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Burak SencerView profile

Associate Professor

• Advanced Manufacturing
• Precision Motion Control
• Optimal Trajectory Generation
• +4 more

Burak Sencer is an Associate Professor of Mechanical Engineering at Oregon State University’s College of Engineering, affiliated with the Mechanical, Industrial, and Manufacturing Engineering department. He directs the Manufacturing Process Control Laboratory and holds the Tom and Carmen West Faculty Scholar title. His expertise lies in advanced manufacturing, precision motion control, and optimal trajectory generation for multi-axis machine tools. B.S., Mechanical Engineering, Istanbul Technical University (2003) M.S., Ph.D., Mechanical Engineering, University of British Columbia (2005, 2009) Postdoctoral Research, Nagoya University, Japan (2009–2012) Assistant Professor, Nagoya University (2012–2015) before joining Oregon State Research focuses on improving manufacturing efficiency through precision control of CNC machines, industrial robots, and machining processes. Key areas include: Vibration suppression in high-speed machining Optimal trajectory generation for multi-axis systems Intelligent process control for challenging materials (e.g., titanium, hardened steel) Robotics for deburring and edge finishing Notable achievements include developing motion control algorithms that boost production speeds by 20–25% for IT products and a chip-pulling device reducing friction in cutting operations. Awards include the JSPE Best Paper Award (2020) and ASME’s Blackall Machine Tool and Gage Award (2019) . Collaborates with industries like Boeing and Japanese machine tool builders for applied projects. Active in teaching graduate students and mentoring through research.

• High-Performance Computing
• LiDAR Technology
• Computer Vision
• +5 more

David López Vilariño is a Lecturer at the University of Santiago de Compostela, affiliated with the Department of Electronics. His research focuses on LiDAR data processing, FPGA acceleration for high-performance computing (HPC), and embedded systems. He teaches courses such as Fundaments of Electronic Instrumentation , Heterogenous Programming , and The Physics of Computing , contributing to bachelor’s and master’s programs in Physics, Informatics Engineering, and High Performance Computing. His research interests span LiDAR-based applications in urban planning, infrastructure monitoring, and medical imaging. He has developed algorithms for LiDAR data analysis, FPGA-based motion estimation, and GPU-accelerated medical image processing. Notable contributions include tools like the Open Lidar Visualizer and Analyser for 3D point cloud visualization. Dr. Vilariño’s work bridges computer architecture, signal processing, and geomatics. His recent publications emphasize optimizing HPC workloads using FPGAs and Intel OneAPI, as well as automated LiDAR-based detection of power lines, road points, and pedestrian zones. He collaborates on interdisciplinary projects involving parallel computing, embedded vision systems, and real-time surveillance applications. Teaching responsibilities include coordinating courses for the Máster Universitario en Computación de Altas Prestaciones, a joint program with the University of A Coruña. No academic awards are listed, but his active role in teaching and research grants underscores his contributions to the field.

Fahad MEHMOODView profile

Assistant Professor

• Supply Chain Management
• Operational Research
• Energy Optimization
• +3 more

Fahad MEHMOOD is an Assistant Professor of Supply Chain and Logistics at EM Normandie since 2023. He holds a PhD and M.S. from the Politecnico di Torino, Italy. Previously, he taught at the University of Dubai and Lahore University of Management Sciences (LUMS). His research focuses on applying operational research to energy optimization, information systems, and supply chain management. He has published in journals like European Journal of Information Systems and Energy Policy . Education: PhD in Management Science, Politecnico di Torino, Italy (2015) M.S. in Management Science, Politecnico di Torino, Italy Research Interests: Energy flexibility and demand response programs Data-driven supply chain decision making Optimization models for energy systems Transportation economics and infrastructure Teaching: Courses include Optimization-based decision systems, Procurement & Logistics, and Production Management.

• Molecular Biology
• Biophysics
• Computational Chemistry
• +3 more

Dr. Jocelyne Vreede is a Researcher at the Van 't Hoff Institute for Molecular Sciences within the Faculty of Science at the University of Amsterdam. Her work focuses on molecular simulations and structural studies of biological systems, particularly exploring protein-DNA interactions, molecular dynamics, and computational biophysics. She is affiliated with the University’s advanced research facilities and contributes to interdisciplinary projects in biophysics and structural biology. Her research interests include the development of enhanced sampling techniques for molecular simulations, the structural basis of DNA compaction by histone-like proteins, and the mechanistic understanding of protein conformational changes in disease contexts such as oncogenic mutations in KRas. She has also contributed to educational studies on academic career development in STEM fields. Dr. Vreede’s publications reflect a strong emphasis on computational approaches applied to biological problems, with recent work exploring novel algorithms for protein-DNA architecture design and the dynamics of transient DNA base pairing states. While no formal awards are listed, her extensive publication record underscores her active role in advancing computational and experimental biophysical research. Her advising activities are not detailed in the provided information, and no ancillary professional roles are explicitly mentioned beyond her primary academic affiliation.

