Find Academic Supervisors

• Quantum Coherence
• Spintronics
• Semiconductor Nanostructures
• +4 more

Andreas Kuhlmann is a Researcher at the Quantum Coherence Lab (Zumbühl Group) within the Department of Physics at the University of Basel. He works on low-temperature quantum transport experiments involving spin qubits, semiconductor nanostructures, and spin-orbit coupling, primarily in GaAs, Ge/Si systems, and quantum wires. His research focuses on quantum computing applications, including tunable microwave photon detectors and spin relaxation mechanisms. Research Interests : Quantum coherence studies in semiconductor nanostructures Spin qubit development for quantum computing Spin-orbit interaction engineering in quantum wells Low-temperature (microkelvin) quantum transport experiments Electron/hole spin manipulation and measurement Exotic quantum phases (Majorana fermions, parafermions) Key Collaborations : Collaborations with Schönenberger, Loss-Klinovaja, Warburton, and Egues groups; industrial partners like Basel Precision Instruments GmbH (BASPI); participation in Swiss national programs NCCR SPIN and SNI. Recent Research Trends : His publications emphasize spin qubit tunability via spin-orbit coupling, machine learning applications in quantum device optimization, and ultra-low temperature measurement techniques (e.g., Coulomb blockade thermometry at 224μK).

Andrew MillerView profile

Researcher

• Gravitational Waves
• Dark Matter Detection
• Primordial Black Holes
• +2 more

Dr. Andrew L. Miller is a postdoctoral researcher at Utrecht University and the National Institute for Subatomic Physics (Nikhef), focusing on gravitational wave detection and dark matter studies. His work bridges theoretical physics and computational methods within the LIGO/Virgo collaborations. PhD from Sapienza University of Rome and University of Florida MS in Physics from University of Florida BS in Physics from The College of New Jersey Andrew's research centers on gravitational waves from neutron stars , primordial black holes , and dark matter interactions . His methodological innovations include: Machine learning algorithms for detector noise classification Pattern-recognition techniques for continuous wave detection Semicoherent analysis for ultralight boson clouds Frequency-Hough transforms for binary inspirals His publications cover LIGO/Virgo data analysis, Einstein Telescope design, and LISA Pathfinder applications for dark matter detection. He has delivered tutorials on: Machine learning classification Parameter estimation in gravitational waves Frequency-Hough transforms Generalized search frameworks

• Power and Energy Systems
• Condition Monitoring
• Fault Diagnosis and Prognosis
• +5 more

Dr Xiandong Ma is a Reader in Power and Energy Systems at Lancaster University's School of Engineering, where he has been a faculty member since December 2008. His research focuses on intelligent condition monitoring and fault diagnosis of power systems, with particular expertise in wind energy systems and smart grid technologies. His educational background includes: BEng in Electrical Engineering from Jiangsu University (1986) MSc in Power Systems and Automation from Nanjing Automation Research Institute (1989) PhD in Partial Discharge based High-voltage Plant Condition Monitoring from Glasgow Caledonian University (2002) Dr Ma's research spans intelligent condition monitoring and fault diagnosis/prognosis of wind power systems and electrical assets, condition-based operations and maintenance of power and energy systems, modeling, optimization, and control of smart/micro grids with renewable energy resources, power conversion and renewable energy integration, and associated machine learning and AI technologies and digital twin solutions. His work bridges theoretical advances with practical engineering applications in the renewable energy sector. His recent publications demonstrate a strong focus on quantum machine learning applications for wind turbine monitoring, electric vehicle-grid integration challenges, wave energy conversion systems, and nuclear fuel inspection technologies. The research shows a clear trajectory toward more sophisticated AI-driven solutions for energy systems, with increasing emphasis on multi-physics modeling and cross-domain applications. Dr Ma has received several prestigious recognitions: Chartered Engineer Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Higher Education Academy (FHEA) Member of EPSRC Peer Review College KTP Fellowship awarded by University of Technology Sydney (2018) Ranked in the world's top 2% scientists by Stanford University He actively supervises numerous PhD students and postdoctoral researchers, with current projects including the Leverhulme Trust-funded "Self-Aware Power Networks: Autonomous Operation at Scale" and several EPSRC-funded initiatives. Dr Ma has secured significant research funding and collaborates extensively with industry partners to translate research into practical applications. Dr Ma leads research within Lancaster's Energy research group, focusing on the integration of advanced sensing, AI, and control techniques for next-generation power and energy systems. His team works closely with industrial partners including ALSTOM Power and various renewable energy companies to develop innovative solutions for real-world energy challenges.

