Find Academic Supervisors

• Quantum Physics
• Quantum Computing
• Quantum Control
• +5 more

Erik Torrontegui Muñoz is a Ramón y Cajal Research Fellow at the University Carlos III of Madrid (UC3M), where he serves as Director of the University Master's Degree in Quantum Technology and Engineering. He is affiliated with the Physics Department and leads research within the QUEST (Quantum Engineering, Science and Technology) research group. His research focuses on quantum technologies, particularly quantum control, quantum computing, quantum sensing, and quantum neural networks. His work bridges theoretical quantum physics with practical applications in quantum information processing. He has made significant contributions to the field of shortcuts to adiabaticity, which enables faster quantum operations while maintaining high fidelity. Torrontegui's recent publications demonstrate expertise across multiple quantum platforms including nitrogen-vacancy centers in diamond, superconducting qubits, trapped ions, and quantum neural networks. His research shows a consistent pattern of developing control techniques that enhance quantum system performance while addressing practical limitations. Ramón y Cajal Research Fellowship (prestigious Spanish research award) As an academic advisor, Torrontegui mentors several PhD students working on quantum technologies. His research is supported by multiple projects including collaborations with IONQ QUANTUM, INC., ARQUIMEA RESEARCH CENTER, and funding from the Spanish Agency for Research (AEI). He maintains active laboratory work in quantum engineering, with particular focus on developing quantum sensing protocols and quantum computing architectures based on NV centers and other quantum platforms.

Jian-Min ZuoView profile

Professor

• Material Characterisation
• Condensed Matter Physics
• Electron Diffraction
• +8 more

Jian-Min Zuo is a Professor of Materials Science and Engineering and Physics at Monash University, and Director of the Monash Centre for Electron Microscopy. Previously, he held the Ivan Racheff Professorship at the University of Illinois, Urbana-Champaign (UIUC), where he remains an Emeritus Professor since July 2025. He earned his Ph.D. in Condensed Matter Physics from Arizona State University in 1989, followed by postdoctoral research at the NSF Center for High-Resolution Electron Microscopy. His research focuses on advanced materials characterization using electron microscopy techniques such as transmission electron microscopy (TEM), diffraction, and spectroscopy. Key areas include high-entropy alloys, quantum materials, semiconductor devices, energy materials, and low-dimensional systems. He pioneered studies on atomic-scale structure analysis of nanostructured materials and ultrafast electron diffraction. Zuo’s expertise spans hyperspectral electron scattering (4D-STEM), defect analysis, and machine learning for data analytics. His work contributes to UN Sustainable Development Goals related to sustainable energy and advanced materials. Education: Ph.D. in Condensed Matter Physics (Arizona State University, 1989) Awards: Ernst Ruska Prize (German Society for Electron Microscopy), Gjonnes Award (International Union of Crystallography), Fellowships from APS and MSA. Grants/Advising: Actively supervising PhD students in high-entropy alloys and electron microscopy techniques. His laboratory develops cutting-edge imaging tools like PINEM and fluctuation cepstral STEM, advancing understanding of material properties at the atomic scale.

Markus BechererView profile

Professor

• Spintronics
• Magnetic Sensor Technology
• Nanomagnetism
• +3 more

Prof. Markus Becherer holds the professorship of 'Chip-based Magnetic Sensor Technology' at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. His research focuses on magnetic components for spin-based signal processing, with emphasis on ferro/ferrimagnetic materials, nanoscale structuring, and applications in digital/analog/neuromorphic systems. He leads ZEITlab, TUM’s central electronics and IT laboratory, and previously served as acting head of the Chair of Nanoelectronics. Education: Vocational training at Sick AG, followed by electrical engineering studies at TUM. PhD (2011) and habilitation (2017) from TUM. Awards include the Kurt Fischer Prize (2011) and multiple scholarships from Bavarian Research Foundation, SICK AG, and the German Academic Scholarship Foundation. Research interests span spintronics, magnetic sensor technology, and nanomagnetic systems. Key innovations include FIB-irradiation techniques for skyrmion control, 3D nanomagnetic integration, and spin-wave-based computing. Recent work explores energy-efficient logic systems and magnonic devices. Key Achievements: Pioneered spin-wave optics in YIG, demonstrated machine-learning-designed magnonic lenses, and advanced skyrmion-based circuitry. Labs/Teams: Leads ZEITlab and collaborates on 3D nanomagnetic logic systems.

