جست‌وجوی استاد و پژوهشگر

Venkata Virajit Garbhapuمشاهده پروفایل

پژوهشگر ارشد

• Optical Communications
• Network Optimization
• Artificial Intelligence in Networking
• +۴ مورد دیگر

Venkata Virajit Garbhapu (Viru) is a Research Fellow at the CONNECT Centre, Trinity College Dublin, specializing in optical networks with emphasis on physical layer optimization and cross-layer integration. Education: PhD, Institut Polytechnique de Paris (CIFRE scholar with Huawei) Laura Magistrale, Politecnico Di Milano His research focuses on maximizing network capabilities through physical layer impairment-aware frameworks, demonstrated by 52.8% training time reduction in distributed AI systems using 800G DCI links and superior failure recovery via optical circuit switching. Key contributions include per-channel power allocation algorithms balancing linear/nonlinear impairments and feasible C-band integration of classical/QKD signals through Raman noise-aware wavelength allocation. His work centers on developing sophisticated simulators enabling cross-layer optimization for functionalities like QKD and packet switching. Analysis of his 2019-2025 publications reveals a dominant trend toward AI-driven network automation, with increasing emphasis on digital twins and large language models for lifecycle management, alongside quantum-classical signal coexistence. The research consistently bridges theoretical algorithms with experimental demonstrations, showing progression from transfer learning for failure detection to advanced AI/quantum integration. No scientific awards were mentioned in available sources. His PhD was supported by a CIFRE industrial scholarship with Huawei, and he participated in the European QSNP project. No student advisement details are provided, though his BONSAI research addressed optical network failure management using transfer learning to overcome data scarcity. He operates within the CONNECT Centre ecosystem at Trinity College Dublin while maintaining active collaboration with Huawei Paris Research Center, reflecting strong industry-academia integration in optical communications research.

Norbert Schererمشاهده پروفایل

استاد

• Biophysics
• Materials Chemistry
• Physical Chemistry
• +۵ مورد دیگر

Norbert F. Scherer is a Professor of Chemistry at the University of Chicago , with affiliations to the James Franck Institute and the Institute for Biophysical Dynamics. His research spans biophysics, materials chemistry, and optical physics, focusing on nonequilibrium systems, nanoplasmonics, and cellular transport mechanisms. B.S., University of Chicago (1982) Ph.D., California Institute of Technology (1989) Research Highlights: Optical matter self-organization and nanoscale light-powered machines Optical magnetism in nanoplasmonic meta-materials Intracellular vesicle transport in diabetic cell models Development of ultrafast lasers, 4D microscopy, and image analysis methods Scientific Contributions: 2022 Optica C.E.K. Mees Medal 2015 Peter Debye Prize 2014 Vannevar Bush Fellowship 1997 Sloan Fellow 1993 Packard Fellow Student Advisees: Charlie Wright (PhD) Daozheng Gong His group collaborates on quantum dot integration, optical vector beam spectroscopy, and machine learning approaches to intracellular dynamics. Laboratory Focus: The Scherer Lab develops photonic methods for optical trapping, super-resolution microscopy, and nonequilibrium systems analysis, with applications in diabetes research and nanoscale machine design.

Damien Coyleمشاهده پروفایل

استاد

• Computational Intelligence
• Artificial Intelligence
• Bio-signal Processing
• +۴ مورد دیگر

Professor Damien Coyle is a leading academic at Ulster University , holding the title of Professor of Neurotechnology and serving as Director of the Intelligent Systems Research Centre (2017-2022) and Research Director in the School of Computing, Engineering and Intelligent Systems. He also leads the Cognitive Analytics Research Laboratory (CARL) and directs Ulster’s Spatial Computing and Neurotechnology Innovation Hub (SCANi-hub) . His research focuses on AI-driven brain-computer interface (BCI) development , translating electrophysiological signals into control signals for applications in rehabilitation, diagnostics, assistive communication, and entertainment. With over 190 publications and an h-index of 31, he pioneered wearable neurotechnology through his spinout company NeuroCONCISE Ltd (founded 2016). 2008 IEEE CIS Outstanding Doctoral Dissertation Award 2011 INNS Young Investigator of the Year 2018 IET Innovation Award Over £18m in external grants Co-investigator for Northern Ireland Functional Brain Mapping Facility As a IEEE Senior Member and founder of the International Brain-Computer Interface Society , he bridges academic research with industrial applications through projects like AI for Intelligent Neurotechnology and Smart Nano-Manufacturing Corridor .

