جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Prediction
• Data Analysis and Visualization
• +۷ مورد دیگر

Eugenio Francisco Sánchez Úbeda is an Associate Professor at the College of Engineering, Comillas Pontifical University, and a researcher at the Technological Research Institute (IIT). He has been affiliated with the institution since 1991 and teaches Statistics, Machine Learning, and Data Analysis at ICAI. His academic and research work spans energy systems, healthcare analytics, and machine learning applications. Education: PhD in Industrial Engineering, Comillas Pontifical University, 1999 Degree in Industrial Engineering (Electronics), Comillas Pontifical University, 1991 His research interests include Machine Learning, Prediction, Data Analysis and Visualization, Nonlinear Statistical Models, and Deep Learning, with a strong focus on energy demand forecasting and probabilistic modeling. He has led numerous research projects in collaboration with major European companies such as Endesa, Enel, and Airbus. Recent publications highlight his work in energy efficiency, healthcare monitoring using wearable devices, microgrid optimization, and pandemic-related energy demand shifts. His research integrates advanced machine learning techniques with real-world applications in energy and public health. Scientific Awards and Recognition: Director of work awarded 'Premio Iberdrola al mejor Trabajo Fin de Master 2020-21' Director of work awarded 'Premio Management Solutions al mejor Proyecto Fin de Carrera 2017-18' He has advised multiple PhD students and serves on thesis committees. He is also an active reviewer for journals such as Energy Economics and Energy . His leadership extends to organizing scientific workshops like the JOSITE series and contributing to funded research projects including those under Horizon 2020 and the Inter-American Development Bank. He has delivered invited seminars on data mining, genetic algorithms, and MATLAB visualization at Comillas and other institutions, and has participated in over 100 research projects in energy and data science.

Steve Braunsteinمشاهده پروفایل

استاد بالینی

• Radiation Oncology
• Pediatric Oncology
• Neuro-Oncology
• +۳ مورد دیگر

Steve Braunstein is a Clinical Professor in the Department of Radiation Oncology at the University of California, San Francisco (UCSF). He serves as Vice Chair and Clinical Director of Radiation Oncology, specializing in pediatric, central nervous system, and soft tissue malignancies. His expertise includes advanced radiation techniques like stereotactic radiotherapy (Gamma Knife, Cyberknife), intraoperative radiotherapy, and intensity-modulated radiation therapy. MD and PhD from New York University School of Medicine Residency in Radiation Oncology at UCSF (2014) Internship in Internal Medicine at Brigham and Women's Hospital (2010) Dr. Braunstein's research focuses on reducing radiation toxicity in pediatric malignancies and optimizing combined immuno- and targeted-therapies for brain tumors. His work spans clinical outcomes, radiation dosimetry, and artificial intelligence applications in oncology. Recent publications highlight his contributions to stereotactic radiosurgery, cancer recurrence modeling, and multi-institutional studies on brain metastases. His work integrates radiation physics, machine learning, and clinical trial design to improve precision therapy. Scientific Awards UCSF Rap Award (2020, 2018, 2017) ARRO Teacher of the Year (2019, 2015) RSNA Roentgen Research Award (2013) UCSF Exceptional Physician Award Dr. Braunstein actively contributes to medical education through undergraduate radiation oncology programs and participates in collaborative research groups like the Radiation Oncology Education Collaborative Study Group. His work bridges clinical practice with cutting-edge research in precision oncology.

