Find Academic Supervisors

• Information Systems
• IT Management
• Digital Transformation
• +3 more

Peter Zahradník is an Assistant Professor at the Department of Information Management under the Faculty of Business Management at the University of Economics in Bratislava. His academic journey includes completing both Master's (2019) and Doctoral (2023) studies in Business Economics/Economics and Management at the same university. Master's: Business Economics (2019) Doctoral: Economics and Management (2023) His research focuses on the intersection of information systems and business management, particularly examining digital transformation, IT governance frameworks, and technology-driven competitiveness. He explores how information systems integrate with corporate operations, emphasizing efficiency gains through digital tools and process optimization. Scientific activities include participation in multiple research projects such as VEGA 1/0662/23 (Digital Transformation & Industry 5.0) and VEGA 1/0388/20 (IT Management Standards). His publications highlight practical applications of technologies like IoT and office software in business contexts. VEGA 1/0662/23: Digital Transformation Readiness KEGA 034EU-4/2023: Innovative Teaching Methods I-22-111-00: Information Technology Management Peter contributes to skill development through pedagogical activities, focusing on digital competencies and critical thinking in business education. His work bridges technical implementation with strategic business outcomes.

Paul CardenView profile

Senior Lecturer

• Computer Science
• Social Science
• E-Learning
• +5 more

Paul Carden is a Senior Lecturer in the Department of Computer Science and Informatics, focusing on integrating social science interpretive approaches with technical computer science methodologies. His current research involves phenomenological studies of academic feedback mechanisms, including dissertation progress reports and final submission marking sheets. Research interests span E-Learning, diversity leadership, followership, and the intersection of art with technology. He organized the 2015 CTRL-ALT-CREATE gallery exhibition and is planning its sequel. His publications highlight trends in E-Learning, educational technology, and human-centered computing. Additional responsibilities include serving as Course Director for Foundation courses and Digital Technology Solutions Professional apprenticeship degrees.

Evgeniy Vladimirovich TankovView profile

Associate Professor

• Cognitive Linguistics
• German Linguistics
• Political Discourse Analysis
• +2 more

Evgeniy Vladimirovich Tankov serves as Associate Professor in the Department of Applied Informatics, Natural Sciences and Humanities at Khakass Technical Institute—a branch of Siberian Federal University in Abakan, Russia. With 23 years of specialized experience, he teaches Foreign Language, Business Foreign Language, Professional Foreign Language, Philosophy, and Foundations of Russian Statehood. Education: Khakass State University named after N. F. Katanova (1996), Qualification: Teacher of German and English Research Focus: Tankov's work centers on Cognitive Linguistics and German Linguistic Consciousness , examining egocentric categories (WUNSCH/Desire), emotion concepts (Neid/envy), and cross-cultural comparisons (TRAUM/DREAM). His recent scholarship extends into Political Discourse Analysis , investigating binary oppositions and messianism in American political rhetoric through cognitive linguistic frameworks. Publication Trends: His 2017-2020 publications demonstrate a methodological shift from pure linguistic analysis toward political discourse, using cognitive linguistics to decode ideological structures in election speeches (notably Biden's) and conceptual oppositions. The work consistently bridges German linguistic space with broader sociopolitical contexts. Scientific Recognition: No awards or fellowships are documented in available materials. Academic Engagement: Tankov has developed two educational textbooks (2015, 2019) and completed advanced training in e-learning technologies (2022) and Russian Statehood pedagogy (2023). No student advisement or grant activities are recorded.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +8 more

Jarosław Wąs is a Professor and Head of the Department of Applied Informatics at the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology in Kraków, Poland. His academic leadership extends to governance roles including the Senate, Faculty College, and Disciplinary Council for Technical Information Technology and Telecommunications. His research spans Artificial Intelligence, Machine Learning, and Data Mining with core expertise in Rough Sets theory. He develops computational models for crowd simulation and pedestrian dynamics critical for evacuation planning, and applies deep learning to renewable energy forecasting and health trajectory prediction. His work bridges theoretical computer science with practical applications in energy systems, healthcare analytics, and cosmic physics. Analysis of his 2023-2025 publications reveals interdisciplinary convergence: Rough Sets combined with cellular automata for crowd modeling, transformer networks for electronic medical records, and hybrid deep learning approaches for renewable energy forecasting. Key trends include zero-shot learning in healthcare, anomaly detection in cosmic data, and data-driven evacuation simulation with social group dynamics. No scientific awards are mentioned in available sources. Information regarding student advising and research grants is not provided in the source material. No laboratory or research team affiliations are specified in the available documentation.

