جستجوی استاد راهنمای دانشگاهی

Felix Friedrich is a researcher at the Technical University of Darmstadt, Department of Computer Science, focusing on AI ethics, fairness, and safety in generative models. He collaborates extensively with Professor Kristian Kersting and other researchers in the AI field, contributing significantly to understanding and mitigating biases in text-to-image generation systems and multilingual AI models. His research interests center on ethical AI development, with particular emphasis on identifying and addressing gender stereotypes, cultural biases, and safety concerns in generative models. Friedrich's work bridges technical AI development with societal implications, examining how language and cultural contexts influence model behavior across different populations. His approach combines rigorous empirical evaluation with practical mitigation strategies to create more responsible AI systems. Friedrich's publication record shows a strong trend toward addressing critical challenges in AI safety and fairness, particularly in multimodal and multilingual contexts. His recent work develops benchmarks and evaluation frameworks to systematically assess issues like cross-linguistic safety gaps, cultural bias magnification, and emotional recognition accuracy in synthetic data. These contributions provide valuable tools for the research community to build more equitable and reliable AI systems. His work has contributed to several collaborative research initiatives focused on AI safety and ethics, working with international teams to develop comprehensive evaluation frameworks and mitigation strategies for emerging AI technologies. Friedrich's research has practical implications for AI developers seeking to create more responsible systems that account for diverse cultural contexts and user needs.

• AI Ethics
• Fairness in AI
• Text-to-Image Generation
• +۵ مورد دیگر

Christian Pölitz is a researcher specializing in artificial intelligence and machine learning with current affiliation at Microsoft Research. Previously associated with Dortmund University of Technology where he completed his PhD in 2016, his research bridges theoretical machine learning with practical applications in programming assistance and data manipulation. His publication record demonstrates sustained contributions to the field spanning over 15 years. Dr. Pölitz's research focuses on the intersection of AI systems and human workflows, particularly examining how large language models can effectively assist with data-centric tasks and programming activities. His recent work (2022-2025) investigates critical aspects of LLM evaluation, human-AI collaboration in programming contexts, and the development of benchmarks for natural language interfaces to data tools like Excel. This represents an evolution from his earlier work (2010-2017) which centered on domain adaptation, topic modeling, and semantic relatedness. Analysis of his publication trends reveals a strategic shift toward contemporary AI challenges, with significant contributions to understanding how generative AI transforms knowledge work. His collaborations with Microsoft researchers including Advait Sarkar, Ben Zorn, and Andrew Gordon indicate his integration into teams exploring the practical implications of AI 'copilots' in professional environments. His work often features in top-tier venues including AAAI, NAACL, EMNLP, and IEEE/ACM conferences. While specific awards aren't documented in the provided information, his consistent publication record in prestigious venues demonstrates recognition within the AI research community. His collaborative approach is evident through numerous co-authorships across academic and industrial research settings, suggesting an active role in interdisciplinary research teams focused on advancing practical AI applications.

• Machine Learning
• Natural Language Processing
• Large Language Models
• +۵ مورد دیگر

Maria Fyta serves as a Professor of Biotechnology at RWTH Aachen University's Faculty of Biology, leading research in the Department of Biotechnology from her lab in the Biology Building (Worringerweg 3, Aachen). Her work bridges computational physics, nanotechnology, and molecular biology to develop next-generation biosensing platforms. Her research focuses on nanopore-based DNA/protein sequencing , utilizing 2D materials (graphene, MoS 2 , h-BN) and nanodiamond functionalization for single-molecule detection. Key projects include ionic liquid catalysis systems , computational alloy design , and molecular dynamics simulations of biomolecular translocation. Recent work emphasizes machine learning integration for signal analysis and materials discovery. Analysis of her 15 most recent publications reveals dominant trends in Nanopore engineering for biomolecular sensing Computational materials design of 2D systems and alloys Machine learning applications in nanofluidics While no specific scientific awards are documented in the provided materials, her extensive publication record demonstrates significant contributions to nanotechnology and biophysics. Professor Fyta's group develops advanced simulation frameworks for biomolecular translocation and collaborates on experimental validation of nanoscale devices. Current efforts focus on enhancing read-out capabilities in functionalized nanopores and designing bio-mimetic sequencing platforms.

