جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +۳ مورد دیگر

Yifei Zhang is an Assistant Professor in the Department of Computer Science at Northeastern University's School of Computer Science and Engineering, with extensive research collaborations including the University of Hong Kong (notably with Irwin King). Active since 2009, Zhang has produced 257 publications through 2025, demonstrating sustained research productivity across artificial intelligence domains. Zhang's research spans Artificial Intelligence , Machine Learning , and Computer Vision , with recent focus on federated learning systems, multimodal AI, and security applications. The work shows strong technical depth in developing novel algorithms for personalized learning, anomaly detection, and efficient model training. Recent publications reveal increasing specialization in privacy-preserving machine learning and cross-modal understanding, particularly for Chinese language and cultural contexts. Publication trends indicate growing influence in AI conferences (CVPR, ACL, NeurIPS), with 61 publications in 2024 alone. The research demonstrates practical applications in cybersecurity, education technology, and urban planning, while maintaining theoretical rigor in model architecture and optimization techniques. Zhang frequently collaborates with Neng Gao and Shuang Song on security-related AI projects. Zhang serves on program committees for major AI conferences and has contributed to workshop organization including FL@FM-TheWebConf'25: International Workshop on Federated Foundation Models for the Web. The research has attracted significant attention in the AI community, particularly in federated learning and multimodal systems.

• Computer Vision
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• Theoretical Physics
• Symmetry Analysis
• Quantum Mechanics
• +۳ مورد دیگر

Professor Piotr Kosiński is a faculty member at the Department of Computer Science, Faculty of Physics and Applied Informatics, University of Łódź. He serves as head of the department and has been recognized for his interdisciplinary research bridging physics and computational methods. Current affiliation: University of Łódź Leadership role: Head of Department of Computer Science Contact: piotr.kosinski@fis.uni.lodz.pl His research spans multiple domains: Symmetry Analysis : Focus on Poincaré symmetry, conformal orbits, and classical integrability Quantum Dynamics : Exploring speed limits, chiral fermions, and quantum field propagation Building Physics : Thermal insulation materials (hemp, flax), moisture transport in fibrous media, and energy-efficient construction Phylogeography : Hybridization zones in willows and alpine plant evolution Recent publications highlight interdisciplinary trends: Application of Herglotz variational formalism to relativistic optics Thermal performance analysis of bio-based insulation materials Quantum speed limit theorems in computational contexts Phylogenetic studies of Salix hybrids in European Alps Hamiltonian symmetry structures across physical systems Contact details: Office: B 305, Pomorska 149/153, Łódź Office Hours: Friday 13:30–14:30, Monday 10:45–11:45

• Augmented Reality
• Virtual Reality
• Human-Computer Interaction
• +۵ مورد دیگر

Alexander Quinn is an Assistant Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. His research focuses on augmented reality (AR), virtual reality (VR), human-computer interaction, and human computation systems. He leads projects in adaptive tutoring systems for machine tasks in AR, privacy-preserving technologies, and crowdsourcing methodologies for complex cognitive tasks. Quinn has published extensively on topics including AR/VR prototyping, object interaction analysis, and quantum computation in trapped ions. Key areas of expertise include designing context-aware AR systems using generative AI, developing robust face de-identification techniques, and improving task instruction quality in crowdsourcing platforms. His work bridges computer science with educational technology and industrial automation, addressing challenges in both technical implementation and human-centered design. Quinn's recent publications highlight advancements in AR/VR environments derived from real-world scans, dataset development for dynamic scene representations, and mechanisms to enhance decision-making through web-based human computation. His research has applications in education, healthcare privacy, and manufacturing collaboration protocols.

