Xiaoli Fern is an Associate Professor in the School of Electrical Engineering and Computer Science at Oregon State University. She holds a Ph.D. in Computer Engineering from Purdue University (2005) and dual degrees (B.S. and M.S.) in Automation and Computer Science from Shanghai Jiao Tong University (2000). Her research focuses on applied machine learning , graph learning , and explainability in AI systems , with applications in microbiome analysis , ecological monitoring , and human-computer interaction . Research Expertise: Unsupervised learning, clustering, correlation analysis, outlier detection, and scientific data mining. Collaborations: Active involvement in the IGERT Ecosystem Informatics program and interdisciplinary projects with ecologists, roboticists, and biologists. Awards: 2011 NSF CAREER Award for early-career excellence in research. Her recent work includes applying deep learning to microbiome data and developing interactive systems that bridge theory with real-world applications in biology and materials science. She mentors students across all academic levels and emphasizes the importance of collaborative, real-world problem-solving in her research lab.
Roles & Affiliations: Professor and President's Chair Professor in Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. Member of the IEEE and IET, with roles in editorial boards of major journals like IEEE Transactions on Network Science and Engineering and IEEE Communications Surveys & Tutorials. Current positions: Editor-in-Chief of IEEE Transactions on Network Science and Engineering, Area Editor in multiple journals including IEEE Communications Surveys and Tutorials. Education: Ph.D. (2008) and M.Sc. (2005) in Electrical and Computer Engineering from the University of Manitoba, Canada. B.Eng. (1999) in Computer Engineering from King Mongkut's Institute of Technology Ladkrabang, Thailand. Research Interests: Focuses on mobile generative AI, edge general intelligence, quantum networking, incentive mechanisms, and wireless communication systems. Explores applications in 6G, IoT, UAV networks, and secure communication. Recent Articles & Trends: 15 most recent articles highlight advancements in generative AI for wireless networks, quantum computing integration, secure communication protocols, and AI-driven network optimization. Key themes include semantic communication frameworks, RIS-aided systems, and federated learning in edge networks. Awards: Over 20+ prestigious awards including IEEE Fellow (2017), IET Fellow (2022), Stuart Meyer Memorial Award (2024), and multiple best paper awards at top conferences like IEEE WCNC and IEEE ICC. Advising & Grants: Advisor to numerous students (not listed explicitly). Active in funding initiatives for AI in wireless systems, quantum networks, and low-altitude economy networking. Leads projects on GenAI for networking and 6G innovations. Labs & Teams: Research focuses on CCDS labs at NTU, collaborating on projects like GenAI for ISAC, mobile edge computing, and secure federated learning systems.
Michele Ruggeri is an Assistant Professor of Numerical Analysis (RTD-B) at the Department of Mathematics, University of Bologna, and an Honorary Lecturer at the University of Strathclyde, UK. His research focuses on numerical analysis of partial differential equations in materials science and engineering, including liquid crystal theory, micromagnetism, nonlinear elasticity, and spintronics. He is also active in developing numerical methods for uncertainty quantification and model order reduction. Education: PhD in Technical Mathematics (TU Wien, Austria), Laurea Magistrale in Mathematics (University of Pavia, Italy), and a Diploma in Science and Technology from Scuola Universitaria Superiore IUSS, Italy. He holds a Fellowship (FHEA) from Advance HE and a PGCert in Learning & Teaching in Higher Education from the University of Strathclyde. His work emphasizes finite element methods and their applications, with contributions to computational micromagnetics and software development (e.g., Commics). He is affiliated with the (AM)² Research Center on Applied Mathematics and ARCES at the University of Bologna. Awards: Fellowship (FHEA), Advance HE, UK. Key Projects: Development of numerical methods for micromagnetic simulations, uncertainty quantification in stochastic models. Contact: m.ruggeri@unibo.it