• Infectious Disease Epidemiology
• HIV Care and Treatment
• Global Health
• +5 more

Kristen A. Stafford is an Associate Professor at the University of Maryland School of Medicine , Department of Epidemiology and Public Health. She serves as Technical Deputy Director for CIHEB and focuses on HIV care and treatment programs, aging in HIV patients, and global health initiatives across sub-Saharan Africa. Education: PhD in Epidemiology (2015), MPH in Health Policy & Management (2004), BA in Psychology (1995) Research Interests include HIV epidemiology, aging with chronic infections, global health systems, and non-AIDS defining conditions. Her work spans infectious diseases, women’s health, and statistical modeling for public health interventions. Publications emphasize HIV treatment outcomes in Nigeria and Zambia, SARS-CoV-2 seroprevalence, cluster-randomized trials in infection control, and methodological advancements in epidemiological research. Scientific Awards: Otani Award (2013) for academic performance Trudy Bush Award (2013) for women’s health research Grants include NIH-funded projects like R21AI179493 (HIV prevalence projections) and CDC sub-contracts for HIV epidemic control in Kenya.

Diogo Ribeiro FerreiraView profile

Associate Professor

• Data Science
• Applied Machine Learning
• Deep Learning
• +6 more

Diogo Ribeiro Ferreira is an Associate Professor of Information Systems at Instituto Superior Técnico (IST) , part of the University of Lisbon . He is also a researcher at the Institute of Plasmas and Nuclear Fusion (IPFN) . His career spans teaching, research, and collaboration with institutions like JET (Joint European Torus) in nuclear fusion. His research focuses on Data Science , Applied Machine Learning , Deep Learning , and Process Mining , with applications in Nuclear Fusion (plasma diagnostics, disruption prediction) and Information Systems (workflow management, healthcare process analysis). He has taught courses like Data Administration in Information Systems and Data Analysis and Integration since 2003. His work bridges Computer Science and Physics , particularly in using Graph Neural Networks for plasma modeling and GPU Acceleration for high-performance computing. He has supervised over 20 PhD and MSc students, including André C. Santos and Fernando Szimanski, and co-authored 15+ recent publications in journals like Journal of Plasma Physics and Fusion Engineering and Design . Scientific Awards: 2012 ICGA Journal Award Best Paper Award at 9th IEEE International EDOC Conference 2005 Collaborations: Affiliated with JET Contributors and IPFN, focusing on real-time tomography and fusion reactor optimization.

• Machine Learning
• Computer Vision
• Emotion Recognition
• +5 more

Marina Marjanovic is a faculty member at Singidunum University in Belgrade, Serbia, affiliated with the Faculty of Informatics and Computing. She maintains an active research profile with numerous publications spanning machine learning, computer vision, and signal processing applications. Her work demonstrates strong interdisciplinary connections between theoretical AI development and practical implementations in healthcare, security, and digital government services. Dr. Marjanovic earned her Doctoral degree in Signals and Systems from Universidad Politecnica de Madrid in 2007, following a Master's degree in the same field from the same institution in 2005. Her foundational education includes basic studies in Telecommunications at the Faculty of Electrical Engineering in Belgrade, and secondary education at the third Belgrade high school with a focus on natural sciences. This strong technical background has enabled her transition from traditional signal processing to contemporary AI research. Her research interests center around machine learning and computer vision, with significant contributions to emotion recognition, sign language processing, and explainable AI. She has demonstrated particular expertise in developing robust AI models that address real-world challenges such as demographic bias in age estimation systems, vulnerability to adversarial attacks in deep learning models, and accurate medical diagnostics through imaging analysis. Her recent work shows increasing focus on healthcare applications, security implications of AI systems, and making AI more transparent and interpretable. Analysis of her publication trajectory reveals a clear evolution from signal processing foundations toward contemporary AI applications. Her recent work (2022-2024) shows strong emphasis on healthcare AI, security applications, and explainability, often combining traditional machine learning approaches with novel optimization techniques. She frequently collaborates with international researchers across multiple institutions, demonstrating the global reach of her work. Dr. Marjanovic has established herself as a productive researcher with consistent publication output across reputable journals and conferences including IEEE transactions, Springer publications, and specialized AI conferences. Her collaborative approach is evident through her extensive co-authorship network spanning multiple Serbian and international institutions. She actively contributes to the academic community through conference organization (including Sinteza conferences) and participation in research projects at Singidunum University's research institutes. Her work bridges theoretical advancements with practical implementations across multiple domains, demonstrating versatility and impact across the AI research landscape.

