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Peter Pal ZubcsekView profile

Senior Lecturer

• Social Networks
• Consumer Behavior
• Customer Relationship Management
• +5 more

Peter Pal Zubcsek serves as Senior Lecturer of Marketing at Tel Aviv University's Coller School of Management, previously holding an Assistant Professor position at University of Florida. His academic work bridges marketing, network science, and consumer psychology through rigorous quantitative analysis. His educational background includes: Ph.D. in Management from INSEAD M.Sc. in Informatics from Budapest University of Technology and Economics Zubcsek's research investigates how social network structures shape consumer behavior, with special focus on mobile advertising effectiveness, customer relationship management, and innovation diffusion. His work employs advanced network analysis to model consumer interactions and predict market responses. His publication trajectory from 2011-2017 reveals evolving expertise: starting with foundational network diffusion models (2011), progressing through mobile advertising frameworks (2016), and culminating in connected consumer intelligence systems (2017). This progression demonstrates increasing sophistication in integrating real-world network data with consumer behavior prediction. Key recognitions include: Journal of Interactive Marketing Best Paper Award (2016) MSI Research Grants totaling over $70,000 for mobile consumer behavior projects International Mathematical Olympiad silver medal (1998) He has secured significant research funding including MSI's $40,000 'Ideas Challenge' grant and leads the 'mLab' mobile research initiative, though specific student mentorship details remain undisclosed. His editorial role at Journal of Interactive Marketing underscores disciplinary leadership. The 'mLab' research initiative represents his current focus on mobile consumer behavior, leveraging collaborative frameworks to study real-time advertising response and device ecosystem interactions.

Cornelius PuschmannView profile

Professor

• Computational Communication Research
• Digital Media Usage
• Hate Speech
• +5 more

Cornelius Puschmann is a Professor of Communication and Media Studies at the University of Bremen's ZeMKI, leading the Digital Communication and Information Diversity (DCID) Lab. He has held affiliations with institutions including Zeppelin University, the Alexander von Humboldt Institute for Internet and Society, and the Leibniz Institute for Media Research / Hans Bredow Institute. His research focuses on computational communication, digital media usage, hate speech, and algorithmic impacts on digital communication. Current projects: Informed by Influencers? (INDI), Political Polarization and Individualized Online Information Environments (POLTRACK) Member of Deutsche Gesellschaft für Publizistik- und Kommunikationswissenschaft (DGPuK), European Communication Research and Education Association (ECREA), and International Communication Association (ICA) His recent publications explore topics such as alternative news consumption, political polarization, and communicative AI. He has also contributed to open-source methodologies like the RPC-Lex dictionary for analyzing right-wing populist discourse. Notable affiliations include visiting scholar roles at the Oxford Internet Institute, Berkman Klein Center for Internet and Society, and University of Amsterdam's Department of Media Studies.

• Eye-tracking
• Virtual Reality
• Extended Reality (XR)
• +7 more

Eakta Jain is an Associate Professor in the Department of Computer & Information Science & Engineering at the University of Florida's College of Engineering. Her research centers on human-computer interaction with a specialized focus on eye-tracking technologies, virtual reality, and privacy-preserving techniques in immersive environments. With over 15 years of sustained academic contributions, she has established herself as a leading researcher in gaze analysis and its applications across multiple domains. Dr. Jain's research interests span eye-tracking, virtual reality, extended reality (XR), privacy in immersive technologies, human-computer interaction, computer vision, and animation. Her work demonstrates a consistent trajectory from fundamental gaze analysis techniques to practical applications addressing critical privacy concerns in emerging technologies. She has made significant contributions to understanding how gaze data can be used to enhance user experience while simultaneously developing methods to protect user privacy in these systems. Analysis of her recent publications reveals a strong focus on privacy challenges in XR environments, with particular attention to gaze data protection, face-swapping technologies, and the psychological impacts of continuous monitoring. Her research bridges theoretical insights with practical implementations, often resulting in novel algorithms and frameworks that address real-world problems in immersive technologies. The interdisciplinary nature of her work connects computer science with cognitive psychology and human factors research. Dr. Jain has received recognition through publications in top-tier venues including IEEE Transactions on Visualization and Computer Graphics, ACM Transactions on Applied Perception, and the Symposium on Eye Tracking Research and Applications. Her work has been influential in shaping the discourse around privacy in immersive environments and has practical implications for the development of ethical XR systems. She actively mentors students and collaborators, with several junior researchers appearing as co-authors on her publications. Her research group appears to focus on the intersection of computer vision, graphics, and human-centered computing, with projects spanning from fundamental gaze analysis to applied privacy-preserving techniques in commercial VR systems. Current projects suggest strong industry connections and potential grant funding supporting her privacy-focused XR research.

