Engineering Group at the Department of Mechanical Engineering, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. He holds a PhD and first degree in Chemistry from the University of Leicester (1987) and focuses on fuel combustion, fuel cells, and process modelling in carbon capture and storage (CCS) systems. His research combines experimental and theoretical approaches, including planar laser diagnostics, quantum chemistry, and CFD simulations. Education: PhD and BSc in Chemistry from University of Leicester. Research Interests: Fuel combustion, pollutant chemistry, PEM fuel cells, CCS process modelling, catalyst development, and combustion in supercritical CO2. Grant Projects: FP7-ENERGY-2010-2 (RELCOM), Gas-FACTS (EPSRC), EP/J020788/1, EP/M001482/1 (Selective EGR), TEABPP (Energy Technology Institute). Scientific Contributions Publications: Over 50 papers on fuel combustion mechanisms, fuel cell optimization, CCS systems, and alternative fuels. Collaborations: Regular work with M. Pourkashanian, D.B. Ingham, S. Michailos, and M.S. Ismail. Technical Expertise Chemical Kinetics Validation Quantum Chemistry Applications Gas Diffusion Layer Analysis Surrogate Fuel Development Supercritical Combustion
Professor Robin Purshouse is a leading academic at the University of Sheffield , currently serving as Professor of Decision Sciences in the Department of Automatic Control and Systems Engineering within the School of Electrical and Electronic Engineering . With a career spanning academia and industry, his work bridges computational modelling , optimization , and systems science to address complex challenges in public health and engineering. His research has been pivotal in developing mechanisms for agent-based modelling and evolutionary multi-objective optimization . Education: PhD in Control Systems (2004), MEng in Control Systems Engineering (1999) from the University of Sheffield Professor Purshouse's research focuses on computational modelling of complex social systems , decision analytics for population health policy , and Bayesian optimization . He has pioneered the integration of machine learning and uncertainty quantification in social science simulations, with notable projects like the Sheffield Alcohol Policy Model and CASCADE initiative. His work spans interdisciplinary domains, including health economics , policy evaluation , and engineering design . Recent publications highlight his expertise in agent-based modelling for smoking/vaping dynamics , intersectional disparities in alcohol consumption , and inclusive economy frameworks . He has secured substantial funding (exceeding £16 million) through grants from NIH , CRUK , UKPRP , and MRC , including his role as co-PI in the HealthMod cluster. His contributions to multi-objective optimization and evolutionary algorithms have advanced methodologies in both engineering and public health domains. Scientific Awards: ESRC Future Research Leaders Award (2012-2015) As a co-developer of the Liger optimization environment , Purshouse has fostered open-source tools for complex decision-making. He leads the SIPHER consortium for systems science in public health and serves on editorial boards for journals like Environmental Modelling & Software . His teaching includes Agent-Based Modelling (ACS6132), and he maintains professional memberships in the Association for Computing Machinery and Research Society on Alcohol .
