جست‌وجوی استاد و پژوهشگر

Vignesh Narayananمشاهده پروفایل

استادیار

• Dynamical Systems
• Artificial Intelligence
• Computational Neuroscience
• +۴ مورد دیگر

Vignesh Narayanan is an Assistant Professor in the Artificial Intelligence Institute (AII) and Department of Computer Science and Engineering at the University of South Carolina (USC), with affiliations to the Carolina Autism and Neurodevelopment (CAN) Research Center. He bridges AI with dynamical systems theory, computational neuroscience, and cyber-physical systems (CPS) to enhance safety and efficiency in human-AI collaboration. Education: B.Tech (SASTRA University, India, 2012), M.Tech (National Institute of Technology, India, 2014), Ph.D. (Missouri University of Science and Technology, 2017), Postdoc (Washington University in St. Louis, 2017-2021) His research spans dynamic modeling , AI integration , and neuroscience-inspired systems . Key projects include: Developing safety-constrained virtual health assistants using knowledge graphs to align with medical protocols Advancing collaborative autonomy for drones/vehicles through event-triggered control mechanisms Creating digital twins for opinion dynamics to analyze information propagation in social systems Designing quantum-echo state networks for chaotic system prediction on NISQ hardware Recent publications demonstrate expertise in reinforcement learning for battery health monitoring ( 2024 ), reservoir computing for time series reconstruction ( 2025 ), and knowledge-infused AI frameworks for explainable decision-making. His lab team includes researchers like Bharath Muppasani and Erik Connerty, while recent grants from NSF , AFOSR , and DoD fund projects on collaborative autonomy and secure cyber-physical control.

Luca Didaciمشاهده پروفایل

دانشیار

• Statistical Pattern Recognition
• Biometric Recognition and Security
• Semi-Supervised Learning
• +۴ مورد دیگر

Luca Didaci is an Associate Professor of Computer Engineering at the University of Cagliari's Department of Electrical and Electronic Engineering (DIEE), within the Faculty of Engineering and Architecture. He leads research in statistical pattern recognition, biometric systems, and EEG signal analysis, focusing on applications in security, healthcare, and machine learning. His work includes developing EEG-based personal identification techniques, semi-supervised learning methods, and advanced tools for histopathological image analysis. With an h-index of 16, his research has been cited over 950 times. He teaches courses on programming, software development, and computer science fundamentals across undergraduate and graduate programs. Didaci is affiliated with the Pattern Recognition and Application Lab at DIEE and contributes to the PhD Program in Electronic and Computer Engineering. His research interests span multiple classifier systems, biometric authentication, and cybersecurity. Recent projects include studying ADaM/SDTM clinical data formats and investigating adversarial machine learning threats to biometric systems. He actively publishes in top-tier journals and conferences, emphasizing interdisciplinary applications of AI in engineering and medicine.

Marco Fumeroمشاهده پروفایل

پژوهشگر ارشد

• Geometric Deep Learning
• Geometry Processing
• Representation Learning
• +۲ مورد دیگر