Dhritiman BhattacharyaView profile

Assistant Professor

• Spintronics
• Energy-Efficient Computing
• Magnetic Nanostructures
• +3 more

Dhritiman Bhattacharya is an Assistant Professor in the Department of Electrical & Computer Engineering at Rowan University, affiliated with the Henry M. Rowan College of Engineering. He holds a Ph.D. in Mechanical and Nuclear Engineering from Virginia Commonwealth University (2020) and a B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2013). Prior to his current role, he was a Postdoctoral Fellow in the Department of Physics at Georgetown University. His research focuses on overcoming energy limitations in CMOS-based computing through spintronic innovations, leveraging magnetic nanostructures and non-volatile materials to develop novel neuromorphic and memory technologies. Key areas include voltage-controlled skyrmions, magneto-ionic devices, and neuromorphic computing architectures. He has published over 25+ journal articles and received the 2021 Best Paper Award at the ASME Smart Materials Conference, alongside recognition as an Outstanding Reviewer for the Institute of Physics. His articles explore topics such as magnetic nanostructure design, strain-mediated switching, and reservoir computing with frustrated nanomagnet arrays. Notable contributions include studies on 3D interconnected nanowire networks and physically secure logic locking mechanisms. Professional memberships include IEEE and the American Physical Society.

• Two-dimensional materials
• Van der Waals heterostructures
• Semiconductor device engineering
• +6 more

Seunghoon Yang is a Research Associate at the Center for Functional Nanomaterials within Brookhaven National Laboratory. His work focuses on van der Waals heterostructures , 2D semiconductor devices , and neuromorphic engineering . PhD in Engineering: Nano Bio-Information Technology from Korea University (2022) BSc in Display and Semiconductor Physics from Korea University (2016) BSc in Biological and Chemical Engineering from Hongik University (2013) His research explores interface band engineering , optoelectronic modulation , and molecular dipole effects in nanoscale devices. Current projects include CMOS integration of neuromorphic devices and oxide-based memristor development . Recent publications highlight van der Waals heterostructures for photodetection , excitonic devices , and spintronic applications . His work bridges materials synthesis , device physics , and advanced computation . 2021: Best Poster Award, The 12th International Conference on Advanced Materials and Devices (ICAMD) 2020: KU Graduate School Achievement Award, Korea University 2020: Best Poster Awards at KPS Fall Meeting, NANO KOREA, and Korean Conference on Semiconductor 2018: Best Poster Paper Award (Silver), 5th International Conference on Advanced Electromaterials Yang contributes to scientific computing through algorithm development for 2D material characterization and is affiliated with Brookhaven National Laboratory's Building 735, Room 2023c facility.

• Electric drives
• Electrical machines
• Power converters
• +7 more

Gianmario Pellegrino is a Full Professor at the Department of Energy (DENERG) , Polytechnic of Turin, Italy. He leads the Power Electronics Innovation Center (PEIC) and serves on the PoliTO Spin-Off Evaluation Commission. His research focuses on electric drives, electrical machines, power converters, and sensorless control , with industry collaborations spanning automotive and aerospace sectors. Professor of Power Converters, Electrical Machines, and Drives IEEE Senior Member and Associate Editor for IEEE Transactions on Industry Applications Research Interests include: Finite element analysis for motor design Optimization algorithms in SyR-e open-source platform Thermal management of high-performance motors Sensorless control techniques Recent Publications highlight advancements in: Thermal transient modeling for supercars Data-driven multi-physics simulations Active fault-reaction strategies Scientific Recognition: IEEE Fellow (2022) for Synchronous Reluctance machine contributions Eight Best Paper Awards and ECCE Third Prize (2011) Advising: Supervises 10+ PhD students in electrical engineering, focusing on motor design and control. Labs: Leads PEEMD research group and contributes to PCIM Europe advisory board.