Naim Dahnounمشاهده پروفایل

استاد آموزشی

• Signal Processing
• Digital Signal Processing
• mmWave Radar
• +۲ مورد دیگر

Naim Dahnoun serves as Professor of Teaching and Learning in Signal Processing at the University of Bristol's School of Electrical, Electronic and Mechanical Engineering, where he contributes to the Visual Information Laboratory and Photonics and Quantum research theme. His career uniquely integrates advanced technical research with pedagogical innovation in engineering education. He holds an Ingenieurd'Etat degree from Oran and a Ph.D. from the University of Leicester, establishing his foundation in both theoretical and applied engineering. His academic journey reflects continuous engagement with cutting-edge signal processing technologies and educational methodologies. Research interests center on Signal Processing with specialized expertise in Digital Signal Processing implementation , mmWave Radar systems for human monitoring, Biomedical Signal Processing (particularly foetal EEG and vital sign detection), and Engineering Education pedagogy. His work develops real-time radar-based solutions for posture estimation, fall detection, and non-contact health monitoring while pioneering FPGA-based DSP teaching approaches. Analysis of his 91 publications reveals a distinct dual trajectory: since 2020, approximately 60% of his work focuses on mmWave radar applications in healthcare (including neonatal monitoring and elderly care), while the remainder advances DSP education through project-based FPGA implementations and e-learning tools. This synergy between technical innovation and teaching methodology defines his scholarly contribution. His recognition includes: Students’ Award for Outstanding Teaching (Engineering), 2018 As a research supervisor, he has guided 7 students through advanced projects while securing funding as co-investigator on interdisciplinary grants including a national database study on neonatal outcomes (2018-2019) and Bristol Harbour water quality monitoring (2017). His grant portfolio demonstrates consistent translation of signal processing techniques to healthcare and environmental applications. Within the Engineering Education Research Group, he leads initiatives in DSP pedagogy and digital learning tools, while his technical work in the Visual Information Laboratory drives radar-based sensing innovations. Current projects emphasize low-cost, real-time implementations for both medical monitoring systems and educational platforms.

Giuseppe Iannacconeمشاهده پروفایل

استاد

• Quantum Electronics
• Neuromorphic Computing
• 2D Materials
• +۵ مورد دیگر

Giuseppe Iannaccone is a full Professor at the University of Pisa , Department of Information Engineering. His research focuses on quantum electronics , neuromorphic computing , and 2D materials for advanced applications in analog circuits and high-temperature electronics. Lead researcher in QUEPE Quantum Engineering and QUEFORMAL projects Pioneer in neuromorphic chip design using silicon and 2D materials Active in developing high-temperature integrated circuits for industrial applications His recent work explores twisted transition metal dichalcogenides for spintronics, inkjet-printed 2D electronics on paper substrates, and wireless power transfer systems for medical devices. The Google Scholar articles show consistent contributions to analog neuromorphic engines , quantum transport modeling , and steep-slope transistor architectures . Collaborations include Gianluca Fiori and Benjamin Zambrano in neuromorphic hardware development. He actively promotes student well-being through institutional initiatives like the Ufficio Benessere at University of Pisa and has taught RFID and IoT courses for PhD students. Current research integrates MoS2/graphene heterostructures and van der Waals junctions for next-generation electronics.

Olav Gaute Hellesøمشاهده پروفایل

استاد

• Photonics
• Micro- and Nanotechnology
• Integrated Optics
• +۴ مورد دیگر

Olav Gaute Hellesø is a Professor and Head of the Department of Physics and Technology at UiT The Arctic University of Norway, where he has been employed since 1998 and led the department since 2021. He is a member of the Ultrasound, Microwaves and Optics research group, focusing on integrated optics and optical trapping of micro- and nanoparticles. Education : Cand. Scient. from University of Bergen (1989), DEA and Dr. from INP de Grenoble, France (1990, 1994) His research spans photonics applications in biomedical engineering, including Raman spectroscopy for extracellular vesicle analysis, metasurface-based optical trapping, and waveguide sensor development. Recent publications highlight innovations in dielectric nanotweezers, BIC-supported trapping systems, and self-supervised learning for spectral analysis. Collaborators include Md Rabiul Hasan, Mathias Novik Jensen, and Balpreet Singh Ahluwalia, with work featured in Nature Photonics , Optics Express , and ACS Applied Optical Materials . His team explores optical manipulation of biological nanoparticles, methane detection via cryptophane-cladded interferometers, and crystalline structure quality assessment using near-infrared tomography. As department head, Hellesø coordinates research in integrated optics and photonics. His laboratory in Teknologibygget Tromsø 3.060 focuses on photonic chip technologies for particle trapping and spectroscopic analysis. Current projects include optimizing metasurface sensitivity, developing low-noise Raman systems, and advancing on-chip methane sensors.

