جست‌وجوی استاد و پژوهشگر

• Molecular Simulations
• Digital Pharmacology
• Computational Neuromedicine
• +۴ مورد دیگر

Prof. Dr. Giulia Rossetti leads the Drug Design Hub for Digital Neuropharmacology at Forschungszentrum Jülich's Institute of Neuroscience and Medicine. Her research integrates molecular simulations and data science to advance CNS drug discovery, focusing on challenging targets like neuronal membrane proteins and RNA. She specializes in computational approaches for neuropharmacology, using advanced methods such as QM/MM simulations and machine learning-enhanced sampling algorithms. Her work explores molecular mechanisms of neurological disorders, including the structural dynamics of ion channels and protein modifications in neurodegeneration. Recent publications demonstrate her focus on ligand-receptor interactions, computational drug design optimization, and the application of supercomputing to biological problems. Research consistently addresses pain pathways, neurodegenerative mechanisms, and innovative therapeutic strategies.

Chenglong Youمشاهده پروفایل

عضو هیئت علمی

• Quantum Optics
• Quantum Metrology
• Plasmonics
• +۳ مورد دیگر

Chenglong You is an Assistant Research Professor at the Department of Physics & Astronomy within the College of Science at Louisiana State University (LSU). His research focuses on quantum optics, plasmonics, and quantum metrology with applications in machine learning-enhanced quantum systems. He explores topics such as multiphoton coherence, quantum plasmonic sensing, and advanced interferometric techniques. Research interests include: quantum coherence dynamics, spatial mode engineering, entanglement-based technologies, and nonlinear optical phenomena. His work bridges classical and quantum optics through studies on Gaussian-Schell models and surface plasmon behavior. Recent advancements involve machine learning applications for turbulence correction and spatial mode reconstruction in structured light systems. Publications highlight breakthroughs in quantum metrology precision, phase sensitivity in SU(1,1) interferometers, and plasmonic systems' quantum statistics. Notable contributions include the van Cittert-Zernike theorem extension to quantum regimes and development of scalable quantum sensing frameworks. While no awards or grants are explicitly listed, his active research portfolio indicates significant contributions to quantum information science and nanophotonics. No advisees are currently documented.

Xiao Shenمشاهده پروفایل

دانشیار

• condensed matter physics
• materials science
• computational physics
• +۴ مورد دیگر

Xiao Shen is an Associate Professor in the Department of Physics and Materials Science at the University of Memphis, College of Arts and Sciences. As a computational physicist, his research focuses on condensed matter physics and modern electronic-structure methods to investigate solid-state phenomena. Research areas: 2D materials (particularly silicon telluride), memristive systems, thermoelectrics, defect engineering, ferroelectric metals, and magnetocaloric effects Teaching: Quantum Mechanics, Statistical Mechanics, and Computational Physics courses Funding: National Science Foundation, ORAU Ralph E. Powe Jr. Faculty Enhancement Award, University of Memphis Research Investment Fund, and College of Arts and Sciences Early Career Research Award His publications (2015-2025) demonstrate expertise in computational modeling of phase transitions in vanadium dioxide, defect engineering in garnet crystals, memristive switching mechanisms, and 2D nanomaterials with applications in electronics and energy systems. Scientific awards include: National Science Foundation grants Ralph E. Powe Jr. Faculty Enhancement Award (ORAU) Early Career Research Award (University of Memphis) Research Investment Fund support Dr. Shen actively supervises research projects and has published extensively on topics ranging from radiation effects in semiconductors to nanostructured supercapacitor materials , with recent work focusing on ultrafast phase transitions and dimensional material properties .