• Perovskite Materials
• Computational Chemistry
• Optoelectronics
• +4 more

Filippo De Angelis is a Professor at the University of Perugia, where he coordinates the Doctoral Program in Chemical Sciences and serves as the University Representative for the National Interuniversity Consortium for Materials Science and Technology (INSTM). His research focuses on the computational and experimental development of advanced perovskite materials for optoelectronic and photovoltaic applications. Research interests span perovskite design, stability engineering, and device integration, with emphasis on: Chiral and low-dimensional perovskites for spintronics Tin-halide systems for LEDs/solar cells Defect physics and doping mechanisms Ab initio molecular dynamics simulations Recent publications (2024–2025) highlight trends in computational materials science, perovskite stability strategies, and chiral structure-property relationships. Works frequently employ machine learning, solid-state NMR, and in situ characterization to address challenges in carrier dynamics, halide segregation, and interfacial phenomena. No awards, students, or grant details are reported in the source text. Laboratory activities are centralized at www.clhyo.org , focusing on hybrid/organic photovoltaics and computational modeling.

• Embedded Systems
• Neural Networks
• Machine Learning
• +3 more

Tamás Majoros is an Assistant Professor at the Faculty of Informatics, University of Debrecen , specializing in Embedded Systems and Neural Networks . His work bridges hardware design and biomedical applications. Primary Affiliation: Faculty of Informatics, University of Debrecen Academic Rank: Assistant Professor Major research areas include: Embedded systems for real-time data processing Neural networks in EEG signal analysis FPGA-based hardware acceleration for medical devices Particle physics collaborations (e.g., sPHENIX) His publications reveal a focus on EEG-based activity recognition , motor imagery classification , and radiation detector stability . Recent work emphasizes edge AI and low-cost neural interfaces . Scientific Awards: None explicitly mentioned. No details provided about advising or grants.

Eli Zysman-ColmanView profile

Professor

• Organic Light-Emitting Diodes (OLEDs)
• Light-Emitting Electrochemical Cells (LEECs)
• Dye-Sensitized Solar Cells
• +7 more

Eli Zysman-Colman is a Professor of Optoelectronic Materials at the School of Chemistry, University of St Andrews , UK. A Royal Society of Chemistry Fellow , his research spans organic electronics, photoredox catalysis, and sustainable materials development. Key affiliations include the Centre for Energy Ethics , Organic Semiconductor Centre , and EaSTCHEM collaborative chemistry network. Research Focus : Thermally Activated Delayed Fluorescence (TADF) for OLEDs and LEECs Photoredox catalysts using Earth-abundant metals Light harvesting dyes for solar cells (DSSCs, OPVs) Supramolecular chemistry in materials design Mechanophotocatalysis for solvent-minimized reactions Teaching : Contributes to CH2501, CH3514, CH4514, CH5517 modules. Projects include SolOLED (TADF dendrimers), Mechanophotocatalysis , and Boron-based NIR emitters . Supervises PhD theses on TADF kinetics and photocatalysis. Scientific Awards : Canadian Foundation for Innovation Leaders Opportunity Fund (2008) McGill University Dean’s Honours List (2004) FRQNT Postdoctoral Fellowship (2004) FRQ Doctoral & Masters Scholarships (2000-2001)

Tom HaywardView profile

Professor

• Nano-scale Magnetism
• Spintronics
• Neuromorphic Computing
• +3 more

Professor Tom Hayward is affiliated with the School of Chemical, Materials and Biological Engineering at the University of Sheffield. His research focuses on nano-scale magnetism , spintronics , and neuromorphic computing , aiming to develop energy-efficient computing devices and understand fundamental magnetic physics. Key research areas include micromagnetic simulation , unconventional computing , and magnetic domain walls . Recent work explores stochastic synapses for machine learning and chirality-encoded logic gates . His publications highlight interdisciplinary applications spanning artificial intelligence and magnetic biosensors . Scientific awards include the EPSRC Career Acceleration Fellowship (2012) . His work is funded by EPSRC Leverhulme Trust European Commission Royal Society Sir Henry Royce Institute . Professor Hayward teaches modules such as MAT6390 (Nanoscale Magnetic Materials and Devices) and supervises projects in materials physics . He leads a research group developing magnetic metamaterials and collaborates with institutions like the University of Cambridge and Royal Society.