• Quantum Computing
• Atomic Electronics
• Quantum Devices
• +2 more

Professor Michelle Y Simmons AO is a Laureate Fellow and Director of the Centre of Excellence for Quantum Computation and Communication Technology at UNSW Sydney. She founded Silicon Quantum Computing (SQC), Australia's first vertically integrated quantum computing company, and pioneered atomic electronics by creating devices in silicon with atomic precision. Research focuses on quantum computing, spin qubits, and atomic-scale engineering Key achievements: first quantum gates with donor atoms, 3D atomic chip architecture Recent publications highlight advancements in silicon qubit fabrication, quantum coherence, multi-qubit control, and noise mitigation. These works emphasize scalable architectures and practical quantum computing solutions. Scientific awards include: 2023 Prime Minister’s Prize for Science 2023 Swiss Erna Hamburger Prize 2022 AmCham Alliance Award 2021 Bakerian Medal 2018 Australian of the Year 2017 L’Oréal-UNESCO Laureate Her leadership in quantum research has secured major industry partnerships (CBA, Telstra, Commonwealth Bank) and driven the development of silicon-based quantum technologies. Current work involves optimizing spin qubit performance and exploring applications of quantum computing in materials science and information processing.

Xingjie NiView profile

Associate Professor

• Metasurface
• Photonics
• Plasmonics
• +5 more

Xingjie Ni is an Associate Professor in the Electrical Engineering department at the Materials Research Institute (MRI) . With a focus on metasurface physics , photonics , and plasmonics , their research spans advanced optical technologies and computational imaging. Research Trends : Recent work explores metasurface design for achromatic lenses and light manipulation machine learning-enhanced polarimetric imaging with encoding metasurfaces ultrathin optical devices enabling geometric image transformations reconfigurable liquid crystal systems for dynamic photonic applications electrically tunable nonlinear optics for ensemble learning nanoscale fabrication techniques for scalable metalenses Grants & Projects : Active grants include NSF funding for Photonic Integrated Guided-Wave-Driven Metasurfaces NASA collaboration on Metalens Origami Deployable Lidar National Institute of Biomedical Imaging and Bioengineering support for Metasurface-Based Endoscope

Athanasios D. PanagopoulosView profile

Professor

• Communications
• Wireless Communications
• Radio Communications
• +9 more

Athanasios D. Panagopoulos is a Full Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in Satellite & Space Communications , Antennas and Propagation , and Quantum Communication . He leads the Division of Information Transmission Systems and Material Technology, with a focus on wireless systems, machine learning, and 5G/6G technologies. Born in Athens (1975), received summa cum laude Diploma and Dr. Engineering from NTUA (1997, 2002). Former roles: part-time Assistant Professor (2003-2007), head of Satellite Division at Hellenic Authority for Information Security (2005-2008). Research Trends emphasize Quantum Key Distribution (QKD) , Reconfigurable Intelligent Surfaces , and Deep Learning Applications in satellite networks. His work bridges atmospheric propagation effects with terrestrial-satellite convergence , including AI-driven excess attenuation prediction and UAV channel modeling. Scientific Awards include URSI General Assembly Young Scientist Award (2002, 2005) Best Paper Awards: IEEE RAWCON 2006, IEEE ISWCS 2015 Grants & Collaborations : Principal Investigator for EU/ESA R&D programs, with editorial roles at IEEE Transactions on Antennas and Propagation, and Elsevier Physical Communication. Member of ITU-R , ETSI Study Groups , and IEEE (Senior) .