Andreas BeyerView profile

Professor

• Aging Research
• Proteomics
• Systems Biology
• +5 more

Prof. Dr. Andreas Beyer holds a faculty position at the University of Cologne, affiliated with the Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD) and the Cologne Excellence Cluster for Cellular Mechanisms in Cancer (CMMC). His research focuses on systems-level analysis of aging processes in humans and model organisms, integrating genomic, proteomic, and computational approaches. Key interests include understanding how genetic variation influences protein networks, developing algorithms for big data analysis, and exploring epigenetic mechanisms related to longevity. Research projects include studying age-associated changes in transcriptional elongation, molecular networks in kidney disease, and the impact of dietary restriction on aging. His group develops tools for proteomics and systems biology, such as methods for analyzing limited proteolysis data and single-cell resolution imaging. Collaborative efforts emphasize translational research in aging-related diseases and drug discovery. Prof. Beyer’s work spans computational biology, molecular genetics, and translational medicine. Notable contributions include identifying epigenetic changes linked to longevity and developing predictive models for age-related disease progression. His lab’s projects often involve multi-omics integration and network-based analyses to uncover disease mechanisms. His research has implications for personalized medicine, cancer biology, and interventions to extend healthspan. Current efforts include optimizing drug combinations targeting aging processes and advancing proteomic technologies for clinical applications.

• Business Informatics
• Artificial Intelligence
• Business Intelligence
• +10 more

Prof. Dr. Harald Ritz serves as Professor of Practical Computer Science, especially Business Informatics, at the Technical University of Central Hesse (THM) within the Department of Mathematics, Natural Sciences and Computer Science since 2003. He holds leadership roles as Chair of Examination Committees for B.Sc. and M.Sc. Business Information Systems and Spokesperson for the MNI department in the Business Informatics Working Group (AKWI). His educational background includes a Diplom in Business Informatics (Dipl.-Wirtsch.-Inform.) and doctorate (Dr. rer. pol.) from the Technical University of Darmstadt, following professional experience at SAP SI AG and a professorship at Heilbronn University of Applied Sciences. Ritz's research centers on AI-driven digital transformation for data-driven enterprises, with focus on the “Data to Decision” value chain encompassing Framing, Allocation, Analytics, and Preparation phases. His work integrates business intelligence, data warehousing, machine learning, and SAP ecosystems to address challenges in SME digitalization, operational IT management, and educational technology. Current projects emphasize AI applications in higher education, including intelligent tutoring systems and automated feedback mechanisms. Analysis of his 15 most recent publications reveals a consistent trajectory toward applied AI solutions in business contexts, particularly in intelligent chatbots for educational support, financial trading algorithms, and cloud-based data infrastructure. The research demonstrates increasing integration of no-code platforms, real-time analytics, and domain-specific AI applications across logistics, banking, and procurement sectors. No scientific awards were documented in the source materials. Professor Ritz actively supervises academic development through bachelor’s and master’s theses, doctoral research, and collaborative projects. Current initiatives include the “Winfy” AI chatbot (v4.0, 2025), AI-based feedback systems for educational content (Freiraum 2025 grant), the frits intelligent tutoring project with Prof. Kammer, and doctoral research on AI adoption in SMEs. His work bridges theoretical research with practical implementation in SAP environments and cloud platforms. He operates within THM’s MNI department infrastructure, collaborating through the Business Informatics Working Group (AKWI) and contributing to the Digital Classroom communication platform for online education.

• Machine Learning
• Optimization
• Federated Learning
• +8 more

Peter Richtarik is a Professor at the King Abdullah University of Science and Technology (KAUST), specializing in Machine Learning, Optimization, and Federated Learning. He actively teaches courses such as Stochastic Gradient Descent Methods and mentors PhD and MS students like Konstantin Burlachenko, Kai Yi, and Lukang Sun. His research spans distributed optimization, parameter-efficient fine-tuning, and theoretical frameworks for non-convex and non-smooth problems. Recent work includes 2025 contributions to Bernoulli-LoRA (theoretical PEFT) and Gluon (LMO-based optimizers). He co-developed Thanos (block-wise pruning) and BurTorch (CPU-optimized training framework). His publications focus on communication efficiency ( ATA , LoCoDL ), differential privacy ( DP-RBCD ), and stochastic proximal methods. Richtarik received the Charles Broyden Prize for his work on quasi-Newton methods. He frequently presents at workshops like MLSS in Senegal and FLOW seminars.