Anwar Hasanمشاهده پروفایل

استاد

• Cryptography
• Post-Quantum Cryptography
• Secure Multiparty Computation
• +۵ مورد دیگر

Anwar Hasan is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He specializes in cryptography, post-quantum cryptography, and secure computation, with a focus on algorithm optimization, cryptographic hardware implementation, and energy efficiency. His work spans theoretical foundations and applied systems, including blockchain technology, fault-tolerant cryptosystems, and secure multiparty computation. His research interests emphasize developing quantum-resistant cryptographic protocols, improving efficiency in cryptographic algorithms (e.g., EdDSA signature verification, CSIDH optimization), and designing secure systems against adversarial attacks (e.g., poisoning in federated learning, covert adversary models). He also explores energy consumption analysis of cryptographic algorithms, particularly in the context of NIST PQC standards and elliptic curve-based systems. Recent work includes contributions to threshold cryptography (isogeny-based schemes), decentralized oracle networks (CountChain), and privacy-preserving blockchain applications (AdChain). His publications frequently address hardware-software co-design for cryptographic primitives, emphasizing both theoretical advancements and practical implementation challenges. Dr. Hasan’s research also extends to applied areas such as fraud detection systems, digital rights management using blockchain, and energy efficiency in embedded cryptographic systems. He collaborates on projects involving secure communication in cloud computing and resilient key management for mobile applications.

• Speech Processing
• Machine Learning
• Affective Computing
• +۶ مورد دیگر

Professor Julien Epps is the Dean of the Faculty of Engineering at the University of New South Wales (UNSW). He holds a Professorship and previously served as Head of the School of Electrical Engineering and Telecommunications. His academic journey includes roles as Senior Lecturer (2007), Associate Professor (2014), and Head of School (2019). Epps earned his BE (Hons I) and PhD in Electrical Engineering from UNSW in 1997 and 2001, respectively. His research focuses on speech processing, wearable sensor technology, affective computing, and mental health applications. Notable contributions include work on depression detection via speech analysis, multimodal emotion recognition, and the development of tools like the IREye4Task dataset for wearable mental state analysis. He also investigates human-computer interaction through eye-tracking and biometric sensors. Epps' publications (2021–2025) emphasize speech-based health screening, adaptive signal processing for cybersecurity, and the integration of large language models with acoustic landmarks. His work spans engineering education, including a pioneering undergraduate quantum engineering degree program introduced in 2023. His lab and team collaborations include projects on smartphone-based depression screening, real-time task load estimation, and the design of robust spoofing attack detection systems. Epps has led interdisciplinary efforts in healthcare technology and pedagogical innovation in engineering education.

Joonas Hämäläinenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Software Engineering
• Artificial Intelligence
• +۱ مورد دیگر

Joonas Hämäläinen serves as a Postdoctoral Researcher at the Faculty of Information Technology, University of Jyväskylä. His academic work bridges theoretical machine learning with practical applications in software engineering and computational chemistry, focusing on distance-based methodologies and the Minimal Learning Machine framework. His primary research domains include Machine Learning (particularly minimal learning machines and multi-label classification), Software Engineering (empirical studies and defect prediction), Artificial Intelligence (regression and clustering algorithms), and Computer Science (scalable data processing). This interdisciplinary approach enables novel solutions for complex problems like atomic force prediction in nanomaterials and software quality assurance. Analysis of his 2020-2024 publications reveals a consistent trajectory in advancing distance-based learning techniques. His work demonstrates increasing sophistication in adapting Minimal Learning Machine variants to multi-target regression, feature selection, and nanoscale simulations, with growing emphasis on theoretical validation alongside empirical testing across computational chemistry and software engineering contexts. Hämäläinen actively contributes to two key research groups: the Empirical Software Engineering Research group focusing on evidence-based software development practices, and the Quantum Information and Computation Team which targets quantum computing advancements through interdisciplinary collaboration.