Georg GraseggerView profile

Researcher

• Rigidity Theory
• Symbolic Computation
• Algebraic Geometry
• +3 more

Georg Grasegger is a Researcher in the Symbolic Computation group at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences. His work focuses on rigidity theory, algebraic geometry, and symbolic computation with applications to combinatorial and geometric structures. Research interests include rigidity of graphs and frameworks, algebraic differential equations, and geometric configurations. Recent work explores paradoxical motions of graphs, flexible polyhedra, and counting realizations of rigid structures using algebraic methods. Publications highlight contributions to graph rigidity, computational geometry, and algebraic combinatorics, with a focus on theoretical foundations and algorithmic approaches. His research bridges discrete mathematics and applied algebraic geometry. Contributions include software tools like FlexRiLoG for studying graph motions and RigiComp for computational rigidity analysis. No notable awards are listed, though his work is widely cited in combinatorial geometry and symbolic computation fields. Labs/Teams: Active in RICAM's Symbolic Computation group, collaborating with institutions on geometric and algebraic problems. Supervises research projects on rigidity and discrete structures.

• Autonomous Vehicles
• Computer Vision
• Intelligent Transportation Systems
• +5 more

Jose Maria Armingol Moreno is a Full Professor at the University Carlos III of Madrid, affiliated with the Department of Systems Engineering and Automation. He serves as Director of the Master's Degree in Internet of Things: Applied Technologies and leads the Intelligent Systems Laboratory research group within the Duque de Santomauro Institute of Motor Vehicle Safety. His expertise spans autonomous systems, computer vision, robotics, and intelligent transportation infrastructure. Key research interests include vehicle safety, sensor fusion, and deep learning applications in traffic monitoring and autonomous navigation. He has authored over 100 peer-reviewed articles, with recent work focusing on 3D vehicle detection, UAV battery systems, and intelligent infrastructure for smart cities. Dr. Armingol has also led numerous projects on autonomous vehicle development, cooperative driving systems, and traffic safety technologies. His contributions include patented innovations in vehicle inspection and collision avoidance systems.

Jérémy DubutView profile

Assistant Professor

• Categorical Aspects of Computer Science
• Concurrency Theory
• Directed Algebraic Topology
• +4 more

Jérémy Dubut is an Assistant Professor (Professeur Assistant Monge) at École Polytechnique, part of the LIX lab and Cosynus team, and affiliated with the chaire Architecture des Systèmes Complexes at IP Paris. His research focuses on categorical aspects of computer science, directed algebraic topology, and formal methods for cyber-physical systems. He holds a PhD from ENS Paris-Saclay under the supervision of Jean Goubault-Larrecq and Eric Goubault. Key contributions include work on bisimulations in category theory, probabilistic safety analysis, and formal verification for automated driving systems. He co-authored a book chapter in Safety Assurance under Uncertainties , addressing temporal specifications. His awards include the Best Paper Award at CALCO'23 and the EATCS Best Paper Award at FoSSaCS'19. He advises PhD student Augustin Albert on categories of modules in directed algebraic topology. His work spans theoretical foundations (e.g., coalgebra, homology) and applications in safety-critical systems, autonomous vehicles, and program logic. He actively contributes to conferences like TACAS, CDC, and FoSSaCS.