• Biometrics
• Computer Vision
• Machine Learning
• +4 more

Marina L. Gavrilova is a Professor at the University of Calgary, Canada. Her research focuses on biometric systems, computer vision, and machine learning with an emphasis on multimodal recognition and security applications. She has authored numerous publications in top journals and conferences, contributing to advancements in fields like emotion-aware de-identification, generative adversarial networks, and ethical AI frameworks in healthcare. Her work spans social behavioral biometrics, gait recognition, masked face recognition, and aesthetic-based person identification. Key contributions include frameworks for ethical AI in care systems, fusion algorithms for multi-biometric systems, and innovations in visual and audio signal processing. Collaborations with experts like Osvaldo Gervasi, Jon G. Rokne, and Padma Polash Paul highlight her interdisciplinary approach. Publications emphasize practical applications such as privacy-preserved biometrics, emotion detection from social media, and adaptive systems for template aging. Despite no explicit mention of grants or labs, her extensive co-author network and frequent citations indicate significant academic influence.

• Proteomics
• Bioinformatics
• Cancer Diagnostics
• +3 more

Young-Hee Lee is a Ph.D. candidate and Lecturer at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design and the Institute for Communications and Navigation. Her research focuses on proteomics, with emphasis on protein citrullination dynamics, phosphoproteomics in cancer diagnostics, and advanced mass spectrometry techniques. Her recent work includes the development of high-throughput proteomic workflows for ischemic stroke biomarker discovery and the application of deep learning to enhance citrullination identification. She contributes to methodological innovations in peptide extraction and single-cell proteomics sensitivity. Lee is part of the Chair of Communication and Navigation led by Prof. Christoph Günther, located at Theresienstraße 90, Munich. Her research bridges computational biology and biochemical analysis, with applications in cancer, neuroscience, and viral proteomics.

• Computer Science
• Machine Learning
• Health Monitoring Systems
• +7 more

Muhammad Waqas is a researcher affiliated with COMSATS University Islamabad , where he holds a position in the Department of Meteorology under the School of Applied Sciences and Humanities . His academic collaborations span institutions like Bahria University, National University of Technology, and University of Bahrain, indicating a multidisciplinary approach. Research interests include Mechanisms for integrating fuzzy logic and machine learning in health monitoring Application of deep learning to medical imaging and clinical diagnostics Development of smart sensors for wearable technology in biomechanics Analysis of social media data for public health surveillance and sentiment analysis Investigation of digital citizenship and ICT leadership in educational contexts Trends in his 15 most recent publications (2025-2024) reveal a focus on medical diagnostics (e.g., monkeypox, breast cancer), smart infrastructure (e.g., sensor placement, structural health monitoring), and social media analytics for health and behavioral insights. These works leverage machine learning , fuzzy systems , and multi-objective optimization .

Thomas LudwigView profile

Professor

• High-Performance Computing (HPC)
• Data Storage Systems
• Energy Efficiency in Computing
• +3 more

Prof. Dr. Thomas Ludwig is the Director of the German Climate Computing Center (DKRZ) and a Professor at the Universität Hamburg. He holds a doctoral degree and habilitation from the Technische Universität München, with expertise in High-Performance Computing (HPC), energy efficiency, and data storage systems. His research focuses on optimizing parallel systems, storage technologies, and computational efficiency for climate science applications. He leads projects like AIMES and PeCoH, advancing HPC storage and energy-aware computing. Education: Doctoral degree and habilitation from TU München (1988–2001). Chair in Parallel Computing at Universität Heidelberg (2001–2009). Research Interests: HPC, data reduction techniques, energy-efficient systems, parallel I/O optimization, and climate modeling infrastructure. Recent Research Trends: His work emphasizes storage system efficiency, machine learning in HPC, and convergence between HPC and Big Data. Key contributions include frameworks for portability (Vecpar), automated performance tools, and energy-aware storage solutions. Awards: Some publications received recognition, e.g., a Best Paper award in 2014 for work on energy efficiency. However, no personal awards are explicitly listed. Advising & Grants: Supervised numerous theses in HPC, I/O optimization, and energy efficiency. Leads major projects funded by national and international initiatives. Labs/Teams: Heads the DKRZ team providing supercomputing and data management for climate research, collaborating with global institutions like the University of Hamburg and European research networks.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +4 more

Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His research focuses on AI, machine learning, and data mining, with applications in medicine and finance. He leads projects in real-time clinical monitoring, anomaly detection, and time-series analysis of EHR data. He has advised numerous PhD and MS students, including notable alumni now at Amazon, DeepMind, and Microsoft. Research interests include reasoning under uncertainty, optimization, and AI-driven medical decision support. Current grants include NIH funding for AI in renal therapy and clinical monitoring. He has published widely in top venues like ICML, NeurIPS, and journals such as Artificial Intelligence in Medicine. His work on conditional outlier detection earned the Homer Warner Award (AMIA 2010). He teaches machine learning and advises on interdisciplinary AI projects.

