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Asai AsaithambiView profile

Professor

• Artificial Intelligence
• Computational Science
• High-Performance Computing
• +3 more

Asai Asaithambi is a Professor and Graduate Program Director in the School of Computing at the University of North Florida (UNF), where he also served as School Director from 2011-2015. With 37+ years of academic experience, he previously held leadership roles including Professor/Chair at the University of South Dakota (2004-2011), Associate Professor/Chair at Saint Louis University (1997-2004), and Assistant/Associate Professor positions at Lincoln University and Mississippi State University. His academic credentials include: PhD in Computer Science, University of Wisconsin-Madison (1985) MS in Computer Science, Indian Institute of Technology-Madras (1980) Bachelor of Technology in Electrical Engineering, Indian Institute of Technology-Madras (1977) Professor Asaithambi's research spans artificial intelligence, computational science, and high-performance computing, with significant contributions to quantum computing algorithms, biomedical image analysis, and numerical methods for differential equations. His work demonstrates consistent focus on applying computational techniques to real-world problems in healthcare, cybersecurity, and engineering, while maintaining strong commitment to broadening participation of underrepresented groups in computing. Analysis of his 15 most recent publications (2019-2025) reveals dominant themes in medical imaging AI (lung X-ray and brain MRI analysis), bioinformatics algorithms for genome rearrangement, quantum computing security implications, and multi-robot coordination systems. His methodology consistently combines theoretical algorithm development with practical implementation, often leveraging advanced optimization techniques and parallel computing approaches. Key research projects include: Getting American Indians to Information Technology (GAIN-IT) as Co-PI (2008-2013) A Computational Intelligence to Gene Structure Prediction as PI (2007-2008) South Dakota Biomedical Research Infrastructure Network as Co-PI (2001-2012) Throughout his career, Professor Asaithambi has directed numerous doctoral dissertations and Master's theses while teaching diverse courses from introductory programming to quantum computing. His teaching philosophy emphasizes critical thinking development for both traditional and non-traditional student populations, with particular attention to mathematical preparation challenges in computational courses.

• Photonics
• Optical Communications
• Photonic Integrated Circuits
• +4 more

Hercules Avramopoulos is a full Professor at the National Technical University of Athens (NTUA) within the School of Electrical and Computer Engineering, where he heads the Photonics Communications Research Laboratory (PCRL). His work bridges academic research and industrial application in photonics, with leadership in European collaborative projects. His educational background includes a BSc and MSc in Physics and Applied Optics from Imperial College London, followed by a PhD in Physics (1989) focusing on nonlinear effects in lasers and optical fibers. Prior to NTUA, he conducted research at AT&T Bell Laboratories. Avramopoulos' research spans photonic integrated circuits , quantum communications , optical interconnects , and biophotonics sensing . Current work emphasizes monolithic/hybrid photonic integration (silicon, InP, polymers), quantum key distribution (QKD) for satellite/terrestrial networks, and photonic sensors for composite manufacturing. His group develops advanced modulation formats, DSP techniques, and all-optical switching systems. Recent publications show strong focus on quantum-classical coexistence in fiber/FSO networks, 6G X-haul architectures combining fiber/mmWave/FSO, and embedded photonic sensors for industrial processes. Key trends include satellite QKD feasibility, SDN-controlled hybrid transport, and machine learning-enhanced optical signal processing. He has supervised 26 completed PhD theses with 12 current doctoral students. Alumni include 10 faculty members at Greek/European universities and professionals at Cisco, Nokia, and the European Patent Office. PCRL has participated in over 60 European projects (coordinating ~30), including current initiatives like POLYNICES and TWILIGHT. PCRL maintains a 30-member team developing photonic solutions across five domains: optical interconnects, DSP, PIC design, biophotonics, and quantum communications. The lab holds 5 patents and has produced 500+ publications (200+ journal articles). Current infrastructure supports quantum communications research for IYQ2025 (International Year of Quantum 2025) initiatives.

