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Colleen B. MouwView profile

Professor

• Biological Oceanography
• Remote Sensing
• Aquatic Optics
• +4 more

Dr. Colleen Mouw is a Professor at the University of Rhode Island's Graduate School of Oceanography and currently serves as the Acting Dean of the Graduate School . Her research focuses on biological oceanography using optical tools to study phytoplankton community structure, carbon cycling, and physical drivers in marine and freshwater systems. Ph.D. in Oceanography, University of Rhode Island M.S. in Oceanography, University of Rhode Island B.S. in Biology, Western Michigan University Her work spans phytoplankton ecology , remote sensing of ocean color , and climate change impacts on marine biogeochemistry. Recent publications highlight advancements in hyperspectral optical property analysis , HAB prediction using imaging , and global carbon cycling models . Dr. Mouw has received prestigious recognitions including the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2016 . She actively mentors students and leads initiatives like the MPOWIR program to support women in physical oceanography. Her lab utilizes cutting-edge facilities including the Imaging FlowCytobot at URI's Coastal Institute.

Fatih IlgınView profile

Lecturer

• High Energy Physics
• Particle Accelerators
• Collision Simulations and Data Analysis
• +4 more

Dr. Fatih Ilgın is a Lecturer in the Faculty of Engineering at Özyeğin University. He holds a PhD in High Energy Physics from Yıldız Technical University (2023), where he also earned his MSc (2017) and BSc (2011) in Physics. His research spans high energy physics, particle accelerators, collision simulations, and machine learning applications. Education PhD in High Energy and Plasma Physics, Yıldız Technical University (2023) MSc in Physics, Yıldız Technical University (2017) BSc in Physics, Yıldız Technical University (2011) Research Areas High Energy Physics Particle Accelerators Collision Simulations and Data Analysis Machine Learning Models Particle Detectors and Calorimeters Deep Neural Networks Publications 2023: Charge Parity Symmetry Violation via Top Quark Chromo-Electric Dipole Moments using Machine Learning 2022: Charge Parity Symmetry Violation via Top Quark Chromo-Electric Dipole Moments using Machine Learning 2017: Effective Mass Calculation of Lambda and Sigma Baryons Containing Tılsım Quark in Lattice QCD

Mario StipčevićView profile

Professor

• Quantum optics
• Quantum information
• Quantum communication
• +5 more

Mario Stipčević is a senior scientist at the Ruđer Bošković Institute (RBI) in Zagreb, Croatia, where he heads the Photonics and Quantum Optics Laboratory within the Division of Experimental Physics. Holding the Croatian rank of scientific advisor in permanent position —equivalent to full professor—he has led national and international research efforts spanning quantum optics, quantum-information science, and high-energy neutrino physics. Education 2011–2012: Visiting Specialization in Experimental Quantum Information, University of California, Santa Barbara, USA 2010–2011: Fulbright Scholar, Experimental Quantum Information, University of California, Santa Barbara, USA 1994: PhD in Particle Physics, Université de Savoie, Chambéry, France 1991: BSc in Theoretical Nuclear Physics, Faculty of Science, University of Zagreb Research Interests His work focuses on quantum communication , quantum randomness , single-photon detection , quantum key distribution , and quantum holography . Early in his career he contributed to neutrino oscillation experiments (NOMAD, OPERA) and calorimetry R&D for the LHC. Scientific Awards Fulbright Scholarship (2010–2011) Golden Medal ARCA 2005, Zagreb International Autumn Fair Golden Medal, Salon International de Inventions, Geneva 2005 Young Investigator Award, Ministry of Science and Technology, Croatia (1998) Projects & Leadership Since 2014 he has been Head of the Photonics and Quantum Optics Research Unit at the RBI Centre of Excellence for Advanced Materials and Sensing Devices. Earlier projects include the Croatian Ministry of Science grant Experiments in Quantum Communication and Quantum Information (2007–2014) and World-Bank-funded development of a Quantum Random Bit Generator (2004–2005). Laboratory & Teams At RBI he directs the Photonics and Quantum Optics Laboratory , a multi-disciplinary group developing photon-counting detectors, entangled-photon sources, and quantum-network testbeds. The lab participates in European initiatives such as the European Quantum Internet Alliance and the ESSnuSB neutrino super-beam design study .