Noah LindenView profile

Professor

• Quantum Information Theory
• Mathematical Physics
• Quantum Computing
• +3 more

Professor Noah Linden is a faculty member in the School of Mathematics at the University of Bristol , holding the title of Professor of Theoretical Physics. His research focuses on Quantum Information Theory , Mathematical Physics , and related areas in quantum computing and thermodynamics. He has contributed to 105 research outputs and leads projects such as Reliable and Robust Quantum Computing and Compilation and Verification of Quantum Software . Key research themes include quantum scrambling, entanglement dynamics, and applications in biophysics. His work has been cited in studies on quantum dots, qubit manipulation, and nonlocality limits. He has also contributed datasets on exciton dynamics in purple bacteria and collaborated on projects analyzing decoherence and disorder effects in photosynthetic systems. Professor Linden serves as an editor for the Journal of Physics A: Mathematical and General and maintains active collaborations across quantum information, quantum computing, and interdisciplinary physics. His research output includes foundational studies on quantum error correction, measurement theory, and computational advantages.

Rodriguez Zalepinos Ramon AntonioView profile

Associate Professor

• distributed systems
• databases
• Big data
• +6 more

Dr. Ramon Antonio Rodriguez Zalepinos is an Associate Professor at the Department of Software Engineering, Faculty of Computer Science, National Research University Higher School of Economics (HSE). With 16+ years of scientific and teaching experience, he specializes in geospatial data systems, distributed databases, and high-performance computing. Doctor of Science in Computer Science (2024) Candidate of Technical Sciences (2013) Master's in Computer Science (2008, Donetsk National Technical University) His research focuses on geospatial array databases , distributed systems , and Big Earth Data engineering , particularly through his ChronosDB and Quantum Tensor DBMS projects. He has pioneered cloud-native solutions for multi-terabyte environmental datasets and developed novel approaches for in-database road traffic simulations. Key publication trends show 7+ years of contributions to VLDB and SIGMOD conferences, with special emphasis on: Quantum-enhanced geospatial processing Web-based array database systems Cellular automata integration High-speed raster data aggregation Scientific recognition includes: Best Teacher award (2017-2021, 2023) HSE Personnel Reserve member Additional post-doctoral funding (2024-2027) Multiple publication bonuses (2019-2023) He supervises student research in geospatial data science and leads projects on satellite data processing systems, with implementations at major institutions including Amazon and Planet Labs.

• Polymer Chemistry
• Functional Polymers
• Energy Storage Materials
• +5 more

Kenichi Oyaizu is a Professor in the Department of Applied Chemistry at Waseda University's School of Advanced Science and Engineering. With over 30 years of academic career, he has established himself as a leading researcher in polymer chemistry, with particular expertise in functional polymers for energy applications. His research group has produced over 300 publications with more than 12,000 citations, demonstrating significant impact in the field. Professor Oyaizu received his education entirely at Waseda University, completing his undergraduate studies in the Department of Applied Chemistry (1986-1990), followed by graduate work in the same department where he earned his PhD in Engineering (1990-1995). His academic journey progressed from JSPS Research Associate to his current position as Professor, with appointments at Tokyo University of Science along the way. Oyaizu's research focuses on polymer synthesis and functional polymers, with particular emphasis on energy storage materials, hydrogen storage systems, high refractive index polymers, and organic battery technologies. His work bridges fundamental polymer chemistry with practical applications in sustainable energy, demonstrating strong interdisciplinary approach that combines materials science, electrochemistry, and informatics. Analysis of his recent publications reveals a strong trend toward developing advanced polymer materials for energy applications, with particular focus on high refractive index polymers for optoelectronics and robust organic materials for batteries. His research increasingly incorporates materials informatics approaches, as evidenced by several publications applying AI and machine learning to polymer design and property prediction. Yamazaki Sho Award (2022) - Polymers for Reversible Hydrogen Storage Society of Polymer Science Japan Award (2022) - Functional Polymers for Energy Storage Minister of Education Science and Technology Prize (2013) Oleo Science Award (2007) Chemical Society of Japan Young Chemists Award (2002) Professor Oyaizu maintains active leadership roles in the academic community, serving on editorial boards and as committee member for multiple professional societies including the Society of Polymer Science Japan and Chemical Society of Japan. His research is supported by significant grants that enable his team to pursue innovative projects at the intersection of polymer science and sustainable energy technologies. The Oyaizu laboratory operates as a dynamic research environment where fundamental polymer chemistry meets practical energy applications, with particular emphasis on developing materials that address global energy challenges.