Marco Fumero is a PostDoctoral Researcher at the Institute of Science and Technology Austria (ISTA), where he conducts foundational research at the intersection of geometry and artificial intelligence. Previously, he completed his Ph.D. in Computer Science at Sapienza University of Rome as a core member of the GLADIA research group under Professor Emanuele Rodolà's supervision, establishing a trajectory bridging theoretical geometry with practical deep learning applications. Ph.D. in Computer Science, Sapienza University of Rome Dr. Fumero's research program centers on exploiting geometric structures to revolutionize artificial intelligence systems, with primary focus on geometric deep learning, geometry processing, and representation learning. He pioneers methodologies for analyzing neural network latent spaces through spectral geometry and dynamical systems theory, developing frameworks that enable cross-model communication and zero-shot transfer. His work systematically addresses challenges in representation alignment, latent space dynamics, and disentangled feature extraction, with direct applications in 3D shape analysis, multimodal learning, and quantum-inspired computing. This research demonstrates exceptional theoretical rigor while maintaining strong connections to real-world problems in computer vision and scientific computing. His publication record reveals a dominant trend toward unifying geometric principles with deep learning architectures, particularly through spectral methods and functional map theory. The 2024-2025 publications showcase a coherent evolution from foundational latent space analysis (e.g., attractor dynamics in autoencoders) to practical frameworks for cross-model communication (e.g., cycle-consistent merging and semantic alignment). Key thematic threads include zero-shot capability development, invariance exploitation, and the translation of classical geometry processing techniques into neural network contexts. These contributions have established new paradigms for latent space manipulation across computer vision, graphics, and multimodal AI. Spotlight presentation at ICLR 2024 for "From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication" Multiple papers accepted at NeurIPS 2024 including "Latent Functional Maps" and "C2M3" During his doctoral training at Sapienza, Dr. Fumero actively mentored junior researchers within the GLADIA group, contributing to the development of next-generation geometric AI specialists through collaborative projects and technical guidance. His research has been supported by institutional funding from Sapienza University and ISTA, with potential backing from European research initiatives targeting foundational AI advances. Current work focuses on scaling geometric deep learning frameworks to complex multimodal scenarios while maintaining theoretical guarantees. Dr. Fumero maintains strong ties to the GLADIA research group at Sapienza University of Rome, which specializes in geometric learning and data analysis. At ISTA, he operates within a highly collaborative interdisciplinary environment that emphasizes theoretical computer science and its applications, contributing to the institute's mission of advancing frontier research through mathematical rigor and computational innovation.

• Computational Biology
• Computational Science
• Monte Carlo Methods
• +۵ مورد دیگر

Yaohang Li is a Professor in the Department of Computer Science at Old Dominion University (ODU), part of the College of Sciences. His research focuses on computational biology, computational science, Monte Carlo methods, and high-performance computing. He holds a Ph.D. in Computer Science from Florida State University (2003), an M.S. from Florida State (2000), and a B.S. from South China University of Technology (1997). Dr. Li has secured over $15M in research funding, including an NSF CAREER Award (2009) and multiple federal/state grants. His work spans protein structure modeling, bio-inspired algorithms, and parallel computing. Notable contributions include novel sampling approaches for protein modeling and GPU-accelerated optimization methods. He has authored/co-authored numerous peer-reviewed articles in journals like Journal of Computational Biology , IEEE Transactions , and Physical Review . Key Research Areas: Protein Loop Prediction, Parallel Tempering Algorithms, Grid Computing Security Grants: Includes $13.5M NOAA ISET Cooperative Center and $1M NSF Bio-inspired Control Systems Project Awards: 2009 NSF CAREER Award, 2005 Young Researcher Award (NCSU), and 2005 Ralph E. Powe Junior Faculty Enhancement Award. His lab collaborates on interdisciplinary projects such as the Consortium for Research Computing for Sciences (CRCSET) and develops tools like the Variational Autoencoder Inverse Mapper (VAIM) for QCD analysis. Advises on HPC systems and security in distributed computing environments.

• Machine Learning
• Health Informatics
• Cybersecurity
• +۵ مورد دیگر

Prof Xujuan Zhou is a Professor of Information Systems at the School of Business, University of Southern Queensland. She is affiliated with the Centre for Heritage and Culture and the Centre for Health Research. Her work focuses on interdisciplinary applications of machine learning and AI in healthcare, cybersecurity, and agricultural systems. She holds qualifications including a BIT (Honours) and PhD from Queensland University of Technology (QUT). Bachelor of IT (BIT), QUT Bachelor of IT (Honours), QUT PhD in IT, QUT Her research interests span AI-driven medical diagnostics (e.g., depression prediction, thyroid care), cybersecurity solutions for 5G networks, blockchain applications in agricultural traceability, and hybrid ML models for fraud detection. She has published extensively on topics like intrusion detection systems, remote patient monitoring, and X-ray-based disease identification using CycleGAN. Her work emphasizes both technical innovation and ethical considerations in AI deployment. Recent publications explore the intersection of social media analysis and mental health monitoring, blockchain for secure recommendation systems, and federated learning for personalized healthcare monitoring. Over 60,000 views and 13,000 downloads of her research outputs highlight the impact of her interdisciplinary work. Her contributions include developing AI tools for sepsis prediction, pain intensity estimation via facial expressions, and automated classification of lung nodules. She has also pioneered work on quantum-inspired algorithms for optimization problems in medical scheduling.