• Quantum Computing
• Optimization Methods
• Smart Grid Technologies
• +۵ مورد دیگر

Junpeng Zhan is an Assistant Professor of Renewable Energy Engineering at the Inamori School of Engineering, Alfred University, NY, USA. He holds a Ph.D. and B.Sc. in Electrical Engineering from Zhejiang University (2014 and 2009). Before joining Alfred, he worked as a Research Associate at Brookhaven National Laboratory (2018–2020), a Postdoctoral Fellow at the University of Saskatchewan and Hong Kong Polytechnic University, and a Visiting Research Assistant at the University of Liverpool. His research focuses on quantum computing, optimization methods, smart grid technologies, machine learning, and renewable energy integration. Notable contributions include developing the Quantum Feasibility Labeling algorithm and the hybrid Variational Quantum Search, which address NP-complete problems like the Vertex Coloring problem. He currently leads NSF-funded projects on variational quantum algorithms for power system simulation and ISO-New England-funded work on quantum computing for unit commitment. Zhan is an Associate Editor of IET Generation, Transmission & Distribution and actively reviews for 20+ journals. His grants include $199,980 from the NSF (2022), $50,000 from ISO-New England (2024–2026), and a co-PI role in a $1.37M NYSERDA-funded workforce training initiative. His work bridges fundamental research and practical energy system challenges, emphasizing computational innovation and scalability. Labs and collaborations include the Engineering Laboratories at Alfred University, with partnerships extending to institutions like RIT and GE. His teaching spans courses in power systems, Python for research, and mechatronics.

• Quantum Cryptography
• Network Security
• Wireless Communication Systems
• +۵ مورد دیگر

Miroslav Voznák is a distinguished academic affiliated with the Faculty of the VSB - Technical University of Ostrava in the Czech Republic. His research focuses on cutting-edge areas such as quantum cryptography, wireless communication systems, and network security. He has collaborated extensively with researchers like Tan N. Nguyen, Miralem Mehic, and Peppino Fazio, producing over 255 publications since 2011. His work spans quantum key distribution (QKD) networks, reconfigurable intelligent surfaces (RIS), and non-orthogonal multiple access (NOMA), with applications in IoT, satellite networks, and 6G technologies. Notably, he contributed to the emulation of national QKD networks and explored security in RIS-aided systems. His research also bridges artificial intelligence, such as generative adversarial networks, with cybersecurity. Voznák’s contributions emphasize practical solutions for secure, reliable, and efficient communication in future networks. His publications highlight expertise in hybrid power-frequency multiple access systems, covert communication, and physical layer security. He has investigated the integration of TinyML for 6G networks and analyzed digital health trends using bibliometric methods. Key findings include optimizing RIS-assisted satellite networks under shadow-Rician channels and enhancing NOMA performance with active repeaters. His work often addresses challenges like co-channel interference and imperfect channel state information (CSI), proposing machine learning frameworks and relay selection algorithms. Voznák’s research bridges theoretical advancements with real-world applications, positioning him as a leader in next-generation communication systems.

• Signal Processing
• Image Processing
• Machine Learning
• +۷ مورد دیگر

Jian Qiu Zhang is a Professor at Fudan University in the School of Information Science and Technology , affiliated with the Key Laboratory for Information Science of Electromagnetic Waves and Research Center of Smart Networks and Systems in Shanghai, China. Previously, he was at the University of Greenwich (1999-2002) and earned his PhD in 1996 from Harbin Institute of Technology in the Department of Electrical Engineering . His research interests span Signal Processing , Image Analysis , and Machine Learning , with a focus on applications in Biomedical Imaging , Hyperspectral Data Analysis , and Smart Network Systems . His work often integrates Wavelet Transforms , Tensor Decomposition , and Bayesian Filtering to solve complex problems in Medical Imaging and Wireless Sensor Networks . Recent publications highlight advancements in Deep Learning for 3D Ultrasound , PARAFAC Decomposition , and Graph Neural Networks for Hyperspectral Classification . His technical contributions include Adaptive Filtering Algorithms , Nonlinear Unmixing , and Wavelet-Based Sensor Analysis . Key collaborations include researchers from institutions such as Harbin Institute of Technology, University of Greenwich, and international teams in IEEE Transactions and IGARSS conferences.

