جست‌وجوی استاد و پژوهشگر

Kurt Hornikمشاهده پروفایل

استاد

• Statistical Data Processing
• Statistical Learning
• Machine Learning
• +۵ مورد دیگر

Kurt Hornik is a Professor at the Vienna University of Economics and Business (WU) , where he heads the Institute for Statistics and Mathematics and the Research Institute for Computationally Intensive Methods . He is renowned for his co-development of the R programming language, a cornerstone in statistical computing with over 15,000 extension packages. His research spans statistical data processing, machine learning, and quantitative risk management, focusing on combining ordinal ratings (e.g., credit scores) into consensus-based models. Education: Technical Mathematics (PhD, 1987, Vienna University of Technology) Postdoctoral work in statistics and mathematics Research Interests: Hornik's work addresses challenges in statistical modeling of ordinal preferences, particularly in creditworthiness assessment. He developed composite likelihood methods to improve calibration of credit rating systems, which underpin key Eurosystem monetary policies. His recent publications explore hyperspherical variational autoencoders, Watson distributions, and Kummer's function bounds, reflecting his contributions to computational statistics and high-dimensional data analysis. Scientific Awards: Golden Decoration of Merit for Services to the Republic of Austria (2007) Contributions: Hornik's research has produced widely used R packages like colors , ROI , and mvord , advancing statistical software infrastructure. He collaborates globally, with publications in journals such as Journal of Statistical Software and Annals of Applied Statistics .

• Automated Reasoning
• Formal Methods
• Program Verification
• +۳ مورد دیگر

Laura Kovacs is a Professor at TU Wien's Research Area Formal Methods in Systems Engineering. Her work focuses on automated reasoning, symbolic computation, and formal verification of (probabilistic) programs. She leads projects like Automated Reasoning with Theories and Induction and Effective Formal Methods for Smart-Contract Certification . Key research areas: Automated Reasoning, Formal Methods, SMT Solving Projects: Game-Theoretic Security, Probabilistic Loop Analysis Recent publications emphasize SAT-based subsumption, induction in saturation, and algebraic loop reasoning. Her team includes researchers like Marcel Moosbrugger and Sophie Rain. She contributes to tools like Vampire and Z3 for symbolic attackers and finite field reasoning.

Sophie Neubauerمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۴ مورد دیگر

Sophie Neubauer is a researcher affiliated with the Faculty of Informatics at Technische Universität Wien , specifically within the Institute of Information Systems and the Knowledge Based Systems research group. Her work bridges theoretical and applied domains in machine learning and systems engineering. Faculty: Faculty of Informatics Department: Institute of Information Systems Research Group: Knowledge Based Systems Research Interests: Sophie focuses on robustness analysis of neural networks, verification of neural ODEs, and applications of machine learning in data-limited scenarios. Her work on Lagrangian reachtubes explores formal methods for dynamic system verification, while her earlier research in optimal bidding strategies contributes to computational economics. Publications Trends: Her publications (2014–2023) span machine learning, formal verification, and systems engineering. Key areas include neural networks, stochastic guarantees, reachability analysis, and predictive modeling in tourism and sponsored search. Collaborations: She collaborates with researchers such as Radu Grosu , Scott A. Smolka , and Ramin Hasani , contributing to both theoretical advancements and practical tools in AI verification.