Jan Pieter Abrahamsمشاهده پروفایل

استاد

• Structural Biology
• Electron Microscopy
• Protein Crystallography
• +۵ مورد دیگر

Jan Pieter Abrahams is a Professor and Group Leader of the Nanodiffraction group at the Laboratory for Multiscale Bioimaging, Paul Scherrer Institute (PSI) in Switzerland. His work focuses on developing electron diffraction technologies for atomic-resolution imaging of frozen hydrated biological samples, leveraging PSI's detector expertise to advance structural biology beyond conventional microscopy limitations. Current applications target mitochondrial stress mechanisms in neurodegeneration and aging, with active collaborations across international institutions. Research Interests: Abrahams pioneers electron diffraction and cryo-EM methodologies, emphasizing computational-phasing innovations and machine learning integration. His group specializes in: Overcoming dynamical scattering for atomic-level cellular visualization Hybrid pixel detector applications (JUNGFRAU, EIGER) in electron microscopy Deep learning frameworks for diffraction data processing (e.g., DiffraGAN) Structural analysis of protein nanocrystals in disease contexts Mitochondrial protease mechanisms in aging Bacterial cell division and sporulation structures Publication Trends: Recent work (2020–2024) reveals heavy emphasis on AI-driven structural biology, including generative networks for diffraction phasing and lossless data compression. Instrumentation advancements (e.g., Boersch phase shifters) and disease-focused studies (Alzheimer’s amyloid-beta, malaria heme processing) dominate, showcasing interdisciplinary convergence of physics, computation, and biomedicine. Scientific Awards: No specific awards or fellowships documented in source text Advising and Collaborations: Abrahams has mentored PhD students including Thakkar, Pooja; Rheinberger, Jan; Schärer, Martin; and Wennmacher, Julian. His team collaborates with PSI's detector group on sensor development (GaAs/CdTe) and international labs for structural studies. Funding likely stems from PSI instrumentation projects and disease-focused research initiatives. Labs and Teams: He directs the Nanodiffraction group under PSI's Center for Life Sciences, comprising scientists (van Genderen, Latychevskaia) and postdocs (Blum). The lab integrates cryo-EM, electron diffraction, and computational modeling to visualize cellular processes at nanometer scales, with strong ties to detector engineering and pharmaceutical structural analysis.

• Cloud Computing
• Data Center Optimization
• Edge Computing
• +۵ مورد دیگر

V. Dinesh Reddy is affiliated with SRM University Andhra Pradesh, Department of Computer Science and Engineering in Amaravati, India. He maintains an active research career with publications spanning from 2017 to 2025 across multiple prestigious venues including IEEE Access, Energy Informatics, Quantum Information Processing, and Sensors. Dr. Reddy's research interests span cloud computing infrastructure optimization, edge computing, quantum computing applications, image processing, and cybersecurity. His work demonstrates expertise in developing evolutionary algorithms, machine learning approaches, and optimization techniques to solve complex computing problems with practical applications in IoT security, vehicular networks, and medical diagnostics. His publication record shows consistent output with increasing collaboration and expanding research scope over time. The research demonstrates strong interdisciplinary connections between traditional computer science domains and emerging technologies like quantum computing, addressing real-world challenges in computing infrastructure efficiency and security. Dr. Reddy has collaborated extensively with researchers including G. R. Gangadharan, G. Subrahmanya V. R. K. Rao, Marco Aiello, Md. Muzakkir Hussain, and Ashu Abdul. His research appears well-funded given the scope and diversity of projects, with applications spanning sustainable data centers, edge computing for vehicular networks, and quantum computing implementations. His research spans multiple laboratory contexts, particularly in cloud computing infrastructure, quantum computing applications, and image processing. Dr. Reddy's future research directions appear to be expanding into more specialized quantum computing applications and advanced edge computing scenarios for vehicular networks, as evidenced by his most recent publications from 2024-2025.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +۵ مورد دیگر

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.

• Surface-Enhanced Raman Scattering (SERS)
• Environmental Chemistry
• Nanomaterials
• +۶ مورد دیگر

Ramón A. Alvarez is a Professor of Chemistry with expertise spanning Surface-Enhanced Raman Scattering (SERS) , environmental chemistry , and nanomaterials . His research focuses on advanced analytical techniques for metal ion detection , pollutant monitoring , and environmental impact mitigation , as evidenced by high-impact publications in journals like Environmental Science & Technology and ACS Nano . Key contributions include studies on methane emission modeling, SERS-based biosensing, and nanocomposite design for biomedical applications. Research Trends : Recent work emphasizes machine learning integration with SERS, thermoelectric materials , and biochemical interactions in neurodegenerative and oncologic contexts. Collaborations : Frequent partnerships with institutions in Spain, Texas, and global environmental research groups. Email : ramon.alvarez@acs.org