• Wireless Communications
• Quantum Machine Learning
• Digital Twin Technology
• +۷ مورد دیگر

Trung Quang Duong is a leading professor in wireless communications and quantum technologies, with a focus on 6G networks, digital twins, and the metaverse. His research spans secure data sharing, UAV-assisted communication, and AI-driven network optimization. He contributes extensively to IEEE journals and conferences as an author and editor. Dr. Duong's work integrates quantum machine learning with digital twin systems to enhance vehicular networks, IoT, and satellite-terrestrial communication. He explores counterfactual quantum protocols, blockchain-based security, and energy-efficient edge computing, positioning himself at the forefront of next-generation wireless standards. His 2025 publications highlight advancements in quantum-enhanced reinforcement learning, UAV swarm efficiency, and digital twin applications for healthcare and environmental monitoring. Collaborations with researchers like Octavia Dobre and Hyundong Shin reflect his role in shaping 6G research agendas through federated learning and semantic communication frameworks.

• Operations Research
• Industrial Engineering
• Quantum Computing Applications
• +۳ مورد دیگر

Malak SAIEM is a researcher affiliated with the Université de Technologie de Compiègne (UTT), specializing in operations research and quantum computing applications. Her work focuses on optimization challenges in industrial engineering, particularly flow shop scheduling problems and predictive maintenance strategies for railway systems. She explores innovative solutions using quantum algorithms (e.g., Grover's algorithm) to address complex combinatorial optimization tasks. Recent research contributions include cost analysis of railway maintenance practices and enhanced quantum unconstrained binary optimization (QUBO) formulations for scheduling. Her interdisciplinary approach bridges quantum computing with transportation and manufacturing systems. No scientific awards or advisory roles are explicitly mentioned in the available data.

Karim Karimمشاهده پروفایل

استاد

• X-ray Imaging Technology
• Semiconductor Materials
• Medical Diagnostic Devices
• +۳ مورد دیگر

Karim Karim is a Professor at the University of Waterloo and serves as Associate Vice-President of Commercialization and Entrepreneurship. His research focuses on advanced X-ray imaging technologies, semiconductor materials, and radiation detection systems. He leads innovations in X-ray detector design, with applications in biomedical imaging and medical diagnostics. Dr. Karim’s work emphasizes materials like TlBr and amorphous selenium, alongside advancements in phase-contrast imaging and detector miniaturization. His research spans semiconductor characterization, detector optimization, and portable diagnostic devices. Key areas include improving temporal performance of detectors, developing high-resolution grating systems, and applying machine learning to image retrieval. Dr. Karim’s contributions address challenges in kidney stone analysis, computed tomography, and point-of-care diagnostics. Publications highlight breakthroughs in multi-layer grating fabrication, dual-energy detectors, and monolithic CMOS-integrated systems. His work bridges material science and engineering, advancing medical imaging capabilities through interdisciplinary approaches.

Toshiya Hachisukaمشاهده پروفایل

دانشیار

• Computer Graphics
• Light Transport Simulation
• Computational Statistics
• +۳ مورد دیگر

Toshiya Hachisuka is an Associate Professor in the Department of Computer Science at the University of Waterloo, part of the School of Computer Science. His research focuses on combining applied mathematics, computer science, and physics to address challenges in visual simulation, particularly in computer graphics, light transport, and computational statistics. He holds a Ph.D. from the University of California, San Diego (2011) and a B.Eng. from the University of Tokyo (2006). Research Interests: Hachisuka’s work spans light transport simulation , Monte Carlo rendering , numerical analysis , and fluid dynamics . He develops algorithms for efficient rendering and simulation, including methods like segment-based light transport and quantum ray marching. His techniques often integrate mathematical rigor with computational efficiency. Publications: His recent work explores advancements in Monte Carlo methods, fluid simulation, and quantum computing applications in rendering. Notable themes include denoising techniques , boundary element methods , and gradient-domain rendering . Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: No specific students or grants listed, but his extensive publication record suggests active research collaboration. Labs/Teams: Involved in computer graphics research groups at the University of Waterloo, contributing to cutting-edge rendering and simulation projects.

