جست‌وجوی استاد و پژوهشگر

Philipp Wendlerمشاهده پروفایل

مدرس

• Software Verification
• Formal Methods
• Program Analysis
• +۵ مورد دیگر

Philipp Wendler is an academic lecturer in the Department of Computer Science at Ludwig-Maximilians-Universität München (LMU Munich). He is affiliated with the Software and Computational Systems Lab and actively involved in research, teaching, and open-source tool development. As an employee representative in the steering committee of the Institute of Informatics, he contributes to institutional governance. His research focuses on software verification, formal methods, and program analysis. Key projects include CPAchecker (a configurable verification framework) and BenchExec (a benchmarking tool). His work emphasizes practical applications in automated testing, energy-efficient algorithms, and reproducible benchmarking. Publications span topics like interpolation-based model checking, energy measurement tools, and strategies for software verification competitions. He has contributed to advancing predicate analysis, k-induction, and refinement selection techniques. Notable achievements include leading the development of CPAchecker and BenchExec, which are widely used in academic and industrial verification efforts. His research addresses challenges in scalable verification, flaky test analysis, and energy-aware computing.

Sohrab Faramarzi Oghaniمشاهده پروفایل

استادیار

• Sustainable Supply Chain Management
• Supply Chain Resiliency
• Facilities Design
• +۳ مورد دیگر

Sohrab Faramarzi Oghani is an Assistant Professor in the Supply Chain Management & Information Systems Academic Area at Rennes School of Business (France). He holds a Ph.D. in Computer Science from Université de Lille (2018) and degrees in Industrial Engineering from the University of Tehran and Tabriz. His roles include Programme Manager for the MSc in Supply Chain Management and teaching courses such as Principles of Supply Chain Management, International Sourcing & Procurement, and Data Science for Advanced Production Systems. His research focuses on Sustainable Supply Chain Management , Supply Chain Resiliency , and Clinical Laboratory Design . He has published in journals like International Journal of Production Research and presented at conferences like META’2018. His work integrates mathematical modeling and simulation to optimize facilities design, particularly in healthcare and logistics sectors. He has contributed to patents in clinical laboratory optimization, including methods for layout design, instrument selection, and cost reduction. His recent studies emphasize sustainable practices and resiliency in supply chains, with a 2023 review paper on meta-heuristics in this domain. Professional activities include organizing international conferences (META, BIOMA, OLA) and collaborating with Inria Lille Nord Europe during his PhD. He advocates for applying advanced analytics to solve real-world operational challenges in both industrial and healthcare environments.

• Computer Architecture
• Embedded Systems
• Hardware/Software Co-Design
• +۵ مورد دیگر

Hammam Alsafrjalani is an Associate Professor of Practice in the Department of Electrical & Computer Engineering at the University of Miami. His roles include teaching undergraduate and graduate courses such as Computer Architecture, Embedded Systems, and Microprocessors, and supervising senior design projects. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from the University of Florida (2015 and 2011, respectively), and a B.S. from the New Jersey Institute of Technology (2007). His research focuses on computer architecture, embedded systems, heterogeneous-and-configurable systems, AI for cybersecurity, and runtime optimization. Notable achievements include developing optimization techniques for energy, performance, and temperature in multicore systems, yielding up to 25% energy savings. His work has been published in peer-reviewed journals and conferences. Awards include the Faculty Learning Community Fellowship (2022 and 2019), Most Innovative Faculty Member (2020 and 2019), and the Student-Choice Best Engineering Faculty Member Award (2019). He has contributed to proposals in computer science, applied science, and multidisciplinary projects with psychology and communication schools. Teaching tools include Quartus Prime, Gem5, and Python, with a focus on hands-on projects. His research emphasizes practical applications in consumer electronics and augmented reality, leveraging industry experience from internships at Intel and Microsoft, and prior roles at Dialogic and Vonage.