Aldo Lipaniمشاهده پروفایل

پژوهشگر

• Information Retrieval
• Search Evaluation
• Bias Correction
• +۳ مورد دیگر

Aldo Lipani is a researcher at the E-Commerce Research Area (E194-04) within TU Wien, specializing in information retrieval and search evaluation methodologies. Holding a Dr.techn. doctoral degree, he has maintained consistent research output since 2014 with significant contributions to IR evaluation frameworks. His educational background includes completing his doctoral thesis titled "On Biases in Information retrieval models and evaluation" at TU Wien in 2018. This foundational work established his research trajectory in evaluation bias and normalization techniques. Dr. Lipani's research centers on critical evaluation challenges in information retrieval, with particular expertise in bias correction mechanisms, document length normalization, and pooling strategies for test collection construction. His work addresses high-recall search scenarios essential for systematic literature reviews and e-commerce applications, often developing visualization tools like VoMBaT to analyze evaluation measure behavior. Recent publications demonstrate growing focus on transformer model position bias mitigation and contextualized re-ranking approaches. Analysis of his 15 most recent publications reveals a sustained commitment to improving IR evaluation reliability through methodological innovations. Key trends include the application of fixed-cost pooling strategies to systematic review automation, development of bias correction frameworks, and integration of visualization techniques for evaluation transparency. His work consistently bridges theoretical model improvements with practical applications in academic search and e-commerce contexts. As an active member of TU Wien's Network Lab within the E-Commerce Research Area, Dr. Lipani collaborates extensively on projects involving search system evaluation, systematic review automation, and e-commerce search technologies. His research group maintains strong connections with international IR communities through regular publications at major conferences.

William Pedenمشاهده پروفایل

پژوهشگر

• Philosophy of Statistics
• Philosophy of Probability
• Philosophy of Artificial Intelligence
• +۴ مورد دیگر

William Peden is a Postdoctoral Researcher at the Institute for Philosophy and Philosophy of Science , Johannes Kepler University Linz, Austria. He previously held positions at Lingnan University (Research Assistant Professor, 2022-2023), Erasmus University Rotterdam (Postdoctoral Researcher, 2020-2022), Polytechnic University of the Marche (Research Fellow, 2019-2020), and Durham University (PhD and Postdoc, 2013-2019). His research focuses on applying formal epistemology to statistical and probabilistic reasoning in economics and artificial intelligence. PhD in Philosophy (2013-2017), Durham University MPhil in History, Philosophy, and Sociology of Science, Technology, and Medicine (2011-2012), Cambridge University MA in Philosophy (2006-2010), Edinburgh University His research explores statistical reasoning through frameworks like Henry E. Kyburg's Evidential Probability and Jon Williamson's Objective Bayesianism, addressing issues in decision theory , inductive logic , probability , and AI epistemology . He uses agent-based modeling to compare reasoning frameworks under ambiguity. His recent publications investigate Bayesianism vs. Imprecise Probability , statistical testing economics , and philosophy of machine learning . He co-authored studies on publication market incentives , personalized medicine evidence , and Keynesian probability theory . William contributes to debates about inductive skepticism , Bayesian updating , and philosophy of economic modeling . He collaborates with researchers across Italy, Netherlands, and the UK.

Hamid Eghbal-zadehمشاهده پروفایل

پژوهشگر

• Adversarial Machine Learning
• Representation Learning
• Generative Models
• +۴ مورد دیگر

Hamid Eghbal-zadeh is a Postdoctoral Researcher at the Institute of Computational Perception at Johannes Kepler University Linz (JKU). Previously, he worked as a Research Scientist at Meta and held roles at the Institute for Machine Learning at JKU. His research focuses on adversarial machine learning, representation learning, and robust deep learning models. He has made significant contributions to acoustic scene analysis, generative models, and reinforcement learning. Education : PhD in Machine Learning from JKU (2019), defended with distinction. BSc/MSc details not explicitly stated in text. Research Highlights : Developed Mixture Density GANs to address mode collapse in GANs Pioneered receptive-field regularization techniques for audio classification Contributed to top-performing systems in DCASE challenges (2016-2019) Active in reinforcement learning and domain adaptation research Awards/Competitions : Multiple top placements in international challenges (DCASE, MediaEval), outstanding reviewer recognitions (ICML 2022, ICLR 2022). Advising : Supervised 6+ students in topics like acoustic scene classification, generative retrieval, and sound event detection.