Dr. Vijay Ganesh is a Professor of Computer Science at Georgia Institute of Technology, where he also serves as Associate Director of the IDEaS Institute and is affiliated with Tech AI. Previously, he held roles as Associate Professor (2018–2023) and Assistant Professor (2012–2018) at the University of Waterloo, and Research Scientist at MIT (2007–2012). He earned his PhD from Stanford University in 2007. His research focuses on SAT/SMT solvers and their applications in AI, software engineering, security, mathematics, and physics. Notable contributions include developing solvers like MapleSAT, Z3str4, and AlphaZ3, and exploring machine learning-augmented reasoning. He has led projects in logic for AI, proof complexity, and security of blockchain technologies. His awards include ACM Impact Paper (2019), ACM Test of Time (2016), and DATE’s Ten-Year Most Influential Paper (2008). He has advised startups like Quantstamp, a blockchain security firm, and co-directed the Waterloo AI Institute (2021–2023). His teaching includes courses on discrete mathematics, software engineering, and AI. Education: PhD in Computer Science, Stanford University (2007); Master’s in Electrical Engineering, Stanford (2000) Research Interests: SAT/SMT solvers, formal methods, automated testing, AI security, combinatorial mathematics Affiliations: Georgia Tech’s School of Computer Science, IDEaS Institute
Matthias Oliver Wilhelm is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His work focuses on advanced theoretical physics topics including scattering amplitudes in gauge/gravity theories, Feynman integrals, special functions, and applications of machine learning in physics. He has contributed to groundbreaking research at the intersection of quantum field theory and mathematical physics, particularly in understanding gravitational wave phenomena and high-energy particle interactions. Research Interests: His research combines quantum field theory with algebraic geometry and computational methods, exploring topics like elliptic Feynman integrals, post-Minkowskian expansions, and machine learning-driven amplitude calculations. Recent work includes leveraging Calabi-Yau manifolds for gravity-related Feynman integrals and developing transformer-based algorithms for scattering amplitude computations. Awards: He received the Velux Grant - Villum Young Investigator in 2018, recognizing his innovative contributions to theoretical physics. Projects: Leveraging Algebraic Geometry for High-Precision Fundamental Physics (2024-2028, DFF-funded) Thermodynamics of strongly coupled Quantum Field Theory (2019-2027, private foundation-funded) Key Themes in Recent Work: His articles emphasize novel computational techniques (e.g., machine learning for integration-by-parts reduction), formal developments in scattering amplitude theory, and geometric approaches to quantum gravity problems. Notable contributions include classifying Feynman integral geometries for black-hole scattering and advancing elliptic function methodologies in perturbative QFT.
Dr. Luiz Felipe Aguinsky is a Lecturer in Computational Nanoelectronics and Deputy Group Leader of the DeepNano Research Group at the University of Glasgow. He holds a PhD (Dr. techn.) from TU Wien, Austria, where he specialized in semiconductor fabrication process modeling. As an Erwin Schrödinger Fellow at ETH Zurich, he developed machine learning-enhanced models for memristors. His research focuses on computational nanoelectronics, combining advanced simulation techniques with cutting-edge materials science. Education: PhD (Dr. techn.) in Microelectronics, TU Wien (Austria), 2019 (with distinction) Erwin Schrödinger Fellowship at ETH Zurich's Computational Electronics Group (2021–2023) Research Interests: His work integrates machine learning with atomistic simulations to address challenges in semiconductor manufacturing. Key areas include: High-performance TCAD for nanofabrication processes Quantum transport and neuromorphic computing Applied computer graphics for nonimaging applications Level-set methods for surface evolution modeling Publications Trends: Recent work emphasizes knudsen diffusion modeling for nanofabrication, atomic layer deposition simulations, and plasma etching optimization. Cross-disciplinary methods like ray tracing and machine learning feature prominently in his latest projects. Awards & Fellowships: EUROSOI-ULIS Best Poster Award (2021) Erwin Schrödinger Fellowship (FWF, 2023–2025) Professional Activities: Active member of IEEE Nanotechnology Council's Modelling & Simulation Technical Committee. Co-author of over 15 peer-reviewed publications since 2019, with contributions to IEEE NANO, SISPAD, and EuroSOI conferences. Labs/Teams: Leads computational modeling efforts in the DeepNano Research Group, collaborating globally on TCAD innovations for next-generation semiconductor devices.