• Knowledge Representation and Reasoning
• Quantum Computation
• Artificial Intelligence
• +3 more

Professor Sanjiang Li is affiliated with the University of Technology, Sydney (UTS) as a Professor in the Faculty of Engineering and Information Technology , specifically within the Centre for Quantum Software and Information . With a PhD in Mathematics from Sichuan University and a BSc from Shaanxi Normal University, his research spans quantum computation , knowledge representation and reasoning , and formal verification of quantum systems . His recent work focuses on quantum circuit transformation , including novel methods like adaptive divide-and-conquer and Monte Carlo Tree Search frameworks. He has advanced symbolic verification techniques using tensor decision diagrams and explored classical-quantum hybrid algorithms for resource-efficient training. Dr. Li has received prestigious awards such as the ARC Future Fellowship and Alexander von Humboldt Research Fellowship . He supervises PhD students including Calum Holker and Guangxi Li , and contributes to quantum software development through funded projects like the Sydney Quantum Academy and ARC Discovery Projects .

• Quantum Information Theory
• Quantum Cryptography
• Quantum Communication
• +4 more

Marco Tomamichel is a Senior Lecturer and Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) fellow at the Centre for Quantum Software and Information, which is part of the Faculty of Engineering and Information Technology at the University of Technology Sydney. He received his Master of Science in Electrical Engineering and Information Technology from ETH Zurich, where he was awarded the prestigious ETH Medal, followed by a doctorate in Theoretical Physics from the same institution. Prior to joining the University of Technology Sydney, Dr. Tomamichel held research positions at the Centre for Quantum Technologies in Singapore and the University of Sydney. Dr. Tomamichel's research lies at the intersection of information theory, computer science, and quantum physics, with a particular focus on the mathematical foundations of quantum information theory. His work explores entropy and other information measures, and addresses theoretical questions in quantum communication and cryptography when resources are limited. His research examines quantum information processing with noisy and limited resources, cryptography in a quantum world, mathematical foundations of quantum information, and other applications of quantum information theory in physics, engineering, and computer science. His recent publications demonstrate a strong focus on quantum information theory, with particular emphasis on entropies, divergences, and their applications. His work spans fundamental theoretical developments in quantum information measures, quantum cryptography protocols, quantum communication theory, and connections to quantum thermodynamics and machine learning. The recurring theme across his publications is the rigorous mathematical treatment of quantum information processing tasks, often focusing on finite-resource scenarios that are relevant for near-term quantum technologies. Dr. Tomamichel has received significant recognition for his research, including: Australian Research Council Discovery Early Career Researcher Award (DECRA) ETH Medal for Master's degree Dr. Tomamichel is available for Masters Research and PhD student supervision. His funded research projects include "Securing the quantum internet with high-dimensional quantum systems" (ARC Discovery Projects), "Topological algebra, first-order logic, and computability" (Royal Society of New Zealand), and "Enhancing Communication using Small Quantum Devices" (ARC DECRA Scheme). Dr. Tomamichel is actively involved in the quantum information research community, serving as an editor for "Quantum - the open journal for quantum science" and participating in numerous conference organizing committees including TQC, QIP, and Quantum Cryptography conferences.

Synge TodoView profile

Professor

• Quantum Many-Body Physics
• Computational Physics
• Quantum Computing
• +5 more

Synge Todo is a Professor in the Department of Physics, Graduate School of Science at the University of Tokyo , with joint appointments at the Mathematics and Informatics Center , Institute for Solid State Physics , Institute for Physics of Intelligence , Quantum Software Project , and Next-Generation AI Research Center . Born in 1968, he earned his B.Sc. and Ph.D. from the University of Tokyo and subsequently held post-doctoral positions at ETH Zürich. Education Ph.D. (Science), University of Tokyo, 1996 B.Sc. (Physics), University of Tokyo, 1991 Research Interests Todo’s research integrates quantum many-body physics , computational physics , and quantum computing . He develops advanced Monte-Carlo algorithms , tensor-network techniques , and renormalization group methods to study strongly correlated electron systems , lattice QCD , quantum phase transitions , and machine-learning applications in physics . His recent work explores fault-tolerant quantum computing architectures , non-variational quantum ground-state preparation , and universal scaling laws in deep neural networks . Scientific Awards Prizes for Science and Technology, The Commendation for Science and Technology by MEXT Japan (April 2019) Grants & Collaborations He currently leads several JSPS KAKENHI projects, including “ Quantum-circuit design for computational materials science ” (2023-26) and “ Enhancement of detailed-balance-violating MCMC methods ” (2020-24). He also co-leads interdisciplinary teams focusing on data assimilation for materials discovery and scalable high-performance computing . Laboratories & Software Todo heads research activities in the HΦ quantum lattice model solver and the MateriApps portal, providing open-source tools for large-scale simulations in condensed-matter and materials science.