• Logistics and Supply Chain Management
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Haibo Wang is a Killam Distinguished Professor at Texas A&M International University, specializing in logistics, artificial intelligence, and quantum computing. With a Ph.D. from the University of Mississippi (2004), he brings over a decade of IT consulting experience to academia, focusing on bridging employer needs and student skills through data analytics education. He teaches in the MBA in Management program, emphasizing analytical skills, research proficiency, and business problem-solving using Excel, R, and Python. His research interests span decision support systems, supply chain resilience, and optimization algorithms. Key contributions include work on quantum-inspired optimization, econophysics frameworks for financial networks, and machine learning applications in supply chain security. He has secured grants related to infrastructure and border security as a principal investigator and expert consultant. Professor Wang has received prestigious awards including the International Faculty of the Year and University Scholar of the Year. His work integrates computational experiments with real-world challenges, such as predicting distressed firms in China and analyzing cross-border transportation data. He mentors students in NSF LSAMP grant programs and advocates for structured learning approaches to master business analytics tools. His academic output includes over 50 refereed journal papers across disciplines like operations research, financial engineering, and AI. Recent research highlights dynamic market relationships (e.g., BRICS stock markets and cryptocurrency), quantum machine learning algorithms, and network resilience in financial systems.

Alicia Troncosoمشاهده پروفایل

استاد

• Time Series Forecasting
• Machine Learning
• Big Data Analytics
• +۳ مورد دیگر

Alicia Troncoso is a Full Professor in the Computer Science Division at Universidad Pablo de Olavide's School of Engineering, where she directs the Master's Degree in Computer Science and co-leads the Data Science & Big Data Research Lab. She received her PhD from the University of Seville, recognized with the best research award from Andalusian Universities. Her research focuses on: Time series forecasting Machine learning algorithms Big data streaming analytics Explainable AI methods Recent publications demonstrate applications in energy forecasting, environmental monitoring, and sustainable computing. Her work integrates deep learning with optimization techniques for high-dimensional data analysis. She has supervised 13 PhD theses to completion and serves as President of the Spanish Association of Artificial Intelligence. Her research has been funded by numerous European and national projects focused on big data infrastructure and sustainable AI.

• Machine Learning
• Neural Networks
• Quantum Computing
• +۳ مورد دیگر

Zheng Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on neural networks, quantum computing, uncertainty quantification, and optimization, with particular emphasis on tensor networks, low-rank compression methods, and hardware-efficient machine learning systems. He leads efforts in developing memory-efficient training algorithms for large language models (LLMs), tensorized optical networks, and physics-informed neural PDE solvers. Key contributions include FLAT-LLM for LLM compression, FETTA hardware accelerators, and DeepOHeat for thermal simulation in 3D-IC design. His work spans cross-disciplinary areas such as quantum-inspired algorithms, stochastic control, and yield-aware optimization of photonic ICs. He holds a faculty position in the College of Engineering and is affiliated with the ECE department. Research trends in his 2025 publications emphasize scalable training techniques for transformers, zeroth-order optimization methods, and optical computing integration. His work consistently addresses computational efficiency, memory constraints, and hardware acceleration across domains like AI, quantum computing, and electronic design automation. Notable grants and lab affiliations include projects on FPGA-based neural training, quantum circuit simulation, and tensor-compressed PDE solvers. He advises on edge computing, neuromorphic systems, and uncertainty-aware design tools for integrated circuits.