• Evolutionary Algorithms
• Machine Learning
• Optimization
• +۷ مورد دیگر

Giovanni Iacca is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science (DISI), where he serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. He leads the Distributed Intelligence and Optimization Lab (DIOL) and teaches courses including Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, and Optimization Techniques across multiple academic programs. PhD in Computer Science, University of Jyväskylä, Finland (2011) MSc in Computer Engineering, Technical University of Bari, Italy (2006) Professor Iacca's research focuses on the intersection of evolutionary computation, machine learning, and optimization with applications in distributed systems and robotics. His work spans from theoretical foundations of memetic computing and multi-objective optimization to practical implementations in soft robotics, embedded systems, and healthcare applications. Recent efforts emphasize interpretable AI, particularly in reinforcement learning contexts, where his team develops methods to make decision processes transparent while maintaining performance. His research bridges the gap between fundamental algorithmic development and real-world engineering challenges, with over 15 years of industrial experience in optimization applied to engineering, logistics, and scheduling. Analysis of his recent publications reveals a strong trend toward interpretable AI systems, particularly in reinforcement learning contexts, with significant contributions to federated learning optimization, evolutionary neural architecture search, and applications in healthcare scheduling. His work consistently combines evolutionary algorithms with modern machine learning techniques to solve complex optimization problems across diverse domains including soft robotics, batteryless edge computing, and supply chain management. Scientific Awards: EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) Professor Iacca actively supervises a large research group with numerous PhD students across multiple doctoral programs, including Information Engineering and Computer Science, Industrial Innovation, and the National PhD in Artificial Intelligence for Society. His lab has secured significant research funding through collaborations with industry partners and international research consortia. Recent grants support work on interpretable reinforcement learning, federated optimization, and applications of evolutionary computation in healthcare and robotics. He has also been appointed to editorial roles for prestigious journals including IEEE Transactions on Evolutionary Computation and Evolutionary Intelligence. The Distributed Intelligence and Optimization Lab (DIOL) under Professor Iacca's leadership comprises over 30 researchers including postdocs, PhD students, and master's students. The lab maintains strong international collaborations and has developed specialized expertise in evolutionary computation, interpretable AI, and optimization for embedded systems. Current projects include work on the EIC Pathfinder Challenge "Awareness Inside," development of methods for batteryless edge intelligence, and applications of evolutionary algorithms to healthcare scheduling problems.

Dario Landa-Silvaمشاهده پروفایل

استاد

• Computational Optimization
• Operations Research
• Artificial Intelligence
• +۴ مورد دیگر

Dario Landa-Silva is a Professor of Computational Optimization at the University of Nottingham and a key member of the Computational Optimization and Learning (COL) Lab . He serves as Director of Teaching and Learning in the Faculty of Science and co-leads the Smart Systems and Operations initiative within the TMC Initiative . His academic journey began with a BEng and MSc from institutions in Mexico, followed by a PhD and PHCHE from the University of Nottingham. Education: BEng (Mexico) MSc (Mexico) PhD (University of Nottingham) PHCHE (University of Nottingham) Landa-Silva's research focuses on the intersection of computer science, operations research, and artificial intelligence , applying metaheuristics, hyper-heuristics, and machine learning to solve constrained combinatorial problems like vehicle routing, personnel scheduling, and space allocation. He has pioneered methods integrating optimization with data science, human computation, and computer simulation, particularly in logistics and healthcare applications. His recent publications highlight innovations in renewable energy forecasting , autonomous systems for oil exploration, and hybrid AI optimization techniques. Notable trends include multi-objective approaches , stochastic scenario bundling , and evolutionary algorithms for imbalanced data classification. Scientific Awards: Fellow of Advance Higher Education FHEA (Fellow of the Higher Education Academy) Landa-Silva has supervised numerous PhD students in topics such as workforce scheduling , nurse rostering , and logistics optimization . He has led over £1.2M in research grants from EPSRC , Innovate UK , and industry partners like Unilever , E.ON Energy , and Netacea Ltd . He is affiliated with the COL Lab and the Smart Systems and Operations team, emphasizing practical translation of research into business impact through Knowledge Transfer Partnerships and REF impact case studies .