• Serious Games
• Learning Analytics
• Educational Technology
• +3 more

Antonio Calvo Morata is an Assistant Professor at the Universidad Complutense de Madrid (UCM), specializing in Software Engineering and Artificial Intelligence. He belongs to the research group "Ingeniería del software y e-learning" and focuses on applying learning analytics and serious games to address educational challenges, particularly in bullying prevention and STEM recruitment. His work integrates AI-driven tools to enhance game development and assessment workflows. Education: Doctorate from UCM (2020) with a thesis on using learning analytics for serious games validation in cyberbullying contexts. Supervisors included Dr. Baltasar Fernández Manjón and others. Research Interests: Development of inclusive serious games, AI applications in education, game-based learning analytics, and tools simplifying game validation (e.g., Simva framework). His studies span STEM education outreach, collaborative learning methodologies, and bioelectrical signal analysis for game evaluation. Key Contributions: Designed the Conectado serious game for bullying awareness, validated through rigorous analytics. Pioneered frameworks blending visual and data mining techniques for educational game improvement. Explored AI tools to streamline serious game authoring and deployment. Labs/Teams: Active in the UCM's Software Engineering and e-learning research group, focusing on educational technology innovation.

• Digital Twin
• Manufacturing Engineering
• Aircraft Assembly
• +5 more

Tengfei Long serves as a Research Fellow at Coventry University's Centre for Manufacturing and Materials, specializing in digital twin applications for advanced manufacturing systems. His active involvement spans multiple high-impact research projects including Power electronics Upscale for Localisation and Sustainable Electrification (2024-2027), WAVETAILOR modular laser production systems (2024-2027), and CLEAN sustainable logistics initiatives (2022-2024). Dr. Long's research integrates Digital Twin technology with manufacturing engineering to solve critical challenges in aircraft assembly, power electronics, and sustainable production. His work employs advanced computational methods including dynamic Bayesian networks, fuzzy rule systems, and IIoT architectures to enhance productivity, accuracy, and resource allocation in complex production environments. Key focus areas include intellectual property management within digital frameworks, lightweight process twin development, and real-time simulation of manufacturing systems. Recent publications demonstrate consistent innovation in applying digital twin technology across diverse manufacturing contexts, with particular emphasis on data capture methodologies, simulation accuracy improvements, and sustainable production integration. His research bridges theoretical computational models with practical industrial applications in aerospace, CNC machining, and personalized production systems. As a Research Fellow, Dr. Long contributes significantly to three major funded projects: Power electronics Upscale (focusing on sustainable electrification), WAVETAILOR (developing modular laser sources for short-run production), and CLEAN (addressing African logistics challenges). These initiatives involve cross-disciplinary collaboration with industry partners and academic researchers across multiple European institutions. He operates within Coventry University's Centre for Manufacturing and Materials, participating in a dynamic research ecosystem that combines additive manufacturing, digital twin technologies, and sustainable production methodologies to advance next-generation manufacturing solutions.

• Clinical Psychology
• Psychotherapy
• Research Methodology
• +3 more

Hans-Jürgen Fercher is a Lecturer at the Alpen-Adria-Universität Klagenfurt, affiliated with the Department of Clinical Psychology, Psychotherapy, and Psychoanalysis, and the Department of Methodology (teaching roles). His research focuses on clinical psychology, psychotherapy methodologies, and quantitative research techniques. He contributes to interdisciplinary projects involving media literacy, educational innovation, and digital transformation. His work spans academic publications in peer-reviewed journals and conference proceedings, addressing topics like online political behavior, swarmalator systems in robotics, and sustainable entrepreneurial ecosystems. Fercher collaborates on EU and national funded projects, including media literacy initiatives and technology-driven solutions for healthcare and education. His teaching emphasizes practical methodologies and evidence-based approaches in clinical and educational settings. Fercher actively engages in academic community-building through workshops and editorial roles in German didactics and interdisciplinary research.

• Business and Management
• Applied Mathematics
• Statistics
• +3 more

Dr. Nuo Xu is an Associate Professor at the Collat School of Business, University of Alabama at Birmingham (UAB), within the Department of Management, Information Systems and Quantitative Methods. He holds a PhD (2007) and MS (2003) in Industrial Engineering from the University of Cincinnati, and a BS in Material Engineering from Shanghai Jiao-Tong University (1997). His research interests span Business Analytics, Applied Mathematics, Statistics, and Manufacturing Engineering. Notable contributions include methodologies in gene expression analysis, feature selection bias reduction, and robust voting machine allocation models. Professional activities include editorial roles for journals like International Journal of Data Science and service on institutional committees such as the IS Search Committee. His academic career at UAB began in 2010, initially in the School of Engineering before transitioning to his current role in the Collat School. He advises students in business honors programs and actively participates in institutional service roles.