Simon Baleمشاهده پروفایل

مدرس ارشد

• Radio Frequency Engineering
• Electromagnetic Compatibility (EMC)
• Atomic Clocks and Oscillators
• +۳ مورد دیگر

Dr. Simon Bale is a Senior Lecturer and 4th year engineering project coordinator at the University of York’s School of Physics, Engineering & Technology. He holds a PhD in radio frequency electronics and microwave engineering (2012) from the same institution. His research focuses on oscillators/atomic clocks, electromagnetic compatibility (EMC), shielding, and bio-inspired optimization methods. He is a co-investigator on major projects such as the EPSRC-funded “Compact Quantum Clocks For Precise And Autonomous Position Navigation And Timing” and the EU’s “European Doctoral Network for Safe and Sustainable Electromagnetic Shielding Solutions for Mobility.” Teaching responsibilities include courses on electrical circuits, AC machines, power electronics, computer architectures, and embedded systems for FPGAs. His academic contributions span over 30 publications in top-tier journals and conferences, emphasizing practical applications in EMC testing, oscillator design, and evolutionary computation. He actively collaborates with industry partners like QinetiQ and Keysight, translating research into real-world solutions. Awards include Fellow of the Higher Education Academy (FHEA). His work bridges theoretical innovation and industrial needs, with a focus on optimizing electronic systems through multi-objective approaches. Ongoing projects explore ultra-low phase noise oscillators, shielding effectiveness measurement methodologies, and AI-driven circuit design optimization.

Alberto Salleoمشاهده پروفایل

استاد

• Organic bioelectronics
• Organic semiconductors
• Structure-property relations
• +۵ مورد دیگر

Alberto Salleo serves as Professor of Materials Science and Chair of the Department of Materials Science and Engineering at Stanford University's School of Engineering since 2019. He joined Stanford in 2005 as Assistant Professor, progressing to Associate Professor (2013) and Full Professor (2019) with a visiting professorship at École des Mines de Saint-Etienne in 2016. His research focuses on structure-property relationships in organic semiconductors , specializing in: Operando characterization techniques (X-ray/electron microscopy) Organic bioelectronics for biosensors and neuromorphic computing Development of artificial synapses and wearable diagnostic devices Electronic transport mechanisms in conjugated polymers His group's work bridges fundamental materials science with applications in healthcare and energy-efficient computing. Salleo's publication record includes multiple Clarivate Highly Cited Papers in Science , Nature Materials , and Journal of the American Chemical Society , demonstrating significant impact across organic electronics and neuromorphic engineering. His research trends show increasing focus on bioelectronic interfaces and energy-efficient computing architectures. Award highlights: Elected Fellow of Materials Research Society (2022) and European Academy of Sciences (2021) Cavaliere Ordine al Merito della Repubblica Italiana (2020) Walter J. Gores Award (Stanford's highest teaching honor, 2016) NSF Career Award and SPIE Early Career Award Salleo directs an active research group developing novel characterization methods and devices, serving as Principal Editor of MRS Communications and board member for the Materials Research Society. His leadership extends to international scientific collaboration through the Italian Scientists and Scholars in North America Foundation.

• Intelligent devices and systems
• Wearable sensors
• Quantum materials
• +۵ مورد دیگر

Professor Roy Vellaisamy is a faculty member at the James Watt School of Engineering, University of Glasgow, specializing in intelligent systems and advanced materials. His research focuses on wearable sensors, quantum materials, neuromorphic devices, and biomedical diagnostics. He has published over 200 articles in journals like Nature Communications and Advanced Materials , and holds 21 patents. Notable achievements include a gold medal for his 'Sensor Platform' at the Geneva International Exhibition of Inventions (2019). His work spans interdisciplinary areas such as point-of-care diagnostics for prostate cancer, thermoelectric energy conversion, and neuromorphic computing. He leads projects on single-cell electrical analysis using transistor-based methods and bio-inspired artificial synapses. Collaborations include developing MXene composites for terahertz shielding and AI-integrated soft sensors for metaverse applications. Roy Vellaisamy's contributions bridge device physics, materials chemistry, and electronics, with applications in healthcare, energy, and advanced computing. His research emphasizes practical solutions for contamination monitoring, wearable health tech, and next-generation electronic systems.