• Nanopore DNA Sequencing
• Computational Materials Science
• 2D Materials Engineering
• +۵ مورد دیگر

Prof. Frank Müller is a Universitätsprofessor at Forschungszentrum Jülich GmbH , leading the Molecular Sensory and Neurobiology research group. His work bridges computational methods and biological systems. Research focuses on: Graph-based machine learning for biological networks Transformer architectures for molecular data Computational approaches to sensory neuroscience Parameter-efficient models for multi-task learning Key article trends show expertise in graph transformers , molecular foundation models , and multi-agent reasoning systems . No awards explicitly mentioned. Current affiliation: IBI (Institute of Biological Information Processing), Forschungszentrum Jülich

• Molecular Biology
• Neurobiology
• Machine Learning
• +۲ مورد دیگر

Prof. Robert Jäschke is a Professor at the Institute of Library and Information Science, Humboldt-Universität zu Berlin, within the Faculty of Arts. His primary research focuses on Recommender Systems, Social Bookmarking, Data Mining & Machine Learning, Web Archiving, and Open Science. He leads projects like AI-SKILLS and UnknownData, and operates BibSonomy, a social tagging system. Jäschke advises on DINI and the Specialized Information Service for Book, Library, and Information Science. His work bridges computational methods with humanities, evident in his contributions to music entity detection, Vossian antonomasia analysis, and digital literary frameworks. Research interests span interdisciplinary applications of AI in cultural studies, formal concept analysis, and web archiving. Notable projects include analyzing regional research networks (REGIO) and evaluating dataset creation for concept detection. Teaching includes courses on information processing, data analysis, and web science. Publications reflect a strong focus on computational linguistics, music informatics, and digital humanities. His blog weltliteratur.net explores world literature in digital contexts. Collaborations emphasize open science practices and tool development for academic communities.

• Recommender Systems
• Social Bookmarking
• Data Mining & Machine Learning
• +۳ مورد دیگر

Kristian Kersting is an Associate Professor in the Computer Science Department at Technical University of Darmstadt, Germany, having moved from TU Dortmund in 2017. He holds additional affiliations including Adjunct Assistant Professor roles and has contributed to institutions like the University of Bonn and Wake Forest University. His research focuses on data mining, machine learning, and statistical relational AI, with applications in medicine, plant phenotyping, and traffic analysis. He has authored over 150 peer-reviewed publications and received numerous awards, including the ECCAI Dissertation Award 2006 and Best Student Paper Award at ECML 2006. Education includes a Ph.D. from the University of Freiburg and postdoctoral research at MIT. He has held roles such as Assistant Professor at the University of Bonn and co-chaired major conferences like ECML PKDD 2013. His work emphasizes interdisciplinary applications, including collaborations with industries like goedle.io and pflegix.de. Scientific contributions span probabilistic logic learning, lifted inference, and neuro-symbolic systems. His research group's activities include projects on plant disease detection, traffic prediction, and collective attention analysis. Key awards highlight his impact in AI and data science.