Sascha Senckمشاهده پروفایل

استادیار

• Additive Manufacturing
• X-ray Computed Tomography
• Materials Science
• +۳ مورد دیگر

Sascha Senck is an Assistant Professor at the University of Applied Sciences Upper Austria, affiliated with the Research Center Wels. He leads research initiatives in Additive Manufacturing, Materials Science, and Advanced Imaging Techniques. His work focuses on X-ray Computed Tomography (X-CT), phase contrast imaging, and defect analysis in advanced materials like carbon fiber reinforced polymers and magnesium alloys. Key projects include PEMOWE (energy transition testing), HyperMAT (hyperspectral material analysis), and TCA5 (diabetic foot imaging). He collaborates with regional industries and academic institutions across Austria and the Czech Republic. His research has been featured in media outlets for innovations in 3D-printed medical implants and non-destructive testing methods. Senck has supervised multiple interdisciplinary projects and holds an h-index of 16 with over 747 citations. His lab focuses on developing advanced manufacturing techniques and medical imaging solutions for industrial and clinical applications.

Milosh Jovanovikjمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Data Engineering
• Semantic Web
• +۸ مورد دیگر

Dr. Milosh Jovanovikj holds the role of Associate Professor at the Faculty of Computer Science and Engineering in Skopje, North Macedonia, and concurrently serves as a PostDoc Researcher at TU Wien in Vienna, Austria. He is also a Senior R&D Knowledge Graphs Engineer at OpenLink Software in London. His primary research focuses on Knowledge Graphs (KGs), including their lifecycle processes such as modeling, enrichment, and analytics-driven AI applications. He has contributed to over 10 international and 25 national research projects, authored 60+ publications, and co-authored 3 books. His expertise spans Data Engineering, Semantic Web technologies, Linked Data, and Applied AI. Significant contributions include developing frameworks like RDFGraphGen for RDF graph generation and PharmKE for pharmaceutical text analysis. He has led initiatives in geospatial semantic web standards (GeoSPARQL) and healthcare informatics, including predictive analytics for nephrology data. Education: PhD in Computer Science and Engineering His work bridges academia and industry, with projects like the 3DFed federation engine and Semantic Sky (a Gmail plugin). He has extensive experience in ontology development (e.g., SEOntology, VEO for CO2 emissions) and open data initiatives, including LinkedDrugs for global drug data consolidation. Active in dissemination, his articles span knowledge graph applications in music recommendation, finance sentiment analysis, and drug-disease relation discovery. He has collaborated on benchmarks like the GeoSPARQL Compliance Benchmark and Mighty Storage Challenge (MOCHA). Grants/Projects: HOBBIT, SAGE, 3DFed, LinkedDrugs He leads teams in advancing semantic web integration, cloud service platforms (Semantic Sky), and educational materials like Web Programming Basics . His labs and teams focus on AI-driven solutions for healthcare, geospatial analytics, and enterprise systems.

• Formal Methods
• Software Engineering
• Machine Learning
• +۳ مورد دیگر

Eva Schenner is a Researcher at TU Wien's Department of Software Engineering, affiliated with both the Office Services and eduLAB teams. She specializes in formal methods, program analysis, and machine learning applications in software engineering. Her work focuses on testing tools, smart contract certification, and probabilistic models for material design. She has led multiple high-profile projects, including the FWF-funded 'Types4Strings' (2024–2027), WWTF's 'Effective Formal Methods for Smart-Contract Certification' (2023–2027), and an Amazon Research Award project on testing Dafny (2024–2027). Her research also spans metamorphic testing of machine-learning models and probabilistic hybrid models for material design. Education: Mag.a phil. (Master of Philosophy) Her awards include Amazon and Google Research Awards, and grants from the WWTF and Max-Planck-Gesellschaft. She actively contributes to initiatives like Bebras, promoting computational thinking education since 2007. Labs/Teams: Involved in the Software Engineering group within the Faculty of Informatics, collaborating with industry partners like Amazon and Google.

