جستجوی استاد راهنمای دانشگاهی

• Quantum Information Science
• Quantum Computing
• Quantum Error Correction
• +۶ مورد دیگر

Dr. Thinh Le is a Postdoctoral Research Fellow at the School of Computer Science , University of Technology Sydney , where he works at the Centre for Quantum Software and Information (since 2023). His academic journey includes research fellowships at the Institute for Quantum Optics and Quantum Information Vienna (2020-2023) and Leibniz University Hannover (2019-2020). He earned his PhD in Physics (2015) and BSc in Physics (2011) from the National University of Singapore . Thinh's research focuses on quantum information science , bridging theoretical foundations and experimental implementations. His work addresses critical challenges in quantum computing (error correction, fault tolerance), quantum tomography (device-independent characterization), and quantum software stack development. He investigates quantum randomness generation, Bell nonlocality, and entanglement distillation for quantum networks, with applications in quantum cryptography and secure communication. His recent publications examine advanced topics like trapped-ion lattice surgery (2025), quantum correlation boundaries (2025), and quantum backflow phenomena (2023). Earlier work established foundational limits for real quantum theory (2021) and developed practical entanglement distillation protocols (2018). Scientific awards include the Lise Meitner Postdoctoral Fellowship (2020-2023) and Humboldt Postdoctoral Fellowship (2019-2020). His funded research projects focus on quantum computing error correction and device-independent quantum tomography , aligning with his theoretical expertise guided by experimental applications.

• Trustworthy computing
• Energy harvesting
• Intelligent system design
• +۷ مورد دیگر

Youhua Shi is a full Professor in the Faculty of Science and Engineering at Waseda University, Japan. He obtained his Doctor of Engineering from Waseda in 2005 and is an active member of IEICE, IPSJ, IEEE, and two Japanese academic societies. His research portfolio integrates trustworthy computing, hardware security of AI accelerators, energy-harvesting interface circuits for triboelectric nanogenerators, and low-power VLSI design-for-test methodologies. Education: Doctor of Engineering, Waseda University (2005) Graduate studies, Waseda University, Division of Engineering (completed 2005) Research Interests: Prof. Shi pursues trustworthy and secure silicon systems, spanning hardware Trojans in automated AI-accelerator flows, radiation-hardened latch design for soft-error resilience, and power-efficient CNN accelerators exploiting zero-gating and data-reuse techniques. Parallel work targets energy-autonomous IoT through advanced interface circuits for triboelectric nanogenerators, achieving record energy-per-cycle beyond the classical CMEO limit. Publication Trends: Recent articles (2024-2025) emphasize two thrusts: (i) security of AI/FPGA accelerators—proposing stealthy hardware-Trojan frameworks embedded within design-space-exploration flows that can misclassify up to 97% of inputs—and (ii) power electronics for triboelectric harvesters—introducing dual-output rectifiers and Bennet-doubler biasing that multiply output power >150× over conventional full-wave rectifiers, enabling battery-free IoT nodes. Scientific Awards: APCCAS Best Student Paper Award – 2020 IEEK Best Paper Award – 2012 Students & Collaboration: He has mentored numerous doctoral and master’s scholars, including Yirui Su, Chao Guo, Jinghao Ye, Lin Ye, Saki Tajima, and Masaru Oya, many of whom serve as first authors on his high-impact publications, indicating an active and productive advising role. Labs & Teams: While the text does not name a specific laboratory, his continued affiliation with Waseda University’s Faculty of Science and Engineering and his extensive project output imply he leads a research group focused on secure & energy-efficient VLSI systems, collaborating closely with colleagues such as Prof. Masao Yanagisawa and Prof. Nozomu Togawa.

Ruzica Piskacمشاهده پروفایل

استاد

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

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

John M. Zobitzمشاهده پروفایل

استاد

• Mathematical biology
• Environmental science
• Mathematical modeling
• +۵ مورد دیگر

