جست‌وجوی استاد و پژوهشگر

• Optical Physics
• Quantum Physics
• Quantum Information
• +۵ مورد دیگر

Dr. Shang Yu is a UKRI Postdoctoral Research Fellow and Marie Curie Fellow in the Department of Physics at Imperial College London. He is affiliated with the Imperial Centre for Quantum Engineering, Science and Technology (QuEST), the Quantum Optics and Laser Science Group, and The Light Community Quantum Optics & Laser Science (QOSL). Education: PhD in Physics from the University of Science and Technology China (USTC), 2020. Shang Yu specializes in photonic quantum computing and quantum optics. His research spans experimental quantum simulation, quantum-enhanced sensors, and error correction for universal quantum computing systems. He has pioneered work on scalable photonic quantum computing architectures and quantum optical neural networks. His recent publications focus on programmable photonic processors, Gaussian boson sampling for drug discovery, and quantum sensing technologies. Shang's work integrates quantum information theory with applied optical physics, emphasizing practical implementations of quantum technologies. Scientific Awards: UKRI Guarantee Marie Curie Fellowship Shang Yu has co-authored over 50 peer-reviewed papers (h-index: 21, citations: 1,570) and contributed to advancements in quantum channel capacity verification, parity-time symmetry sensors, and temporally encoded photonic quantum computing systems applied to real-world problems.

• Quantum Computing
• Quantum Physics
• Quantum Information Science
• +۲ مورد دیگر

Sabrina Maniscalco is an Adjunct Professor in the Department of Applied Physics at Aalto University. Her research focuses on quantum computing, quantum information science, and open quantum systems. Research Interests: Her work spans quantum algorithms, quantum simulation of dissipative systems, quantum entanglement detection, and applications of quantum walks in network science. She also contributes to quantum education methodologies. Publication Trends: Recent publications (2022-2024) emphasize quantum simulation on noisy hardware, multipartite entanglement detection, quantum walks for network analysis, and quantum measurement theory. Her work bridges theoretical quantum physics with practical computational applications.

Markus Reiherمشاهده پروفایل

استاد

• Theoretical Chemistry
• Quantum Chemistry
• Electronic Structure Calculation
• +۷ مورد دیگر

Markus Reiher is a Full Professor at the Department of Chemistry and Applied Biosciences, ETH Zurich, where he leads a research group focused on theoretical chemistry since 2006. His work bridges quantum mechanics and computational chemistry, emphasizing electronic structure theory and algorithm development for chemical reaction exploration. BSc in Chemistry (University of Bielefeld, 1995) PhD in Chemistry (University of Bielefeld, 1998) Habilitation in Chemistry (University of Erlangen, 2002) His research spans quantum computing applications in chemistry, machine learning potentials, and advanced methods for reaction networks. Recent publications highlight quantum-classical hybrid models, vibronic structure simulations, and solvent effects in chemical processes. Scientific awards include the ADUC Annual Prize (2004), Emmy Noether Habilitation Award (2003), Golden Owl Award for Teaching (2010), Löwdin Lecture (2018), and Heilbronn-Hückel Lectureship (2023). He was elected to the International Academy of Quantum Molecular Science in 2022.

Nick Mayhallمشاهده پروفایل

استاد

• Computational Chemistry
• Quantum Information Science
• Quantum Algorithms
• +۳ مورد دیگر

Professor Nick Mayhall is a faculty member in the Department of Chemistry at Indiana University Bloomington, set to begin his role in Fall 2025. Prior to this, he served as an Assistant Professor at Virginia Tech since 2015, where he received tenure in 2021 and was promoted to Full Professor in 2025. His research focuses on computational chemistry and quantum information science, integrating quantum algorithms with classical simulations to advance molecular modeling techniques. Education B.S. in Chemistry, University of Southern Indiana (2006) Ph.D. in Theoretical Chemistry, Indiana University Bloomington (2011), advised by Krishnan Raghavachari Research Interests The Mayhall Group bridges computational chemistry and quantum information science, developing methods to simulate chemical systems on quantum computers and improving classical computational techniques through quantum insights. Key subfields include: Quantum Computing Quantum Algorithms Molecular Simulation Theoretical Chemistry Strongly Correlated Systems Computational Chemistry Scientific Awards NSF CAREER Award Alfred P. Sloan Fellowship Professional Trajectory After postdoctoral work at UC Berkeley under Martin Head-Gordon (2011–2015), Prof. Mayhall will transition his research group to Indiana University Bloomington in Fall 2025. His work emphasizes interdisciplinary approaches at the intersection of quantum information and chemical theory.