Eva ArtingerView profile

Researcher

• Human-Computer Interaction
• Tangible User Interfaces
• Multitouch Interaction
• +4 more

Eva Artinger is a Researcher at the Chair for Computer Aided Medical Procedures & Augmented Reality within the Faculty of Informatics at Technical University of Munich . Her work focuses on Human-Computer Interaction with specific emphasis on Multitouch and gestural User Interfaces , Information Visualization , and Tangible User Interfaces . Current position: Researcher at TUM Degree: Master of Science Her research particularly addresses the challenges of designing user interfaces for emergency response systems , including applications for Mass Casualty Incidents (MCIs) where she has developed innovative map interaction techniques and gesture-based text input systems like Gestyboard 2.0. Eva has contributed to multiple publications analyzing usability in high-stress environments and exploring interaction paradigms for augmented reality applications in medical and architectural contexts. Her recent work has focused on designing interfaces that maintain functionality while being operated in unstable conditions, with research trends showing increasing specialization in emergency response UI/UX , mobile device interaction , and real-time data visualization . Eva has also been actively involved in teaching activities at TUM, covering topics in Augmented Reality applications and Human-Computer Interaction for both desktop and mobile platforms.

Nicolas BallierView profile

Professor

• Corpus prosody
• Neural machine translation
• Automatic analysis of learner corpora
• +5 more

Nicolas Ballier is a Professor at Université Paris Cité (formerly Université Paris Diderot), where he teaches and conducts research in linguistics and digital humanities. Previously, he taught at the Université de Rouen and Paris 13. His work focuses on the intersection of linguistics, computational methods, and language learning technologies, with particular expertise in neural machine translation and speech processing. His primary research interests include: Corpus prosody Neural machine translation Automatic analysis of learner corpora Digital humanities Epistemology of linguistics (third revolution of grammatisation) Interpretability of neural machine translation systems Representation of speech in Whisper audio models Dr. Ballier's recent work explores how computers transform linguistic data (the 'third revolution of grammatisation'), with a focus on neural machine translation interpretability and speech analysis using large language models like Whisper. His research bridges theoretical linguistics with practical applications in language learning and translation technologies, particularly focusing on how these technologies can be made transparent and useful for translators and language learners. His 15 most recent publications (2022-2024) demonstrate a strong focus on neural machine translation interpretability, Whisper applications for language assessment, and learner corpus analysis. The publications span multiple prestigious venues including EAMT, ACL, LREC-COLING, and specialized journals in speech technology and computational linguistics, showing consistent productivity and impact in his fields. Dr. Ballier has been PI or team member on numerous European-funded projects including DOKTORAND (2012-2016), KVARK project (2014-2026), PHC Ulysses (2019), multitraiNMT (2021), and LT-LIder project (2024-Nov 2026). He has developed platforms like PAPTAN for neural machine translation experiments and MAKE-NMT VIZ for investigating machine translation interpretability. He has supervised PhD students through collaborative projects and has been involved in research initiatives like DLLA (Deep Learning for Language Assessment) exploring CEFR levels with keylog data, Neuroviz (2021-2022), and SPECTRANS (2020-2022) focusing on specialized neural translation and probing information flow in neural networks.

Shashikant IlagerView profile

Assistant Professor

• Distributed Systems
• Energy Efficiency
• Machine Learning
• +4 more

Dr. Shashikant Ilager is an Assistant Professor at the Informatics Institute (IVI) , University of Amsterdam. His research focuses on distributed systems , energy efficiency , and machine learning , with a specific emphasis on sustainable large-scale AI platforms. Current affiliation: University of Amsterdam (Oct 2024–present) Previous roles: Postdoctoral Researcher at TU Wien; Visiting Research Scientist at IBM PhD: CLOUDS Lab, University of Melbourne Research Focus Dr. Ilager develops data-driven approaches to optimize cloud/edge platforms for environmental and economic sustainability , particularly in AI workloads. His work bridges system characterization with learn-centric optimization techniques. Recent Publications 2025: ACM e-Energy (LLM carbon amortization), TAAS (self-adaptive edge monitoring), CCGRID (code generation efficiency). 2024: ICSOC (edge time series classification), EdgeSys (federated learning with GANs). Awards Best Paper Award @ ACM/IEEE UCC 2023 Community Engagement Organizer of the GreenSys workshop (2025) at EuroSys. Member of HPDC 2025 Technical Program Committee.