• Quantum Computing
• Computational Complexity
• Communication Complexity
• +۳ مورد دیگر

Dr Nikhil Mande is a researcher at the University of Liverpool's Department of Computer Science within the School of Electrical Engineering, Electronic and Computer Science. His work bridges quantum computing and classical complexity theory, with a focus on decision trees, communication complexity, and Fourier-sparse Boolean functions. His research includes groundbreaking work in quantum query complexity and communication complexity, supported by a Royal Society grant (2024-2026) titled "Power of Knowledge in Explorable Uncertainty." He has developed novel frameworks for comparing quantum and classical algorithms and analyzing complexity lower bounds. Key research outputs include studies on quantum sabotage complexity (2024), parity decision trees (2024), and randomized vs deterministic decision tree size (2023). His theoretical work challenges established conjectures, such as the Log-Approximate-Rank Conjecture (2020). He co-authored publications in venues like Quantum journal and LIPIcs conference proceedings. Scientific awards include: Royal Society Grant (2024-2026)

• Virtual Reality
• Scientific Visualization
• Computer Graphics
• +۴ مورد دیگر

Aleksandr Zagarskikh is an Associate Professor at the Game Development School of ITMO University, specializing in virtual reality, scientific visualization, and high-performance computing. He has led projects in quantum chemistry visualization, flight simulators, and urban simulation technologies. Developed real-time graphics systems for ultra-realistic image synthesis Created high-performance network protocols for distributed visualization Current research focuses on big data decision-making in finance and multiscale urban modeling His work spans predictive modeling, GPU optimization, and cloud-based infrastructure visualization, with publications in Procedia Computer Science. He teaches courses in game technologies, VR, and scientific computer graphics.

Ruzica Piskacمشاهده پروفایل

استاد

• Software Verification
• Security and Applied Cryptography
• Automated Reasoning
• +۴ مورد دیگر

Ruzica Piskac is a Professor of Computer Science at Yale University, where she leads the Rigorous Software Engineering (ROSE) group. She has made significant contributions to the fields of software verification, security, automated reasoning, and code synthesis, focusing on improving software reliability and trustworthiness through formal techniques. Dr. Piskac received her PhD from the Swiss Federal Institute of Technology (EPFL) in 2011, where her dissertation won the Patrick Denantes Prize. Prior to joining Yale, she led an independent research group at the Max Planck Institute for Software Systems in Germany (2012-2013). Her research spans several key areas: symbolic execution for Haskell (G2), privacy-preserving formal methods (PPFM), functional reactive synthesis, verification of configuration files, and analysis of software updates. Her work consistently bridges theoretical formal methods with practical applications in real-world systems. Dr. Piskac's recent publications demonstrate a strong trend toward applying formal verification techniques to emerging challenges including large language models, quantum computing security, legal accountability of automated systems, and cyber-physical systems. Her research increasingly intersects with AI, cryptography, and legal domains while maintaining strong foundations in formal methods. Her scientific achievements have been recognized with numerous prestigious awards: Multiple Amazon Research Awards Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation (SEIF) Patrick Denantes Prize for her PhD dissertation Dr. Piskac has graduated five PhD students, four of whom have gone on to become assistant professors of computer science. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and is on the Steering Committee of the Formal Methods in Computer-Aided Design conference. She leads the Rigorous Software Engineering (ROSE) group at Yale, which focuses on several key projects including: Symbolic Execution Engine for Haskell (G2) Privacy Preserving Formal Methods (PPFM) Functional Reactive Synthesis Verifications for Configuration Files Analysis of Software Updates and Configuration Files

Mustafa Demirمشاهده پروفایل

مدرس

• Human-Machine Teaming
• Human-AI Teaming
• Advanced Statistical Modeling
• +۱۱ مورد دیگر