Laura Emilia Maria Ricci is a Full Professor at the University of Pisa's Department of Computer Science, leading the Pisa Distributed Ledger Laboratory (Pisa DLT Lab). Her research focuses on blockchain technology, layer-2 solutions, cryptographic techniques, and self-sovereign identity frameworks. She coordinates the National PhD program in Blockchain and Distributed Ledger Technologies and leads the PRIN research project 'AWESOME' (2023-2025). Ricci serves as an associate editor for the ACM Distributed Ledger Technologies: Research and Practice journal and the Springer Nature SN Computer Science section on blockchain innovations. Her research emphasizes blockchain scalability, transaction analysis, and social network dynamics. Recent work includes studies on NFT architectures, post-quantum cryptography in Ethereum, and query authentication protocols. She co-organized the 7th IEEE International Conference on Blockchain and Cryptocurrencies (2025) and actively participates in global blockchain initiatives. Ricci has been awarded Best Paper Awards for her contributions to decentralized cloud scheduling, hybrid architectures for online games, and distributed virtual environments. She advises numerous PhD students and oversees grants like the H2020 'HELIOS' project. Ricci's academic roles include teaching blockchain, peer-to-peer systems, and web scraping at the University of Pisa. Her lab collaborates on projects such as the AQuSDIT grant (2024-2025) and the Ethereum Foundation's 'Cross Chain Authenticated Queries.' She also chairs conferences like IEEE Blockchain and co-edits special issues on blockchain-based pervasive systems and social media analysis.
Naveed Mahmud is an Assistant Professor at the Department of Electrical Engineering and Computer Science, Florida Institute of Technology. He specializes in quantum computing, hybrid quantum-classical systems, and reconfigurable computing architectures. His research focuses on optimizing quantum algorithms, data encoding/decoding techniques, and secure communications using quantum technologies. Research interests include quantum-classical integration, algorithm emulation on high-performance reconfigurable computers, and applications of quantum computing in pattern recognition and cryptography. Key areas of exploration are hybrid quantum-classical machine learning, quantum wavelet transforms, and securing free-space optical communications with quantum key distribution. His recent work emphasizes scalability and efficiency in quantum computing frameworks, including frameworks like QASM-to-HLS for quantum circuit acceleration, and decoherence-optimized quantum circuits. Articles highlight advancements in quantum data decoding, algorithm emulation, and secure communication systems. No scientific awards or formal advisees are listed. His profile includes links to ORCID, Google Scholar, and ResearchGate for further details on publications and collaborations.
Dr. Yi Wang is an Associate Professor and Department Chairperson of the Electrical and Computer Engineering Graduate Programs at Manhattan College, New York. He also serves as Director of the Electrical & Computer Engineering Graduate Program. His research focuses on machine learning, deep learning, cybersecurity, blockchain, and their applications in cyber-physical systems. Dr. Wang is an IEEE Senior Member (since 2021) and has secured NSF grants totaling over $149,000. Education: Ph.D. Computer Engineering, University of Alabama in Huntsville M.S. Computer Science, Wuhan University of Science and Technology B.S. Information Systems, Wuhan University of Science and Technology Research Interests: Machine learning and deep learning algorithms Cybersecurity for IoT and smart grids Blockchain applications in attribute-based access control Adversarial machine learning defense mechanisms Optical fiber communication systems Recent Publications Trends: His work spans blockchain platform comparisons, adversarial attack mitigation in power systems, and AI-driven smart home solutions. He frequently publishes in IEEE journals and conferences. Awards: Best Paper Award at 2017 IEEE UEMCON Best Paper Award at 2015 ICDIP Grants and Advising: Principal Investigator for NSF-funded UIRiSCS project (2022–2025). Co-Principal on NYC DOT Loading Zone Study. Recipient of Manhattan College Faculty Summer Grants (2021, 2017). Advises graduate students in cybersecurity and IoT research. Labs/Teams: Collaborates with the University of Zaragoza, Spain on smart systems research. Leads projects on plastic optical fiber networks and AI-driven smart home systems.