Silvia Curteanuمشاهده پروفایل

استاد

• Neural Networks
• Bio-Inspired Computing
• Chemical Engineering Applications
• +۶ مورد دیگر

Silvia Curteanu is a Professor at the Faculty of Chemical Engineering and Environmental Protection of the Gheorghe Asachi Technical University of Iasi, Romania. Her academic career spans over 40 years, with roles ranging from researcher to PhD supervisor in chemical engineering and applied informatics. She holds a PhD in Chemical Engineering (1998) and a License in Chemical Engineering (1981). University: Gheorghe Asachi Technical University of Iasi School: Faculty of Chemical Engineering and Environmental Protection Department: Department of Chemical Engineering Academic Rank: Professor Research Interests: Specializing in artificial intelligence applications for chemical processes, Silvia Curteanu has developed methodologies using neural networks , genetic algorithms , and hybrid models for tasks like process modeling, optimization, and inverse problem solving. Her work addresses polymerization , bio-processes , electrochemical treatments , and molecular design , with notable contributions to soft sensors and multi-objective optimization . Scientific Output: With over 192 papers (133 ISI-indexed), 23 books/chapters, 14 patents, and 32 research grants, her articles focus on neural network topology , biologically inspired algorithms , and chemical process optimization . Her work has been published in journals like Journal of Chemical Engineering , Environmental Science and Pollution Research , and Applied Soft Computing . Scientific Awards: Best Paper Award (2016) for 'Performance Comparison of Different Regression Methods for a Polymerization Process with Adaptive Sampling' Grants & Projects: 32 research grants, including international collaborations 11 projects as director (1 international) Advising: Acted as PhD supervisor since 2005, mentoring students in chemical engineering and AI applications. Her laboratory collaborates with institutions like Oxford University and Aristotle University of Thessaloniki.

• Power Electronics
• DC-DC Converters
• Inverters
• +۴ مورد دیگر

Andrzej Mondzik serves as a Professor in the Department of Power Electronics and Automation of Energy Conversion Systems at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering in Kraków, Poland. His academic role encompasses both research and teaching responsibilities within power electronics, with particular emphasis on converter topologies for energy systems. His research spans power converter design including DC-DC converters, inverters, and renewable energy integration systems. Key specialties involve switched-capacitor and switched-inductor topologies, resonant converters, and applications of wide-bandgap semiconductors (GaN/SiC). Current investigations focus on soft-switching techniques, voltage stress reduction, and grid-connected photovoltaic systems, addressing critical challenges in efficiency and thermal management for high-power applications. Analysis of his 2020-2024 publications reveals consistent innovation in multilevel inverter topologies (T-NPC, ANPC) and high-gain DC-DC converters, with strong emphasis on practical implementation using advanced semiconductor devices. His work demonstrates particular expertise in balancing theoretical circuit analysis with experimental validation, as evidenced by frequent references to laboratory prototypes and measured efficiency data across diverse operating conditions.

• Power Electronics
• Renewable Energy Systems
• Multilevel Inverters
• +۲ مورد دیگر

Adam Penczek is a Professor at the AGH University of Science and Technology , affiliated with the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. He works in the Department of Power Electronics and Automation of Energy Conversion Systems, focusing on advanced power electronics technologies for energy conversion and automation. Research Interests: His work primarily addresses Multilevel inverter topologies Switched-capacitor converter designs Soft switching techniques Wide bandgap semiconductor applications (SiC/GaN) Renewable energy system integration High-frequency power conversion Recent Publications demonstrate expertise in optimizing voltage gain, reducing switching losses, and implementing passive snubber circuits. His studies span from 2019 to 2024, addressing both theoretical and practical challenges in power electronics. Contact: penczek@agh.edu.pl

