جست‌وجوی استاد و پژوهشگر

• Materials Science
• Mechanics of Materials
• Electron Microscopy
• +۷ مورد دیگر

Dr. Antoine Guitton is a Tenured Full Professor in Materials Physics at Université de Lorraine and an Adjunct Professor at Georgia Institute of Technology. He works at LEM3 laboratory, a collaborative research center affiliated with CNRS and Arts et Métiers ParisTech. His teaching spans Université de Lorraine (Metz) and Georgia Tech Europe. He co-founded the Met’Hy student-researcher club and leads the Micro- and nano-Mechanics team at LEM3. His research focuses on crystalline defects, electron microscopies, and hydrogen storage in advanced materials. Université de Lorraine – Full Professor (2024–now) Georgia Institute of Technology – Adjunct Professor (2023–now) Laboratoire d’Étude des Microstructures et de Mécanique des Matériaux (LEM3) Research Interests Antoine’s work addresses critical challenges in materials science, including: Minimizing global warming through innovative materials (metallic alloys, ceramics, composites) Hydrogen industry and nuclear material optimization for energy transition Numerical transition via defect characterization in semiconductor electronics Defense sector innovation for national security Sustainability pathways through materials science Grants & Projects ANR PRC MAMIENOVA (Machine Learning for Micromechanics): 608k€ (PI) HORIZON AddMorePower: 6M€ (Local PI) CNRS MITI H2MX: 140k€ (PI) ANR PRC HEIRMAX: 712k€ (Local PI) Popularization Efforts He actively communicates science through: 'La Lorraine des Sciences' radio podcast (CNRS-labeled for Year of Physics 2023-2024) 'La Science en 4 Saisons' book series (EDPSciences) Public lectures on materials science, hydrogen energy, and deformation physics Scientific Expertise Member of PhD juries (EPFL, UGA) Expert in electron microscopy (KAI GmbH, 2023–now) Scientific council member (LEM3, IRMA)

• Human-AI Interaction
• Social Media Psychology
• Cognitive Psychology
• +۷ مورد دیگر

Jürgen Buder is a research scientist at the Leibniz-Institut für Wissensmedien (IWM) in Tübingen, Germany, and has been a key member since 2008. He serves as Deputy Head of the Perception and Action lab since 2022 and is also the ombudsman for good scientific practice at IWM. He is affiliated with Eberhard Karls University Tübingen, where he completed his habilitation in psychology in 2021. His research focuses on human-AI interaction, processing of conflicting information on social media, attitude polarization, knowledge communication, and computer-supported collaborative learning (CSCL). His work bridges cognitive psychology, educational technology, and social media dynamics. He has led major projects such as 'Info-noise', 'How do people deal with opposing opinions?', and 'ArchiveGPT', exploring the psychological effects of digital environments. The most recent articles highlight trends in AI perception, trust in artificial agents, polarization in online discourse, and cognitive mechanisms in digital learning. These publications appear in high-impact journals such as Computers in Human Behavior , Psychological Science , and New Media & Society , reflecting interdisciplinary engagement across psychology, education, and human-computer interaction. Scientific Awards: Dissertation Award, Faculty of Social Sciences, University of Tübingen (2002) He actively supervises and collaborates with PhD students and postdocs, though specific advisees are not listed. He has secured multiple research grants through DFG and Leibniz ScienceCampus funding. He has organized numerous conferences and workshops, including the 'WCT meets HCI' series and symposia on belief polarization. He teaches courses such as 'Knowledge media in education, work, and leisure' and seminars on anthropomorphism at the University of Tübingen. He is a core member of the Perception and Action lab at IWM, contributing to its mission of understanding human cognition in digital environments. His work emphasizes both theoretical frameworks and practical applications in educational and public policy contexts.