• High Energy Physics
• Beyond-the-Standard-Model Physics
• Dark Matter Direct Detection
• +3 more

Christina (Yang) Gao is an Assistant Professor at the Southern University of Science and Technology (SUSTech) in the Department of Physics under the Faculty of Science. Her research focuses on theoretical investigations beyond the Standard Model, particularly in dark matter phenomenology and quantum systems for dark matter detection. PhD in Physics from University of California, Davis (2012-2018) Bachelor and Master of Physics from University of Oxford (2008-2012) Dr. Gao's work spans multiple high-energy physics domains: Dark Matter Direct Detection Particle Cosmology Quantum Systems in Physics Machine Learning Applications Her publications highlight collaborative efforts in axion detection methods using superfluid helium-3 and superconducting cavities. 2023 Simons Foundation Travel Grant 2009 New College Scholarship Contact: gaoy3@sustech.edu.cn

• Offshore Geotechnical Engineering
• Marine Foundation Engineering
• Marine Mooring and Anchoring Technologies
• +4 more

Dr. Fu Yong is an Assistant Professor and doctoral supervisor at the Department of Marine Science and Engineering , Southern University of Science and Technology (SUSTech), Shenzhen, China. He earned his PhD in Geotechnical Engineering from the National University of Singapore (NUS) in 2018, preceded by a Master's (Tongji University, 2013) and Bachelor's (Shanghai University, 2010) in Civil Engineering . Current Role: Assistant Professor, SUSTech (2020–present) Prior Roles: Research Fellow (NUS, 2018–2020), Research Engineer (NUS, 2017–2018) Dr. Fu specializes in marine geotechnical engineering , with key focuses on offshore wind power foundation design, deep-sea anchoring technologies, and underground space development. His technical expertise includes large deformation finite element modeling (CEL/RITSS methods), stochastic analysis , and machine learning (GWO-MLP optimization). His work bridges laboratory experiments (centrifuge and field tests) with computational simulations to address challenges in marine infrastructure. Marine foundation design for offshore wind turbines and marine ranches Deepwater dynamic penetration anchoring systems Horizontal deep soil mixing equipment for underground space Finite element simulation of large deformations and thermal effects His publication trends reveal a consistent focus on offshore anchoring mechanics, shale fracture modeling, and machine learning integration for geotechnical predictions. Recent works (2024–2025) emphasize torsional effects in suction anchors, sand-GFRP interface behavior, and advanced constitutive models for cemented soils. Key journals: Geotechnique , Ocean Engineering , Physics of Fluids , Computers and Geotechnics Dr. Fu has received multiple scientific awards , including the 2024 SUSTech Undergraduate Teaching Award, 2022 SUSTech Outstanding College Mentor Award, and NUS Research Scholarship (2013–2017). Selected Honors: 2024 First Prize, SUSTech Undergraduate Teaching Award 2022 SUSTech Outstanding College Mentor 2020 Shenzhen Overseas High-level C Talent He actively contributes as a reviewer for 18+ international journals and serves on editorial boards for journals like Sustainable Structures . His work on machine learning-driven anchor penetration prediction (2024) and hybrid phase-field shale fracture modeling (2024) exemplifies interdisciplinary innovation.

• Atmospheric Science
• Climate Science
• Environmental Science
• +3 more

Tuomo Nieminen is a University Lecturer at the Institute for Atmospheric and Earth System Research (INAR) , University of Helsinki. He serves as Supervisor for the Doctoral Programme in Atmospheric Sciences , focusing on global atmosphere-earth surface feedbacks. Institute for Atmospheric and Earth System Research (INAR), University of Helsinki Doctoral Programme in Atmospheric Sciences His research spans atmospheric particle formation, aerosol dynamics, and climate feedback mechanisms. Key collaborations include the CLOUD experiments at CERN and international partnerships with institutions like the School of Earth and Environment, University of Leeds (2015 visiting researcher). Recent publications analyze machine learning applications for boreal forest carbon exchange, parameterization of particle formation rates, and Himalayan aerosol factory modeling. His work connects sulfuric acid dynamics, biogenic emissions, and aerosol-climate interactions. Academic activities include participation in the NOSA-FAAR Symposium (2015 poster), CLOUD data workshops (2015 attendee), and Centre of Excellence in Atmospheric Science seminars.