Michael NothnagelView profile

Professor

• Statistical Genetics
• Genetic Epidemiology
• Population Genetics
• +5 more

Michael Nothnagel is a Professor at the University of Cologne, where he leads the Department of Statistical Genetics and Bioinformatics within the Cologne Center for Genomics (CCG). His work spans statistical genetics, genetic epidemiology, and forensic genetics, focusing on methodological development and large-scale genomic data analysis. His research interests encompass theoretical and applied statistical genetics, with emphasis on human genetic diversity, disease etiology, and forensic applications. Key areas include Y-chromosomal phylogeography, genome-wide association studies for complex diseases, development of statistical methods for variant interpretation, and forensic marker optimization. His group leverages next-generation sequencing data and specialized forensic markers to address questions in population history, disease mechanisms, and identification systems. Recent publications reveal a strong focus on computational approaches to genetic analysis, including spatial frequency interpolation for haplogroup mapping, polygenic risk score applications for behavioral traits, and advanced methods for variant classification. His work demonstrates consistent integration of statistical theory with practical applications in medical and forensic genetics, often through international collaborations like the VISAGE Consortium. Nothnagel maintains active involvement in the Cologne Center for Genomics, contributing to seminars and collaborative projects including the upcoming 34th International Genetic Epidemiology Society meeting. His research group operates at the intersection of computational biology and medicine, with particular strengths in handling complex genomic datasets and developing novel analytical frameworks for genetic epidemiology.

• Hybrid Intelligence
• Human-centered Generative AI
• Federated Learning
• +1 more

Florian Leiser is a Professor at the Chair of Information Infrastructures (led by Prof. Dr. Ali Sunyaev) at Technical University of Munich's Heilbronn campus. His research focuses on human-AI collaboration, privacy-preserving algorithms, and explainability in machine learning systems. Current research areas include Hybrid Intelligence, Human-centered Generative AI (LLMs), Federated Learning, and Health Information Systems Recent publications demonstrate expertise in Explainable AI for medical imaging LLM hallucination detection Federated learning architectures Human-in-the-loop systems Healthcare data applications He contributes to teaching through Human-Centered Artifact Design courses Collaborative teaching roles in machine learning Supervising student projects

Ralf KriegelView profile

Professor

• Sports Management
• Fitness Facility Leadership
• Human Resources Management
• +3 more

Prof. Ralf Kriegel holds a part-time professorship (0.5 PE) for Sports Science at the German University of Health and Sport (DHGS) in Ismaning, Faculty of Sport and Training Sciences. He concurrently serves as COO and authorized signatory at functional training movement GmbH (FTM), Munich, and previously held leadership roles in fitness management including Management Board membership at ACISO/ELEMENTS and regional manager at Migros Freizeit Deutschland GmbH/ELEMENTS. Education includes a PhD (2004) from the University of the Federal Armed Forces Munich on sports recommendations via internet applications, and a Diploma in Sports Science (1994) from the same institution. Research focuses on fitness facility management, human resources in sports sectors, and marketing strategies for fitness industries. His work bridges academic research (e.g., diabetes and exercise studies) with practical business applications in gym operations and client retention. Professional experience spans over 20 years in fitness industry leadership roles combined with academic teaching. Publications span strategic management in fitness, exercise physiology for chronic diseases, and innovative digital tools for sports recommendations.

• Cyber Security
• Cryptography
• Steganography
• +6 more

Rainer Böhme is a Professor at the University of Münster, Germany. His research focuses on cyber security, cryptography, steganography, digital forensics, and blockchain technology. He has made significant contributions to understanding cyber risk quantification, central bank digital currencies (CBDCs), and the socio-technical challenges in cryptocurrency systems. Key areas of research include steganalysis (analysis of hidden data in digital media), forensic techniques for neural network inference pipelines, and the economic implications of cyber insurance. He actively contributes to conferences like the Financial Cryptography Workshops (FC) and the Workshop on Information Hiding and Multimedia Security (IH). Böhme's work bridges theory and practice, addressing real-world issues such as privacy in CBDCs, adversarial attacks on AI systems, and the role of law enforcement in cryptocurrency markets. His recent studies explore the security implications of image orientation in JPEG files and the detection of privileged parties in blockchain transactions. He has collaborated with institutions like the University of Vienna and ETH Zurich, and his research has been published in top-tier venues such as IEEE Transactions on Information Forensics and Security and the ACM Conference on Computer and Communications Security (CCS).