Marcus Handteمشاهده پروفایل

پژوهشگر

• Computer Science
• Pervasive Computing
• Sustainable Mobility
• +۳ مورد دیگر

Dr. Marcus Handte is a Senior Researcher at the University of Duisburg-Essen, focusing on networked embedded systems, context-aware computing, and sustainable mobility. His academic journey includes a Habilitation in Computer Science (2013) and a PhD in Natural Sciences (2009) from Universität Stuttgart, alongside a Master's degree from Georgia Institute of Technology (2002). Research Interests Context-aware applications Localization and location-based systems Sustainable mobility solutions Internet of Things (IoT) Smart city infrastructure Privacy-preserving technologies His recent work involves developing platforms for multimodal mobility analysis (MOBYDEX), wireless EV charging systems (TALAKO, FAIR), and innovative approaches to indoor localization. Publications span journals like Machine Vision and Applications and conferences in pervasive computing. Scientific Recognition Best Poster Award at ACM KMIS 2023 Dr. Handte has contributed to projects such as ATMo2, INNAMORUHR, and GAMBAS, and maintains active collaborations across institutions. His expertise in adaptive middleware and distributed systems continues to shape research in smart mobility and ambient intelligence.

Jisu Kimمشاهده پروفایل

استادیار

• International Migration
• Computational Demography
• Big Data Analytics
• +۹ مورد دیگر

Jisu Kim is an Assistant Professor at Utrecht University in the Netherlands, affiliated with the Faculty of Social Sciences and the Department of Sociology and Inequality. She was previously an Affiliate Researcher at the Max Planck Institute for Demographic Research (MPIDR), where she worked in the Laboratory of Migration and Mobility as part of the Digital and Computational Demography group. Research Interests: Her work lies at the intersection of demography, data science, and computational social science. She specializes in using big data—especially digital trace data from social media platforms like Twitter—to study international migration, social integration, gender differences in migration, cultural assimilation, and public sentiment around migration. She applies advanced computational methods including network analysis, natural language processing, and machine learning to analyze large-scale datasets for demographic insights. The recent articles highlight a consistent focus on leveraging social media data to understand migration dynamics, integration patterns, and public discourse. Her publications span computational demography, migration policy, gender and migration, and digital polarization, reflecting a strong interdisciplinary approach. She frequently publishes in top journals and conference proceedings in both demography and computational social science. Scientific Awards and Honors: DGD Best Paper Award for 'Gender Differences in the Migration Process: A Narrative Literature Review' Seed Money Award in Migration and Societal Change, Utrecht University Best Poster Award at Complenet 2023 1st Prize for Flash Talk at 'New data for the new challenges of population and society' Advising and Grants: While no formal students are listed, she actively mentors through programs like the Population and Social Data Science Summer Incubator. She has secured competitive funding such as the Seed Money Award and has been involved in EU-funded projects like PREMIUM. She regularly organizes workshops and training sessions, indicating strong engagement in academic leadership and community building. Labs and Teams: She was a key member of the Laboratory of Migration and Mobility at MPIDR and contributed to the Digital and Computational Demography group led by Emilio Zagheni. She has organized multiple editions of the International Workshop on Migration and Mobility Research in the Digital Era (MIMODE), demonstrating leadership in advancing methodological innovation in the field.

• Mobile Edge Computing
• Cloud Computing
• Internet of Things
• +۱۲ مورد دیگر

Junaid Shuja is a researcher with significant contributions to Mobile Edge Computing , Cloud Environments , and IoT Systems . Collaborating with scholars like Kashif Bilal , Abdullah Gani , and Ehzaz Mustafa , his work spans computation offloading, resource allocation, and security frameworks. Research Highlights 2017: Analysis of Vector Code Offloading in Heterogeneous Architectures 2021: Survey on Machine Learning for Edge Caching 2023: Reinforcement Learning for Computation Offloading in Vehicular Networks 2024: Blockchain Applications in Land Lease Systems and Employee Transfers 2025: Deep Reinforcement Learning for Resource Optimization His recent work focuses on Deep Learning and Blockchain for latency-sensitive applications in IoT and vehicular networks, published in IEEE Access , Cluster Computing , and Telecommunication Systems . Key co-authors include Faisal Rehman , Abdallah Namoun , and Muhammad Bilal .