Mostafa BedewyView profile

Associate Professor

• Advanced manufacturing
• Nano- & micro-fabrication
• Flexible electronics
• +11 more

Dr. Mostafa Bedewy is an Associate Professor at the University of Pittsburgh's Swanson School of Engineering, with primary appointment in Mechanical Engineering & Materials Science and secondary appointments in Chemical & Petroleum Engineering and Industrial Engineering. He leads the NanoProduct Lab , focusing on nanomanufacturing and advanced materials research. Education: Postdoctoral training, Massachusetts Institute of Technology (MIT), 2014-2016 Ph.D. Mechanical Engineering, University of Michigan–Ann Arbor, 2009-2013 M.S. Mechanical Design and Production Engineering, Cairo University, 2008 B.S. Mechanical Design and Production Engineering, Cairo University, 2006 Research Interests: Dr. Bedewy's work spans advanced manufacturing, nanoscale materials synthesis, and bio-inspired design. Key areas include laser processing, carbon nanotube growth dynamics, self-folding polymer systems, cybermanufacturing, and machine learning applications for materials optimization. His interdisciplinary approach bridges nanotechnology with flexible electronics, biomedical devices, and sustainable manufacturing. Publication Trends: Recent articles (2020-2025) demonstrate strong focus on precision nanofabrication techniques, including laser-induced graphene synthesis, chemical vapor deposition optimization for carbon nanotubes, and intelligent polymer systems. Machine learning integration for materials characterization and process control emerges as a growing theme alongside biomedical applications like biosensors and magnetically directed nanoparticles. Awards and Honors: NSF CAREER Award (2023) TMS Frontiers of Materials Award (2022) IISE Outstanding Young Investigator Award (2020) SME Outstanding Young Manufacturing Engineer Award (2018) ORAU Ralph E. Powe Junior Faculty Enhancement Award (2017) American Carbon Society Robert A. Meyer Award (2016) University of Michigan Richard and Eleanor Towner Prize (2014) MRS Silver Award (2013) Research Leadership: As director of the NanoProduct Lab, Dr. Bedewy oversees projects funded by NSF and other agencies, including his CAREER award on cybermanufacturing frameworks. The lab develops novel in-situ characterization tools and data-driven manufacturing platforms, collaborating with biomedical and industrial partners to translate nanomaterials into functional devices.

• Additive Manufacturing
• Computational Mechanics
• Topology Optimization
• +4 more

Albert To is a Professor at the Swanson School of Engineering, University of Pittsburgh, where he holds the William Kepler Whiteford Professorship. He serves as Director of both the MOST-AM Consortium and the ANSYS Additive Manufacturing Research Laboratory. Since joining Pitt in 2008, he has advanced from assistant to associate (2014) and full professor (2019). Education: BS, MS, and PhD from UC Berkeley; MS from MIT Postdoctoral Research: Northwestern University with Wing Kam Liu Dr. To's primary research interests center around design optimization for additive manufacturing, multiscale methods, and computational mechanics. His work focuses on fast process modeling and topology optimization for metal additive manufacturing. He directs the ANSYS Additive Manufacturing Research Laboratory, which houses advanced metal 3D printers including EOS DMLS, Optomec LENS, and ExOne binder jetting systems. In 2016, he founded the MOST-AM Consortium, which now includes over 30 member companies and research labs collaborating on additive manufacturing research. His research has been consistently supported by major funding agencies including NASA, DOD, DOE, NSF, America Makes, and industry partners like ANSYS. The recent publications demonstrate a strong focus on addressing key challenges in metal additive manufacturing processes, particularly laser powder bed fusion and wire-arc directed energy deposition technologies. His work spans from fundamental process modeling to practical applications in materials science and mechanical engineering. NSF BRIGE Award (2009) Air Force Summer Faculty Fellowship (2009) Board of Visitors Faculty Award (2016) Carnegie Science Award (2018) Best Student Paper Award, 46th Acoustic Emission Working Group Meeting (2003) Dr. To has secured substantial research funding from government agencies and industry partners to advance additive manufacturing technologies. His MOST-AM Consortium facilitates collaboration between academia and industry, accelerating the translation of research findings into practical applications. He has advised numerous graduate students who have contributed to his extensive publication record in top journals. His laboratory at the University of Pittsburgh is equipped with state-of-the-art metal 3D printing systems, enabling both fundamental research and applied development in additive manufacturing. The MOST-AM Consortium provides a framework for industry collaboration, ensuring research addresses real-world challenges in the field.

• Soft Matter Physics
• Computational Physics
• Machine Learning in Materials Science
• +7 more

Laura Filion is an Associate Professor at the Faculty of Science, Utrecht University , specializing in Soft Condensed Matter within the Debye Institute for Nanomaterials Science . Her research leverages advanced computational and machine learning algorithms to investigate self-assembly processes in soft matter systems, with applications to sustainable material design. Email: l.c.filion@uu.nl Phone: +31 30 253 3519 Research Interests span soft matter physics , colloidal self-assembly , machine learning in materials science, crystal nucleation , and defect analysis . Her work bridges computational methods with experimental validation, focusing on structure-dynamics relationships in supercooled liquids and active colloids. Scientific Contributions include pioneering studies on quasicrystal stability , entropic interactions , and microtubule self-organization . Key publications in Physical Review Letters , Nature Reviews Physics , and Soft Matter highlight her expertise. Awards NWO-Veni Fellowship (2013) NWO-Vidi Fellowship (2019) Collaborative Efforts involve interdisciplinary projects with physicists, biologists, and materials scientists. She supervises doctoral and master's students indirectly through her research group's publications and network.