Dr. Mustafa Demir serves as an Associate Research Scientist at Arizona State University's Biodesign Center for Applied Structural Discovery and Faculty Associate in the Ira A. Fulton Schools of Engineering. His interdisciplinary work integrates cognitive science and engineering to optimize human-AI collaborative systems across healthcare, transportation, and defense domains through human-centered design principles. Education: Ph.D. in Simulation, Modeling, and Applied Cognitive Science, Arizona State University (2017) Dr. Demir's research centers on human-machine teaming dynamics, employing advanced statistical and nonlinear dynamical systems modeling. His expertise includes quantum cognitive approaches to decision-making, team cognition analysis, and machine learning applications for real-time physiological monitoring. Current projects focus on AI-powered stress management tools, curiosity-driven STEM education systems, and human-autonomy coordination in driving and command environments using eye-tracking and biometric sensing. Analysis of his 2023-2025 publications reveals methodological innovation in dynamical systems analysis (DSA Toolbox) and quantum probability modeling applied to trust calibration in autonomous vehicles, educational technology, and digital health interventions. His work consistently bridges theoretical modeling with real-world implementation in complex sociotechnical systems. Dr. Demir mentors students in cognitive engineering and applied data science while leading multi-institutional research initiatives funded by NSF, AFRL, and DARPA. His grant portfolio supports experimental work across simulated and operational environments including remotely piloted aircraft systems and urban search-and-rescue scenarios. He contributes to the Biodesign Center for Applied Structural Discovery and HLA-Inception research group, developing computational models of team interaction and adaptive AI systems for healthcare and education applications.

Stefanos Kourtisمشاهده پروفایل

استادیار

• Strongly Correlated Quantum Systems
• Tensor Network Methods
• Topological Quantum Matter
• +۴ مورد دیگر

Stefanos Kourtis serves as an Assistant Professor in the Department of Physics at the University of Sherbrooke, holding the prestigious MEI Chair in Quantum Computing. His academic profile centers on theoretical quantum physics and computational methods development. His educational qualifications include: M.Sc. in Physics from National Technical University of Athens (2009) Ph.D. in Physics from Technische Universität Dresden (2014) Dr. Kourtis specializes in strongly correlated quantum systems, where he develops tensor network methodologies to model quantum materials and topological states of matter. His research bridges theoretical physics with computational science, focusing on numerical prediction of quantum material properties and solving complex combinatorial problems in decision-making, optimization, and counting through quantum-inspired algorithms. This interdisciplinary approach creates synergies between condensed matter theory and quantum computing. Analysis of his 2016-2019 publications reveals consistent methodological innovation using tensor networks across diverse applications—from topological quantum computing and Weyl semimetals to quantum spin liquids—demonstrating how computational physics techniques can address fundamental questions in quantum matter while advancing quantum algorithm development. His scientific recognition includes: MEI Chair in Quantum Computing No information regarding student advising or research grants was provided in the source documentation. The available text contains no details about laboratory facilities or research team structures associated with his work.

Sangeeta Sharmaمشاهده پروفایل

استاد

• Theoretical Condensed Matter Physics
• Ultrafast Phenomena
• Spintronics
• +۳ مورد دیگر

Prof. Dr. Sangeeta Sharma is a Principal Investigator at the Max Born Institute (MBI) in Berlin, leading Project A04 in the TRR 227 "Ultrafast Spin Dynamics" collaboration. Her work centers on theoretical condensed matter physics, with a focus on ultrafast phenomena in quantum materials. Her research interests include spintronics, valleytronics, and light-matter interaction. She develops models for controlling spin and valley currents using ultrafast light pulses, particularly in two-dimensional materials like graphene, with applications in next-generation electronic devices. Analysis of her recent publications (2024-2025) reveals a cohesive trend toward manipulating valley and spin currents via tailored light pulses in quantum materials. Her theoretical frameworks bridge computational modeling and experimental validation, advancing ultrafast magnetism and quantum transport research. As a Principal Investigator in TRR 227, she leads DFG-funded research and collaborates within Project B4 "Theory for Dynamics in Quantum Materials" at MBI, contributing to international efforts in spin dynamics and correlated matter systems.

Philip Tarantoمشاهده پروفایل

مدرس

• Quantum Information Theory
• Open Quantum Systems
• Quantum Thermodynamics
• +۷ مورد دیگر

