جست‌وجوی استاد و پژوهشگر

Cynthia Liemمشاهده پروفایل

دانشیار

• Multimedia Computing
• Human-Centered AI
• Music Information Retrieval
• +۱۰ مورد دیگر

Cynthia Liem is an Associate Professor at the Multimedia Computing Group, part of the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology (TU Delft). Her work uniquely bridges computer science and music, leveraging her dual expertise in both domains. Education: BSc and MSc in Media and Knowledge Engineering (Computer Science), TU Delft (2007, 2009); PhD, TU Delft (2015); BMus and MMus in Classical Piano Performance, Royal Conservatoire, The Hague (2009, 2011). Her research centers on human-centered multimedia analysis, with two core themes. First, she investigates how to algorithmically surface information in large multimodal archives that users would not discover through conventional search or recommendation systems, aiming to broaden user perspectives and help uncover underutilized digital content. Second, she focuses on validation and validity in data science, particularly in contexts where human responses are implicit and hard to measure, drawing inspiration from psychometric validity in the social sciences. Her work emphasizes real-world applicability, especially in music and cultural heritage domains. The two recent publications highlight her engagement with cutting-edge AI challenges: one explores algorithmic recourse dynamics, while the other examines evolutionary algorithms in adversarial example generation, indicating strong interests in robustness, explainability, and human-aligned AI systems. Notable scientific recognitions include: Google Anita Borg Scholarship (2008) Google European Doctoral Fellowship (2010) Top-5 nominee, New Scientist Science Talent Prize (2016) Cynthia Liem has been involved in several significant research projects, including the H2020 TROMPA project on crowd-powered enrichment of public-domain music, an NWO-KIEM project on musician well-being, an NWO-Veni project on perspective-broadening in recommender systems, and an ERASMUS+ initiative on big data in psychological assessment. She collaborates with key institutions such as the National Library of The Netherlands and CDR/Muziekweb. Beyond academia, she remains active as a performing pianist, notably in the Magma Duo, which won first prize at the A Feast of Duos competition and was part of the Dutch Classical Talent Tour, leading to a national concert tour. There is no indication of lab or research team leadership beyond project involvement, though her role as an Associate Professor suggests supervision of students and researchers. Future work appears directed toward socially responsible AI, improved validation frameworks, and interdisciplinary applications of music and data science.

Marten van Dijkمشاهده پروفایل

استاد

• Computer Security
• Cryptography
• Secure Processor Design
• +۷ مورد دیگر

Dr. Marten van Dijk is a Full Professor in the Computer Security department at Vrije Universiteit Amsterdam (VU) since 2022 and a Group Leader for Computer Security at CWI since 2020. He also holds a Gratis Full Research Professor position at the University of Connecticut's ECE Department since 2020. Previously, he served as Associate and Full Professor at the University of Connecticut and held research roles at MIT CSAIL, RSA Laboratories, and Philips Research. PhD in Mathematics (1997, Eindhoven University of Technology) M.S. in Mathematics (Cum Laude, 1993) M.S. in Computer Science (Cum Laude, 1991) His research focuses on foundational computer security problems using cryptographic principles, including secure processor design, oblivious computation, and privacy-preserving machine learning. Notable contributions span Physical Unclonable Functions (PUFs), Aegis secure processor architecture, and oblivious RAM protocols. 15+ publications in 2023-2025 address topics like PUF cryptanalysis, differential privacy in federated learning, and Byzantine fault tolerance Key journals: IEEE Transactions on Computers, Journal of Cryptology, ACM CCS Conference Award highlights include: IEEE Fellow (2022) for secure processor design and encrypted computation IEEE Technical Achievement Award (2023) Intel Test of Time Award (2022) ACM CCS Best Paper (2013) A. Richard Newton Technical Impact Award (2015) His technical leadership spans hardware security (blu-ray error correction codes), cryptographic protocol design, and machine learning privacy frameworks. Current projects focus on secure processors with hardware-enforced isolation and differential privacy optimization.