• Algorithms for hybrid and embedded systems
• Level set methods
• Hamilton-Jacobi equations
• +4 more

Professor Ian Mitchell is a faculty member in the Department of Computer Science at the University of British Columbia's Faculty of Science. He received his B.A.Sc. and M.Sc. from UBC and PhD in Scientific Computing and Computational Mathematics from Stanford University. His research focuses on algorithms for hybrid systems, level set methods for Hamilton-Jacobi PDEs, control systems for cyber-physical applications, assistive technology development, and reproducible research practices. Key research contributions include the Toolbox of Level Set Methods Development of Dijkstra-like ordered upwind methods Advancements in safety-preserving control algorithms Applications in smart wheelchair navigation and user interface design His recent publications demonstrate expertise in Hamilton-Jacobi equation solving, robotics applications of level set methods, and assistive technology design. While no explicit awards are listed, his work spans multiple disciplines including computer science, control theory, and biomedical engineering. He teaches both graduate (CPSC 5xx) and undergraduate (CPSC 4xx and below) courses in computational robotics and parallel computing.

• Linguistics
• Corpus Linguistics
• Terminology
• +3 more

Anne Condamines is a Research Director at CNRS, affiliated with the CLLE (Cognition, Languages, Language, Ergonomics) laboratory at University Toulouse - Jean Jaurès. Her work bridges corpus linguistics, terminology, and cognitive ergonomics, focusing on specialized communication and controlled language applications. Key affiliations: CNRS, CLLE, EDF R&D, CERN collaborations Research themes: Terminology construction, linguistic variation in specialized contexts, cognitive aspects of term usage Research Highlights : 2024: Analyzed controlled language effectiveness in technical communication 2022: Explored knowledge-rich contexts and exobiology terminology 2021: Investigated terminological anomalies and conceptual relations Academic Contributions : Co-developed textual terminology frameworks Founded MAR-REL (Conceptual Relation Markers Database) Pioneered ergonomic linguistics for language engineering

David RichterView profile

Professor

• Computational Fluid Dynamics
• Turbulence Modeling
• Air-Sea Interaction
• +4 more

David Richter is a Professor at the University of Notre Dame in the Department of Civil and Environmental Engineering and Earth Sciences , with a concurrent appointment in the Department of Aerospace and Mechanical Engineering . He serves as the Frank M. Freimann Collegiate Professor of Environmental Fluid Dynamics and leads the Richter Lab , focusing on multiphase turbulent processes in environmental systems. Education: Ph.D. in Mechanical Engineering, Stanford University (2011) M.S. in Mechanical Engineering, Stanford University (2011) B.S. in Mechanical Engineering, University of Massachusetts (2006) Appointments: 2025–Present: Professor, University of Notre Dame 2019–2025: Associate Professor, University of Notre Dame 2013–2019: Assistant Professor, University of Notre Dame 2011–2013: Postdoctoral Fellow, National Center for Atmospheric Research Richter’s research spans computational fluid dynamics , air-sea interactions , and geophysical flow modeling , addressing challenges in hurricane boundary layer dynamics , sediment transport , and cloud microphysics . His work employs direct numerical simulations (DNS) and large-eddy simulations (LES) , integrating physics-informed machine learning for predictive models. Recent projects include storm surge forecasting in Alaska and air-sea flux studies via NSF and ONR grants. Scientific contributions include 101 publications, with recent articles analyzing marine fog microphysics , tropical cyclone intensification , and microplastic transport . His 15 most recent articles (2021–2025) emphasize turbulent flow modeling , particle-laden systems , and environmental impact prediction . Awards: 2025: Frank M. Freimann Collegiate Professor 2021: Outstanding Teacher, University of Notre Dame Engineering Office of Naval Research Young Investigator Award Richter mentors graduate students in atmospheric and oceanic sciences , with lab alumni pursuing academic and industry roles. His collaborative projects extend to institutions like McGill University , University of Chile , and Michigan Tech .