Héctor Chinchero Villacisمشاهده پروفایل

استادیار

• Smart Buildings
• LED Lighting Systems
• IoT Applications
• +۳ مورد دیگر

Héctor Chinchero Villacis is an Assistant Professor in the Department of Electronics at the University of Alcalá. His research focuses on smart lighting systems, IoT integration in buildings, and energy management solutions. He is a member of the GEINTRA research group, which specializes in Electronic Engineering applications for intelligent spaces and transport. Chinchero holds a PhD from the University of Oviedo (2021), where his thesis addressed LED driver development for smart buildings using controllable reactive elements. Education: PhD in Electronics Engineering, Universidad de Oviedo (2021) Research Interests: His work combines power electronics innovation with IoT technologies to enhance energy efficiency in smart infrastructure. Key areas include: Magnetic control of DC-DC converters for LED applications Energy management systems for public lighting and microgrids IoT-based precision agriculture and home energy monitoring Human-centered lighting parameters for wellbeing optimization Recent Contributions: Recent publications highlight advancements in low-cost emergency lighting solutions, magnetic control techniques for power electronics, and distributed energy systems for Amazonian regions in Ecuador. His work bridges theoretical power electronics with practical smart infrastructure implementations. Labs/Teams: Active contributor to the GEINTRA research group, focusing on applied electronic engineering innovations for smart environments.

• Automated Theorem Proving
• Machine Learning Integration
• Formal Verification
• +۷ مورد دیگر

Konstantin Korovin is a Reader at the Department of Computer Science, University of Manchester, with over 20 years of experience in automated reasoning and formal verification. He previously held roles as Senior Lecturer (2015-2023) and Royal Society University Research Fellow (2007-2015). His research spans automated theorem proving, machine learning integration, and constraint solving for hardware/software verification. Doctoral thesis: Knuth-Bendix orders in automated deduction (2003, University of Manchester) Current teaching: Logic and Modelling (COMP21111), Automated Reasoning (COMP60332) Research interests include: Automated Theorem Proving (ATP) and SMT solving Combining machine learning with formal verification Nonlinear constraint analysis via δ-complete procedures DNA computing and bio modeling applications Recent publications focus on symbolic ML verification (SMLP), conflict-driven quantifier elimination (VIRAS), and enhanced C++ model checking (ESBMC). His work has been recognized with best paper awards at FroCoS'19 and CP'09. Supervised: 9 PhD/PostDocs including Maryam Abdullah, Franz Brausse, and Andrzej Kucik Collaborated with industry (Intel, Google, MathWorks) on verification tools Contact: konstantin.korovin@manchester.ac.uk

Heike Trautmannمشاهده پروفایل

استاد

• Multi-criteria optimization
• Evolutionary optimization
• Automated algorithm selection and configuration
• +۶ مورد دیگر

Prof. Dr. Heike Trautmann is a leading researcher in statistics and optimization at the University of Twente (2021-2026) and former Professor at WWU Münster (2013-2016). Her work bridges computational statistics, evolutionary optimization, and social media analytics. She has held visiting positions at TU Dortmund, Leiden University, and RWTH Aachen. Current affiliation: University of Twente (Data Science: Statistics and Optimization) Previous roles: WWU Münster (Professor for Information Systems and Statistics), TU Dortmund (Postdoctoral researcher) Research Focus: Multi-criteria optimization, automated algorithm selection, data stream mining, and disinformation detection in social media. Her methodological innovations in exploratory landscape analysis and evolutionary computation have transformed algorithm configuration practices. Developed COSEAL consortium for algorithm selection Co-founder of Benchmarking Network (2019) Principal investigator in projects like PropStop and MODERAT! Academic Contributions: Over 150 publications in top venues like GECCO, PPSN, and Evolutionary Computation journal. Pioneered feature-based landscape analysis tools (flacco, pflacco) and stream clustering frameworks.

