جست‌وجوی استاد و پژوهشگر

• Edge Computing
• Cloud Computing
• Artificial Intelligence
• +۷ مورد دیگر

Hadi Tabatabaee is an Assistant Professor at the School of Computer Science, University College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). His research focuses on sustainable orchestration of services across edge-cloud environments, emphasizing energy efficiency, carbon-aware systems, and AI-driven applications like large language models (LLMs). Key roles include Associate Editor for IEEE Access and Management Committee member of COST Action CA22151 (CYPHER). He holds a PhD in Computer Engineering from the University of Isfahan and has held academic positions at Maynooth University, Shahid Beheshti University, and Trinity College Dublin's CONNECT research program. Education: PhD (Computer Engineering, University of Isfahan), MSc (Computer Engineering), with a research visit at TU Delft (2010-2011). Certifications include Epigeum's Research Leadership and Research Integrity courses. Languages: Persian (fluent), Azerbaijani (spoken). Research Interests: Edge-cloud continuum, dynamic service placement, distributed AI workloads, LLM optimization, and sustainable resource management. Recent work includes zero-trust vehicular networks, parallel algorithms for recommender systems, and geospatial event processing. Awards: None explicitly listed, though his contributions include over 20 journal articles in IEEE/Elsevier/Springer venues. Professional Activities: IEEE Senior Member, TPC member for IEEE conferences, and reviewer for multiple journals. Teaching: Coordinates/teaches Cloud Computing, Computer Networks, and Principles of Computer Organization at UCD.

Kanu Sinhaمشاهده پروفایل

استادیار

• Quantum Optics
• Quantum Information
• Open Quantum Systems
• +۵ مورد دیگر

Kanu Sinha is an Assistant Professor of Optical Sciences and Physics at the University of Arizona, serving as Joint Faculty in the College of Optical Sciences. His research focuses on quantum fluctuation phenomena, cavity and waveguide quantum electrodynamics (QED), collective atom-field interactions, and non-Markovian open quantum systems. He leads the Quantum Optics and Open Quantum Systems Group, which explores applications in quantum information processing and quantum sensing. His work emphasizes engineering light-matter interfaces to study macroscopic quantum behaviors. Education: Ph.D. in AMO Physics from the University of Maryland, College Park (2015). Research interests include collective radiation dynamics, quantum Brownian motion, and decoherence mechanisms. Recent publications highlight advancements in quantum sensing, entanglement engineering, and non-Markovian systems. His group collaborates closely with experimental teams to bridge theoretical models with real-world applications. Key contributions include studies on vacuum-induced quantum beats, collective decay mechanisms, and Casimir-Polder interactions. Current projects explore quantum fluctuation forces in nanoscale systems and mechanical quantum sensing for dark matter detection.

• High-Performance Computing (HPC)
• Computational Fluid Dynamics
• Quantum Computing
• +۳ مورد دیگر

Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.

• Metal-metal bonding
• Ligand design
• Catalysis
• +۳ مورد دیگر

Prof. Connie Lu is a Professor at the Institute of Inorganic Chemistry within the Faculty of Mathematics and Natural Sciences at the University of Bonn. She leads the Lu Lab, which focuses on developing homogeneous catalysts for converting abundant small molecules like N₂ and CO₂ into useful chemical feedstocks such as ammonia and methanol. Her research interests encompass metal-metal bonding, innovative ligand design, sustainable catalysis, and energy conversion technologies. The lab specializes in creating ligand frameworks that enable transition metals to facilitate multi-electron and proton-coupled reactions essential for small-molecule reduction. This approach allows rapid screening of catalyst families for efficient small-molecule activation. Prof. Lu's work bridges fundamental inorganic chemistry and applied catalysis, with particular emphasis on reaction mechanisms in bimetallic systems and sustainable chemical synthesis. Her research contributes to renewable energy solutions and environmentally friendly chemical processes.

• Nanotechnology
• Condensed Matter Physics
• Materials Science
• +۳ مورد دیگر

Chang Liu is an Assistant Professor at the Department of Economics, Stony Brook University. He specializes in advanced microscopy techniques and nanoscale material characterization, with a focus on plasmonics, 2D materials, and quantum phenomena. His research employs cutting-edge tools like scattering-type scanning near-field optical microscopy (s-SNOM) and terahertz spectroscopy to explore electronic and optical properties at the nanoscale. His work spans interdisciplinary areas including graphene heterostructures, Dirac materials, and phase transitions in correlated oxides. Liu’s contributions include developing novel imaging modalities (e.g., BOSON) and advancing understanding of polaritonic systems. He collaborates with synchrotron facilities like NSLS-II for ultra-high resolution studies. Notable research trends include exploring nanoscale structural phase separations, tunable phonon polaritons in van der Waals heterostructures, and nano-photocurrent dynamics in twisted bilayer systems. His technical innovations, such as machine learning for nano-optical data analysis and cryogenic s-SNOM setups, enhance experimental precision. Liu’s lab is part of the Stony Brook Center for Game Theory, though his direct research themes focus on materials physics. Current projects include roadmap development for 2D material photonics and exploring moiré ferroelectricity in twisted WSe₂ systems. No awards or grants are explicitly listed in the provided materials.