• Parallel Computing
• Quantum Computing
• Self-Adjusting Computation
• +4 more

Umut A. Acar is a Professor at the Computer Science Department , Carnegie Mellon University , and an Amazon Scholar. His research focuses on bridging formal methods with systems , algorithms , and AI , aiming to integrate safety and performance in parallel computing. His research spans multiple domains: Quantum Computing : Developing optimization techniques for quantum circuits (e.g., Quartz , Atlas ). Self-Adjusting Computation : Advancing dynamic algorithms and incremental programming frameworks (e.g., Diderot , MPL ). Concurrency & Parallelism : Designing efficient scheduling mechanisms and disentanglement strategies. Recent publications highlight his work on parallel functional programming , quantum simulation , and cache coherence optimization . Notable awards include Best Paper (QCE 2025) , Distinguished Paper (POPL 2024) , and the Intel Award (2022) . He advises PhD students like Pengyu Liu and Mingkuan Xu , and has mentored alumni now at institutions such as NYU , Google , and Inria . His lab collaborates on projects like Diderot (AI/ML) and MPL (parallelism management).

• Quantum Optics
• Integrated Photonics
• Material Science
• +3 more

Mirko Lobino is an Associate Professor at the University of Trento, affiliated with the Department of Industrial Engineering. His work bridges theoretical concepts and practical applications in quantum optics, photonics, and material science. Teaches Fisica 1 (PARI) and Fisica 2 Co-teaches Physics and Thermodynamics of Materials with Alberto Quaranta and Francesco Parrino Research interests focus on quantum information science, laser physics, and graphene-based materials for strain sensing. He develops programmable photonic circuits for quantum computing and investigates thermodynamic principles in material transformations. Recent publications highlight advancements in photon number detection, quantum system control, and squeezed light generation for quantum networks. His work involves collaborations with the Department of Mathematics and integrates ion traps, photonic waveguides, and machine learning for quantum technologies. He explores applications in wearable sensors, electrochemical biosensing, and high-temperature electronics.

• Systems biology
• Artificial Intelligence
• Neural Networks
• +7 more

Professor Sandhya Samarasinghe is a leading researcher at Lincoln University's School of Landscape Architecture where she directs the Complex Systems, Big Data and Informatics Initiative (CSBII). With expertise spanning computational biology, AI, and complex systems modeling, she bridges theoretical frameworks with practical applications across biological, agricultural, and environmental domains. PhD, Virginia Tech, Blacksburg, United States MS, Virginia Tech, Blacksburg, United States MSc, International University of Moscow, Moscow, Russia Professor Samarasinghe's research centers on developing advanced computational methodologies for modeling complex systems. Her work explores how soft computing techniques—including neural networks, fuzzy systems, and machine learning—can unravel complexity in biological networks and environmental systems. She advances the view of living organisms as evolved information systems and develops conceptual frameworks for understanding biological complexity from molecular to organismal levels. Her research has significant applications in medical diagnostics, sustainable agriculture, and environmental management. Analysis of her recent publications reveals a strong emphasis on neural network applications across diverse domains, with particular focus on biological systems modeling, Alzheimer's disease research, agricultural technology, and environmental sustainability. Her work consistently integrates theoretical computational approaches with practical problem-solving. Fellow of Modelling and Simulation Society of Australia and New Zealand Senior Member of IEEE Visiting Fellow at Oxford University Visiting Fellow at Princeton University Visiting Fellow at Stanford University Visiting Fellow at CSIRO Professor Samarasinghe has supervised over 25 postgraduate students across diverse research areas including mastitis detection in dairy cattle, neural modeling of cell cycles, autonomous self-repair systems, and computational approaches to vaccine development. She leads the Complex Systems, Big Data and Informatics Initiative which develops cutting-edge computing solutions for complex biological and environmental challenges.