Syed Hassan Shahمشاهده پروفایل

استاد مدعو

• Wireless Networks
• Cyber-Physical Systems
• Smart Cities
• +۷ مورد دیگر

Dr. Syed Hassan Shah is a distinguished adjunct faculty member at California State University, Fullerton's Department of Computer Science, where he teaches graduate-level courses. Concurrently, he serves as a Wi-Fi connectivity subject matter expert and Product Director for Short Range Technologies at Quectel Inc., and previously held roles as Product Manager for Mobile & Compute Connectivity at Qualcomm Inc., focusing on Mi-Fi, CPE, and UWB technologies. Ph.D. in Computer Science & Engineering, Kyungpook National University, South Korea (2013-2017) BS in Computer Science, Kohat University of Science & Technology, Pakistan (2007-2012) Dr. Shah's research spans interdisciplinary domains in Wireless Communications , Cyber-Physical Systems , and Smart Cities , with significant contributions to Vehicular Networks , Internet of Things , and Future Internet Architectures . His recent publications demonstrate expertise in Neural Networks , Optimization Theory , and Security & Privacy applications. Scientific recognition includes: Qualcomm Innovation Award (2016) IEEE Senior Member (2018) ACM Distinguished Speaker (2018) Best Research Contribution Award (Brain Korea, 2016) Multiple travel grants from ACM and IEEE KNU Honors Scholarship (2013) Dr. Shah actively contributes to academic governance as: Editorial Board Member for 60+ special issues in top-ranked journals TPC Member for 100+ international conferences including IEEE Globecom and ACM MobiHoc IEEE Vehicular Technology Society Board appointee (2018-2019)

Erik-Jan van Kesterenمشاهده پروفایل

استادیار

• Data Science
• Computational Social Science
• Bayesian Statistics
• +۷ مورد دیگر

Dr. Erik-Jan van Kesteren is an Assistant Professor in the Methodology and Statistics department at Utrecht University , where he leads the ODISSEI Social Data Science (SoDa) team. His work bridges statistical methodology with computational social science, with a focus on synthetic data, Bayesian inference, and open research infrastructure. Current research initiatives include: Developing privacy-preserving synthetic data frameworks Improving computational approaches to structural equation modeling Addressing geospatial sampling bias in volunteer-collected datasets Exploring text-based personality computing and language association tools He maintains the department's compute server infrastructure and contributes to open-source projects like ArtScraper and osmenrich. Contact via e.vankesteren1@uu.nl or erikjanvankesteren@pm.me

• Machine Learning
• Artificial Intelligence
• Ethics
• +۷ مورد دیگر

Dr. ir. Cynthia C.S. Liem is an Associate Professor in the Multimedia Computing department at Delft University of Technology, specializing in ethical and reliable AI frameworks. Her work bridges machine learning, human-computer interaction, and societal impact. Academic Rank: Associate Professor Department: Multimedia Computing School: Electrical Engineering, Mathematics and Computer Science Liem’s research focuses on Explainable AI , Algorithmic Transparency , and AI Ethics , emphasizing fairness in machine learning and societal implications of autonomous systems. Recent publications explore adversarial testing, energy-constrained counterfactuals, and critiques of unscientific AGI claims. Her most recent articles highlight trends in autonomous vehicle testing , AI explainability , and ethical algorithmic design , leveraging techniques like conformal prediction and differential evolution . Scientific Awards : SBFT 2023 Best Paper Award TU Delft Education Fellow WWW 2018 Challenge Winner 2024 Women in AI Netherlands Diversity Leader Award Liem serves as an advisor for DUO’s research ethics audits and frequently contributes to public discourse on AI’s societal role, appearing in national media outlets like Delta and Trouw . She also leads datasets on music representation learning and algorithmic recourse.

Zerrin Yumakمشاهده پروفایل

استادیار

• Virtual Humans
• Social Robotics
• Affective Computing
• +۳ مورد دیگر

Zerrin Yumak is an Assistant Professor at the Department of Information and Computing Sciences, Faculty of Science, Utrecht University, specializing in virtual humans and social robotics. She leads Task 3.2: Embodiment and Physical Interaction in the RAGE project. Her research focuses on Social and emotional behavior analysis for virtual characters Machine learning applications in motion capture data Constrained motion graph search algorithms Lip-sync and gesture animation generation Real-time multimodal interaction systems Notable projects include the Virtual Human Controller asset for Unity 3D, integrated with BiP Media and Randstad applications. Recent work spans 2025: SemGes for semantics-aware gesture generation 2024: ProbTalk3D emotion-controllable facial animation 2023: Diffusion model-based facial animation (Facediffuser) 2020: Music-driven expressive gesture generation She contributes to workshops like GENEA (2021-2022) and MASSXR (2025), advancing non-verbal behavior modeling for embodied agents.