Hussam Amrouchمشاهده پروفایل

استاد

• AI Hardware Acceleration
• Emerging Nanoelectronics
• Cryogenic Circuits
• +۴ مورد دیگر

Prof. Hussam Amrouch is a Full Professor of AI Processor Design at the Technical University of Munich (TUM), leading the TUM School of Computation, Information and Technology. His research focuses on ultra-efficient embodied AI, reliable designs in emerging technologies, and cryogenic circuits for quantum computing. He holds a Dr.-Ing. from Karlsruhe Institute of Technology (2015, Summa cum Laude) and previously led the "Dependable Hardware" group at KIT and the Chair of Semiconductor Test and Reliability at University of Stuttgart. He is affiliated with Munich Quantum Valley (MQV) and Munich Institute of Robotics and Machine Intelligence (MIRMI). Research interests include ferroelectric FETs, in-memory computing, cryogenic electronics, and neuromorphic systems. Key achievements include 10× HiPEAC Paper Awards, 3× DAC/DATE best paper nominations, and pioneering work on FeFET-based AI accelerators. His work bridges nanoelectronics with AI, addressing challenges in energy efficiency, reliability, and quantum integration. Publications span cutting-edge topics like cryogenic FinFETs, hyperdimensional computing, and monolithic 3D integration. He has developed novel testing methodologies, self-aware silicon systems, and energy-efficient architectures for edge-AI. Current projects explore cryogenic circuit design, radiation-resistant FeFETs, and carbon-efficient 3D neural networks. His awards reflect contributions to high-performance and embedded architectures. Research groups under his leadership focus on device-level innovations and system-level integration of emerging technologies. He actively contributes to interdisciplinary initiatives at MQV and MIRMI, advancing quantum computing and AI hardware frontiers.

Wilfred G. van der Wielمشاهده پروفایل

استاد

• Nanoelectronics
• Quantum Devices
• Brain-Inspired Computing
• +۱۲ مورد دیگر

Wilfred G. van der Wiel is a Full Professor in the Nano Electronics department at the MESA+ Institute for Nanotechnology, University of Twente. His work bridges nanotechnology, quantum physics, and unconventional computing, focusing on novel hardware for machine learning and brain-inspired systems. Full Professor, Nano Electronics, MESA+ Institute, University of Twente Research Interests: His research lies at the intersection of nanoelectronics and cognitive computing. He investigates disordered dopant-atom networks in silicon , gold nanoparticle assemblies , and in-materio computing to develop energy-efficient, adaptive hardware. His work explores how physical systems can inherently perform computation, bypassing traditional von Neumann architectures. Publication Trends: Recent articles (2023–2025) highlight a shift toward practical implementations of neuromorphic computing, including speech recognition in physical systems and systematic reviews of brain-inspired architectures. Earlier works focus on fundamental transport mechanisms in nanostructures. Collectively, his publications reveal a trajectory from basic physics of nanodevices to applied neuromorphic engineering and software frameworks like Brains-PY. Scientific Contributions: While specific awards are not listed, his high-impact publications in journals like Nature and Physical Review Applied , along with continuous funding and supervision of research, indicate significant recognition in the field. Advising and Grants: He has supervised at least 29 research projects, including PhD and postdoctoral work, as indicated by 'Supervised Work (29)'. He leads or participates in externally funded projects related to nanotechnology and neuromorphic computing, evidenced by collaborative publications and datasets. Labs and Teams: He is a key member of the MESA+ Institute for Nanotechnology at the University of Twente, working within the Nano Electronics group. He collaborates extensively with researchers in physics, materials science, and computer science, particularly on projects involving unconventional computing and nanofabrication.