Jens StoyeView profile

Professor

• Bioinformatics
• Computational Biology
• Comparative Genomics
• +3 more

Jens Stoye is a Professor of Genome Informatics at the Faculty of Engineering , Bielefeld University, where he has held this position since 2002. He leads the Genome Informatics Working Group at CeBiTec and serves as Managing Director of the Bielefeld Institute for Bioinformatics Infrastructure (BIBI). Additionally, he is a member of the Board of Directors at the Center for Interdisciplinary Research (ZiF) and held numerous administrative roles, including Dean of the Faculty of Technology and Speaker of the DFG Research Training Group. Education: Diploma in Informatics in the Natural Sciences (1995) and PhD (1997) from Bielefeld University Current Roles: Full University Professor, Managing Director of BIBI, Executive Director of ZiF (2023-2025) His research focuses on computational genomics , including genome rearrangement, comparative genomics, and pangenomics. Recent work explores the double distance problem , natural genome phylogeny reconstruction, and efficient pangenome storage techniques. He has developed algorithms for genome assembly benchmarking (GABenchToB), rearrangement epidemiology (pling), and core genome detection. Scientific Contributions include best paper awards and editorial roles at IEEE/ACM Transactions on Computational Biology and Bioinformatics , BMC Bioinformatics , and Discrete Applied Mathematics . He has served on over 20 conference committees (WABI, ISMB, RECOMB) and advised 165+ theses (36 PhD, 71 Master/Diploma, 58 Bachelor). Leadership Roles: Vice-Speaker of German Bioinformatics Society (2002-2014) Dean of Faculty of Technology (2007-2009) Speaker of DFG Research Training Group (2013-2019) Member of DAAD Postdoc Fellowship Committee (2014-2025)

• Science and Technology Studies
• Digital Methods
• Generative AI for Social Research
• +5 more

Anders Kristian Munk is a Professor at DTU Management, Technical University of Denmark, based in the Department of Technology, Management and Economics within the Division for Technology and Business Studies. His research focuses on the critical intersection of technology and society through Science and Technology Studies (STS) and advanced digital methodologies. His primary research domains include: Generative AI integration in social research Digital methods development (notably data sprints) Controversy mapping of sociotechnical issues Computational ethnography and network analysis Critical examination of data imaginaries Urban studies through computational visual methods Recent publications (2023-2025) demonstrate a strategic pivot toward generative AI applications, with significant contributions in developing AI-native research frameworks like synthetic interlocutors and irreductionist mapping. His work uniquely bridges STS theory with practical tool development, exemplified by Gephisto for critical network analysis. Key application areas span public health (e.g., inflammatory bowel disease trend analysis in Africa), cultural industries (film audience analytics), and urban studies (pandemic place attachments). Prof. Munk co-developed the data sprint methodology for collaborative controversy mapping and has contributed to major projects including Climaps and EMAPS. His interdisciplinary approach combines social science rigor with computational innovation through partnerships across computer science, design, and domain-specific fields. Current work emphasizes ethical AI integration and methodological adaptation for complex sociotechnical challenges.

Gary WeissmanView profile

Assistant Professor

• clinical research informatics
• natural language processing
• acute care clinical decision support
• +2 more