Johan Perssonمشاهده پروفایل

دانشیار

• Computer Aided Product Development
• Surrogate Models
• Finite Element Analysis (FEM)
• +۴ مورد دیگر

Johan Persson is an Associate Professor and Head of Unit at Linköping University's Department of Management and Engineering (IEI). He leads the Product Realisation (PROD) division, focusing on advancing Computer Aided Product Development (CAD) and optimization techniques. His research emphasizes computationally efficient surrogate models to enhance FEM/CFD analyses and streamline product development processes. Key areas of interest include design automation frameworks, additive manufacturing integration, and multidisciplinary optimization strategies. He teaches courses such as Machine Elements (TMKT39), Design Optimization (TMKT48), and Collaborative Multidisciplinary Optimization (TMKT79). His work bridges academic research and industry applications, with notable contributions to frameworks supporting Additive Manufacturing (AM) and OpenMDAO-based optimization. Research trends in his publications highlight advancements in AM applications, design automation tools, and collaborative optimization methods. Notable projects include a framework for AM design integration and studies on hydraulic pump design using additive techniques. Johan supervises PhD students including Erik Gustafsson and Javier Villena Toro. His research is supported by collaborations with colleagues like Anton Wiberg and Johan Ölvander. The Automation Lab, a key platform for his work, pioneers innovations in CAD automation and digital design processes.

Felix Ulrich-Olteanمشاهده پروفایل

مدرس

• Constraint Satisfaction
• Combinatorial Optimization
• Boolean Satisfiability
• +۳ مورد دیگر

Dr. Felix Ulrich-Oltean is a Lecturer in the Department of Computer Science at the University of York. His research focuses on constraint satisfaction, combinatorial optimization, boolean satisfiability, and machine learning applied to automated algorithm selection and configuration. He holds a PhD in Computer Science from the University of York (2019–2023), a PGCE in Secondary Mathematics from Leeds Trinity University, and a BSc in Computer Science from the University of York. His recent work emphasizes SAT encoding techniques for pseudo-boolean and linear integer constraints, with contributions to automated tabulation methods in constraint models. Research trends include integrating machine learning for algorithm selection, optimizing constraint satisfaction problem resolution, and developing efficient SAT-based solutions for complex combinatorial problems. No scientific awards or grants are explicitly mentioned in the provided texts. He advises no listed students and has no disclosed lab affiliations. His office is located at CSE/108, and contact is facilitated through a university web form to protect email privacy.

• Industry 4.0 Technologies
• Material Flow Simulation
• Production Planning and Control Optimization
• +۴ مورد دیگر

Prof. Dr. Sven Völker is a faculty member at Technische Hochschule Ulm , affiliated with the Faculty of Production Engineering and Production Management and the Institute for Business Organization and Logistics (IBL) . He specializes in technologies for Industry 4.0 , with a focus on material flow simulation, digital factory design, and optimization methods in production planning and control. Teaches Enterprise Information Systems and Digital Factory Planning in the Systems Engineering and Management program. Active in simulation-based optimization and digitalization scenarios for logistics and manufacturing systems. Research Interests: His work bridges industrial engineering and computer science , emphasizing simulation tools, ERP systems, and lifecycle management of digital models. Recent publications explore Machine Learning applications in production planning, OPC UA communication for logistics systems, and augmented reality in manufacturing demonstrations. Publications: His research spans 2010–2023, covering simulation-based optimization of supply networks, discrete-event modeling, and Industry 4.0 integration. Keywords include Operations Research , Robotics , and Digital Twins , with sub-fields like Logistics Automation and Flexible Manufacturing . Memberships: Institute for Higher Education Didactics (IHD) Institute for Applied Research (IAF)