• LiDAR in Agriculture
• Machine Learning
• Hyperspectral Image Analysis
• +4 more

Sourabhi Debnath is a part-time lecturer and researcher at the School of Computing, Mathematics, and Engineering at Charles Sturt University since 2018. Her work focuses on LiDAR in Agriculture, Machine Learning, Hyperspectral Image Analysis, and Digital Health. Research Interests: LiDAR applications in agricultural monitoring Machine Learning for crop analysis Hyperspectral imaging in plant health AI-driven health monitoring systems Recent Publications include work on graphene-based wearable sensors and hyperspectral imaging for nutrient deficiency detection. She has received grants for AI models in brain disease diagnosis and biosensor development. Her research intersects digital health and agricultural technology , with collaborations on quantum image processing and cybersecurity analytics.

• Brain-inspired Circuit Design
• Artificial Intelligence Acceleration
• Spiking Neural Networks
• +3 more

Jason Eshraghian is an Assistant Professor at the Department of Electrical and Computer Engineering, University of California, Santa Cruz. He leads the UCSC Neuromorphic Computing Group, focusing on brain-inspired circuits for AI acceleration and spiking neural networks. His work bridges biological principles with hardware implementation to solve computational challenges in AI efficiency. Assistant Professor, Electrical and Computer Engineering UCSC Neuromorphic Computing Group leader Research intersections: Neuromorphic engineering, AI hardware, spiking networks Research Interests: Dr. Eshraghian's work centers on neuromorphic computing and spiking neural networks for AI acceleration. His lab explores Hardware-software co-design for ultra-low-power systems Memristor-based neural architectures Event-driven medical diagnostics Spiking language models (e.g., SpikeGPT) Closed-loop neurostimulation Article Trends: Recent publications emphasize Scalable neuromorphic architectures for AI (2025: Ising machines, FPGA implementation) Medical applications including cytometry and brain-computer interfaces Energy-efficient designs for language models and tracking systems Hybrid attention mechanisms and temporal learning frameworks

Nickvash KaniView profile

Academic

• Mathematical Language Processing
• Natural Language Processing
• Artificial Intelligence
• +4 more

Nickvash Kani is a Teaching Assistant Professor in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign since August 2023. He is affiliated with the Coordinated Science Lab and leads research in Mathematical Language Processing, focusing on developing systems that can process mathematical data with the sophistication currently applied to natural language. His educational background includes: Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2017) M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2013) B.S. in Computer Engineering from Boston University (2010) Dr. Kani's research centers on Mathematical Language Processing, which he describes as essential for achieving true artificial intelligence since mathematics represents axiomatic knowledge. His work bridges natural language processing with mathematical reasoning systems, developing algorithms to extract knowledge from mathematical literature. He emphasizes that "true intelligence is axiomatic" and believes focusing on mathematical literature enables automated collection of axiomatic knowledge from unlabeled sources. His publication record shows a clear evolution from traditional electrical engineering topics like nanomagnetics and spintronics to cutting-edge AI research. Recent publications (2023-2025) focus on mathematical expression representation, formula search, and enhancing mathematical reasoning in language models. His work spans multiple venues including Transactions on Machine Learning Research, IEEE/ACM Transactions, and NeurIPS workshops, demonstrating interdisciplinary impact across computer science and engineering. His notable achievements include: George Anner Excellence in Undergrad Teaching Award (September 2023) 1st Place - NL4Opt-Task2 challenge at NIPS 2022 (December 2022) 2023 Engineering Council Outstanding Advisors award Dr. Kani actively mentors students and has built a research group dedicated to Mathematical Language Processing. His graduate teaching assistants, including Neeraj Gangwar and Suyuan Wang, have been instrumental in his research and teaching. He has taught ECE 374 (Introduction to Algorithms and Models of Computation) to 200-300 students and developed ECE 364 (Data Science and Engineering), focusing on implementation with auto-differentiation tools like PyTorch. He leads the Mathematical Language Processing Group, which is dedicated to developing algorithms that process mathematical data and literature, with the long-term goal of enabling AI systems to reason axiomatically. His teaching philosophy emphasizes "slow, iterative changes" to improve course accessibility and support student learning objectives in STEM education.