Adam Kaufman is an Associate Professor and Adjoint Fellow at JILA, a joint institute of the University of Colorado Boulder and NIST. He holds a faculty appointment in the Department of Physics at CU Boulder and collaborates closely with NIST researchers. His research focuses on quantum science, leveraging atomic, molecular, and optical physics to explore entanglement in complex systems, quantum coherence, and precision measurement. Key interests include quantum metrology with atomic clocks, quantum simulation using optical tweezers, and the development of qubit architectures with alkaline-earth atoms. Research Interests: - Investigating entanglement in quantum systems for both fundamental understanding and practical applications in condensed matter physics. - Pushing the limits of quantum coherence in optical tweezers to build scalable quantum states. - Developing tools like microscopy and precision spectroscopy to study ultra-cold atoms. Publications reflect advancements in optical clocks, quantum error correction, and boson sampling with atoms. Notable trends include innovations in multi-qubit gates for precision timing, Rydberg atom arrays for quantum magnetism, and cryogenic systems for extended coherence times. Scientific Awards: PECASE, Gordon and Betty Moore Foundation Grant, Friedrich Wilhelm Bessel Research Award. Advising includes mentoring students like Aaron Young (Deborah Jin Award recipient) and Matthew Norcia (IUPAP Prize). Grants include NSF Q-SEnSE funding and collaborations through CUbit Quantum Initiative. Lab activities focus on JILA’s optical tweezer arrays, cryogenic systems, and Ytterbium-based qubit development. Projects aim to realize scalable quantum processors and novel quantum sensors.
Bernd Sturmfels is a leading mathematician serving as Director of the Max Planck Institute for Mathematics in the Sciences in Leipzig since 2017. He is also Professor Emeritus of Mathematics, Statistics, and Computer Science at the University of California, Berkeley, and holds honorary professorships at the Technical University of Berlin and the University of Leipzig. His research bridges pure and applied mathematics, with foundational contributions to algebraic geometry, combinatorics, and computational biology. Education: Sturmfels earned dual Ph.D. degrees in 1987 from the University of Washington and Technische Universität Darmstadt, followed by an honorary doctorate from Goethe University Frankfurt in 2015 and additional honorary doctorates from the University of Bern (2023) and the University of Chicago (2024). Research Interests: His work spans algebraic geometry , combinatorics , commutative algebra , algebraic statistics , convex optimization , and computational biology . He explores deep connections between abstract algebraic structures and practical applications in statistics, optimization, and the life sciences. Publications and Trends: With over 300 research articles and 11 books, his recent work (2022–2025) focuses on advanced topics like Grassmannian geometry, tropical implicitization, quantum chemistry applications, and algebraic statistics. His research increasingly integrates computational methods with theoretical insights, addressing problems in machine learning, phylogenetics, and optimization. Awards and Honors: Sturmfels has received numerous prestigious awards, including: George David Birkhoff Prize in Applied Mathematics (2018) SIAM von Neumann Lecturership (2010) Humboldt Senior Research Prize (2007–2008) David and Lucile Packard Fellowship (1992–1997) Fellowships of the AMS and SIAM Membership in the Berlin-Brandenburg Academy of Sciences and Humanities Mentoring and Grants: He has supervised 60 doctoral students and numerous postdocs, with many securing positions at leading institutions. His mentoring philosophy emphasizes diversity and excellence, as highlighted in his Notices of the AMS article. Funding sources include the NSF, DARPA, and the German National Science Foundation (DFG). Labs and Teams: At the Max Planck Institute, he leads the Nonlinear Algebra group, fostering interdisciplinary collaboration between mathematics and the sciences. His team focuses on developing algebraic methods for data analysis, optimization, and theoretical physics.