Philip Taranto is a Lecturer (Assistant Professor) at The University of Manchester's Physics & Astronomy department, where he leads the Quantum Information & Spatiotemporal Phenomena (QuISP) research group. He also serves as an editor for the Quantum journal. Originally from Melbourne, Australia, Taranto completed his undergraduate studies and Masters at Monash University under Dr. Kavan Modi and Dr. Felix A. Pollock, focusing on memory effects in open quantum systems. He then earned his PhD at the University of Vienna under Dr. Marcus Huber, studying quantum thermodynamics and complex temporal correlations. Following this, he held a JSPS Postdoctoral Fellowship at the University of Tokyo in Dr. Mio Murao's group before joining the University of Manchester. Taranto's research centers on quantum complexity, exploring how quantum systems' intricate behaviors can be harnessed for computational advantages. His primary focus areas include quantum information theory, open quantum dynamics, quantum thermodynamics, quantum foundations, correlations & entanglement, stochastic & complex processes, and quantum computation & simulation. His methodological approach heavily relies on the framework of higher-order quantum operations—transformations that act upon transformations themselves—which has proven valuable for developing optimal quantum interactive strategies, clarifying memory effects in open quantum processes, and analyzing foundational notions like causality. He also employs tensor networks, graphical calculus, and semidefinite programming in his research. His recent publications reveal a strong focus on quantum thermodynamics, higher-order quantum operations, and quantum memory effects. Taranto has made significant contributions to understanding the relationship between Landauer's principle and Nernst's unattainability principle in quantum cooling, developing protocols for efficient quantum system cooling with finite resources, and characterizing multi-time quantum processes with classical memory. His work on the quantum switch and higher-order quantum operations has advanced our understanding of quantum causality and indefinite causal order. JSPS Postdoctoral Fellowship (2022-2025) Editor of Quantum Journal (since June 2024) Taranto actively collaborates with multiple research groups globally, including the Murao group at the University of Tokyo, the Huber group at TU Wien, and the Modi group at SUTD Singapore and Monash University. He has worked with prominent researchers such as Simon Milz, Jessica Bavaresco, Marco Túlio Quintino, Felix Binder, Martí Perarnau-Llobet, Patryk Lipka-Bartosik, and Andrea Smirne. He is currently accepting PhD students and encourages collaboration with researchers sharing similar interests. Taranto is also committed to social responsibility, advocating for open science, climate justice, and empowering historically excluded and marginalized groups.

• Aerospace Engineering
• Spacecraft Dynamics
• Orbital Mechanics
• +۵ مورد دیگر

Rafael Vazquez Valenzuela is a University Professor in the Department of Aerospace Engineering and Fluid Mechanics at the Higher Technical School of Engineering, University of Seville. His research focuses on control systems for aerospace applications, particularly in spacecraft dynamics, orbital mechanics, and partial differential equation (PDE) control systems. Professor Vazquez Valenzuela's research spans several key areas in aerospace engineering and control theory. His primary interests include spacecraft guidance and navigation, particularly for asteroid exploration and rendezvous operations. He has made significant contributions to the field of backstepping control for PDE systems, with applications ranging from fluid dynamics to spacecraft control. His work also extends to stochastic analysis of aircraft performance, thermoacoustic instability control, and the development of advanced algorithms for unmanned aerial vehicles. Notably, his research bridges theoretical control theory with practical aerospace applications, resulting in numerous high-impact publications in top journals. His publication record shows a clear trend toward increasingly sophisticated control algorithms for complex aerospace systems. Recent work focuses on predictive control for spacecraft operations near asteroids, chance-constrained optimization for halo orbit rendezvous, and prescribed-time control methods. His research consistently addresses the challenging intersection of theoretical control design and practical implementation constraints in aerospace applications. Professor Vazquez Valenzuela has been involved in numerous research projects, including "APPLICATION OF LEADING TECHNOLOGY TO UNMANNED AERIAL VEHICLES FOR RESEARCH AND DEVELOPMENT IN ATM (ATLANTIDA)" "Diseño de Algoritmos de Guiado y Control Innovadores para Aplicaciones Avanzadas de Rendezvous: Órbitas Halo y Exploración de Asteroides" "SESAR WP-E ComplexWorld Network" These projects highlight his expertise in both theoretical control systems and practical aerospace applications. He is affiliated with the INGENIERÍA AEROESPACIAL (GIA) research group at the University of Seville, where he collaborates with researchers on advanced control and navigation systems for autonomous aerospace vehicles.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۵ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the CMAP (Applied Mathematics Department). His research focuses on computational statistics, machine learning, and stochastic methods for Bayesian inference and generative models. Key areas include Monte Carlo methods, stochastic optimization, and Riemannian geometry in optimization. He has contributed to theoretical advancements in Markov processes and their applications in sampling algorithms. Affiliations: École Polytechnique (CMAP), ENS Paris-Saclay (teaching roles), and other academic collaborations. Teaching: Courses on mathematical statistics, stochastic methods, probability theory, and optimization at ENS Paris-Saclay and École Polytechnique. Research Interests: Development of Monte Carlo algorithms, stochastic approximation schemes, and theoretical analysis of Markov chain convergence. Specializes in Bayesian statistics, generative models, and high-dimensional inference. His work bridges theory and application, with notable contributions to Langevin dynamics, nonreversible MCMC methods, and federated learning. He has received a Best student paper award at ICASSP 2020 for collaborative research on Bayesian computation using Sliced-Wasserstein distances. Alain’s publications span prestigious journals like the Annals of Applied Probability and SIAM, with a focus on algorithmic innovation and rigorous mathematical foundations.