Felix DietrichView profile

Professor

• Physics-Enhanced Machine Learning
• Scientific Computing
• Harmonic AI
• +7 more

Prof. Dr. Felix Dietrich holds the Professorship for Physics-Enhanced Machine Learning at the Technical University of Munich (TUM), within the TUM School of CIT and Department of Computer Science. He is actively engaged in teaching courses including Scientific Computing and Machine Learning for the Winter semester 2025/26 and supervises Master's theses in Machine Learning in Crowd Modeling & Simulation. His research focuses on the intersection of scientific computing and machine learning, particularly through his 'Harmonic AI' framework that bridges linear operator theory with deep learning. Key research areas include physics-enhanced machine learning, Koopman operator theory, scientific machine learning, and numerical algorithms for complex systems. His work applies to diverse domains such as crowd dynamics, quantum systems, molecular dynamics, and civil engineering applications like structural defect detection. Prof. Dietrich leads several major research initiatives including the Emmy Noether group 'Harmonic Artificial Intelligence based on Linear Operators', the Scientific Machine Learning Focus Group with the IAS, GNI projects DeepMonitor and FORWARD, and AutoMD-AI for molecular dynamics simulations. His recent publications demonstrate strong activity in connecting mathematical rigor with machine learning applications. His scientific contributions span developing data-driven algorithms that combine the reliability of traditional scientific computing with the flexibility of AI approaches. This work has resulted in numerous publications in top-tier journals including SIAM Journal on Scientific Computing, Chaos, Nature Communications, and PNAS Nexus. Emmy Noether group on Harmonic AI Scientific Machine Learning Focus Group with IAS GNI project DeepMonitor for structural defect detection GNI project FORWARD for pedestrian flow prediction AutoMD-AI for molecular dynamics surrogate modeling Prof. Dietrich actively mentors PhD students and postdoctoral researchers, with several advisees including Chinmay Datar, Erik Lien Bolager, and Vladyslav Fediukov appearing as co-authors on recent publications. His research group (SCML) develops open-source software for physics-enhanced machine learning applications.

• Fluid Engineering
• Thermal Engineering
• Aerospace Engineering
• +3 more

Professor Matsuda Yu at Waseda University 's Faculty of Science and Engineering (School of Creative Science and Engineering) is a leading researcher in fluid engineering and aerospace systems. With a Doctor of Engineering from Nagoya University, he has developed innovative measurement techniques for micro/nano-scale phenomena. Current position: Professor, Waseda University (2022-present) Previous roles: Japan Science and Technology Agency (2018-2022), Nagoya University (2008-2018) Research Focus spans thermal engineering , microfluidics , and quantum-inspired data analysis . His recent work involves pressure-sensitive paint optimization, single-particle tracking , and quantum annealing applications for fluid dynamics. Scientific Recognition includes multiple JSME awards, MEXT Commendation for Young Scientists, and the 2025 Ichiro Tanaka Award . His 45+ scientific awards highlight contributions to measurement science and fluid dynamics. Technical Innovations include ambient-light-resistant PSP methods, quantum-optimized sensor placement, and bioluminescent temperature-pressure sensors. His 95+ publications with 1497 Google Scholar citations demonstrate significant impact in microscale flow analysis and nanoparticle dynamics .