Yu [Kevin] Caoمشاهده پروفایل

استاد

• Microelectronics
• Computer Engineering
• Semiconductor Technology
• +۵ مورد دیگر

Yu [Kevin] Cao is the Louis John Schnell Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on microelectronics co-design for energy-efficient computing, spanning integrated circuit design, semiconductor physics, and machine learning methodologies. He leads the Microelectronics Co-design Research Group and actively collaborates with institutions like Georgia Institute of Technology, Sandia National Laboratories, and Notre Dame. His research interests include AI hardware acceleration , in-memory computing , cryogenic CMOS design , and 3D integration of heterogeneous chiplets . Current initiatives explore reconfigurable on-package systems for AI, spiking neural networks on neuromorphic hardware, and low-temperature logic technologies. Recent publications and projects highlight advancements in AI accelerators , RRAM-based compute-in-memory , graph convolutional networks , and 3D integration . His group develops tools like MN-SIM 2.0 for memristor modeling and investigates novel materials for neuromorphic systems. Grants include collaborative NSF funding for chiplet-based AI systems, CoCoSys center funding from SRC, and DOE/Sandia projects on neuromorphic hardware. Future work emphasizes scalable co-design frameworks for intelligent systems and heterogeneous integration challenges.

• Neuroscience
• Complex Systems Analysis
• Statistical Physics
• +۳ مورد دیگر

Ben Fulcher is an Associate Professor and Senior Lecturer in the School of Physics at The University of Sydney, leading the Dynamics and Neural Systems Group. His research focuses on applying statistical and physics-based methods to study complex systems, with a strong emphasis on neuroscience. He investigates brain structure and function dynamics, particularly exploring the interplay between genetics, neural connectivity, and information processing mechanisms. Fulcher has pioneered tools like hctsa (highly comparative time-series analysis) for analyzing large-scale neuroimaging and physiological data. Research Interests Complex systems analysis Neural dynamics and connectomics Feature-based time-series analysis Gene-brain connectivity relationships Imaging transcriptomics Recent Work Trends Fulcher's recent articles emphasize time-series analysis applications in neuroscience, including studies on anesthesia effects, brain connectivity, and criticality in neural systems. His work bridges computational methods (e.g., generative models, matrix-product states) with biological insights, often involving interdisciplinary collaborations. Key themes include understanding how brain geometry influences dynamics and translating genetic data into connectomic predictions. Grants & Projects 2024: ARC Future Fellowship for sleep research using complex systems methods 2023: Discovery Project on network neuroscience and AI integration Advising & Labs Fulcher supervises research students and directs the Dynamics and Neural Systems Group, which develops open-source tools like the theft R package for time-series analysis. His lab’s work has been featured in media outlets discussing brain structure-function relationships and comparative neurobiology.

• Privacy-Preserving Protocols
• Secure Multi-Party Computation
• Homomorphic Encryption
• +۵ مورد دیگر

Daniel Demmler is an Assistant Professor at Darmstadt University of Technology's Department of Computer Science, specializing in privacy-preserving protocols and cryptographic systems. His research focuses on practical implementations of secure multi-party computation, homomorphic encryption, and privacy-preserving machine learning frameworks. Dr. Demmler's primary research interests lie in making cryptographic protocols practical for real-world applications. His work spans secure multi-party computation, threshold homomorphic encryption, federated learning security, and defenses against property inference attacks. He has made significant contributions to frameworks like MOTION for mixed-protocol computation and Noah's Ark for threshold-FHE systems. His research bridges theoretical cryptography with practical implementation challenges, focusing on efficiency and real-world applicability. Analysis of his recent publications reveals a strong focus on threshold cryptography and privacy-preserving machine learning. His work shows increasing sophistication in balancing security guarantees with computational efficiency, particularly in distributed settings. The trend indicates growing interest in quantum-resistant cryptographic approaches and defenses against emerging machine learning privacy threats. Best Paper Award at SECRYPT 2021 Distinguished Paper Award at CCS 2018 Dr. Demmler leads the Cryptology and Privacy Research Group at TU Darmstadt, collaborating extensively with international researchers in the field. His team focuses on developing practical implementations of advanced cryptographic protocols that can be deployed in real-world systems while maintaining strong security guarantees. Current projects include threshold homomorphic encryption systems and privacy-preserving machine learning frameworks.