• Quantum Machine Learning
• Optimization Algorithms
• Satellite Networks
• +۳ مورد دیگر

Dr. Antonio Macaluso is a researcher at the German Research Center for Artificial Intelligence (DFKI) within the Agents and Simulated Reality department in Saarbrücken, Germany. His work focuses on quantum computing applications for complex optimization problems. Research trends from his publications indicate expertise in: Quantum machine learning for autonomous systems Quantum annealing algorithms for distributed networks Hybrid quantum-classical optimization techniques Contact: Antonio.Macaluso@dfki.de

Raju Venugopalanمشاهده پروفایل

استاد

• Quantum Chromodynamics
• High Energy Physics
• Color Glass Condensate
• +۵ مورد دیگر

Raju Venugopalan is a distinguished theoretical physicist serving as a Distinguished Scientist at Brookhaven National Laboratory (BNL) and Director of the BNL EIC Theory Institute since October 2022. He also holds an Adjunct Professor position at Stony Brook University since March 2009 and previously served as Group Leader of the BNL Nuclear Theory group from June 2010 to September 2021. His career spans over two decades at BNL, where he was awarded tenure in June 2002. Venugopalan received his B.S. from the University of Chicago in June 1987 and his Ph.D. from Stony Brook University in August 1992. Following his doctorate, he completed postdoctoral appointments at the Theoretical Physics Institute at the University of Minnesota (1992-1994), the National Institute for Nuclear Theory at the University of Washington (1994-1996), and as a Danish Research Council Fellow at the Niels Bohr Institute in Copenhagen (1997-1998). His research primarily focuses on Quantum Chromodynamics (QCD) at high energies, where he has made seminal contributions to the development of the Color Glass Condensate (CGC) effective theory. Venugopalan has pioneered numerical and analytical techniques in real-time classical-statistical field theory and co-invented the IP-Glasma model, which provides the most successful description of high-energy heavy-ion collisions to date. He has also been an early proponent of the Electron-Ion Collider (EIC), having organized the first eRHIC workshop at BNL in December 1999. His work explores interdisciplinary connections between high-energy QCD and other areas of physics, ranging from Planck scale phenomena to ultracold atomic systems. Venugopalan's recent publications demonstrate a strong focus on the intersection of QCD, gravity through double copy formalisms, quantum information aspects of QCD (particularly entanglement), and quantum computing applications to field theory problems. His work on the QCD-gravity double copy in Regge asymptotics represents a cutting-edge research direction connecting strong force physics with gravitational phenomena. He has also been increasingly active in quantum information applications to nuclear physics, particularly in studying entanglement in QCD systems. Co-PI of Simons Foundation Collaboration on Confinement and QCD strings Co-author of Phys. Rev. D 50th Anniversary Milestone Paper (2020) Suffolk County NY Distinguished Asian American Award (2019) BNL Science & Technology Award (2018) Humboldt Research Award (Humboldt Prize, 2016) Fellow of the American Physical Society (2007) Venugopalan has supervised numerous undergraduate, master's, and Ph.D. students throughout his career. He has secured significant research funding including co-PI roles on DOE Topical Theory Collaborations (BEST, TMD, SURGE) and the Simons Foundation Collaboration on Confinement and QCD strings. His leadership extends to chairing the Steering Committee of the Joint BNL/Stony Brook Center for Frontiers in Nuclear Science and serving on multiple national and international advisory committees. As Director of the BNL EIC Theory Institute, Venugopalan leads theoretical efforts supporting the development of the Electron-Ion Collider, a major DOE project currently under construction at BNL. He also contributes to quantum computing initiatives through his role as Theory sub-Thrust co-Leader for the Co-design Center for Quantum Advantage (C2QA) at BNL.