• Nanoionics
• Atomic Switches
• Reservoir Computing
• +5 more

Tsuyoshi Hasegawa is a Professor at Waseda University's School of Advanced Science and Engineering, holding a Ph.D. in Physics from Tokyo Institute of Technology. With over two decades of research experience including positions at National Institute for Materials Science (NIMS) and Yokohama City University, he leads cutting-edge work in nanoionics and neuromorphic computing. His research focuses on atomic switches , reservoir computing , and memristive systems , with particular expertise in Ag 2 S-based physical reservoirs that process information through ionic diffusion and filament formation. Hasegawa's work bridges materials science, electronics, and artificial intelligence, developing hardware solutions for edge computing that operate with minimal power consumption. Analysis of his 15 most recent publications reveals a strong trend toward practical implementations of physical reservoir computing, with applications in optical signal processing, tactile sensation, game theory systems, and deep learning hardware. His team consistently achieves high accuracy rates (81-98%) while addressing critical challenges like fabrication yield, noise sensitivity, and operational stability. SSDM Award 2020 for Quantum Point Contact Switch Tsukuba Prize 2017 Japan Society of Applied Physics Outstanding Paper Award 2012 NIMS Chairman's Research Achievement Award 2010 Science and Technology Prize of MEXT 2007 Hasegawa maintains active collaborations with institutions including NIMS and RIKEN, securing significant research funding for nanoarchitectonics projects. His laboratory specializes in in-materio computing using atomic switch networks, with recent work demonstrating million-cycle endurance and direct optical signal processing capabilities. Current research emphasizes practical deployment of reservoir computing systems for robotics and edge AI applications.

• Theoretical Chemistry
• Quantum Chemistry
• Photodissociation
• +4 more

Johannes Dietschreit is an active researcher at the Institute for Theoretical Chemistry within the Faculty of Chemistry at a German-speaking institution. Holding a doctorate along with B.Sc. and M.Sc. degrees, his research focuses on advanced computational methods in theoretical chemistry. His research interests center on theoretical and computational chemistry , with specific expertise in photodissociation mechanisms, collective variables for molecular dynamics, nonadiabatic processes, and machine learning applications in chemical systems. His work bridges physics-based modeling with data-driven approaches to solve complex chemical problems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional quantum chemistry methods. His work focuses on developing robust computational frameworks for studying molecular dynamics, particularly in challenging areas like photodissociation pathways and energy barrier calculations. The research demonstrates increasing sophistication in handling complex electronic state transitions and developing more accurate interatomic potentials. Dr. Dietschreit has been recognized as an invited speaker at multiple scientific events in 2025, including presentations on robust data sets for free energy estimates and artificial light harvesting systems. His research shows active collaboration with international teams, particularly with researchers from groups focused on machine learning applications in chemistry.

Adam FontecchioView profile

Professor

• Nanophotonics
• Electro-optics
• Liquid Crystals
• +7 more

Adam Fontecchio is a Professor in the Electrical and Computer Engineering Department at Drexel University and the inaugural Director of the Center for the Advancement of STEM Teaching and Learning Excellence (CASTLE) . He has held leadership roles including Vice-Dean of the Graduate College, Vice-Chair of the IEEE Philadelphia Section, and Associate Dean for Academic Affairs in the College of Engineering. PhD, Brown University, 2002 MS, Physics, Brown University BA, Physics, Brown University Fontecchio's research spans nanophotonics , liquid crystal technology , and smart textile engineering , with applications in biomedical monitoring, hyperspectral imaging, and educational outreach. His work includes tunable electro-optic devices, holographic polymer dispersed liquid crystals, and wearable RFID sensors. He integrates engineering education into K-12 curricula through NSF-funded programs like STEM GK12. His educational research focuses on project-based learning , team-teaching , and STEM engagement , particularly among underrepresented populations. Recent publications highlight advancements in wearable sensors, tunable optical systems, and interdisciplinary educational frameworks. 2015 Delaware Valley Engineer of the Year NASA New Investigator Award Drexel Graduate Student Association Outstanding Mentor Award Drexel University ECE Outstanding Research Achievement Award International Liquid Crystal Society Multimedia Prize Drexel President’s Award for Diversity (2018) Fontecchio has secured over $33M in grants, including NSF STEM GK12, DoE GAANN, and NSF Nanoscale Undergraduate Education awards. He mentors high school students through Drexel’s Summer Mentorship Program and leads initiatives to enhance STEM education via academic makerspaces and international partnerships.