• Bioinformatics
• Protein Stability
• Molecular Biology
• +۳ مورد دیگر

Amelie Stein is an Associate Professor at the Department of Biology, University of Copenhagen, specializing in Bioinformatics and RNA Biology. Her research focuses on protein stability, molecular mechanisms of disease variants, and computational methods for protein design. She is affiliated with the UCPH Quantum Hub, reflecting interdisciplinary interests in biological systems. Her work integrates bioinformatics tools, mutational scanning, and structural biology to understand protein degradation pathways and their relevance to human diseases such as Lynch syndrome and metabolic disorders. Key research areas include analyzing protein variants using deep learning models (e.g., SSEmb), developing web-based tools like MutationExplorer for 3D visualization, and characterizing disease-linked mutations in proteins such as Parkin and MLH1. Her publications highlight breakthroughs in rapid protein stability predictions, degon mapping, and the interplay between protein toxicity and degradation. No scientific awards are explicitly mentioned in the provided texts. Stein’s research also explores the application of computational approaches to biotechnology and therapeutic development, emphasizing translational applications of her findings. Her lab, linked to the SCARB research group (https://www1.bio.ku.dk/english/research/scarb/), focuses on structural and computational biology, with ongoing projects involving protein quality control networks and enzyme variant analysis. Collaborations span molecular biology, bioinformatics, and interdisciplinary quantum-related research through her UCPH Quantum Hub membership.

Christina Rostمشاهده پروفایل

استادیار

• High Entropy Ceramics
• Compositionally Complex Materials
• Bulk and Thin Film Synthesis
• +۳ مورد دیگر

Christina M. Rost is an Assistant Professor in the Department of Materials Science and Engineering at Virginia Polytechnic Institute and State University (Virginia Tech) , part of the College of Engineering . She holds a Ph.D. in Materials Science and Engineering from North Carolina State University (2016), following a B.S. and M.S. in Physics from Indiana University of Pennsylvania. Prior to joining Virginia Tech, she served as an Assistant Professor of Physics at James Madison University. Research Focus: Atomic-level disorder in ceramics, particularly high entropy and compositionally complex systems. Key Techniques: X-ray absorption/emission spectroscopy, multi-length-scale characterization. Applications: Functional materials for extreme environments, energy, and electronics. Research Interests include designing materials with tunable properties via controlled disorder, leveraging high entropy oxides and amorphous systems. Her work integrates advanced spectroscopy, computational modeling, and machine learning to accelerate materials discovery. Recent emphases include understanding local structure- property relationships in entropy-stabilized oxides and spinel ferrites. Publications reflect a focus on high entropy materials' synthesis, thermal stability, magnetic behavior, and functional applications. Notable trends include: (1) exploring cation roles in rock salt oxides, (2) optimizing thin film growth for pyroelectric applications, and (3) studying phase evolution under thermal/mechanical stress. Scientific Awards : 2023 Provost Award for Excellence in Research (James Madison University) 2017 Postdoctoral Teaching Fellowship (University of Virginia) 2015 First Place, Best Student Presentation (ACerS) Lab Team includes 5 graduate students (e.g., John Barber, Gerald Bejger), 4 undergraduates, and former members now at institutions like Pratt & Whitney. Lab Facilities : Located in Holden Hall, equipped with state-of-the-art characterization tools.

Alexis Kirkeمشاهده پروفایل

عضو هیئت علمی

• Music Technology
• Computational Music
• Human-Computer Interaction
• +۳ مورد دیگر

Dr Alexis Kirke is a Senior Research Fellow in Computer Music at the School of Art, Design and Architecture (Faculty of Arts, Humanities and Business) at the University of Plymouth. As a composer-in-residence, he specializes in interdisciplinary research at the intersection of music, computing, and healthcare. His work includes developing adaptive music systems like RadioMe for dementia care, applying quantum computing to music composition, and exploring affective computing through brain-computer interfaces. Teaching roles include associate lecturer positions in modules such as Collaborative Practice (BA Sound and Music Production), Psychology (BA Music), and MRes Computer Music. He has supervised five PhD students as a second supervisor and served as an internal PhD examiner. His research focuses on algorithmic composition, music technology for healthcare, quantum computing applications in music, and multi-agent systems inspired by natural phenomena like humpback whale song evolution. Recent projects include the Plymouth Marine Institute collaboration and the Cloud Chamber performance involving real-time interaction with subatomic particles. Key contributions include innovative systems like RadioMe, which combines adaptive radio with reminder systems for dementia patients, and Q-Muse, a quantum computer music system. His work bridges computational creativity with human-centric applications, emphasizing ethical and accessible technology. Grants & Awards: No specific awards listed, but active in collaborative research projects Lab/Teams: Plymouth Marine Institute, Cloud Chamber Project