Jingjie Yeoمشاهده پروفایل

استادیار

• Computational Materials Science
• Engineered Living Materials
• Multiscale Modeling
• +۱۷ مورد دیگر

Prof. Jingjie Yeo is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University and the Principal Investigator of the J² Lab for Engineering Living Materials. His research integrates computational modeling, machine learning, and biomaterials to design sustainable, adaptive materials for healthcare and environmental applications. He is also a co-instructor at Station1, a nonprofit advancing socially-directed STEM education. Ph.D. and B.Eng., Nanyang Technological University, Singapore Postdoctoral training, Tufts University and Massachusetts Institute of Technology Research Scientist, Institute of High Performance Computing, Singapore Prof. Yeo’s research focuses on computational design of engineered living materials , particularly bacteria-based systems and biopolymers. His lab employs multiscale simulations —from quantum mechanics to continuum modeling—and integrates machine learning and materials informatics to accelerate discovery. Key areas include nanomechanics of mucus-bacteria interactions, mucoadhesive biomaterials, sustainable materials, and living materials for gut health and infrastructure. The lab pioneers AI-driven frameworks for materials-by-design, aiming to advance a "Materials 4.0" paradigm. The lab’s recent publications reveal a strong trend in AI-enhanced materials discovery , bio-inspired soft materials , and biomechanical modeling of biological systems . Themes include antimicrobial surfaces, solid electrolytes, hydrogels, and sustainable carbon materials. The work is highly interdisciplinary, combining physics, biology, and computation. Notable scientific awards include: NSF CAREER Award (2024) Dennis G. Shepherd Excellence in Teaching Award, Cornell College of Engineering (2023) ASME Rising Star in Mechanical Engineering Emerging Investigator, Journal of Materials Chemistry B (2020) Multiple NSF grants including EFRI and Convergence Accelerator awards Prof. Yeo has advised numerous PhD, MS, and undergraduate students , including the lab’s first PhD graduates, Drs. Haoyuan Shi and Tianjiao Li. His research is supported by NSF, Cornell, and international seed grants . He serves on editorial boards for STEM Education and International Journal of AI for Materials and Design , reflecting his dual commitment to research and education innovation. The J² Lab is part of the Cornell Engineered Living Materials Institute and collaborates with researchers at MIT, Montana State University, Zhejiang University, and others. The lab emphasizes interdisciplinary teamwork, computational rigor, and socially impactful science.

Stephan Günnemannمشاهده پروفایل

استاد

• Machine Learning
• Data Analysis
• Graph Neural Networks
• +۴ مورد دیگر

Prof. Stephan Günnemann is a Professor of Data Analytics and Machine Learning at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He leads the Munich Data Science Institute as Executive Director and directs the Konrad Zuse School of Excellence in Reliable AI. His research focuses on enhancing the reliability of machine learning systems, particularly in graph-based and temporal data analysis. Prof. Günnemann holds a PhD from RWTH Aachen University (2012) and has held postdoctoral and senior research positions at Carnegie Mellon University (USA), Simon Fraser University (Canada), and Siemens AG. He founded the Emmy Noether Research Group at TUM in 2015 and has been recognized with prestigious awards including the Heinz Maier-Leibnitz Medal (2022) and the ACM SIGKDD Best Paper Award (2018). His research interests span adversarial robustness, graph neural networks, and molecular data analysis. Recent work emphasizes certifiable AI safety, efficient data pruning, and uncertainty estimation in heterogeneous systems. He has contributed to over 150 peer-reviewed publications, with a focus on foundational ML challenges and real-world applications. Key Awards: Heinz Maier-Leibnitz Medal (2022), Google Faculty Award (2020), DFG Emmy Noether Programme (2015) Leadership Roles: Executive Director of Munich Data Science Institute, Director of Konrad Zuse School Past Roles: Postdoctoral Fellow at CMU, Researcher at Siemens His lab actively explores cutting-edge AI topics such as graph representation learning, quantum chemistry simulations, and trustworthy ML systems. Current projects include developing certifiable defense mechanisms against adversarial attacks and scalable molecular generation frameworks.