Piotr Szwedمشاهده پروفایل

مدرس

• Data Mining
• Software Engineering
• Computational Intelligence
• +۵ مورد دیگر

Dr inż. Piotr Szwed is a Lecturer in the Department of Applied Computer Science at AGH University of Science and Technology, Kraków. He is affiliated with the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering (EAIiIB), where he teaches courses ranging from imperative and object-oriented programming to data exploration and computational intelligence. Research Interests: Data mining and knowledge discovery Fuzzy cognitive maps and their applications in transportation, risk assessment, and security Software engineering methodologies, including agile and ontology-driven development Intelligent transportation systems (ITS) and real-time traffic management Natural language processing for Polish texts, stylometry, and authorship attribution Ontology engineering, enterprise architecture verification, and model checking His work often bridges theoretical computer science with practical applications in urban mobility, safety systems, and enterprise software. Scientific Contributions: Pioneered the integration of fuzzy cognitive maps with evolutionary algorithms to predict traffic flows. Developed rule-based and machine-learning approaches to determine speed limits from geospatial data. Contributed to the INSIGMA intelligent transportation system aimed at enhancing urban mobility in Polish cities. Created formal verification techniques for ArchiMate business processes using NuSMV model checking. Advanced stylometric analysis for Polish texts, introducing part-of-speech features for authorship attribution. Teaching & Academic Service: Regularly teaches Programming (C, C++, Java), Software Engineering, Data Exploration, and Computational Intelligence. Supervises engineering and master theses in applied computer science. Maintains a public wiki with course materials, lab exercises, and consultation schedules for students. Utilizes Git-based repositories to streamline code submission and continuous assessment in laboratory classes. Laboratory & Facilities: He is located in building C-2, room 403, at AGH’s main campus, al. Mickiewicza 30, 30-059 Kraków. The lab supports courses that emphasize practical software development, version control, and collaborative project work.

John Rubinمشاهده پروفایل

استادیار بالینی

• Extended Reality (XR) in Medicine
• Medical Simulation Education
• Ultrasound-Guided Regional Anesthesia
• +۵ مورد دیگر

John Rubin is a Clinical Assistant Professor at Weill Cornell Medical College's Department of Anesthesiology since 2023. He is also an Assistant Attending Anesthesiologist at NewYork Presbyterian Hospital. Educational Background: MD from New York Medical College (2016) BS in Biochemistry from University of Minnesota (2010) Dr. Rubin is an international leader in Extended Reality (XR) applications in medicine, co-directing the Extended Reality Anesthesiology Immersion Laboratory (XRAIL). His research focuses on: XR integration for procedural training and simulation education Generative AI conversational agents for soft skills training Pain outcome optimization in cardiac surgery patients Ultrasound-guided nerve block techniques His 2023-2025 publications demonstrate expertise in spatial computing applications across: Interventional pain procedures Neuraxial training Crisis management Obstetric anesthesia Thoracic nerve blocks Surgical risk assessment Key scientific awards include: NYP Role Model of the Year (2024) Marjorie Topkins Teacher of the Year (2023) Founding Member of American Medical Extended Reality Association Chief Resident recognition (2019-2020) As Director of Simulation Education, he specializes in regional anesthesia, acute pain medicine, thoracic anesthesia, and obstetric anesthesia. He is board-certified by the American Board of Anesthesiology.

• Computer Graphics
• Physics-Based Simulation
• Fluid Dynamics
• +۳ مورد دیگر

Prof. Dr. Jan Bender holds a professorship in Computer Animation at RWTH Aachen University's College of Engineering. As a leading researcher in physics-based simulation methods, his work focuses on developing advanced numerical techniques for fluid dynamics, deformable solids, and multi-physics interactions through Smoothed Particle Hydrodynamics (SPH) and Finite Element Methods (FEM). Key Contributions: Invented PF-FLIP for two-phase flows, developed SymX symbolic framework for energy-based simulations, created STARK unified solver for robotics applications, and introduced implicit boundary handling for SPH Methodologies: Specializes in hybrid Eulerian/Lagrangian approaches, differentiable physics, adaptive discretization, and machine learning integration for simulation acceleration Research Impact: 2023 & 2024 Best Paper Awards in VMV and SCA conferences. His work enables billion-particle fluid simulations and realistic multi-body interactions for robotics, with applications in welding, thermal spraying, and soft robotics. Collaborations: Works extensively with robotics institutes (Gazebo Fluids extension) and materials science departments (TIG welding, thermal spray modeling). Maintains open-source code repositories for simulation frameworks.