• Particle Physics
• Machine Learning
• High Energy Physics
• +3 more

Dr. Barry Dillon serves as a Lecturer in Mathematics at Ulster University's Intelligent Systems Research Centre (ISRC) within the School of Computing, Engineering and Intelligent Systems. His academic profile combines expertise in theoretical physics with advanced machine learning applications for high-energy physics research. His educational foundation includes: PhD in Theoretical Physics from the University of Sussex Postdoctoral positions at the University of Plymouth (UK), Jožef Stefan Institute (Slovenia), and University of Heidelberg (Germany) Dillon's research centers on machine learning applications for Large Hadron Collider (LHC) data analysis, specifically developing techniques to identify signatures of new particles through anomaly detection and representation learning. His work bridges Beyond the Standard Model physics, strong-field quantum electrodynamics, and particle phenomenology, with recent publications demonstrating innovative approaches to physics-informed neural networks and self-supervised learning frameworks. Analysis of his 15 most recent publications (2022-2025) reveals a dominant research trajectory in physics-guided machine learning, with 80% focusing on anomaly detection methodologies for LHC data, 60% exploring representation learning techniques, and significant contributions to Snowmass Reports on computational physics frontiers. His fingerprint analysis confirms specialization in Representation Space, Contrastive Learning, and Anomaly-Based Detection within computer science applications to physics. Based at Ulster University's Derry~Londonderry campus in Room MS129, Dillon actively contributes to the Intelligent Systems Research Centre's interdisciplinary initiatives in computational intelligence and data-driven scientific discovery.

• Quantum Computing
• Theoretical Physics
• Neutrino Physics
• +4 more

Marek Góźdź is a University Professor and Head of the Department of Computer Science Basics at the Faculty of Mathematics, Physics and Computer Science, Maria Curie-Skłodowska University (UMCS) in Lublin, Poland. His academic work bridges theoretical physics and computer science with particular focus on quantum phenomena and computational applications. Dr. Góźdź's research interests span multiple disciplines with strong emphasis on quantum mechanics and computational methods: Quantum Computing and Quantum Information Time in Quantum Systems Quantum Evolution Formalisms Neutrino Physics Computer Security Systems Epidemiological Modeling using Computational Methods His scholarly output demonstrates a clear trajectory toward understanding quantum time and its implications for fundamental physics. Góźdź has developed the projection evolution formalism as a framework for addressing the problem of time in quantum systems, with applications ranging from quantum computing to understanding relativistic particles. His work on the RSIR model shows his ability to apply computational methods to real-world problems like pandemic forecasting, demonstrating the interdisciplinary nature of his research. With 34 publications and h-indices of 7 (Google Scholar), 1 (Scopus), and 6 (Web of Science), his work shows significant scholarly impact. Dr. Góźdź serves as thesis advisor for both diploma and doctoral students at UMCS. His supervision spans computer science topics including quantum computing, Linux system security, and physical simulations, as well as theoretical physics topics such as non-standard physics, neutrino physics, and quantum mechanics fundamentals. His office is located in Room 305 of the Institute of Computer Science building where he holds consultations either on scheduled dates or by prior arrangement.

Anthony LaingView profile

Professor

• Quantum Technologies
• Quantum Simulations
• Photonic Chips
• +3 more

Professor Anthony Laing is a Professor of Physics at the School of Physics, University of Bristol, where he leads research in quantum technologies at QET Labs. He holds a 5-year EPSRC fellowship in quantum technologies and serves as an investigator on the UK Quantum Communications Hub. His research focuses on harnessing quantum physics to develop new technologies with potential societal impact comparable to the information age or industrial revolution. Dr. Laing's research interests center on quantum simulations using photonic chips to model microscopic physical systems that are too complex for conventional computers. His work specifically targets simulating molecular quantum dynamics using single photons controlled by programmable optical chips, with applications in chemistry, particle physics, and understanding energy transport in biological molecules. His research fingerprint shows strong activity in Photonics (100%), Quantum Technology (48%), Quantum Information Science (39%), Quantum Dot (25%), Machine Learning (22%), and Integrated Optics (22%). His recent publications demonstrate a clear focus on advancing integrated quantum photonics, with emphasis on developing practical quantum computing and simulation platforms. The research trajectory shows progression from fundamental quantum simulation techniques toward more complex integrated photonic systems capable of high-dimensional quantum computation and near-ideal photon sources. Scientific recognition includes: Lecturer in Physics (awarded by competition, 2016) Professor Laing actively supervises research students (13 supervised works documented) and leads multiple research projects including the QCS Hub with funding from EPSRC. His group collaborates extensively within the quantum technology community, evidenced by numerous conference participations and co-authorships with leading researchers in the field. Based at QET Labs (Quantum Engineering Technology Labs) at the University of Bristol, Professor Laing's research group works at the intersection of quantum physics, photonics, and computing, developing experimental platforms for quantum simulation and computation with potential applications across chemistry, materials science, and fundamental physics.