Hamza Fawziمشاهده پروفایل

استاد

• Mathematical Optimization
• Quantum Information Theory
• Convex Optimization
• +۵ مورد دیگر

Hamza Fawzi is a Professor in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, where he leads a research group focused on mathematical optimization with applications in quantum theory. His work bridges theoretical mathematics and practical computational methods for quantum information processing. Professor Fawzi received his PhD in 2016 from MIT under the supervision of Pablo Parrilo. His research interests span mathematical optimization, quantum information theory, convex optimization, quantum many-body systems, and semidefinite programming. He has developed significant theoretical frameworks and practical algorithms for problems in quantum theory, including the design of optimization algorithms specifically tailored for quantum information applications. His recent publications demonstrate a strong focus on quantum information processing through the lens of optimization theory. The research trends show consistent advancement in developing certified algorithms for quantum Hamiltonian problems, quantum relative entropy optimization, and quantum conic programming. His work often involves applying convex optimization techniques to solve challenging problems in quantum information theory, with particular emphasis on developing reliable numerical methods and theoretical guarantees. Professor Fawzi leads a research group with current members Ahmad Al-Sulami, Giorgio Facelli, Samuel O. Scalet, and Shengding Sun. Former group members include Kun Fang (now Assistant Professor at CUHK-Shenzhen) and Oisín Faust (now at Tower Research Capital). He has developed notable software tools including QICS (a convex optimization solver for quantum information theory) and CVXQUAD (a MATLAB-based package for quantum entropies and divergences). He teaches advanced courses at Cambridge including Part II Numerical Analysis (Michaelmas 2023), Part IB Numerical Analysis (Lent 2020), and Part III Topics in Convex Optimization (Michaelmas 2019). His teaching covers fundamental numerical methods, optimization algorithms, and their applications in mathematical physics.

• Machine Learning
• Computational Biology
• Structural Biology
• +۳ مورد دیگر

Valentina Sora is a Postdoc in the Machine Learning section at the Department of Computer Science , University of Copenhagen , focusing on interdisciplinary research at the intersection of machine learning and structural biology. Research Interests Developing machine learning algorithms for quantum-classical simulations in computational chemistry . Modeling biological data with applications in autophagy , cancer genomics , and protein structure networks . Designing computational tools for protein interaction network analysis and high-throughput mutational scans . Key Trends in Publications Her work bridges machine learning and quantum computing with structural biology , emphasizing protein dynamics , mutation effects , and biomolecular simulations . Recent studies apply deep learning to gene expression and genomic variant interpretation , alongside quantum embedding for molecular assemblies. Collaborations span computational biology, quantum chemistry, and cancer research with institutions like the SCIENCE AI Centre at the University of Copenhagen and researchers such as Yevgeny Seldin and Andrea Krogh .

• Machine Learning
• Quantum Computing
• AI Fairness
• +۲ مورد دیگر

Simon Krogh Anderson serves as a Lecturer at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. He is an active member of the Machine Learning section which focuses on theoretical foundations and applications across domains including natural language processing, medical image analysis, and biological data modeling. The department participates in the SCIENCE AI Centre and maintains powerful compute resources including the TreeSense platform for remote sensing. His research spans multiple cutting-edge areas in artificial intelligence with particular emphasis on machine learning, quantum computing applications, and algorithmic fairness. Key interests include sustainable AI development, reproducibility in recommender systems, and cross-cultural adaptation frameworks. His work often bridges theoretical computer science with practical applications in environmental monitoring, healthcare analytics, and quantum information processing. Recent publications demonstrate strong interdisciplinary connections across quantum computing, sustainable AI, and fairness metrics. Trends show increasing focus on environmentally conscious AI development, integration of quantum methods with classical machine learning, and ethical considerations in recommendation systems. His work frequently leverages Denmark's extensive health registries and environmental data resources. Anderson actively contributes to the department's research ecosystem through teaching and collaboration within the Machine Learning section. While specific grants aren't detailed in available materials, his publications indicate involvement in projects related to quantum computing infrastructure, environmental monitoring systems, and AI ethics frameworks. The department provides significant computational resources including a dedicated cluster and specialized labs like TreeSense for remote sensing applications. His work appears connected to the SCIENCE AI Centre's initiatives in sustainable computing and quantum information processing.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +۱۰ مورد دیگر