• Data Mining
• Machine Learning
• Statistical Relational Artificial Intelligence
• +۵ مورد دیگر

Dimitrije Marković is a Researcher in the Department of Psychology at Technische Universität Dresden, affiliated with the Faculty of Science. His work bridges cognitive neuroscience, theoretical neuroscience, and machine learning, focusing on computational models of human decision-making and adaptive behavior. He employs concepts from information theory and probability to develop and test experimental predictions from these models. Education: PhD in Physics (2013), Goethe University Frankfurt, advised by Prof. Claudius Gros Diploma in Theoretical and Experimental Physics (2007), Belgrade University Undergraduate studies in Physics (2002–2007), Belgrade University Research Focus: His research emphasizes understanding neurophysiological and computational mechanisms underlying human decision-making, particularly in dynamic environments. He explores topics like adaptive learning, Bayesian inference, and neural modeling of uncertainty and agency perception. Publications Trends: Recent work includes advancements in active inference frameworks, machine learning models for decision-making, and applications to mental health (e.g., depression). His 2020–2025 publications highlight innovations in scalable models (e.g., AXIOM), Bayesian methods, and interdisciplinary approaches linking neuroscience with AI. Professional Experience: Postdoc at TU Dresden (2014–present) Guest Researcher, Max-Planck Institute for Human Cognitive and Brain Sciences (2013–2015) Postdoc, University Clinic Jena (2013–2014) Labs/Teams: Affiliated with TU Dresden’s Chair for Neuroimaging, focusing on neuroimaging techniques and collaborative projects in computational psychiatry and AI-driven neuroscience.

• Cognitive Neuroscience
• Theoretical Neuroscience
• Machine Learning
• +۴ مورد دیگر

Prof. Dr.-Ing. Thomas Esch is a Professor at the Aachen University of Applied Sciences in the Department of Aerospace and Automotive Engineering . He specializes in Thermodynamics and Combustion Technology, with consultation hours held weekly. His institutional affiliations include the European Center for Sustainable Mobility (ECSM). His research spans thermodynamics, combustion systems, sustainable mobility, fuel cell technology, and propulsion engineering. Recent work focuses on thermal management in electric vehicles, rocket propulsion optimization, and AI-driven energy systems. Prof. Esch publishes extensively on sustainable energy solutions, with recent articles (2020-2025) covering fuel cell efficiency, hydrogen vehicle optimization, rocket engine design, and thermal management. His research consistently addresses industrial applications of thermodynamics and propulsion. He teaches courses including: Combustion Technology (Lecture, Exercises, Internships) Global Automotive Homologation Practical Engineering and Social Competencies for Industrial Development Prof. Esch leads academic initiatives like the Erasmus+ United Future Fuel Design Training Center, facilitating international collaborations in sustainable fuel research.

• Thermodynamics
• Combustion Technology
• Automotive Engineering
• +۳ مورد دیگر

Prof. Dr. Joachim Schwarz is a Professor for Market Research and Quantitative Methods at the Business Department of the University of Applied Sciences Emden/Leer. His expertise spans empirical-quantitative methods in business management, with a focus on data analysis, statistical modeling, and market research. Contact: joachim.schwarz@hs-emden-leer.de Research Interests Electromobility in rural areas: Developing energy system models to analyze bidirectional charging impacts on regional self-sufficiency Generative AI applications in statistics education Consumer behavior and market acceptance of autonomous vehicles Unpackaged food product marketing Fintech evolution and Industry 4.0 in financial markets Recent Publications (2014-2022) demonstrate expertise in fintech, market acceptance studies, statistical modeling, and business analytics. His work combines quantitative methods with practical applications in finance, marketing, and energy systems.

• Market Research
• Quantitative Methods
• Data Analysis
• +۳ مورد دیگر

Volha Petukhova is a Researcher at Saarland University, specializing in computational semantics, dialogue theory, and cognitive modeling. She contributes to ISO standards for semantic annotation frameworks (ISO 24617-2 and -5). Her work focuses on dialogue acts, multimodal communication, and argumentation systems. She has led projects like DialogBank and participated in initiatives such as METALOGUE and DBOX. She teaches courses including Multimodal Dialogue Systems and Computational Pragmatics. Her research interests include dialogue management, belief-intention representation, and context-based reasoning. She has published extensively on topics like metacognitive event classification, argument quality assessment, and negotiation strategies. Her recent projects involve personalized health intervention messages and customer-oriented dialogue analysis. She has advised numerous students on topics like negotiation outcome prediction, argument generation, and multimodal depression detection. She collaborates on creating annotated dialogue corpora, such as the DBOX and Metalogue Debate Trainee Corpus. Her work bridges theoretical dialogue semantics with practical applications in healthcare and education.