• Social Media Analysis
• Online Political Polarization
• Misinformation Detection
• +۷ مورد دیگر

Ashwin Rao is a Research Professor at the University of Southern California's Information Sciences Institute (ISI) within the Viterbi School of Engineering. With a research career spanning over two decades, his work bridges computer science, social sciences, and political science, focusing on understanding human behavior through digital footprints. His academic journey began with signal processing research in the 1990s before evolving into network protocols and mobile computing, and most recently into computational social science and AI ethics. Rao's research interests encompass Social Media Analysis, Online Political Polarization, Misinformation Detection, Network Protocols, Mobile Computing, Privacy in Mobile Applications, Natural Language Processing, and AI Ethics. His work demonstrates a consistent trajectory from technical networking research to the societal implications of technology. His most recent publications reveal a strong focus on understanding political discourse online, particularly examining polarization, emotional responses to events, and the impact of social media algorithms on information ecosystems. His interdisciplinary approach combines computational methods with social science theories to address pressing issues in digital society. Rao has published extensively across top venues including ICWSM, WWW, ACL, IEEE Transactions, and numerous conferences in networking and systems. His work often appears with Kristina Lerman, with whom he collaborates closely at USC ISI. His recent research portfolio shows a sophisticated integration of machine learning techniques with social science questions, particularly examining how language models reflect and potentially amplify societal biases. Rao has made significant contributions to understanding privacy issues in mobile applications, network protocols, and social media dynamics. His research has been influential in both technical communities studying network performance and social science communities examining online behavior. His work on BitTorrent performance, mobile privacy, and social media analysis has been widely cited across disciplines. His laboratory work appears to focus on computational social science methodologies, developing tools and frameworks for analyzing large-scale social media data while addressing ethical considerations in AI and data analysis. Recent projects suggest strong connections with public health research through social media analysis during the pandemic.

• Face Recognition
• Biometrics
• Computer Vision
• +۶ مورد دیگر

Anjith George is a researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, working within the Biometrics Security and Privacy Laboratory. His research focuses on advancing face recognition systems with particular emphasis on security, efficiency, and cross-domain applications. He maintains a strong collaborative relationship with Professor Sébastien Marcel's research group at EPFL. George's research interests span multiple critical areas in modern biometrics including face recognition systems, face anti-spoofing techniques, heterogeneous face recognition across different modalities (such as visible to infrared), and efficient model deployment for edge devices. His work addresses fundamental challenges in biometric security by developing robust systems that can withstand presentation attacks while maintaining high accuracy across diverse conditions. He has made significant contributions to the field of synthetic data generation and utilization for improving face recognition systems, exploring how knowledge can be effectively transferred from synthetic to real-world domains. Analysis of George's recent publications reveals a clear research trajectory focused on solving practical challenges in face recognition. His work has evolved from fundamental eye tracking and gaze direction research in the early 2010s toward increasingly sophisticated face recognition systems addressing security vulnerabilities, efficiency constraints, and domain adaptation problems. A significant portion of his recent work explores the potential of synthetic data to overcome limitations in real-world training data, while also investigating how to bridge the gap between different face recognition modalities. His research demonstrates a strong emphasis on practical applications, particularly in resource-constrained environments where edge deployment is necessary. George has been actively involved in major biometrics competitions and challenges, including the FRCSyn Challenge and EFaR (Efficient Face Recognition) competition, contributing to community benchmarking efforts and advancing state-of-the-art solutions. His collaborative work spans multiple institutions globally, reflecting the international nature of biometrics research.

• Face Recognition
• Biometrics
• Computer Vision
• +۳ مورد دیگر

Alexander Unnervik is an active researcher in computer vision and biometrics with a focus on face recognition systems, synthetic data generation, and security of deep learning models. His work spans multiple prestigious conferences including CVPR, WACV, BIOSIG, and ECCV workshops. His research interests center around critical challenges in biometric systems, particularly addressing security vulnerabilities in face recognition systems through anomaly detection approaches for backdoored neural networks. He has made significant contributions to synthetic data generation for improving face recognition systems, organizing and analyzing multiple FRCSyn challenges that have become important benchmarks in the field. His work bridges theoretical security concerns with practical applications in biometric systems. Analysis of his publication trends reveals a strong progression from automotive applications (driver monitoring systems around 2019-2020) to specialized biometric security research (2022 onward). His recent work demonstrates increasing leadership in organizing major challenges in synthetic data for face recognition, with numerous high-impact publications in 2024 across multiple venues. The research shows a clear trajectory toward addressing security concerns in biometric AI systems while developing robust evaluation frameworks. Unnervik has established a strong collaborative network with leading researchers in biometrics, particularly working closely with Sébastien Marcel and other members of the international biometrics research community. His work appears in top-tier computer vision and biometrics venues, indicating recognition by the research community. His laboratory and team affiliations appear connected to major biometrics research initiatives, particularly those focused on synthetic data generation and security evaluation for face recognition systems. He has been instrumental in organizing the FRCSyn challenges, which have brought together researchers from multiple institutions worldwide to address critical problems in biometric security.