Tobias Nordholm-Højskov is an Instructor at the Department of Computer Science , University of Copenhagen (DIKU). His research intersects machine learning with healthcare, sustainability, and quantum computing, focusing on theoretical foundations and applications in medical data analysis, climate-aware AI, and quantum systems. He is affiliated with the SCIENCE AI Centre and contributes to projects like QDarts (quantum dot array simulation) and TreeSense (remote sensing for environmental monitoring). His work spans diverse subfields, including Explainable AI for healthcare records Federated Learning in rare disease research Quantum-inspired neural networks Retrieval-Augmented Generation frameworks Environmental impact mitigation in AI

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

Thor Alexander Bøje Simonsen is an Instructor at the Department of Computer Science , University of Copenhagen , focusing on interdisciplinary research at the intersection of machine learning, quantum computing, and real-world applications. He is affiliated with the SCIENCE AI Centre and contributes to projects in sustainability, medical data analysis, and quantum-enhanced algorithms. His research spans theoretical and applied domains, including: Quantum computing for biomolecular simulations Environmentally sustainable AI systems Medical data analysis and clinical decision support Image reconstruction and remote sensing Explainable AI for large language models Thor's work engages with cutting-edge challenges in machine learning, from hardware acceleration to ethical considerations in clinical contexts. He is part of the university's Machine Learning Section , which has access to a dedicated compute cluster and collaborates with initiatives like TreeSense for global tree resource monitoring.

• Theoretical Physics
• Computational Physics
• Soft Matter
• +۱۰ مورد دیگر

Halim Kusumaatmaja is an Honorary Visiting Professor in the Department of Physics at Durham University. He is actively involved in theoretical and computational research at the intersection of physics, chemistry, engineering, and biology. His work is closely associated with the Durham Centre for Soft Matter, the Biophysical Sciences Institute, and the SOFI CDT, reflecting his interdisciplinary approach to scientific problems. Professor Kusumaatmaja's research spans multiple areas of soft matter and biophysics, with particular focus on wetting phenomena on structured surfaces, membrane biophysics, liquid-liquid phase separation, multi-stable elastic structures, crystallography on curved surfaces, the Lattice Boltzmann Method, and energy landscape exploration. His work combines theoretical modeling with computational techniques to address fundamental questions in fluid mechanics and biological systems. He has developed novel approaches for computing free energy landscapes of continuum models, which has applications across various soft matter systems. His recent publications (2021-2025) demonstrate a strong trend toward interdisciplinary research that bridges traditional boundaries between physics, biology, and computational science. His work spans from fundamental studies of wetting and capillary phenomena to applications in biological systems like stress granule condensates and membrane biophysics. Notably, he has recently expanded into quantum computing applications for classical physics problems, showing his ability to adapt to emerging computational paradigms. His research consistently focuses on understanding complex energy landscapes, interfacial phenomena, and the mechanical properties of soft materials. Professor Kusumaatmaja has supervised PhD students including Ke Sun and Listra Ginting, and actively welcomes talented undergraduates, PhD students, postdocs, and visitors to join his research group. He has secured funding through various prestigious fellowships including EPSRC, Leverhulme Early Career, Marie Curie, Newton, and Royal Commission for the Exhibition of 1851 fellowships, demonstrating his ability to attract competitive research funding. His research group operates at the forefront of computational soft matter physics, utilizing advanced simulation techniques to tackle problems ranging from fundamental wetting phenomena to biological membrane interactions. The group maintains strong collaborations with experimental colleagues, ensuring that theoretical predictions can be validated against real-world observations. Professor Kusumaatmaja is also a co-author of the authoritative text 'The Lattice Boltzmann Method: Principles and Practice' (Springer, 2017), which has become a standard reference in the field.