Alan Hanjalicمشاهده پروفایل

استاد

• Multimedia Information Retrieval
• Affective Video Content Analysis
• Recommender Systems
• +۵ مورد دیگر

Alan Hanjalic is Professor of Computer Science at Delft University of Technology, holding the Antoni van Leeuwenhoek Chair and serving as Head of the Multimedia Computing Group and Head of the Department of Intelligent Systems within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). Recognized as an IEEE Fellow and AAIA Fellow, he has been a driving force in multimedia information retrieval for over two decades. Education: 1995: Dipl.-Ing. in Electrical Engineering, Friedrich-Alexander University Erlangen-Nuremberg, Germany (specialization in motion estimation for video compression) 1999: PhD in Computer Science, Delft University of Technology (specialization in video indexing and search) Research Interests: Prof. Hanjalic’s research spans the broad domain of multimedia information retrieval , with pioneering contributions to affective video content analysis . By modeling videos as trajectories in the arousal-valence emotional space, he enabled indexing and retrieval based on emotional impact, laying the groundwork for an entire subfield. His group further advances socially-responsible recommender systems , cross-modal retrieval and generation , graph learning , and network influence prediction . Recent work also delves into fairness in recommendation and deep-learning-based side-channel security . Publication Trends: His latest publications (2024–2025) showcase a diversification into fairness and reproducibility in recommender systems, security-oriented deep learning for side-channel analysis, and advanced metrics for influence prediction in complex networks. These papers reflect both foundational algorithmic contributions and critical validation studies that strengthen the reliability of machine-learning approaches across multimedia and security domains. Scientific Recognition: IEEE Fellow (2016) – for pioneering contributions to multimedia information retrieval AAIA Fellow – acknowledging outstanding impact in artificial intelligence and multimedia Named Antoni van Leeuwenhoek Professor – university-wide recognition of research excellence Education, Mentorship & Leadership: Over 25 years at TU Delft, Prof. Hanjalic has served as lecturer, course coordinator, and chair of the Computer Science Board of Studies. He has promoted more than 30 PhD students and held leadership roles such as: Head, Intelligent Systems Department (2018–present) Member, EEMCS Career Development Committee Chair, TU Delft central appointment advisory committee for full professors Management team member, 4TU.NIRICT (Netherlands Institute for Research on ICT) TU Delft representative, ICT Research Platform Netherlands (IPN) Laboratory & Research Environment: He founded the Delft Multimedia Information Retrieval (DMIR) Lab , which evolved into the current Multimedia Computing (MMC) section . Under his guidance, the group has led national (FES, BSIK) and European Union projects—including a Network of Excellence—advancing responsible and human-centered multimedia technologies.

Mahdi Khodadadzadehمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Process Mining
• +۹ مورد دیگر

Mahdi Khodadadzadeh is an Assistant Professor in the Department of Geo-information Processing, specializing in machine learning, data mining, and geospatial analysis. His work bridges traditional and deep learning methods with applications in mineral exploration, environmental modeling, and circular economy initiatives. Research outputs include novel cross-validation techniques for geospatial machine learning (Spatial+, dissimilarity-adaptive methods), hyperspectral mineral mapping for ore characterization, and hybrid models combining artificial neural networks with optimization algorithms. He actively contributes to open-access datasets and methodologies, particularly in drill-core analysis and multi-source data fusion. Collaborations with researchers like R. Zurita-Milla and R. Gloaguen are evident through co-authored publications and shared datasets. His work impacts domains such as mineral exploration, geospatial modeling, and sustainable resource management, with a focus on robust statistical evaluation frameworks and spectral-spatial analysis.

Estefanía Talavera Martínezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Estefanía Talavera Martínez is an Assistant Professor specializing in Datamanagement & Biometrics , with research spanning artificial intelligence, computer vision, and health informatics. Her work addresses surveillance, emotion recognition, and egocentric data analysis.

• Deception detection
• Legal psychology
• Human resilience
• +۶ مورد دیگر

Bennett Kleinberg is an Associate Professor in Behavioural Data Science at the Department of Methodology and Statistics at Tilburg University (The Netherlands) and the Department of Security and Crime Science at University College London (UK). His academic journey includes a PhD from the Department of Psychology at the University of Amsterdam and a previous position at the UCL Dawes Centre for Future Crime. Professor Kleinberg's research revolves around the interplay between psychological research and computational methods to study human behaviour. His work addresses two key questions: How can computational methods enhance our understanding of the human mind and behaviour? And how can psychological research methods inform our understanding of computational model behaviour? His substantive research interests include deception detection and legal psychology, human resilience, and machine behaviour. In the Computational Psychology and Computational Methods (CPCM) Lab, his team collects data through psychological experiments and applies techniques from natural language processing, adversarial machine learning, and statistical modeling to better understand human behavior. His recent publications (2023-2025) demonstrate a strong interdisciplinary focus, spanning psychology, computer science, criminology, and AI research. Key trends include the application of computational methods to deception detection, analysis of large language models, and examination of human-AI interactions. His work often bridges theoretical psychological concepts with practical computational approaches, resulting in publications in high-impact journals across multiple disciplines. Scientific publications that use promotional language receive more citations Verbal deception detection enhanced through adversarial attacks Computational text analysis for identifying embedded lies Analysis of cognitive phenomena in large language models Applications of NLP to legal and security contexts Professor Kleinberg actively supervises students through limited positions to ensure close mentorship quality. His teaching responsibilities include the joint BSc in Data Science and the innovative MSc programme 'AI for Psychological Research.' The Computational Psychology and Computational Methods Lab offers structured pathways for PhD students, thesis projects, and research internships (minimum 6 months), with all projects required to align with the lab's core focus on computational methods to study human behavior and psychology.