John M. Zobitz is a Professor of Mathematics at Augsburg University. His academic journey includes a Ph.D. in Mathematics from the University of Utah, an M.A. in Mathematics from the University of Utah, and a B.A. in Mathematics and Spanish with a minor in Physics from St. John's University. Dr. Zobitz's research focuses on the intersection of mathematics, biology, and environmental science. He has developed expertise in mathematical modeling, particularly in the application of mathematics to solve biological and environmental problems. His work often involves data assimilation techniques and statistical approaches to understand complex ecological systems. His research interests span mathematical biology, environmental science, carbon cycle dynamics, and ecological modeling. Dr. Zobitz has made significant contributions to understanding ecosystem carbon exchange, atmospheric monitoring of CO 2 sources, and the development of statistical methods for analyzing ecological data. His interdisciplinary approach bridges the gap between pure mathematics and practical environmental applications, with a particular emphasis on using mathematical tools to address climate-related questions. Dr. Zobitz has developed computational tools for data assimilation in ecological modeling, including MCMC (Markov Chain Monte Carlo) methods. His research has important implications for understanding climate change impacts and developing more accurate predictive models of ecosystem behavior. Ph.D. in Mathematics, University of Utah M.A. in Mathematics, University of Utah B.A. in Mathematics and Spanish (with a minor in Physics), St. John's University His publications reflect a strong commitment to both theoretical mathematical development and practical environmental applications. Dr. Zobitz has also contributed to educational efforts, as evidenced by his publication in Science Education and Civic Engagement. Scientific Contributions Development of MCMC methods for ecological modeling Research on carbon cycle dynamics and ecosystem respiration Atmospheric monitoring techniques for urban CO 2 sources Statistical methods for isotopic analysis in carbon cycle research Dr. Zobitz has been active in academic presentations, including an Augsburg Mathematics Colloquium Talk on "Math and Climate," demonstrating his commitment to sharing knowledge and engaging with the academic community.

Ahmed H. Elsheikhمشاهده پروفایل

استاد

• Subsurface Engineering
• Artificial Intelligence in Energy Systems
• Machine Learning Applications
• +۵ مورد دیگر

Professor Ahmed H. Elsheikh is a faculty member at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, within the Institute for GeoEnergy Engineering. He holds a Professor title since 2021, previously serving as Associate (2017-2021) and Assistant Professor (2013-2017). His educational background includes a Ph.D. (2010) and MASc (2002) from McMaster University, and a BASc from Al-Azhar University (1999). Research Interests: Fluid control via AI, predictive machine learning, generative modeling, data assimilation, Bayesian uncertainty quantification, and subsurface engineering. His work addresses challenges in reservoir modeling, CO2 sequestration, seismic inversion, and geophysical data analysis. Publications span 2004-2025 with a focus on machine learning applications in energy systems, stochastic field generation, and subsurface flow modeling. Notable contributions include AI-driven seismic inversion, deep learning for reactive transport, and ensemble-based history matching. Key Achievements: Developed novel methodologies for uncertainty quantification, GAN applications in geological modeling, and real-time reservoir monitoring systems. His work contributes to UN Sustainable Development Goals related to climate action (SDG 13) and affordable clean energy (SDG 7).

• convex optimization
• optimal transport
• distributed optimization
• +۱ مورد دیگر

Dvinskikh Darina Mikhailovna serves as both Associate Professor and Senior Research Fellow at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science, the Institute of Artificial Intelligence and Digital Sciences, and the Department of Big Data and Information Retrieval. She earned her PhD from Humboldt University of Berlin in 2021, following Master's (2018) and Bachelor's (2016) degrees in Applied Mathematics and Physics from Moscow Institute of Physics and Technology. Prior to joining HSE in 2022, she conducted research at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin (2018-2021). Dr. Dvinskikh specializes in convex optimization, optimal transport, distributed optimization, and numerical optimization methods. Her research bridges theoretical foundations with practical applications in machine learning and artificial intelligence, with particular expertise in zeroth-order optimization methods, stochastic optimization frameworks, and decentralized computing approaches. Her work has advanced the field through novel algorithms for black-box optimization and improved complexity bounds for Wasserstein barycenter problems. Her publications span top venues including NeurIPS, Optimization Methods and Software, and Computational Mathematics and Mathematical Physics, demonstrating her contributions to both theoretical and applied aspects of optimization. She has received institutional recognition including a Letter of gratitude from the First Vice-Rector of HSE (May 2024) and a publication bonus for work in List A journals (2023-2024). Dr. Dvinskikh teaches Mathematical Statistics 1 and Modern Algorithmical Optimization for undergraduate and graduate students, emphasizing both theoretical understanding and practical implementation of optimization algorithms. Her teaching philosophy aims to provide students with a comprehensive understanding of how optimization techniques power modern machine learning systems.