• Particle Physics Phenomenology
• Quantum Computing in High-Energy Physics
• Beyond Standard Model Physics
• +۵ مورد دیگر

Professor Michael Spannowsky serves as Director of the Institute of Particle Physics Phenomenology and Professor in the Department of Physics at Durham University. His research bridges theoretical particle physics with cutting-edge computational techniques. Spannowsky's primary research interests include Particle Physics Phenomenology , Quantum Computing applications in High-Energy Physics , and Beyond Standard Model physics . His work focuses on Higgs boson physics, collider phenomenology, and the development of machine learning techniques for particle physics data analysis. Recent publications demonstrate strong emphasis on quantum simulation methods for field theories and novel detector applications. Analysis of his 15 most recent publications (2024-2025) reveals a clear trend toward quantum computing applications in particle physics, with over 60% of recent work exploring quantum algorithms for scattering processes, field theory simulations, and data analysis. His research increasingly integrates machine learning with traditional phenomenological approaches, particularly using graph neural networks for new physics searches. His supervisory role includes PGR student Puya Mirkarimi. Spannowsky actively contributes to major collaborations including the LHC experiments and quantum computing initiatives for high-energy physics.

Artur Izmaylovمشاهده پروفایل

استاد

• Quantum Dynamics
• Theoretical Chemistry
• Quantum Computing
• +۵ مورد دیگر

Artur Izmaylov is a Professor of Theoretical Chemistry at the University of Toronto with dual departmental appointments: the Department of Chemistry at the St. George campus and the Department of Physical and Environmental Sciences at the University of Toronto Scarborough (UTSC). He leads the Izmaylov Research Group and is affiliated with the Center for Quantum Information and Quantum Control. His offices are located at EV356 (UTSC) and LM420C (St. George), and he can be contacted at artur.izmaylov@utoronto.ca or via phone at 416-208-2951 (UTSC) / 416-946-8405 (St. George). Professor Izmaylov's research develops novel theoretical and computational approaches to quantum dynamics in complex systems. Key focus areas include: Quantum processes in organic photovoltaics, biomolecules, and catalytic surfaces Hybrid quantum-classical methodologies for subsystem-environment interactions Renormalization techniques for efficient quantum dynamics simulations Quantum computing applications for chemical problems and electronic structure Nonadiabatic dynamics near conical intersections and spin-charge transfer His recent publications (2023-2025) demonstrate strong emphasis on quantum algorithm development for chemical applications, particularly: Advancements in variational quantum eigensolver (VQE) methodologies Quantum resource optimization and error mitigation strategies Novel Hamiltonian decomposition techniques for efficient simulation Applications in molecular vibrations, electronic structure, and materials science Hybrid quantum-classical approaches for scalable computations The Izmaylov Research Group actively recruits graduate students and postdoctoral researchers, with opportunities through NSERC USRA, CQIQC, CHMD90/91, CHM499Y/PHY479Y courses, and Mitacs Globalink programs. Current research directions emphasize quantum computing implementations for chemical dynamics and surface interactions.

Dvira Segalمشاهده پروفایل

دانشیار

• Chemical Physics
• Quantum Dynamics
• Quantum Thermodynamics
• +۳ مورد دیگر

Dvira Segal is an Associate Professor in the Department of Chemistry at the University of Toronto with a cross-appointment in the Department of Physics. They hold the Canada Research Chair in Theoretical Chemistry (2014-2024) and are a Distinguished Fellow at the Hebrew University of Jerusalem's Faculty of Sciences (2024-2026). Their research focuses on quantum dynamics, quantum thermodynamics, and nonequilibrium systems, particularly in nanoscale quantum devices and molecular-level electronics. Research interests include theoretical and computational studies of dissipative quantum systems, heat and charge transfer in molecular junctions, and quantum control strategies. Key areas involve spin dynamics in non-equilibrium systems, quantum thermal machines, and energy management at the nanoscale. Notable awards include the Canada Research Chair and Distinguished Fellowship. Segal leads the Centre for Quantum Information and Quantum Control and is part of the Chemical Physics Theory Group. Their work bridges quantum mechanics, statistical mechanics, and nanotechnology, with applications in quantum computing and energy science. Current and past students and postdocs include over 30 researchers, many now in academic and industry roles. Segal teaches courses on quantum mechanics, statistical mechanics, and physical chemistry, emphasizing graduate-level theoretical training.