• Smart Grids
• Electric Vehicles
• Energy Resource Management
• +۷ مورد دیگر

José Almeida is a Researcher at the Polytechnic Institute of Porto, specifically at the School of Engineering's Research Group in Engineering and Intelligent Computing for Innovation and Development (GECAD). He is concurrently pursuing a PhD in Computer Engineering at the University of Salamanca since 2022, focusing on explainable decision support systems for energy management in smart grids with high electric vehicle penetration. His academic background includes a Master's in Electrical Engineering - Electrical Energy Systems (2021) and Bachelor's in Electrical and Computer Engineering (2019), both from the same institution. His research spans smart grid technologies, computational intelligence, and energy resource management with particular emphasis on electric vehicles, optimization, and explainable AI. Key areas include local energy communities, risk-based scheduling, metaheuristic optimization, and renewable energy integration. His work bridges theoretical algorithm development with practical energy system applications, frequently employing evolutionary computation techniques. Analysis of his recent publications reveals a strong focus on community-based energy sharing, fairness metrics in local markets, and explainability in optimization systems. His research trajectory shows increasing sophistication in handling uncertainty through risk assessment frameworks while maintaining practical applicability to real-world grid operations and EV integration challenges. Best Paper Award at CPEEE 2025 for energy community formation research 2nd Prize REN Best Master Thesis Award (2022) ICEER Best Oral Presentation Award (2021) As a co-supervisor for 5 MSc dissertations and 2 BSc completion works, he actively mentors next-generation researchers in energy systems. His grant participation demonstrates significant collaborative work through projects like Honda Research Institute consultancy, NGS Innovation Pact, RIBIERSE-CYTED Network, and multiple FCT-funded initiatives. He leads the META-ERM platform development for energy resource management and organizes the annual Competition on Evolutionary Computation in the Energy Domain.

• Electrical Engineering
• Electrical Machines
• Magnetic Materials
• +۵ مورد دیگر

Andrea Cavagnino is a Full Professor in the Energy Department (DENERG) at the Polytechnic University of Turin. His research focuses on advanced design of electrical machines, analytical-numerical methods, magnetic materials, and thermal modeling. He leads projects like SUPERDRIVE (superconductive synchronous machines) and TUrBO (high-speed reluctance motors). Cavagnino has held editorial roles in IEEE Transactions journals and chairs conferences like the IEEE International Electric Machines and Drives Conference. He supervises PhD students in high-speed motor design and power systems. His awards include multiple Publication Awards from Politecnico di Torino and an ERC Advanced Grant proposal recognition. Cavagnino also consults for industry on motor design and optimization. His recent work addresses ultra-high-speed motors, additive manufacturing rotors, and cryogenic cooling impacts on machine performance.

• Aerospace Engineering
• Materials Science
• Mechanical Engineering
• +۳ مورد دیگر

Michele Pasquali is an Associate Professor at the Department of Mechanical and Aerospace Engineering of Sapienza University of Rome. His research focuses on advanced materials for aerospace and high-energy physics applications, structural health monitoring, Industry 4.0 integration in aerospace manufacturing, and preliminary design methodologies for satellite systems and collimation systems in particle accelerators. His work often intersects with experimental facilities at CERN, particularly the HiRadMat facility for material testing under extreme conditions. Key research themes include: Development of advanced composite materials for aerospace and accelerator components Integration of AI and immersive technologies (AR/VR) in manufacturing and assembly processes Optimization of beam collimation systems for high-luminosity particle accelerators like the HL-LHC and FCC Economic analysis of space infrastructure concepts such as lunar propellant mining Systems engineering approaches for Earth observation satellite constellations His recent projects include: Design of a Cyber-Physical System for AR-assisted assembly of aerospace components Material characterization studies for vacuum beam windows at CERN facilities Preliminary analysis of moon-mined propellant viability for on-orbit refueling Collaborations involve major scientific institutions such as CERN and contributions to Future Circular Collider (FCC) conceptual design reports. His work bridges theoretical modeling with practical implementation in cutting-edge engineering systems.