• Computational Semantics
• Dialogue Theory
• Computational Pragmatics
• +۳ مورد دیگر

Prof. Dr. Nicole Böhmer is a Professor of Economics and Social Sciences at Osnabrück University of Applied Sciences, specializing in international human resource management and talent development. She holds a PhD from Carl von Ossietzky University Oldenburg (2005) and has over two decades of professional experience in banking and academia. Her research focuses on global talent management, AI-driven HR practices, and gender equality marketing, emphasizing organizational resilience in multinational contexts. Education: 2002–2005: PhD in Human Resource Management, Oldenburg University 1993–1999: Economics & Vocational Teacher Training, Oldenburg University 1995–1996: Exchange at Manchester Metropolitan University Research Interests: Global Talent Management Strategies A.I. Integration in HR Operations Cross-Cultural Leadership Gender Equality in Workplace Systems Digital Transformation in Organizational Structures Her recent work explores frontline workforce dynamics, agile workplace adaptation, and ethical AI applications in HR. Articles analyze topics like Barbie's gender equality campaigns and automotive industry diversity initiatives. Leadership Roles: 2019–2025: Senate Member, Osnabrück University of Applied Sciences 2020–2025: Board Member, University Foundation Expertise includes case-based teaching methods and professional development in elementary education. Her publications span 35 refereed articles, book chapters, and industry analyses since 2021.

• International Human Resource Management
• Global Talent Management
• Incentive Systems
• +۵ مورد دیگر

Prof. Dr. Hans H. Jung is a Professor for International Marketing at Munich Business School (MBS) since 2012. He holds a doctorate from the University of Mannheim and has extensive experience in both academia and industry. Prior to joining MBS, he served as CEO of the Institute for Marketing at Mannheim University, a partner at ISUMA Consulting, and held managerial roles at Daimler AG and Nexolab GmbH. Since 2011, he has been a Senior Manager at UNITY AG, a management consulting firm. His research focuses on digital transformation, customer relationship management, and strategic foresight, with notable contributions to automotive industry strategy and blockchain applications in healthcare. Education: PhD in Business Administration from University of Mannheim (1997), completed apprenticeship as a banker. Professional Highlights: Consulted for premium car manufacturers, led projects on electro-mobility, and developed frameworks for digital business models in industries like agriculture and retail. His work emphasizes leveraging AI and blockchain for ethical innovation in healthcare and marketing. Recent publications include analysis of ChatGPT in strategic foresight and decentralized consent management systems for clinical studies. At MBS, he teaches international business courses and has advised on over 50 student theses, many of which led to industry applications. He actively collaborates with industry leaders, hosting guest lectures from BMW, Google, and HARMAN Europe to bridge academic and practical insights.

• International Marketing
• Digital Transformation
• Customer Relationship Management (CRM)
• +۸ مورد دیگر

Dr. Sabine Hossenfelder is a Researcher at Ludwig-Maximilians-Universität München (LMU), where she explores foundational questions in physics. She holds a PhD in Physics from the University of Frankfurt (2003) and has conducted research across particle physics, quantum gravity, dark matter, and quantum foundations. Her current work focuses on superdeterminism and locality in quantum mechanics, challenging conventional assumptions about Bell’s theorem and finetuning. Dr. Hossenfelder is also a prominent science communicator, hosting the YouTube channel “Science without the Gobbledygook” and authoring books like *Lost in Math* (2018) and *Existential Physics* (2022), which critique biases in theoretical physics and explore the philosophical dimensions of science. Her research interests include the mathematical and philosophical underpinnings of quantum mechanics, with recent emphasis on experimental tests of superdeterminism and its implications for locality. She has also contributed to astrophysics studies, such as analyzing dark matter profiles in galaxies using superfluid models. Through her writing and media work, she advocates for scientific rigor and skepticism of unfalsifiable theories.