Roni Granotمشاهده پروفایل

استاد

• Music Cognition
• Music Psychology
• Neuroscience of Music
• +۵ مورد دیگر

Roni Granot is a Professor and Principal Investigator at the Department of Musicology, Hebrew University of Jerusalem, where they lead the Music Cognition Lab. With 61 publications and nearly 2,000 citations, Granot has established themselves as a prominent researcher in the interdisciplinary field connecting music with cognitive science, neuroscience, and psychology. Granot's research focuses on music cognition, particularly the psychological and neural mechanisms underlying emotional responses to music. Their work examines how musical elements like rhythm, melody, and structure influence listeners' emotional experiences, social bonding, and cognitive processing. Recent research has explored music's role in well-being during crises, such as the COVID-19 pandemic, investigating how individuals across different cultures use music to achieve emotional regulation and social connection. Analysis of Granot's publication record reveals a strong interdisciplinary approach combining neuroscience, psychology, and music theory. Their research demonstrates particular interest in predictive coding models of music perception, examining how listeners' expectations influence emotional responses. The work spans experimental studies using fMRI, behavioral experiments, and cross-cultural investigations, with significant contributions to understanding musical humor, social bonding through music, and the neural correlates of musical pleasure. As Principal Investigator of the Music Cognition Lab, Granot has fostered numerous international collaborations with researchers in neuroscience, psychology, and musicology. Their work has been cited by scholars across multiple disciplines, indicating significant impact in both music research and cognitive science communities.

Daniel F. Stoneمشاهده پروفایل

دانشیار

• Behavioral Economics
• Microeconomics
• Game Theory
• +۴ مورد دیگر

Daniel F. Stone is an Associate Professor of Economics and Chair of the Economics Department at Bowdoin College, where he has been teaching since 2012 after previously serving as an assistant professor at Oregon State University (2008-2012). His academic home is within the Faculty of Arts and Sciences at Bowdoin College, located in Brunswick, Maine. Dr. Stone's educational background includes a PhD in Economics (2008) and MA in Economics (2006) from Johns Hopkins University, along with a BS in Applied Mathematics from Yale University (2001). His research expertise spans behavioral economics, microeconomics, game theory, information economics, political media, affective polarization, and socially responsible capitalism. Stone's scholarly work demonstrates a clear trajectory toward understanding polarization dynamics through behavioral economics frameworks. His recent publications and book on the behavioral economics of affective polarization examine how cognitive biases drive relationship conflicts and political polarization. His research integrates psychological principles with economic modeling to explain phenomena ranging from sports performance ('Hot Hand,' 'PGA Prospect Theory') to media effects on political attitudes ('Fox News Gridlock,' 'Media Slant NCAA Football Suspense'). His academic contributions include numerous publications on SSRN and SocArXiv, with research focusing on measurement error, selective engagement, and understanding partisan bias through economic lenses. Stone actively participates in academic discourse through Twitter (@d_f_stone) and has delivered a TEDx talk related to his work on polarization. As department chair and educator, Stone teaches courses across the economics curriculum including Behavioral Economics (Econ 3533), The Behavioral Economics of Affective Polarization (Econ 3559), Behavioral Economics and Socially Responsible Capitalism (Econ 3561), Behavioral Economic Theory (Econ 3355), Economics of Information (Econ 2323), Game Theory (Econ 3305), Intermediate Microeconomic Theory (Econ 2555), and Principles of Microeconomics (Econ 1101).

Sourav Duttaمشاهده پروفایل

پژوهشگر ارشد

• Model Order Reduction
• Computational Fluid Dynamics
• Scientific Machine Learning
• +۴ مورد دیگر

Sourav Dutta is a Research Fellow at the Oden Institute for Computational Engineering & Sciences at the University of Texas at Austin, where he works with Clint Dawson in the Computational Hydraulics Group. Prior to joining UT Austin in October 2022, he was an ORISE postdoctoral fellow at the Coastal & Hydraulics Laboratory of the U.S. Army Engineer Research and Development Center (ERDC) from September 2017 to August 2022. Dr. Dutta's research lies at the intersection of physics-based computational methods and data-driven machine learning techniques for environmental flow problems. His work focuses on developing efficient numerical approximations by combining physical principles with modern machine learning algorithms. His primary research areas include: Model Order Reduction Computational Fluid Dynamics Scientific Machine Learning Uncertainty Quantification Applied Mathematics His publication record reveals a strong trajectory in reduced order modeling for environmental hydrodynamics, particularly for advection-dominated problems that challenge traditional numerical methods. His research has evolved from foundational work on hybrid numerical methods for porous media flows to cutting-edge applications of deep learning and neural ODEs in computational hydraulics. Dr. Dutta has presented his research at major venues including SIAM conferences, Computational Methods in Water Resources meetings, and the HydroML Symposium. In September 2022, he co-organized a minisymposium on Machine Learning and Data-Driven Methods for Forward and Inverse Problems at the SIAM Mathematics of Data Science meeting. His educational background includes a PhD in Mathematics from Texas A&M University (2017), supervised by Dr. Prabir Daripa, and an Integrated BSc & MSc in Mathematics & Computing from the Indian Institute of Technology, Kharagpur (2010). At the Oden Institute, Dr. Dutta is currently working on compound flood simulation capabilities within Adaptive Hydraulics (AdH), reduced order modeling for coastal engineering applications, and physics-based operator learning frameworks for environmental flows. He has also developed pyNIROM, an open-source Python package for non-intrusive reduced order modeling of time-dependent problems.