• Face Recognition
• Synthetic Data Generation
• Deep Learning
• +۲ مورد دیگر

Andrea Atzori is an active researcher in the field of computer science, with a primary focus on face recognition, synthetic data applications, and bias mitigation in AI systems. He has collaborated extensively with Gianni Fenu, Mirko Marras, and other scholars in his publications. Research Focus Face recognition systems Impact of synthetic data on model performance Gender and ethnicity bias analysis Accuracy-fairness trade-offs in AI Forensic applications of face recognition Publication Trends Since 2021, his work has centered on evaluating how synthetic data affects face recognition accuracy and fairness. Recent papers analyze balancing real/synthetic datasets, security-fairness trade-offs, and forensic AI applications.

Oliver Hohlfeldمشاهده پروفایل

استاد

• Network Security
• Network Measurements
• Internet Architecture
• +۳ مورد دیگر

Oliver Hohlfeld is a Professor at the University of Kassel where he leads the Distributed Systems Group . Prior academic appointments include professorships at Brandenburg University of Technology (heading the Computer Networks group) and RWTH Aachen University , with earlier work at TU Berlin/Deutsche Telekom Innovation Laboratories . He has served as visiting scholar at University of Wisconsin-Madison (Paul Barford's group) and holds a Dr. rer. nat. in Computer Science (2013) from TU Berlin under advisor Anja Feldmann. B.Sc. and M.Sc. in Computer Science from Darmstadt University of Applied Sciences, Institute Eurecom, and Darmstadt University of Technology Former Fraunhofer IGD researcher (2004-2006) working on telemedical network architectures His research interests focus on data-driven analysis of internet performance and security through empirical network measurement, psychological user studies, and machine learning. Key areas include DDoS defense mechanisms, TLS deployment analysis, social media interaction patterns, caching strategies, and internet protocol evaluation (HTTP/2, QUIC, BGP). Recent work examines cross-border information control circumvention through unconventional user reviews. Oliver has served on technical program committees for 25+ conferences including SIGCOMM'24 , NSDI'25 , and IMC'24 . His scientific leadership includes: 2022 IETF/IRTF Applied Networking Research Prize 2020 ACM Senior Member status 2018-2021 conference co-chair roles He leads major research projects : AIDOS: AI-based DDoS Mitigation at DE-CIX Internet Exchange (BMBF funded) DFG SFB 1053 MAKI: Multi-Mechanism Internet Adaptation (2017-2020) EU Horizon 2020 SSICLOPS: Secure Cloud Operations Internet Observatory Initiative

• Real-Time Networking
• Data Center Networks
• Industrial Networks
• +۳ مورد دیگر

Philip Diederich is a researcher at the Chair of Communication Networks at Technical University Munich (TUM), where he has been since 2021. He holds an M.Sc. in Electrical and Computer Engineering from TUM (2021) and a B.Sc. from the same institution (2019). His research focuses on real-time networking in diverse environments, including data centers, industrial networks, and wide area networks. Key interests include deterministic networks with hard/soft real-time guarantees, software-defined networking (SDN), and the integration of real-time systems with best-effort networks. Recent work explores topology effects on real-time network performance, resilience in time-sensitive networks, and affordable latency measurement setups. He collaborates on projects like integrating deterministic networking with 5G and developing hybrid models for TSN resource allocation. Philip co-advises student theses on topics like end-to-end scheduling in large-scale deterministic networks and has contributed to open research directions in network security and protocol synthesis.