• Databases
• Artificial Intelligence
• Text Mining
• +۲ مورد دیگر

Ingo Feinerer serves as an Associate Professor at Vienna University of Technology's Faculty of Informatics, specifically within the Institute of Information Systems Engineering (E192-02). His research bridges theoretical computer science and practical applications in database systems, artificial intelligence, and text mining, with significant contributions to configuration management and formal methods. His educational background includes: PhD Dissertation (2007): A formal treatment of UML class diagrams as an efficient method for configuration management Diploma Thesis (2005): Formal program verification: a comparison of selected tools and their theoretical foundations Feinerer's research focuses on database theory (particularly schema mapping and dependencies), AI-driven configuration systems using integer linear programming, and text mining infrastructure development in R. His work combines theoretical rigor with practical tool development, notably through the textcat and tm packages. The integration of formal methods in software engineering remains a consistent thread throughout his publications. Analysis of his 15 most recent publications reveals strong specialization in database constraints (40%), text mining applications (30%), and software configuration systems (30%), with increasing interdisciplinary work connecting computer science to digital humanities. His notable recognition includes: INiTS Award (2007) for technology commercialization potential Feinerer has supervised doctoral research including F. I. Chertes' work on schema mapping languages and diploma theses on UML semantics and probabilistic databases. He led major research projects HINT (2012-2017) and SEE (2012-2016) focusing on database technologies and information systems. His work demonstrates consistent funding through Austrian national research programs. As a core member of the Databases and Artificial Intelligence research group, he collaborates extensively on R package development for text analysis and contributes to international workshops on theoretical aspects of software engineering.

• Dependable Systems
• Probabilistic Verification
• Concurrency Theory
• +۵ مورد دیگر

Holger Hermanns is a Professor at Saarland University , holding the Chair for Dependable Systems and Software within the Faculty of Mathematics and Computer Science on the Saarland Informatics Campus. He has held academic positions at Universiteit Twente (Netherlands), INRIA Rhône-Alpes (France), and Universität Erlangen-Nürnberg (Germany), with part-time roles at INRIA (2007-2010) and Universiteit Twente (2001-2006). His research spans modeling and verification of concurrent systems , resource-aware embedded systems, compositional performance evaluation , and energy informatics . Recent work focuses on satellite network architectures, AI-driven system management, and ethical considerations in algorithmic accountability. Authored over 200 peer-reviewed papers (ha-index 92, h-index 56) Co-chaired program committees for CAV, CAV, CONCUR, TACAS, and QEST Keynote speaker at numerous international conferences Honors include the ERC Advanced Grant (2016), Preis des Fakultätentages Informatik (2013), and membership in Academia Europaea (2013). He serves on steering committees for ETAPS and TACAS, and has led Dagstuhl Foundation initiatives.

Phokion Kolaitisمشاهده پروفایل

استاد

• Principles of database systems
• Computational complexity
• Logic in computer science

Phokion Kolaitis is a Professor of Computer Science at the University of California Santa Cruz (UC Santa Cruz) and a Principal Research Staff Member (part-time) at the IBM Almaden Research Center. He has held leadership roles as Chair of UC Santa Cruz's Computer Science Department (1997-2001, 2014-2015) and Senior Manager at IBM's Computer Science Principles and Methodologies Department (2004-2008). His research bridges database systems, computational complexity, and logic in computer science, with significant contributions to schema mappings, data exchange, and constraint satisfaction problems. His work spans foundational studies in database theory, logic, and algorithms, influencing both academic research and industrial applications. Kolaitis has received numerous accolades, including two ACM PODS Alberto Mendelzon Test-of-Time Awards, IBM Research Division Outstanding Innovation and Technical Achievement Awards, and Best Paper Awards at top conferences like ICDT and FCT. He is a Fellow of AAAS and ACM, a Foreign Member of the Finnish Academy of Science and Letters, and holds an Honorary Doctorate from the University of Athens, Greece. 2017 Best Paper Award (FCT 2017) 2015 IBM Outstanding Innovation Award 2014 Honorary Doctorate (University of Athens) 2008 IBM Outstanding Technical Achievement Award Fellow of AAAS and ACM Foreign Member, Finnish Academy of Science Kolaitis collaborates extensively, co-authoring with researchers like D. Burdick, R. Fagin, and M.Y. Vardi. At UC Santa Cruz, he advises on database systems and logic, while his part-time role at IBM focuses on theoretical advancements in data management and complexity.