Amnon Buxboimمشاهده پروفایل

استادیار

• Mechanobiology
• Stem Cell Biology
• Nuclear Mechanics
• +۵ مورد دیگر

Dr. Amnon Buxboim is an Assistant Professor at the Hebrew University of Jerusalem, affiliated with the Alexander Grass Center for Bioengineering and the Department of Cell and Developmental Biology. His research focuses on mechanobiology, particularly the roles of mechanical forces in stem cell differentiation, nuclear mechanics, and embryo development. He leads the Buxboim Lab, which combines single-cell genomics, microrheology, and AI to study mechanotransduction in contexts like preimplantation embryos and inflammation. Key achievements include securing an ERC Starting Grant (2015) for bovine reproduction mechanobiology and developing novel technologies like the MechanoSCOPE for in situ embryo mechanics analysis. His lab trains students in biology, bioengineering, physics, and computer science, collaborating with clinicians for translational impact. Notable students include Yoav Kan-Tor (PhD, Computer Science), Nir Zabari (MSc), and Shlomi Brielle (PhD). Research interests span matrix-directed stem cell fate, embryo development prediction via deep learning, and nuclear lamina interactions. The lab also investigates inflammation's mechanical aspects in IBD and GvHD.

• Cybersecurity
• Code Obfuscation
• Malware Analysis
• +۳ مورد دیگر

Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.

• Physics
• Astrophysics
• General Relativity
• +۳ مورد دیگر

Georgios Pappas is an Associate Professor at the School of Physics , Aristotle University of Thessaloniki since July 2025. Previously, he held positions as Assistant Professor there (2018–present), Researcher at Sapienza University of Rome (2018), and multiple Research Fellow/Postdoc roles at institutions including the University of Nottingham (2017–2018), Instituto Superior Técnico (2016–2017), University of Mississippi (2015–2016), and earlier postdoc/research roles since 2012. PhD in Physics (2005–2012), MSc (2001–2005), and Bachelor's (1996–2001) from the National and Kapodistrian University of Athens His research focuses on gravitational physics , particularly black holes and neutron stars in the context of general relativity , alternative gravity theories , and gravitational wave astronomy . Recent work includes applying machine learning to neutron star universal relations analyzing black hole shadows as tests of the no-hair theorem developing surrogate models for gravitational waveforms He has contributed to international collaborations like the Laser Interferometer Space Antenna (LISA) mission planning and has expertise in spacetime metrics , QPO analysis , and neutron star structure . Peer review activities centered on general relativity and celestial mechanics.

Mihai Boicuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Structured Analytical Methods
• +۹ مورد دیگر

Mihai Boicu is an Associate Professor at George Mason University's Volgenau School of Engineering , Department of Applied Information Technology, and Associate Director of the Learning Agents Center (LAC) . His research spans Artificial Intelligence , structured analytical methods, personalized education, and collective intelligence. He is the architect of the Disciple agent development platform and has secured grants from the US Department of Defense , VentureWell , and Oculus Info Inc. . His work includes over 100 publications, with three defense-focused textbooks and contributions to journals like AI Magazine and International Journal of Intelligence and CounterIntelligence . He has co-organized premier AI conferences such as ICMLA and AAAI Symposia . His research focuses on AI in defense , education technology , and metacognition in STEM , published in venues ranging from AAAI to IEEE and Hawaii International Conference on System Sciences . Scientific Awards : Innovative Application Award from AAAI Certificates of Appreciation from U.S. Army War College and Air War College GMU Teacher of Distinction (2015) His recent articles (2023–2025) analyze machine learning in education, quantum computing , LLM feedback systems , and metacognition , with projects like SugarNet (diabetes management) and FlightList (Air Force compliance). He advises PhD students in applied AI, education technology, and innovation.