• Semantic Communication
• Resource Allocation Mechanisms
• 6G Networks
• +۳ مورد دیگر

Alexander Griessel is a Researcher and PhD candidate at the Chair of Communication Networks (Prof. Wolfgang Kellerer) at the Technical University of Munich (TUM). He holds a B.Sc. (June 2020) and M.Sc. (December 2021) in Electrical and Computer Engineering from TUM. His research focuses on Resource Allocation Mechanisms in Semantic Communication , particularly addressing resilience and efficiency in 6G channels. He contributes to the Semantic Communication project and serves as a teaching associate for the Internet Kommunikation course. His work spans semantic resilience strategies , real-time video/image communication , and 6G network design . Recent research includes lossy coding techniques for semantic images, adaptive systems for challenging 6G environments, and non-invasive sensor solutions for infrastructure monitoring. His publications also explore cybersecurity (website fingerprinting) and reinforcement learning for network trace generation. Alexander actively supervises master's theses on topics like semantic symbol prioritization and reliability analysis in 5G/6G channels. He collaborates on TUM's 6G testbed initiatives and contributes to the 6G-life and Flexibility research networks. His interdisciplinary approach bridges communication theory with practical network implementations.

• Networked Control Systems
• Software-Defined Networking
• Cybersecurity
• +۳ مورد دیگر

Hasan Yagiz Özkan is a Scientific Staff member at the Chair of Communication Networks at Technische Universität München. His research focuses on networked control systems, software-defined networking, cybersecurity, and real-time communication protocols. He contributes to projects such as the ERC Network Flexibility initiative and the 6G Future Lab Bavaria. Recent work includes advancements in task-oriented scheduling for networked control systems and intrusion detection using machine learning. His publications span topics like bug resolution prediction in softwarized networks and age-of-information-aware frameworks. He has collaborated on papers addressing network reliability, software-defined radio implementations, and contrastive pretraining for cybersecurity. His inactive status may pertain to a specific role, but he remains active in academic research and publications.

• Federated Learning
• Edge Computing
• Mobile Computing
• +۴ مورد دیگر

Zimu Zhou is an Assistant Professor at Tsinghua University's School of Software, Department of Computer Science and Technology, specializing in federated learning, edge computing, and mobile systems. With an extensive publication record spanning from 2018 to 2025, Dr. Zhou has established themselves as a leading researcher in distributed machine learning systems. Dr. Zhou's research focuses on federated learning systems , mobile AI optimization , and spatial data processing . Their work addresses critical challenges in distributed machine learning including data heterogeneity, communication efficiency, privacy preservation, and practical deployment constraints. Recent research has expanded into federated large language models and advanced personalization techniques for mobile environments. Analysis of Dr. Zhou's publication trends reveals a strong emphasis on practical deployment of federated learning systems, with increasing focus on real-world applications in mobility, urban computing, and AIoT. Their work bridges theoretical advances with practical implementations, as evidenced by multiple publications in top-tier systems and AI conferences. Best Paper Award, MobiCom 2024 ACM SIGSPATIAL Best Paper Honorable Mention, 2022 Dr. Zhou has supervised several PhD students who have become active contributors in the field, including Xiaochen Li, Sicong Liu, and Yexuan Shi. Their research has been supported by multiple grants focused on edge intelligence and privacy-preserving distributed learning. Current projects include federated reasoning with large language models and resource-efficient AIoT systems. Dr. Zhou leads the Distributed Intelligence Lab at Tsinghua University, which focuses on building practical frameworks for decentralized machine learning across mobile and edge environments. The lab collaborates with industry partners to deploy federated learning solutions in real-world settings.

• Information Retrieval
• Natural Language Processing
• Social Media Analysis
• +۳ مورد دیگر

Marco Viviani is a researcher affiliated with University of Milano-Bicocca (Italy) and University of Torino (Candiolo, Italy), focusing on information retrieval , health informatics , and social media credibility . His work spans multi-dimensional relevance assessment generative AI applications misinformation detection privacy-aware systems Research highlights include ROMCIR workshops on reducing online misinformation, Web2Vec for structural content analysis, and LLM reasoning capabilities evaluation. Key collaborations with Gabriella Pasi, Rishabh Upadhyay, and Marinella Petrocchi appear across 103 publications from 2004-2025. His work integrates machine learning and knowledge graphs for healthcare applications, with recent focus on blockchain social media dynamics and privacy-utility trade-offs . He contributes to ECIR WI/IAT CLEF eHealth MDAI conferences and journals like IEEE Access and Frontiers in Artificial Intelligence .