Bettina Könighoferمشاهده پروفایل

استادیار

• Artificial Intelligence
• Reinforcement Learning
• Formal Methods
• +۳ مورد دیگر

Bettina Könighofer is an Assistant Professor at the Technische Universität Graz (TU Graz), affiliated with the Institute of Information Security. Her academic background includes a BSc, Dipl.-Ing. (Diplom-Ingenieur), and Dr.techn. (Technical Doctorate). She specializes in formal methods applied to AI safety, reinforcement learning, and autonomous systems. Her research emphasizes ensuring safety and ethical compliance in decision-making systems through techniques like shield synthesis and formal verification. Research Interests: Safe Reinforcement Learning Algorithmic Fairness and Ethical AI Formal Verification of Learning Systems Autonomous Systems Safety Legal Accountability in Automated Decision-Making Her recent work explores safety shields for real-time decision enforcement, tools for legal accountability (e.g., soid ), and adaptive architectures for safety-critical systems. Notable projects include contributions to QComp verification competitions and Dagstuhl seminars on trustworthy AI. She holds a leadership role in academic conferences, including co-chairing the AISoLA 2023 Track C2. Her research often intersects with practical applications in mobility systems and regulatory compliance.

Franz Pernkopfمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Graphical Models
• Signal Processing
• +۴ مورد دیگر

Franz Pernkopf is a Professor at the Signal Processing and Speech Communication Laboratory, Graz University of Technology, Austria. He holds a MSc from Graz University of Technology (1999) and a PhD from the University of Leoben (2002). His research focuses on machine learning, probabilistic graphical models, and signal processing applications in speech recognition, medical data analysis, and radar technology. He has led the Christian Doppler Laboratory for Dependable Intelligent Systems and contributed to national research networks in signal processing. His work emphasizes resource-efficient deep learning, Bayesian networks, and time-series analysis. Notable contributions include interference mitigation in radar systems and acoustic event detection. Awards include the Erwin Schrödinger Fellowship (2002) and Young Investigator Award (2010). He advises on projects ranging from lung sound analysis to embedded system optimizations. His lab hosts courses on signal processing and machine learning.

Shalom Lappinمشاهده پروفایل

استاد

• Syntax
• Probabilistic modelling of linguistic knowledge
• Semantics
• +۳ مورد دیگر

Shalom Lappin is a Professor of Computational Linguistics at the University of Gothenburg and King’s College London. He has held multiple academic roles, including Director of the Centre for Linguistic Theory and Studies in Probability at Gothenburg since 2015. Ph.D. in Philosophy, Brandeis University (1975) M.A. in Philosophy, Brandeis University (1973) B.A. in Philosophy, York University (1970) Lappin’s research focuses on the intersection of syntax, semantics, and computational modeling of linguistic knowledge. He explores probabilistic approaches to grammar induction, the philosophy of language, and the application of machine learning in understanding linguistic structures. His publications highlight trends in syntactic theory, anaphora resolution, and computational linguistics. Notable works include critiques of the Minimalist Program and foundational contributions to natural language processing algorithms. ESRC Professorial Fellowship (2012-2015) Elected Chair of the British Academy’s Linguistics and Philology Section (2011-2014) Fellow of the British Academy (2010) Lappin has led research teams and centers, notably the Centre for Linguistic Theory and Studies in Probability at Gothenburg. His career spans institutions like Tel Aviv University, University of London, and IBM Watson Research Center, with grants supporting interdisciplinary research in linguistics and cognition.