Davi Soaresمشاهده پروفایل

استادیار

• Electrochemical Energy Storage
• Materials Science
• Nanotechnology
• +۳ مورد دیگر

Davi Soares is an Assistant Professor in the Department of Electrical and Computer Engineering at Wichita State University (WSU). He is affiliated with the College of Engineering and is based at Wallace Hall on the main campus. His research focuses on advanced materials for electrochemical energy storage systems, including batteries and supercapacitors, with an emphasis on lithium-ion, sodium-ion, and potassium-ion technologies. He also explores applications of machine learning for battery health monitoring and additive manufacturing for electrode fabrication. His work spans material science, nanotechnology, and renewable energy, with a particular interest in transition metal dichalcogenides (TMDs), Weyl semimetals, and carbon-based composites. He has published extensively on topics like battery degradation modeling, composite insulator design, and electrode material optimization. His research aims to enhance energy storage efficiency, resilience, and sustainability in infrastructure applications. Recent publications (2021–2025) highlight trends in physics-informed machine learning frameworks for battery health prediction, novel anode materials for next-gen batteries, and composite materials for supercapacitors. His contributions bridge fundamental material science with applied engineering solutions for energy systems. Dr. Soares collaborates with WSU’s main campus labs and has contributed to peer-review processes, as evidenced by his acknowledgment in the 2022 Acknowledgment to Reviewers of Batteries . His lab’s work is accessible via the campus website.

Jordan R. Raneyمشاهده پروفایل

دانشیار

• Mechanical Metamaterials
• Architected Materials
• Nonlinear Dynamics
• +۶ مورد دیگر

Jordan R. Raney is an Associate Professor in the Department of Mechanical Engineering & Applied Mechanics at the University of Pennsylvania's School of Engineering and Applied Science. He leads the Architected Materials Laboratory, focusing on advanced manufacturing, nonlinear mechanics, and smart materials. His work integrates 3D printing, mechanical metamaterials, and autonomous systems to design materials with programmable behaviors. Research Thrusts: Geometric Control of Nonlinear Behavior : Studies wave propagation, phase transitions, and energy absorption in metamaterials. 3D-Printable Composites : Develops sustainable composites like amylopectin-based materials and fiber-reinforced systems. Mechanical Logic & Autonomous Materials : Creates systems that perform decision-making via material properties, eliminating traditional electronics. Funding: Supported by NSF, Army Research Office, Air Force Office of Scientific Research, and DARPA. His lab's innovations span robotics, aerospace, and biomedical applications. Teaching: Courses include MEAM/MSE 507: Fundamentals of Materials , MEAM 321: Vibrations of Mechanical Systems , and MEAM 211: Engineering Mechanics: Dynamics . Lab & Team: The Architected Materials Lab includes PhD students, master’s candidates, and undergraduates. Notable alumni have joined industry leaders like Intel and Lam Research.

Louise Skinnariمشاهده پروفایل

استادیار

• Experimental Particle Physics
• High Energy Physics
• Top Quark Studies
• +۴ مورد دیگر