• quantum many-body physics
• topological phases of matter
• quantum simulations
• +۲ مورد دیگر

Michele Burrello is an Associate Professor in Condensed Matter Physics at the Niels Bohr Institute, University of Copenhagen. He joined NBI in 2016 and was promoted to Associate Professor in 2021. He is affiliated with the Condensed Matter Theory group (CMT), the Center for Quantum Devices (QDev), and the Niels Bohr International Academy (NBIA). Professor Burrello's primary research interests focus on quantum many-body physics, topological phases of matter, and quantum simulations. His work bridges theoretical physics with experimental implementations, particularly in the realm of quantum computing and quantum information processing. He has made significant contributions to understanding Majorana fermions, topological quantum systems, and quantum simulation techniques using Josephson junctions and related devices. Analysis of his recent publications reveals a strong focus on topological quantum systems, quantum simulation platforms, and the physics of exotic quantum states such as Majorana modes and parafermions. His research often combines theoretical modeling with considerations for experimental implementation, particularly using superconducting circuits and quantum devices. Villum Young Investigator grant (2019) Professor Burrello has received significant research funding through the Villum Young Investigator grant, which supports his work on quantum simulation and topological phases of matter. His research group collaborates extensively with experimental teams working on quantum devices, particularly those involving superconducting circuits and topological materials. He is an active member of the Condensed Matter Theory group at NBI and contributes to the research activities of the Center for Quantum Devices (QDev) and the Niels Bohr International Academy. His work forms part of Copenhagen's strong research ecosystem in quantum physics and quantum information science.

Yipeng Huangمشاهده پروفایل

استادیار

• Quantum Computing
• Computer Architecture
• Analog Computing
• +۵ مورد دیگر

Yipeng Huang is an Assistant Professor in the Department of Computer Science at Rutgers University, School of Arts and Sciences. His research focuses on building and helping programmers use quantum, analog, and other emerging computer architectures for the post-Moore's Law era of computing. He teaches undergraduate and graduate courses including Computer Architecture (CS 211) and Quantum Computing: Programs and Systems (CS 558/443). Huang actively mentors graduate students and recruits undergraduates for research in quantum computing and computer architecture. His PhD advisees include Zirui Li, Jonathan Garcia-Mallen, Adeeb Kabir, Haoyan Luo, Enhyeok Jang, and Seungwoo Choi, with undergraduate researchers including Pooja Kedia and Winston Li. Huang's research spans quantum computing, computer architecture, and analog computing. His work examines quantum error correction, quantum software development, and hybrid analog-digital systems for scientific computation. He has published extensively on quantum error correction, quantum compilation frameworks, and analog accelerators for solving differential equations and linear algebra problems. Huang's recent publications show a strong focus on practical quantum computing systems, with several 2025 papers on high-dimensional quantum error correction, qudit simulation, and optimized quantum program generation. His work bridges theoretical quantum computing with practical implementation challenges. 2024 ISCA Distinguished Artifact Award for Tetris: A Compilation Framework for VQA Applications in Quantum Computing 2021 MICRO Top Picks honorable mention 2017 MICRO Top Picks honorable mention 2016 MICRO Top Picks Huang serves on program committees for major computer architecture conferences including ISCA, ASPLOS, HPCA, and MICRO. He is actively involved in science education outreach, organizing the Workshop on Broadly Accessible Quantum Computing and serving on committees for the Summer Science Program and FIRST Robotics Competition.

Alexandru Andreiمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Particle Physics
• Quantum Chromodynamics (QCD)
• +۳ مورد دیگر

Alexandru Andrei is a Professor of Physics at George Washington University, affiliated with the Department of Physics within the Columbian College of Arts and Sciences. His expertise lies in theoretical nuclear and particle physics, with a focus on lattice quantum chromodynamics (QCD), quantum computing applications to gauge theories, and non-perturbative methods in high-energy physics. Research interests include the study of QCD dynamics at finite temperature, hadron structure (polarizabilities, form factors), and numerical techniques to address challenges in lattice simulations such as the sign problem. He explores novel approaches for quantum algorithms targeting bosonic and fermionic field theories, including qubitization strategies and complex path integration methods. Recent work emphasizes infrared QCD phenomena, pion electromagnetic properties, and the development of efficient computational frameworks for lattice gauge theory and quantum computing. His contributions address fundamental questions in particle physics, such as the nature of the QCD vacuum, quark-gluon dynamics, and the implementation of gauge theories on quantum hardware. Publications since 2023-2024 highlight advancements in polarizability calculations, solutions to variance issues in fermionic systems, and innovative methods for simulating frustrated quantum systems. His research bridges theoretical innovation with computational breakthroughs, aiming to resolve longstanding challenges in both lattice field theory and quantum information science.