Matteo Tamburini is a Group Leader at the Max Planck Institute for Nuclear Physics (MPIK) in Heidelberg, leading the Extreme Field Quantum Plasma Dynamics and Relativistic Laboratory Astrophysics group. He also serves as a Lecturer at the International Max Planck Research School in Quantum Dynamics (IMPRS-QD). His academic journey includes a PhD in Physics from the University of Pisa, Italy, and postdoctoral research at MPIK. Research Interests: Tamburini specializes in quantum plasma dynamics, strong-field quantum electrodynamics (QED), and high-intensity laser-plasma interactions. He focuses on topics such as radiation reaction effects, relativistic astrophysical simulations, and the generation of ultra-high energy particles and gamma-ray bursts. His work bridges theoretical modeling with experimental validation at facilities like FACET-II (SLAC), Gemini (UK), and DESY. Experimental Contributions: Key projects include devising experiments to probe quantum radiation reaction (E-332, E-320, E-305), developing the SFQEDtoolkit for QED simulations, and advancing polarized laser-wakefield acceleration. His research often involves close collaboration with international teams and leverages cutting-edge facilities like the Gemini laser and FACET-II. Awards & Service: Tamburini is recognized as an IOP trusted reviewer for peer review excellence. He organizes the Seminar Theoretical Quantum Dynamics and contributes to reviewing for journals like Physical Review Letters and Nature Physics. He has secured significant funding, including a 4-year scholarship for student Michael Quin. Lab/Teams: Leads the Extreme Field Group at MPIK, focusing on advancing understanding of quantum plasma phenomena and relativistic astrophysical processes through theoretical and computational approaches.
Lamine M. Mili is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His expertise spans power systems, signal processing, and robust estimation theory. He holds an IEEE Fellowship (2016) for contributions to robust state estimation in power systems. Mili's research focuses on advancing methodologies for power system reliability, control, and integration of renewable energy sources. His work includes studies on dynamic state estimation, nonlinear dynamics, bifurcation theory, and quantum computing applications. He has contributed extensively to resilience engineering and computational social science in power systems. Mili’s recent articles address challenges in smart grids, quantum circuit error prediction, and multifractal signal analysis in EEG. His research often combines advanced statistical techniques with real-world grid data, emphasizing robustness and adaptability in dynamic environments. Education: Ph.D., University of Liège, 1987 M.S., University of Tunis, 1983 B.S., Swiss Federal Institute of Technology, Lausanne, 1976 Research Interests: Power system stability and control State estimation and robust filtering Quantum computing for power systems Resilience and cyber-physical-social systems Nonlinear dynamics and bifurcation analysis His recent publications reflect a focus on hybrid power systems, probabilistic methods, and data-driven approaches for grid optimization. The 2025 articles highlight advancements in photovoltaic state estimation, quantum error prediction, and robust modulation techniques. Mili’s work often bridges theoretical models with practical grid applications, emphasizing uncertainty quantification and real-time monitoring.
Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.
Dr. Muhammad Fahim is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on AI-driven solutions for healthcare, including wearable sensor data analysis, deep learning models for healthcare platforms, and integrating intelligence into digital twins for societal impact. He specializes in multimodal sensor data, energy expenditure estimation, and activity recognition in smart homes. His work spans projects such as TUDOR (Ubiquitous 3D Open Resilient Network) and MISO (Atmospheric Carbon Monitoring). He has been awarded the Queen's Merit Fellowship and SEDA Leading Undergraduate Programmes Certification. His research interests include edge intelligence, quantum-based models, and proactive health monitoring systems. Dr. Fahim collaborates internationally and has contributed to over 60 publications in areas like smart healthcare systems, network security, and environmental modeling. He actively participates in academic activities, including PhD examinations and invited talks on AI and wearable computing.