Jonas M. KüblerView profile

Researcher

• Quantum Computing
• Machine Learning
• Artificial Intelligence
• +3 more

Jonas M. Kübler is a Doctoral Researcher at the Max Planck Institute for Intelligent Systems in the Empirical Inference department. His research focuses on the intersection of quantum computing and machine learning, particularly quantum kernels, hypothesis testing, and data-efficient learning methods. Education International Max Planck Research School for Intelligent Systems (IMPRS-IS): Ph.D. candidate supervised by Bernhard Schölkopf, expected completion in 2022. LANL Quantum Computing Summer School: Participant (2019). Master’s in Quantum Causality (2018), published in New Journal of Physics . Research Interests Jonas investigates how quantum computers can redefine machine learning models, with a focus on theoretical rigor and empirical validation. His work spans quantum kernel methods , hypothesis testing , and variational quantum algorithms , emphasizing mathematical foundations and computational efficiency. Current projects include analyzing inductive biases in quantum models and optimizing kernel-based statistical tests. Scientific Contributions His publications highlight trends in quantum-enhanced learning , statistical inference , and data-efficient algorithm design . Notable themes include bridging quantum information theory with ML, advancing kernel methods for hypothesis testing, and exploring the limitations of quantum optimization techniques. Scientific Awards: AISTATS 2022 Best Reviewer Award Affiliations & Activities Jonas is affiliated with the IMPRS-IS and has held internships at Amazon (2021) and Los Alamos National Laboratory (2019). He serves as a member of the Tübingen City Council, demonstrating interdisciplinary engagement beyond research.

• Quantum Many-Body Physics
• Quantum Machine Learning
• Quantum Metrology
• +2 more

Dr. Tie-Cheng Guo is a researcher at the International Research Centre MagTop in Warsaw, Poland, specializing in quantum information science. He earned his PhD in Physics from Tsinghua University (2021) and Bachelor's in Applied Physics from Tianjin University (2016). Education: PhD, Physics, Tsinghua University (2021) BSc, Applied Physics, Tianjin University (2016) His research focuses on quantum many-body systems, quantum machine learning, and quantum metrology. He pioneered the "Time Order" framework for analyzing quantum phases through temporal evolution and investigates quantum-enhanced sensing using critical phenomena. This work bridges theoretical condensed matter physics with quantum information applications, targeting precision measurement breakthroughs and topological quantum computing foundations. At MagTop, Dr. Guo contributes to Poland's national Metrology II initiative, advancing quantum technologies within the center's mission to develop next-generation measurement standards and quantum devices.

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +3 more

Zhan Su serves as a Guest Researcher within the Programming Languages and Theory of Computation group at the Department of Computer Science, Faculty of Science, University of Copenhagen. His research bridges theoretical computer science with practical applications in artificial intelligence and privacy. Education: Ph.D. in Computer Science, University of Copenhagen, 2024 (Thesis: Information Propagation in Modular Language Modeling and Web Tracking) Research Interests: Dr. Su's work centers on quantum-inspired neural architectures for natural language processing and information retrieval systems. He pioneers density matrix representations for semantic modeling and investigates conversational search mechanisms enhanced by large language models. His research extends to privacy vulnerabilities in educational technology, examining web tracking evolution across digital learning platforms. The integration of quantum computing principles with classical machine learning frameworks represents a distinctive thread throughout his publications. Publication Trends: Analysis of Dr. Su's 8 publications (2023-2025) reveals three dominant research trajectories: (1) Quantum-AI hybrid systems (40% of output), featuring entanglement-embedded networks and density matrix word representations; (2) Conversational information retrieval (38%), with innovations in history-aware dense retrieval and query alignment; (3) Privacy/security applications (22%), particularly in online education ecosystems. His 2025 works demonstrate increasing sophistication in quantum-classical model fusion, while 2024 outputs show substantial conference presence at ACL, CIKM, and WWW venues. Advising and Grants: No advisory roles or grant funding details are documented in the available materials. Current research appears conducted within collaborative frameworks at the University of Copenhagen. Labs and Teams: Dr. Su operates within the Programming Languages and Theory of Computation research environment at DIKU (Department of Computer Science), engaging with interdisciplinary teams focused on language modeling, quantum computing applications, and web security analysis. His collaborations span institutions including the University of Copenhagen's Department of Computer Science and external partners in natural language processing research.