• Teleoperation systems
• Human-robot interaction
• AI-based telerobotics
• +۱۰ مورد دیگر

Ziwei Wang is a Lecturer in Robotics at Lancaster University and co-theme lead of the Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRA). He holds a Ph.D. in Automation from Tsinghua University and previously worked as a Research Associate at Imperial College London's Department of Bioengineering before joining Lancaster. Research Focus : Teleoperation systems, human sensorimotor augmentation, AI-based telerobotics, haptic interaction, and control technologies for extreme environments. His work addresses challenges in remote surgery, space robotics, and nuclear decommissioning through prescribed-performance control, reinforcement learning, and fuzzy modeling. Scientific Contributions : Over 70 publications in journals like IEEE Transactions on Automatic Control and IEEE/ASME Transactions on Mechatronics . Key projects include the Future AI and Robotics for Space (FAIR-SPACE) Hub and RAICo Fellowships. Awards include Best Paper at ICAC 2022 and IROS 2024 New Generation Star recognition. Academic Roles : Associate Editor for IET Journal of Engineering and Biomimetic Intelligence and Robotics , with leadership in IEEE conference committees. Teaches Mechanics Engineering and Machine Learning in Engineering at Lancaster.

Sima Asadiمشاهده پروفایل

استادیار

• Infectious Disease Transmission
• Bioaerosol Science
• Transport Phenomena
• +۳ مورد دیگر

Dr. Sima Asadi serves as the Clare Boothe Luce Assistant Professor of Chemical Engineering at the University of Notre Dame since January 2024, conducting pioneering research at the intersection of transport phenomena, soft matter rheology, and bioaerosol science to address infectious disease transmission challenges. Her work integrates experimental and theoretical approaches to develop medical technologies targeting respiratory pathogen spread. Her educational background includes a Ph.D. in Chemical Engineering from UC Davis (2020), where her dissertation on 'airborne infectious disease transmission via expiratory aerosol particles and aerosolized fomites' earned the Zuhair A. Munir Award for Best Doctoral Dissertation, an M.S. in Chemical/Polymer Engineering from Sharif University of Technology (2012), and a B.S. in Chemical Engineering from the University of Tabriz (2009). Postdoctoral training at MIT Chemical Engineering under Prof. Patrick Doyle focused on shape-engineered alginate particles for biomedical applications. Research interests center on three critical areas: elucidating physics of pathogen-laden respiratory droplet formation in human airways; developing single-droplet analysis platforms for bioaerosol composition measurement; and investigating aerosolized fomites (non-respiratory particles from contaminated surfaces) in disease transmission. Her experimental toolkit includes microfluidics, rheometry, high-speed imaging, human/animal experiments, and infectivity assays to bridge engineering principles with health outcomes. Publications reveal consistent focus on airborne transmission mechanics, with 15 recent articles spanning influenza, COVID-19, and fundamental aerosol physics. Key trends include quantifying speech-induced aerosol emission, mask efficacy limitations due to sealing imperfections, and the controversial role of aerosolized fomites in bypassing respiratory transmission pathways. Her work has reshaped pandemic response strategies through evidence-based analysis of ventilation, vocalization, and surface contamination risks. Major recognitions include the Burroughs Wellcome Fund Career Award at the Scientific Interface (2022) and UC Davis College of Engineering's top dissertation prize (2021). Additional honors comprise multiple travel grants from the International Aerosol Conference and UC Davis Graduate Studies, graduate poster prizes, and Iranian national fellowships from Pars Oil and Gas Company and the Iran Nanotechnology Initiative Council. Dr. Asadi actively recruits PhD students and postdoctoral scholars through the Notre Dame CBE program, currently leading a lab focused on microfluidic device design for respiratory droplet analysis. Current projects include 'Elucidating the physics of pathogen-laden respiratory droplet formation' with human experiments and rheometry, and developing single-droplet pathogen load measurement techniques. Her team collaborates across medical, engineering, and public health disciplines to translate fundamental findings into infection control solutions.

• Residual Stress Analysis
• Mechanical Design
• Materials Science
• +۵ مورد دیگر

Vincenzo Moramarco is an Assistant Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari, Italy. His research focuses on residual stress analysis, mechanical design, and materials science in engineering contexts. Position: Assistant Professor Department: Mechanics, Mathematics & Management Email: vincenzo.moramarco@poliba.it His research interests include: Residual stress measurement in additive manufacturing and welding Mechanical behavior of polyurethane foams and composite materials Surface treatments for fatigue enhancement in aerospace components Application of optical methods in stress analysis Biomechanics of human intervertebral discs and lumbar spine Recent publications highlight advancements in: Fused Deposition Modeling (FDM) material characterization Laser peening techniques for stress optimization Residual stress evaluation in dissimilar welded joints Mechanical testing of soft foams using digital image correlation Coatings for biomedical tool performance