• Quantum Foundations
• Quantum Mechanics
• Dark Matter
• +۳ مورد دیگر

Prof. Dr. Rolf Grossmann is a Professor at Leuphana Universität Lüneburg, affiliated with the Institute of Culture and Aesthetics of Digital Media (ICAM). His research focuses on Sound Studies, Digital Cultures, and Cultural Informatics, exploring themes like media theory, music technology, and audio culture. He has organized conferences such as 'The Sound of Sound Studies' (2021) and contributed to projects like 'Instrumentality meets Materiality' (2019). Grossmann's publications span peer-reviewed journals like Paragrana and Positionen , as well as edited volumes on topics like sound art and media anthropology. He has been featured in media discussions about AI in music production, digital copyright, and the cultural implications of sampling. His work bridges musicology, media studies, and philosophy, addressing contemporary issues in digital sound culture. Research Interests: Sound Studies investigates how sound shapes cultural practices and technologies. His work on digital cultures examines transformations in music production and consumption, while cultural informatics explores computational methods in media analysis. Key projects include analyzing the PRC's Social Credit System through media theory and critiquing sonic media's role in societal norms. Media Contributions: Grossmann has participated in radio discussions (NDR, Deutschlandfunk) about AI's impact on music (2023), sampling ethics (2015), and digital music's future (2012). His work emphasizes interdisciplinary approaches to understanding sound's societal role.

• Sound Studies
• Digital Cultures
• Cultural Informatics
• +۵ مورد دیگر

Prof. Andrey Ustyuzhanin is an Adjunct Professor of Computer Science at Constructor University's School of Computer Science & Engineering and a Visiting Research Professor at the National University of Singapore (NUS), affiliated with the Institute for Future Intelligent Machines (IFIM). He holds a PhD in Computer Science from the Institute of System Programming (Russian Academy of Sciences) and advanced degrees from Moscow Institute of Physics and Technology (MIPT). His research focuses on developing machine learning methods to address complex scientific challenges in particle physics, materials science, and data-driven discovery. He has contributed to projects like the LHCb experiment at CERN, optimizing online triggers and BDT-based processing, and has pioneered initiatives like the Tracking Machine Learning Challenge and the Code4ML dataset. His work bridges AI and fundamental science, emphasizing interdisciplinary applications. He is also the Director of AI/ML Research at Acronis and a co-organizer of international summer schools in machine learning for particle physics. Education PhD in Computer Science, Institute of System Programming (RAS), 2007 M.Sc. in Applied Mathematics & Physics (Autonomous Control Systems), MIPT, 1994–2000 M.Sc. in Innovative Management, MIPT, 1998–1999 B.Sc. in Applied Mathematics, MIPT, 1994–1998 Mathematics & Physics, Moscow Chemical Lyceum, 1991–1994 Research Interests Prof. Ustyuzhanin specializes in machine learning for scientific discovery, including particle physics (LHCb experiment), materials science (defect analysis in 2D materials), and AI-driven experimental optimization. His work also explores symbolic expression generation, code semantics classification (Code4ML), and cybersecurity frameworks like EAGLEEYE for malicious event detection. He advocates for reproducible science and end-to-end optimization of experimental designs using differentiable programming. Key Projects & Contributions Co-developed the Tracking Machine Learning Challenge to advance high-throughput physics analysis Co-created the Code4ML dataset for annotated machine learning code Designed algorithms for LHCb’s online triggers and scintillator tracking systems Co-founded the annual summer schools on ML in particle physics Labs & Collaborations Director of AI/ML Research at Acronis Head of the LAMBDA Lab at HSE University PI at IFIM, NUS Collaborator on CERN-Yandex research programs

• Machine Learning
• Particle Physics
• Data Science
• +۵ مورد دیگر