Christopher MonroeView profile

Professor

• Quantum Physics
• Quantum Information Science
• Quantum Computing
• +3 more

Christopher Monroe is the Gilhuly Family Presidential Distinguished Professor at Duke University, with joint appointments in the Pratt School of Engineering (Department of Electrical and Computer Engineering) and the Department of Physics. He serves as Director of the Duke Quantum Center , leading cutting-edge research in quantum information science and trapped ion systems. Education: B.Sc. from MIT (1987), Ph.D. from University of Colorado, Boulder (1992) Key Research Areas: Quantum computing, quantum networking, quantum simulation, and multi-body entanglement Monroe's work focuses on trapped ion quantum systems , advancing remote entanglement, mid-circuit measurement, and N-body interactions. His team has demonstrated high-fidelity quantum gates , efficient cooling methods, and photonic interconnects for quantum networks. Recent projects include simulating quantum field theories and studying discrete time crystals with programmable ion chains. Scientific accolades include election to the National Academy of Sciences (2016). He has pioneered multi-species ion trapping for scalable quantum computers and machine learning integration with quantum circuits. Monroe's leadership extends to national policy through the U.S. National Quantum Initiative , where he testified before Congress on quantum computing's strategic importance. His lab develops hybrid quantum-classical architectures and error-corrected qubit memories , with applications in quantum gravity-inspired algorithms like thermofield double states . Monroe's group also explores quantum scrambling and verified quantum information processing , pushing boundaries in both fundamental physics and practical quantum technology.

Huachao MaoView profile

Assistant Professor

• Additive Manufacturing
• Precision Manufacturing
• Smart Manufacturing
• +8 more

Dr. Huachao Mao serves as an Assistant Professor at Purdue University's School of Engineering Technology, where he directs the Additive & Intelligent Manufacturing (AIM) Lab. His research bridges nanoscale precision with additive manufacturing, focusing on applications in optics, microfluidics, and biomedical technologies through innovations in process engineering and computational modeling. His academic foundation includes: Ph.D. in Industrial and Systems Engineering, University of Southern California, 2019 M.S. in Computer Science, University of Southern California, 2018 M.S. in Control Science and Engineering, Tsinghua University, China, 2014 B.E. in Mechanical Engineering and Automation, Beihang University, China, 2011 Dr. Mao's research centers on precision Additive Manufacturing processes for optics, functional polymers, microfluidics, and structural composites, coupled with advanced computing for manufacturing including digital twin development, topology optimization, and AI/ML-driven process control. His work pioneers techniques like adaptive slicing, mask image optimization, and precision spin coating to achieve nanoscale accuracy in complex 3D geometries, enabling breakthroughs in optical devices and biomedical applications. Analysis of his recent publications reveals a dominant focus on vat photopolymerization advancements, particularly for optical and microfluidic applications. His work consistently integrates AI/ML for process optimization while expanding into functional materials like index-matching resins and conductive polymers, with growing emphasis on educational frameworks for smart manufacturing. His scientific contributions have been recognized with: Best Paper Award, ASME MSEC 2021 Best Paper Award, NAMRC 2016 Best Paper Award, NAMRC 2018 Purdue Departmental Achievement Award 2024 Outstanding Faculty in Discovery at Purdue Polytechnic 2025 Dr. Mao actively mentors graduate researchers and leads NSF-funded projects developing 3D-printed optical AI processors and robotic e-skin. His lab currently recruits Ph.D. students for Fall 2025 to advance multi-material printing and digital twin applications. As an editorial board member for Micromachines and Digital Manufacturing Technology, he shapes the field's scholarly discourse while reviewing for top journals including Nature and Science Robotics. The Additive & Intelligent Manufacturing Lab serves as the operational hub for Dr. Mao's research, driving innovations in liquid crystal display-based printing, microfluidic device fabrication, and precision optical manufacturing through interdisciplinary collaboration between materials science, robotics, and computational engineering.