Ivan PopovView profile

Senior Lecturer

• Engineering Design
• Reverse Engineering
• Engineering Metrology
• +2 more

Dr. Ivan Popov is a Senior Lecturer in the School of Electrical and Mechanical Engineering within the Faculty of Technology at the University of Portsmouth. His research focuses on engineering design, reverse engineering, metrology, quality control, and advanced manufacturing processes for composite materials. Key research areas: Machining optimization for natural fiber composites Tool design and drilling technology Geometric modeling for robotics Sustainable manufacturing techniques

Souma ChowdhuryView profile

Associate Professor

• Nature-inspired computing
• Swarm systems
• Autonomous systems
• +7 more

Souma Chowdhury is an Associate Professor in the Department of Mechanical and Aerospace Engineering and an Adjunct Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo. His work focuses on nature-inspired computing, swarm systems, autonomous robotics, and UAV design, integrating AI, optimization, and physics-infused machine learning. He leads the ADAMS Laboratory, advancing robotics, human-swarm interaction, and energy systems. Education: PhD, Mechanical Engineering, Rensselaer Polytechnic Institute (2012) MS, Mechanical Engineering, Florida International University (2008) BTech, Indian Institute of Technology (2007) Research Interests: Swarm robotics and collective behavior Autonomous systems and UAVs Physics-aware AI and machine learning Multi-fidelity optimization and metamaterials Human-swarm interaction Energy systems and smart grids His recent work emphasizes real-time decision-making in robotics, space debris capture systems, and urban air mobility. Over 50+ publications demonstrate his contributions to AI-driven robotics and sustainable energy solutions. Awards: NSF CAREER Award (2021) SEAS Early Career Researcher of the Year (2019) Renewable Energy Top Paper Award (2015) Grants & Projects: $5K Summer Foundry funding (RPI, 2012) NYSERDA CleanTech funding (2012) Labs & Teams: ADAMS Laboratory (Autonomous & Distributed Agents for Multi-domain Systems) Develops open-source tools like SHaSTA for human-swarm collaboration

• Operations Research
• Logistics and Supply Chain Management
• Transportation Engineering
• +3 more

Christian Truden is a Researcher at the Alpen-Adria-Universität Klagenfurt, affiliated with the Department of Production Management and Logistics and the Institute for Production, Energy and Environmental Management. He holds roles as Deputy Director of the Institute and Member of the Curricular Commission for Information Management (TEWI and WIWI). His work focuses on optimizing logistics, transportation systems, and emergency management through computational methods and GIS applications. Truden's research interests span operations research, vehicle routing algorithms, sustainability in transportation, and the integration of UAVs in civilian emergency services. He has contributed to solving complex problems such as emergency contact point allocation during power outages, fleet management optimization, and microtransit scheduling in rural areas. His methodologies include Bayesian modeling, heuristic decomposition, and nearest neighbor Gaussian processes for predictive analytics. His recent publications (2020–2025) emphasize practical applications like defibrillator drone deployment in mountainous regions, grocery home delivery systems, and time-window constrained vehicle routing. These studies address real-world challenges in logistics efficiency, environmental impact reduction, and accessibility improvements. Truden's work bridges theoretical optimization with operational feasibility, often leveraging geographic and computational tools. He is reachable at christian.truden@aau.at and located in room p.2.45 of the Main building's South Wing East.

• High-speed Networks
• Big Data
• Performance Modeling
• +3 more

Chase Wu is a Professor in the Department of Data Science and Director of the Center for Big Data at New Jersey Institute of Technology (NJIT). He previously held the rank of Associate Professor in Computer Science. His research focuses on high-speed networks, big data systems, performance modeling, and machine learning applications. He leads projects funded by the National Science Foundation (NSF), including collaborative research on big data workflows and system performance evaluation. Wu has authored over 300 publications and holds an h-index of 37 with 6,77 citations. Research Interests: His work spans network optimization, big data transfer efficiency, blockchain security, and AI-driven systems. Notable contributions include frameworks for automated parameter tuning in software systems and intelligent scheduling algorithms for distributed computing. His team develops tools like PTSSBench for performance testing and SRGTNet for image retrieval. Grants & Projects: Two NSF-funded projects include the ESPRIT testbench for system evaluation and collaborative research on big data scientific workflows. He has secured federal grants totaling over $2M since 2015. Labs & Teams: Directs the Center for Big Data at NJIT, leading interdisciplinary teams in computational steering, edge intelligence, and cybersecurity solutions. His lab focuses on real-world applications like air quality monitoring systems and radiation detection networks.