• Network Security
• Software Defined Networking
• Traffic Engineering
• +۷ مورد دیگر

Xingang Shi is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With over 126 publications spanning from 2006 to 2025, he maintains an active and influential research program in computer networking, with recent expansion into quantum network optimization. His work consistently appears in top-tier venues including INFOCOM, IEEE/ACM Transactions on Networking, and SIGCOMM. Dr. Shi's research focuses on network security, software defined networking, traffic engineering, and network anomaly detection. His recent work shows increasing emphasis on quantum network optimization and advanced security verification techniques. He has developed innovative approaches to routing protocols, congestion control, and inter-domain routing analysis, with practical applications in real-world network infrastructure. His publication trends reveal a steady evolution from traditional networking topics toward more sophisticated security and optimization frameworks. The 15 most recent publications demonstrate strong integration of machine learning techniques with networking fundamentals, particularly in anomaly detection and traffic engineering. His work increasingly addresses quantum networking challenges while maintaining strong contributions to conventional network security and performance optimization. As a leading researcher in his field, Dr. Shi has made significant contributions to network verification methodologies and security protocol testing frameworks. His recent papers on model checking-based security testing and proactive network policy verification represent important advances in ensuring network reliability and security. Through his extensive collaboration network (particularly with Zhiliang Wang, Xia Yin, and Han Zhang), Dr. Shi has built a productive research ecosystem that bridges theoretical networking concepts with practical implementation challenges. His work on quantum network optimization represents an emerging frontier in his research portfolio.

Xiaoyu Chenمشاهده پروفایل

استادیار

• Machine Learning
• Advanced Manufacturing
• Cyber Manufacturing Systems
• +۵ مورد دیگر

Xiaoyu Chen serves as an Assistant Professor in the Department of Industrial and Systems Engineering within the School of Engineering and Applied Sciences at the University at Buffalo. His academic foundation includes a PhD in Industrial and Systems Engineering (2021) and an MEng in Computer Science (2021) from Virginia Tech, complemented by a BS in Optical Information Science and Technology from Beijing Institute of Technology (2015). Dr. Chen's research spans machine learning methodology for advanced manufacturing, networked data analytics in cyber manufacturing systems, statistical methods for Bayesian deep learning, smart connected health applications for organ transplantation and perioperative medicine, and human behavior/performance modeling. His work bridges industrial engineering with cutting-edge AI techniques to solve complex problems in manufacturing and healthcare systems. Analysis of recent publications reveals a strong focus on developing interpretable and context-aware AI systems for manufacturing and clinical applications. Key trends include multimodal learning frameworks, distributed data processing for fog manufacturing, human-AI collaboration paradigms, and medical diagnostics through novel data modalities like breath analysis. Dr. Chen actively teaches core engineering courses including Probability & Statistics for Engineers (IE 360/560), Simulation (IE 541), and Predictive Analytics (Deep Learning) (IE 463/663), demonstrating commitment to both foundational and emerging topics in industrial engineering education.

Tuomas Välimäkiمشاهده پروفایل

پژوهشگر

• Robotics
• Automation Engineering
• Sensor Calibration
• +۳ مورد دیگر

Tuomas Välimäki is a Doctoral Researcher affiliated with the Faculty of Engineering and Natural Sciences. His research focuses on robotics and automation, with a strong emphasis on sensor calibration, visual navigation, and control systems for hydraulic manipulators. He holds a Master of Science (Technology) in Automation Engineering. Master of Science (Technology), Automation Engineering (2015) Lower-Degree Level Tertiary Education in Technology (2015) His research explores Robotics Engineering , Motion-based Calibration , and Probabilistic Movement Primitives . Key themes include optimizing sensor configurations for autonomous systems, trajectory tracking in hydraulic manipulators, and gaze-direction strategies for visual navigation tasks. His work intersects with Computer Vision , Control Systems , and Mechatronics . Tuomas’s publications highlight advancements in sensor fusion , multi-camera systems , and probabilistic motion planning . These studies focus on improving accuracy and efficiency in robotic perception and control, particularly for mobile platforms and industrial manipulators.