Satwik Patnaikمشاهده پروفایل

استادیار

• VLSI Design
• Hardware Security
• Computer-Aided Design (CAD)
• +۳ مورد دیگر

Satwik Patnaik is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. His research focuses on strengthening hardware security through CAD tools, leveraging machine learning, and exploring emerging technologies. He holds a Ph.D. in Electrical Engineering from New York University (2020) and was a postdoctoral researcher at Texas A&M University (2020–2021). Education: Ph.D., Electrical Engineering, Tandon School of Engineering, New York University (2020) Postdoctoral Research, Texas A&M University (2020–2021) Research Interests: VLSI design, hardware security (e.g., logic locking, side-channel attacks), computer-aided design (CAD), and applications of machine learning in securing global semiconductor supply chains. His work emphasizes 3D integration, emerging devices (e.g., ferroelectric tunnel junctions), and reinforcement learning for adversarial testing. Key Contributions: Developed tools like SecureX, VIGILANT, and Titan. Published over 48 peer-reviewed articles in top venues (e.g., ICCAD, IEEE Transactions). Co-organized global competitions like HeLLO-CTF 2021 and AI vs. Humans 2022. Served as security track chair for ACM CADAthlon (2022) and technical program committee member for ICCAD, ASP-DAC, etc. Awards: ACM/SIGDA Bronze Medal (2018) Best Paper Award at ARC 2017 Third Place at ARC 2021 Expertise: Integrates hardware design, security, and AI to address vulnerabilities in semiconductor supply chains. Active in standardizing secure CAD flows and educating the next generation of hardware security researchers.

David Yaronمشاهده پروفایل

استاد

• Computational Chemistry
• Quantum Chemistry
• Materials Science
• +۶ مورد دیگر

David Yaron is a Professor of Chemistry at Carnegie Mellon University's Mellon College of Science. His research focuses on computational chemistry, machine learning applications in chemical modeling, and educational technology. He holds a Ph.D. from Harvard University (1990) and a B.S. from Wilkes College (1983). Yaron's work includes developing semiempirical quantum chemical models, designing dyes for biological imaging, and creating virtual labs like ChemCollective and ChemVLab+. He has received numerous awards, including the Richard Moore Award (2023) and the Henry Dreyfus Teacher-Scholar Award (2000). His educational initiatives emphasize data-driven learning and open-ended problem-solving. Research Interests: Machine Learning for Chemistry, Photophysics, Materials Theory, Chemical Education Key Projects: Quantum chemical models via ML, design of fluorogenic dyes, light-driven molecular motors Education: Ph.D. Harvard (1990), B.S. Wilkes College (1983) Awards highlight both research (e.g., Camile & Henry Dreyfus Foundation) and teaching (e.g., Teaching Innovation Awards). His lab has advised over 30 graduate and undergraduate students. Publications span quantum chemistry methods, polymer science, and educational technology.

• Quantum Computing
• Spin Qubits
• Semiconductor Nanostructures
• +۴ مورد دیگر

Dr. Frederico Martins is a Senior Research Fellow and solid-state physicist specializing in quantum computing with spin qubits. He currently leads the Spin Qubit team at QuantrolOx, a University of Oxford spinout developing machine-learning-based software for quantum processor automation. Previously, he led the Quantum Information team at Hitachi Cambridge Laboratory (University of Cambridge). His work bridges quantum hardware and intelligent software to address scalability and reliability in quantum architectures. Research interests include spin qubit control in semiconductor nanostructures, machine learning integration for device calibration, and overcoming challenges in multi-dot systems such as charge sensing and Pauli spin blockade detection. He focuses on high-fidelity spin manipulation, readout, and scaling to enable robust quantum computing platforms. His articles demonstrate expertise in silicon spin qubits, quantum dot characterization, and noise mitigation. Collaborations with industry and academia aim to advance scalable quantum technologies. Current projects emphasize automating quantum device control through AI-driven algorithms.

• Mathematical Physics
• Differential Geometry
• Topology
• +۴ مورد دیگر

Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.