Louise Skinnari is an Assistant Professor in the Department of Physics at Northeastern University, affiliated with the College of Science. Her research focuses on experimental particle physics using the CMS detector at CERN's Large Hadron Collider (LHC), particularly studying top quarks and upgrading the CMS trigger system to enhance charged particle trajectory identification. Prior to Northeastern, she was a research associate at Cornell University working on the CMS experiment, and completed her PhD at UC Berkeley investigating physics beyond the Standard Model with the ATLAS experiment. Education: PhD in Physics from University of California, Berkeley (ATLAS Collaboration); prior work at Cornell University (CMS Collaboration). Research Interests: Experimental particle physics, top quark dynamics, dark matter detection, Standard Model precision measurements, and detector development for high-luminosity LHC runs. She contributes to the Future Circular Collider feasibility studies and explores novel signatures of beyond-Standard-Model physics through CMS data. 2025 Breakthrough Prize in Fundamental Physics (shared with Northeastern team) 2020 Department of Energy Physics Grant recipient Her work involves collaborations on detector upgrades, including the CMS Phase-2 Outer Tracker, and employs advanced analysis techniques such as machine learning for event classification. Active in both proton-proton and heavy ion collision studies, her research bridges fundamental physics questions with cutting-edge experimental techniques. Labs/Groups: CMS Collaboration at CERN, Northeastern High Energy Physics Group, and Future Circular Collider Studies.

Efstathios Stiliarisمشاهده پروفایل

دانشیار مدعو

• Experimental Nuclear Physics
• Medical Imaging
• Artificial Intelligence and Machine Learning
• +۵ مورد دیگر

Efstathios Stiliaris is an Experimental Nuclear Physicist at the Department of Physics of the National & Kapodistrian University of Athens since 2016, where he also served as Acting Head of the Nuclear and Particle Physics Laboratory until 2022. He concurrently holds the position of Adjunct Associate Professor at The Cyprus Institute in Nicosia, Cyprus since 2013, leading the research group at the SPECT Laboratory (SPECT-Lab). His primary research focuses on experimental nuclear physics, medical imaging technologies, and hadronic physics, with a strong emphasis on developing advanced imaging modalities such as SPECT, Compton cameras, and AI-driven solutions for radiological diagnostics. His expertise spans radiation detection technologies, including gamma-camera systems, thermal tomography, and the development of dual-modality contrast agents for SPECT/MRI and PET/MRI applications. He has pioneered novel reconstruction algorithms like RISE and ComptonRec, addressing challenges in image quality enhancement and inverse problem solutions. His work bridges nuclear physics fundamentals with clinical applications, leveraging machine learning for therapeutic and diagnostic advancements. Key contributions include optimizing semi-pixelated scintillation crystals for planar imaging, exploring proton structure through pion photoproduction analyses, and advancing thermal tomography through physics-informed neural networks. His multidisciplinary approach integrates computational physics, material science, and biomedical engineering to address unresolved challenges in medical imaging instrumentation and radiation detection systems. Stiliaris has collaborated on projects involving the CMS Collaboration at CERN and contributed to experimental methodologies for thermal loss estimation in molten salt energy systems. His research group maintains active involvement in both fundamental physics and translational medical imaging technologies, emphasizing lab-to-clinic transitions through rigorous validation protocols and novel experimental setups.

• Femtosecond Laser Fabrication
• Optical Fiber Sensors
• Photonics
• +۳ مورد دیگر

Dr. Mohan Wang is a Postdoctoral Researcher at the University of Oxford's Department of Engineering Science. Her work focuses on femtosecond laser fabrication techniques for advanced optical devices, particularly in developing robust optical fiber sensors capable of operating in extreme environments. She holds a PhD from the University of Pittsburgh for her thesis on photonic device fabrication on transparent platforms. Her research interests include 3D photonic circuit fabrication, optical fiber sensor systems for industrial applications, and laser material processing. She collaborates with researchers such as Dr. Julian Fells, Dr. Patrick Salter, and Professor Martin Booth within the Dynamic Optics and Photonics research group. Dr. Wang’s publications span topics like nanograting-based sensors, high-temperature stable interferometric systems, and topological photonics. Her work bridges fundamental physics with applied engineering, addressing challenges in energy systems, additive manufacturing, and infrastructure monitoring through innovative optical sensing solutions. Notable contributions include pioneering reel-to-reel fabrication of fiber sensors and integrating machine learning with distributed fiber sensors for pipeline protection. Her interdisciplinary approach combines photonics, materials science, and data analytics to advance next-generation sensing technologies.