• Digital Signal Processing
• Image Processing
• Multimedia Communications
• +۳ مورد دیگر

Marco Carli is a Full Professor at the Department of Industrial, Electronic, and Mechanical Engineering, Roma Tre University, Italy. He holds a Laurea in Telecommunication Engineering from Università degli Studi di Roma 'La Sapienza' and a Ph.D. from Tampere University of Technology. His research focuses on digital signal and image processing applied to multimedia communications, including digital watermarking, multimedia quality evaluation, and information security. He has led projects such as EHEM (medieval architecture modeling), ISEEYOO (AI-based anomaly detection), and INSECTT (secure IoT-AI systems). Carli serves as an Associate Editor for IEEE Transactions on Image Processing and Area Editor for Signal Processing: Image Communication. He is an IEEE Senior Member. His work spans over 55 journal publications and numerous conferences, addressing topics like immersive VR applications, cybersecurity, and perceptual quality metrics. Education: Laurea in Telecommunication Engineering, Università di Roma 'La Sapienza', 1990s Ph.D. in Telecommunication Engineering, Tampere University of Technology, Finland Projects: EHEM: Digital modeling of medieval architecture and art ISEEYOO: Anomaly detection in Cyber-Physical Systems INSECTT: Secure IoT systems with AI Research Interests: Signal/image processing for multimedia Quality evaluation and watermarking Cybersecurity and IoT applications Awards: None explicitly listed Grants/Advising: Multiple EU-funded projects (e.g., ImmerSAFE, RESISTO, ATENA) and collaborations with industry partners like Thales Alenia Space.

• Geologic Carbon Storage
• Geomechanics
• Enhanced Oil Recovery
• +۳ مورد دیگر

Brian J. McPherson is a Professor in the Department of Civil & Environmental Engineering at the University of Utah. He specializes in geologic carbon storage, geomechanics, and subsurface fluid dynamics, with extensive research on CO₂ sequestration, enhanced oil recovery (EOR), and reservoir characterization. His work integrates experimental, numerical, and field studies to assess risks and optimize subsurface storage. McPherson's research focuses on: Geomechanical risk assessment for CO₂ storage Multiphase flow dynamics in porous media Machine learning applications in reservoir forecasting Impacts of CO₂ leakage on groundwater systems His publications emphasize carbon capture and storage (CCS) technologies, hydraulic fracturing, and subsurface monitoring methods. Recent articles frequently address uncertainty quantification and predictive modeling using Bayesian frameworks and AI. He leads major DOE-funded initiatives, including the San Juan Basin CarbonSAFE project and the Uinta Basin CarbonSAFE II feasibility study. These projects focus on ensuring safe long-term CO₂ storage in saline reservoirs. McPherson advises graduate students through thesis research courses and directs the SMART Machine Learning Initiative as an External Advisory Board member.

• Antenna Engineering
• Radar Technology
• Electromagnetic Fields
• +۳ مورد دیگر

Prof. Dirk Heberling is a Universitätsprofessor at RWTH Aachen University, leading the Institute for High Frequency Technology. His research focuses on advanced antenna systems, radar technology, and electromagnetic field exposure assessment in 5G/6G networks. He specializes in high-frequency components, including leaky-wave antennas, reconfigurable intelligent surfaces, and robot-based antenna measurement systems. His work addresses challenges in antenna design, signal processing, and environmental compliance for next-generation communication infrastructure. Key research areas include: Development of wideband antennas for IoT and smart building applications Numerical analysis of near-field antenna measurements and phase recovery techniques Assessment of radio frequency exposure from massive MIMO base stations using digital twins Integration of radar systems with automotive technologies for self-localization and object detection Optimization of antenna arrays to mitigate grating lobes and phase synchronization issues His lab operates a state-of-the-art robot-based mm-wave test system capable of spherical near-field measurements and real-time auralization of aircraft noise. This infrastructure supports both fundamental research and industry collaborations in telecommunications, automotive radar, and environmental monitoring. Current projects emphasize: Exposure modeling for 6G networks considering increased base station utilization Generative adversarial networks for radar data synthesis Calibration techniques for polarimetric radar systems