Esra Mete Güneyisiمشاهده پروفایل

استاد

• Civil Engineering
• Earthquake Engineering
• Reinforced Concrete Structures
• +۷ مورد دیگر

Professor Esra Mete Güneyisi is affiliated with Gaziantep University since 2020, serving within the Faculty of Engineering, Department of Civil Engineering . She holds a PhD and MSc in Civil Engineering from Boğaziçi University (2007 and 2002, respectively) and a BSc from the same institution (2000). Her research focuses on seismic analysis, composite structures, sustainable construction, and advanced material modeling. Education : PhD (Boğaziçi University, 2007), MSc (Boğaziçi University, 2002), BSc (Boğaziçi University, 2000) Professional Roles : Professor at Gaziantep University (2020–present), Associate Professor (2013–2020), Doctor Lecturer (2008–2013), Research Assistant at Boğaziçi University (2001–2008) Her research integrates artificial intelligence and finite element analysis for structural design, with a focus on reinforced concrete , steel structures , and seismic retrofitting . She has contributed to self-healing concrete and recycled aggregate applications . Her 15 most recent articles explore topics like rubberized CFST columns, eccentric bracing effects, and AI-driven design models, spanning civil engineering, materials science, and structural safety. She has supervised 19 PhD and 49 Master’s theses, addressing nonlinear structural analysis, seismic retrofitting, and composite materials. Her projects include TÜBİTAK-funded studies on structural behavior and international collaborations like the PROHITECH initiative. Memberships include the Structural Steel Association (2010) and the TMMOB-Chamber of Civil Engineers (2007).

Marco Cuturiمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Statistics
• +۳ مورد دیگر

Marco Cuturi is a Professor of Statistics at ENSAE (École Nationale de la Statistique et de l'Administration Économique), part of Institut Polytechnique de Paris, where he has been affiliated since 2016, working part-time since 2018. He is also a CREST Permanent Member and a Research Scientist at Apple ML Research in Paris, having previously worked at Google Brain from 2018 to 2022. His academic journey includes positions as an Associate Professor at Kyoto University's Graduate School of Informatics (2010-2016), a lecturer at Princeton University's ORFE department (2009-2010), and postdoctoral research at the Institute of Statistical Mathematics in Tokyo (2005-2007). Dr. Cuturi earned his Ph.D. from École des Mines de Paris in 2005 and holds a master's degree from ENS Cachan, having graduated from ENSAE in 2001. His research centers on the application of optimal transport theory to machine learning and statistics, with significant contributions to computational optimal transport, kernel methods, and time series analysis. His influential work includes the development of the Sinkhorn distance for fast computation of optimal transport, soft-DTW for time series alignment, and various applications of optimal transport in machine learning. His research program explores how geometric structures can be leveraged in statistical learning, with recent work focusing on entropic optimal transport solvers, Gromov-Wasserstein distances, and applications to single-cell biology and multimodal learning. Dr. Cuturi's publications reveal a consistent trajectory of developing theoretically grounded yet computationally efficient methods that bridge optimal transport theory with practical machine learning applications across diverse domains including computer vision, natural language processing, and computational biology. Among his professional recognitions is a Notable Paper Award at AISTATS 2020 for his work on regularization in optimal transport. He serves as an Associate Editor for JMLR and has held senior area chair positions at NeurIPS and ICML. Dr. Cuturi has organized major machine learning events including the Machine Learning Summer School in Kyoto (2012, 2015) and co-organized workshops on optimal transport at major conferences. Dr. Cuturi has supervised numerous doctoral students including Nina Vesseron, Théo Uscidda, Charlotte Bunne, and Othmane Sebbouh, with research spanning optimal transport applications in generative modeling, representation learning, and biological data analysis. His current research group at ENSAE and Apple continues to push the boundaries of optimal transport methodology and its applications to cutting-edge machine learning problems.