Gary Weissman, MD, MSHP, serves as an Assistant Professor of Medicine with a research program centered at the intersection of clinical medicine and advanced informatics. His work bridges critical care practice with data science to develop innovative solutions for patient care improvement, focusing on clinical research informatics, natural language processing, acute care clinical decision support, population health management, and health informatics policy. Dr. Weissman's research program addresses fundamental challenges in acute care delivery through sophisticated analysis of electronic health record data. He has pioneered methods for identifying patients with acute respiratory distress syndrome (ARDS) and sepsis using natural language processing of clinical notes, developed standardized data frameworks like the Common Longitudinal Intensive Care Unit (CLIF) format, and investigated health equity implications of clinical algorithms. His work on goal-concordant care measurement and ventilator management optimization demonstrates commitment to both technical innovation and ethical healthcare delivery. Current investigations examine social determinants of health impacts on respiratory outcomes and biases in pulmonary function test interpretation. Analysis of his 2022-2025 publications reveals a cohesive research trajectory focused on critical care informatics with three dominant themes: 1) Development and validation of machine learning models for retrospective identification of critical conditions (ARDS, sepsis); 2) Standardization of ICU data formats and ventilator management protocols; 3) Rigorous evaluation of health equity dimensions in clinical algorithms and diagnostic criteria. His multi-center studies consistently address real-world implementation challenges while advancing methodological approaches for EHR-based research. While specific details regarding teaching appointments, grant funding, and advising responsibilities are not documented in available sources, Dr. Weissman's prolific publication record (over 50 papers since 2022) indicates an active research program with significant implications for critical care practice, health policy, and medical informatics infrastructure development. His work exemplifies the integration of computational methods with clinical expertise to solve pressing healthcare challenges.

• Database Systems
• Integration of heterogeneous databases
• Data quality and data integrity
• +6 more

Thomas Kudraß serves as Professor for Database Systems at the Faculty of Computer Science, Mathematics and Natural Sciences (IMN) at Leipzig University of Applied Sciences (HTWK Leipzig). With over 25 years of academic and industry experience, he teaches courses including Database Basics, Database Application Programming, Data Warehousing, and Big Data Technologies. His leadership roles include Internship Coordinator for Computer Science since 2001, Member of the Academic Senate since 2011, and Contact person for the IMN faculty alumni association since 2005. Dr. Kudraß earned his Dipl.-Ing. in Computer Science from Technical University of Dresden (1985-1990) and completed his doctorate at Darmstadt University of Technology (1992-1997), where his dissertation received the prestigious Jos Schepens Memorial Award. Prior to academia, he worked as an Information Systems Architect at UBS AG Zurich and Database Specialist at Swiss Bank Corporation Basel. His research spans database technologies with focus on heterogeneous database integration, data quality, data privacy, and modern database concepts. Recent work explores cloud-native billing applications for 5G, NoSQL databases, and privacy-preserving record linkage techniques. His publication record shows continuous evolution from traditional database systems to contemporary challenges in big data and distributed systems. Jos Schepens Memorial Award for doctoral dissertation (1997) Active participation in German Informatics Society (GI) specialist groups Organizer of multiple database-related workshops and conferences Professor Kudraß has significantly contributed to academic governance as E-Learning Officer (2000-02), Faculty Council member (2009-12), and liaison lecturer for the German Society for Computer Science (2001-14). He has coordinated numerous student projects and served on program committees for major conferences including INFORMATIK 2017 and BTW series.

Murat ElhüseyniView profile

Lecturer

• Engineering Fundamental Field
• Industrial Engineering
• Modeling and Optimization
• +1 more

Murat Elhüseyni serves as a Lecturer in the Department of Industrial Engineering at Yeditepe University's Faculty of Engineering. His academic career spans multiple institutions including Uskudar University, Bogazici University, and University of Primorska, with expertise in optimization and industrial systems. Education PhD in Industrial Engineering, Bogazici University (2012-2018) Master's in Industrial Engineering, Istanbul Technical University (2010-2012) BSc in Industrial Engineering, Yildiz Technical University (2006-2010) Post-Doctoral Research, Sabanci University (2019-2020) Post-Doctoral Research, University of Primorska (2023-2024) His research centers on Operations Research with emphasis on Modeling and Optimization. Key contributions include vehicle maintenance scheduling under single dead-end track constraints and service level agreements, electricity market modeling to prevent tacit collusion, and biomedical applications using hybrid learning frameworks for gene expression analysis in breast cancer. His methodological approach integrates mathematical modeling, heuristic algorithms, and simulation techniques. Publication trends (2012-2021) reveal consistent focus on industrial optimization problems, particularly vehicle scheduling systems, with expanding applications into energy markets and bioinformatics. His work demonstrates evolution from simulation-based textile workshop scheduling to complex trilevel optimization models for electricity markets. He contributed as a scholarship holder to the TÜBİTAK 1001 project 'Preventing Tacit Collusion in Restructured Electricity Markets' (2020-2021) and serves as a member of INFORMS since 2019.