Vicente Alarcon-Aquinoمشاهده پروفایل

استاد

• Cybersecurity
• Network Monitoring
• Anomaly Detection
• +۷ مورد دیگر

Dr. Vicente Alarcon-Aquino is a Professor in the Department of Computing, Electronics, and Mechatronics at Universidad de las Americas Puebla (UDLAP), Mexico. He received his Ph.D. and D.I.C. degrees in Electrical and Electronic Engineering from Imperial College London in 2003. He previously served as department head from October 2012 to June 2018 and spent a research stay at King's College London in 2017. Dr. Alarcon-Aquino is a Senior Member of IEEE, a Level I member of the Mexican National System of Researchers (SNI), and a Fellow of the Mexican Academy of Sciences. His educational background includes: Ph.D. and D.I.C. in Electrical and Electronic Engineering, Imperial College London, UK (2003) Dr. Alarcon-Aquino's research focuses on cybersecurity, network monitoring, anomaly detection, wavelet analysis, and machine learning. His work spans theoretical foundations to practical applications in network security, with significant contributions to intrusion detection systems, cryptographic techniques, and machine learning approaches for security applications. He has developed innovative methods combining wavelet transforms with neural networks for various security and signal processing applications. His scholarly contributions include over 180 research articles in refereed journals and conference proceedings, a book on MPLS networks, and numerous citations. As an editor, he serves as Associate Editor for IEEE Access Journal and as Academic & Section Editor for PeerJ Computer Science, focusing on Security & Privacy. Notable professional recognitions include: Senior Member of IEEE Mexican National System of Researchers (SNI Level I) Member of the Mexican Academy of Sciences Dr. Alarcon-Aquino has supervised over 70 theses, including 8 Ph.D. dissertations, 15 Master's theses, and more than 37 Bachelor's theses. His supervision spans topics including network intrusion detection, information security, encryption algorithms, wavelet-based signal processing, neural networks, EEG signal processing, and biometric cryptosystems. He has hosted international research students from institutions including the Polytechnic University of Madrid and Kiel University of Applied Sciences. His research group at UDLAP focuses on developing advanced security solutions for modern network environments, with particular emphasis on applying machine learning techniques to cybersecurity challenges. Current projects include blockchain-based security solutions, federated learning approaches for intrusion detection, and advanced anomaly detection systems for IoT environments.

Ahmed Elazabمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۷ مورد دیگر

Ahmed Elazab serves as an Associate Researcher at Shenzhen University's School of Biomedical Engineering since January 2021, following a Postdoctoral Research Fellowship at the same institution from January 2018 to April 2020. He holds a Ph.D. in Pattern Recognition and Intelligent Systems from the Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences (2017). Education Ph.D. in Pattern Recognition and Intelligent Systems, Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences, China (2017) Research Focus Dr. Elazab's work centers on machine learning and deep learning applications in biomedical contexts, with specialized expertise in medical image analysis , brain anatomy analysis , and computer-aided diagnosis . His research integrates computer vision, bioinformatics, and data science to develop AI-driven solutions for complex medical challenges including neurodegenerative diseases and infectious outbreaks. Publication Trends Analysis of his recent publications (2020-2023) reveals a concentrated focus on deep learning for medical diagnostics, particularly in Alzheimer's disease staging from MRI data and COVID-19 detection from X-ray images. His work consistently incorporates domain-specific knowledge into neural architectures, with growing emphasis on generative models for medical image segmentation and vaccine development. Cross-cutting themes include handling multi-modal biomedical data and addressing real-world clinical constraints. Scientific Recognition Active Academic Editor for PeerJ Computer Science with 1,600 contribution points Reviewer for prestigious international journals across computer science and biomedical domains Author/co-author of over 80 peer-reviewed publications Academic Engagement Dr. Elazab maintains significant scholarly involvement through editorial work at PeerJ Computer Science, where he has handled manuscripts on deep learning applications in medical imaging since 2020. His extensive publication record demonstrates consistent research productivity, though specific grant funding or student supervision details are not documented in available sources. He contributes to multiple subject areas including Artificial Intelligence, Bioinformatics, and Computational Biology. Research Environment As part of Shenzhen University's School of Biomedical Engineering, Dr. Elazab operates within a multidisciplinary research ecosystem focused on AI-driven medical solutions. His work intersects with ongoing initiatives in medical image computing and computational diagnostics, leveraging institutional resources for biomedical data analysis without specified laboratory affiliations.