Alex OrailogluView profile

Professor

• Electronic Design Automation
• VLSI Test
• Fault-Tolerant ICs
• +3 more

Alex Orailoglu is a Professor at the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD), since 1987. He leads the Reliable System Synthesis Group and specializes in Electronic Design Automation, VLSI Test, and Synthesis of Fault-Tolerant ICs. His work has produced over 80 publications and has advised 6 Ph.D. and 2 M.S. students. Education: S.B. in Applied Mathematics (Harvard University, 1977) M.S. in Computer Science (University of Illinois, Urbana, 1979) Ph.D. in Computer Science (University of Illinois, Urbana, 1983) Research focuses on hardware security, VLSI testing, and neural network robustness. His recent work includes logic obfuscation techniques to counter reverse engineering and methods for enhancing DNN fault tolerance. Professional involvement includes roles as General Chair, Program Chair, and Steering Committee member for conferences like IEEE VLSI Test Symposium and High-Level Design Validation Workshops. He has secured funding from the National Science Foundation, Intel, and other industry partners. Advising & Grants: Current advising includes 6 Ph.D. and 2 M.S. students. Notable sponsored research includes collaborations with NASA, NATO, and major tech firms like Agilent and Hewlett-Packard. Labs: Directs the Reliable System Synthesis Group (RSS Group) at UCSD, focusing on secure hardware design and embedded systems.

Wei WangView profile

Professor

• Wireless Networks
• Multimedia Communications
• QoE/QoS Optimization
• +8 more

Dr. Wei Wang is a Professor of Computer Science at San Diego State University (SDSU) within the College of Sciences . He joined SDSU in August 2014 after serving as an Assistant Professor at South Dakota State University (2010-2014). He earned his Ph.D. in Computer Engineering with Computer Science Emphasis from the University of Nebraska-Lincoln (2009), an MS in Information and Telecommunications Systems from Xian Jiaotong University (2005), and a BS in Computer Science/Engineering from the same institution (2002). Dr. Wang's research focuses on Wireless Networks , Quality of Experience (QoE)/Quality of Service (QoS) Optimization , Network Economics , Blockchain Applications , and Internet of Things (IoT) . His recent work explores Semantic Communications , Crypto-Economic Incentives for wireless systems, and Quantum Annealing Approaches for multimedia optimization. Over 15 years, he has published extensively on topics like NOMA Resource Allocation , Game-Theoretic Pricing Models , and Energy-Harvesting Wireless Sensors . Trends in Publications : His 2017-2025 work shows increasing focus on blockchain-integrated wireless systems (2024-2025), machine learning for signal processing (2023), and traditional wireless network economics (2018-2022). Scientific Awards : Excellent Educator Award, SD-SAIC Hackathon (2024) Best Paper Award, IEEE CAMAD (2022) SDSU CS Faculty of the Year (2016) IEEE Senior Member (2016) ACM Service Awards (2013-2015) Advising & Grants : Mentored 30+ students across Ph.D., M.S., and B.S. levels. Principal Investigator for $571,489 in NSF grants (2014-2023) and other industry-funded projects. Labs & Teams : Leads the Cyber Innovation Research Group at SDSU, focusing on QoE-driven wireless solutions and IoT optimization . Collaborates with IEEE MMTC (served as Director of Publicity Board) and NSF panels.