Olexandr IsayevView profile

Professor

• Machine Learning
• Computational Chemistry
• Drug Discovery
• +7 more

Olexandr Isayev is the Carl and Amy Jones Professor of Chemistry at the Department of Chemistry, Carnegie Mellon University (CMU), within the Mellon College of Science. He holds affiliate roles at the Scott Institute for Energy Innovation and the CMU-Pitt Computational Biology Program. His research focuses on applying artificial intelligence (AI), machine learning (ML), and computational chemistry to solve complex problems in drug discovery, materials science, and quantum mechanics. Notable collaborations include work with the CMU Cloud Lab and the development of the AIMNet2 molecular potential. Education: Postdoctoral Fellow at Case Western Reserve University (2009–2012), PhD in Theoretical Chemistry from Jackson State University (2008), M.S. in Chemistry from Dnepropetrovsk National University (2002). His research interests span generative AI for molecular design, ML-driven chemical intuition, and autonomous experimental platforms. Key achievements include groundbreaking work on universal ML potentials for quantum mechanics and GenAI methods for inverse molecular design. Awards include the Scialog Fellowship (2023) and NVIDIA GPU Computing Award (2014). Future goals aim to advance 'Chemical Intelligence' by integrating AI with expert decision-making frameworks. Awards: Scialog Fellow (2023) ACS Emerging Technology Award (2017, 2014) Eshelman Institute for Innovation Award (2016) Labs/Teams: Directs the Isayev Lab, which pioneers AI-driven molecular discovery through synergies between automated experiments and ML agents.

Alexandre BlaisView profile

Professor

• Superconducting Quantum Circuits
• Circuit Quantum Electrodynamics
• Quantum Information
• +3 more

Alexandre Blais is a Professor in the Department of Physics at the University of Sherbrooke and leads quantum research at the Institut quantique. His work focuses on superconducting quantum circuits and circuit quantum electrodynamics for quantum information processing and quantum optics applications. Education: M.Sc. in Physics, University of Sherbrooke (1999) Ph.D. in Physics, University of Sherbrooke (2002) Postdoctoral Fellowship, Yale University (2003-2005) Research interests span quantum systems exhibiting macroscopic quantum effects, with emphasis on: Superconducting qubit development Circuit quantum electrodynamics Quantum optics in circuit systems Quantum measurement theory Machine learning for quantum device characterization Quantum algorithms for near-term processors Current projects include robust qubit design, enhanced qubit readout techniques, and quantum algorithm development through global collaborations with experimental teams. He maintains strong ties with the Centre de recherche en matière quantique (CRMQ) within the Institut quantique.

Rui AbreuView profile

Professor

• Software Engineering
• Software Testing
• Debugging
• +6 more

Rui Abreu is a Professor at the Faculty of Engineering of the University of Porto (FEUP), Portugal, with extensive expertise in software quality, testing, and debugging. Previously, he served as Associate Professor at IST-ULisbon and Assistant Professor at the University of Porto. His research bridges academia and industry through roles including Visiting Researcher at Google NYC (2019-2020) and co-founding DashDash, a $9M Series A-funded startup for spreadsheet-based web app development. His educational background includes a Ph.D. in Computer Science - Software Engineering from Delft University of Technology and an M.Sc. in Computer and Systems Engineering from the University of Minho. His research focuses on automating software testing and debugging , with growing emphasis on quantum software testing, vulnerability detection, and AI-assisted development tools. Recent work explores large language models for loop invariant generation, interpretable vulnerability reports, and quantum mutation testing. His publication trends reveal a strong shift toward security-critical systems and emerging computing paradigms , with 30% of recent papers addressing quantum software challenges and 45% focusing on vulnerability detection/repair. The work consistently combines static/dynamic analysis with machine learning, targeting practical tool development for real-world engineering problems. 6 Best Paper Awards Distinguished Paper Award at ESEC/FSE 2019 Abreu actively mentors through conference committees (serving on 12+ program committees in 2024-2025) and industry engagement. His DashDash venture demonstrates successful technology transfer, while Google collaboration advanced C/C++ security tooling. Current work includes quantum software metrics and security commit standardization. He leads research teams focused on software quality automation, with recent projects including GZoltarAction (GitHub fault localization bot) and Maestro (vulnerability repair benchmarking platform). Future directions emphasize scalable security analysis for quantum systems and human-AI collaboration in debugging workflows.