Find Academic Supervisors

• Cloud-climate interactions
• High-cloud (cirrus) microphysics
• Tropical anvil cloud lifecycle
• +4 more

Blaž Gasparini is an Assistant Professor (Universitätsassistent) at the University of Vienna , Faculty of Earth Sciences, Geography and Astronomy, based in the Department of Meteorology and Geophysics and affiliated with the Environment and Climate Research Hub . He leads an active research programme on cloud-climate interactions and teaches four courses each academic year. Education & early career: MSc ETH (Swiss Federal Institute of Technology) – atmospheric / climate science PhD studies (University of Vienna) focusing on cirrus-cloud geo-engineering with global climate-model simulations Post-doc/assistant professor appointment at University of Vienna since 2020 Research interests sit at the intersection of cloud microphysics and climate dynamics . Using high-resolution models, satellite observations and theory he investigates how high clouds (especially tropical anvil and cirrus) form, evolve and interact with radiation, and how these processes may change as the planet warms. A second strand explores deliberate climate intervention (cirrus thinning, stratospheric aerosols) and associated physical and ethical uncertainties. His recent publications (2024-2025) reveal three dominant themes: (i) lifecycle and radiative impacts of tropical anvil clouds, (ii) turbulence and microphysical controls on cirrus ice formation, and (iii) critical evaluation of geo-engineering proposals. Collectively the work advances understanding of cloud-radiative feedbacks that govern climate sensitivity and the hydrological cycle. Teaching & outreach: Each semester Dr Gasparini coordinates 2–3 courses including Modelling and Data Analysis , Thermodynamics of the Atmosphere and the Paper Club seminar. He frequently appears on Austrian/Slovenian radio and podcasts explaining climate science and geo-engineering, co-authored the youth brochure "WTF is Climate Change?!" (EN/SL/HU), and maintains an educational blog on high-performance computing for climate modelling. Grants & projects: He is principal investigator on Austrian Science Fund (FWF) and EU-funded projects examining cloud-radiative heating, cirrus microphysics and aerosol-cloud interactions, and participates in international model inter-comparison activities (RCEMIP, CMIP6 analyses). No doctoral students or major scientific prizes are listed in the supplied material.

Lukas SteinbergerView profile

Associate Professor

• Statistical Machine Learning
• Differential Privacy
• High-dimensional Data Analysis
• +3 more

Lukas Steinberger serves as Associate Professor for Statistical Machine Learning in the Department of Statistics and Operations Research at the University of Vienna's Faculty of Business, Economics and Statistics. He is also affiliated with the university's Data Science research network. Prior to his current position, he was a post-doctoral researcher with Angelika Rohde at the University of Freiburg and held a temporary Full Professorship in the Department of Mathematics at the Technical University of Munich. Steinberger's research program centers on statistical inference under differential privacy, high-dimensional data analysis, and uncertainty quantification in statistical learning. His work bridges theoretical statistics with practical machine learning applications, particularly focusing on how to perform valid statistical inference while preserving data privacy. His research has significant implications for fields requiring sensitive data analysis including healthcare, finance, and social sciences where privacy constraints are paramount. His publication record demonstrates consistent contributions to top-tier statistics journals including multiple papers in the Annals of Statistics. His recent work shows a clear trajectory toward developing theoretically sound methods for privacy-preserving data analysis that maintain statistical efficiency. The research themes across his publications reveal strong connections between differential privacy mechanisms, high-dimensional statistical theory, and practical machine learning applications. Scientific Awards: Award for dissertations in the field of theoretical statistics (2016) Steinberger maintains an active research agenda with numerous conference invitations and seminar presentations internationally, reflecting his standing in the statistical community. His current research project "Classification - Preprocessed and high-dimensional data" (2021-2025) indicates ongoing funding and research direction. He serves as Vice-Director of Studies for the Business, Economics and Statistics program and teaches courses including Statistics 2, Computational Statistics, and Classification, Clustering and Discrimination at the master's level. As part of the Data Science research network at University of Vienna, Steinberger collaborates with interdisciplinary teams working at the intersection of statistics, computer science, and domain-specific applications. His research group focuses on developing theoretically grounded approaches to statistical machine learning problems under privacy constraints.

• Algorithms
• Sparse Matrices
• Parallel Computing
• +3 more

Robert Ernstbrunner is a researcher affiliated with the Faculty of Computer Science , contributing to algorithm development and parallel computing. His work focuses on low-rank approximations and fault-tolerant strategies in linear algebra methods. Research Interests: His research spans computational efficiency, numerical linear algebra, and fault tolerance in high-performance computing environments. Key areas include sparse matrix operations and resilience mechanisms for iterative algorithms. Publications: He has published two peer-reviewed papers: (1) a 2022 study on precision-cost trade-offs in low-rank matrix approximations, and (2) a 2020 framework for node-failure resilience in iterative linear algebra methods. Academic Activities: Robert has presented at international conferences, including the 2022 IEEE IPDPS and the 2020 FTXS workshop.

Manijeh RazeghiView profile

Professor

• Semiconductor Physics
• Optoelectronic Devices
• Quantum Structures and Devices
• +7 more

Manijeh Razeghi is the Walter P. Murphy Professor of Electrical and Computer Engineering and Director of the Center for Quantum Devices at Northwestern University. She also serves as an Adjunct Professor at the Optical Sciences Center, University of Arizona, and maintains significant professional connections with institutions in France and Switzerland. With a career spanning over three decades at Northwestern, Professor Razeghi has established herself as a world-leading authority in semiconductor physics and optoelectronic devices across the electromagnetic spectrum. Her educational background includes: 1980: Docteur d'etat es Sciences Physiques, Universite de Paris, France 1977: Docteur 3eme Cycle, Solid State Physics, Universite de Paris, France 1976: DEA, Science des Materiaux, Universite de Paris, France Professor Razeghi's research focuses on cutting-edge developments in semiconductor technology, particularly in the areas of quantum structures and devices spanning the electromagnetic spectrum from deep ultraviolet to terahertz frequencies. Her pioneering work in epitaxial manufacturing techniques has enabled significant advances in optoelectronic devices, particularly in infrared and terahertz technologies. She has made substantial contributions to condensed matter physics and engineering, with particular emphasis on nonlinear optics and semiconductor physics and technology. Her current research explores gallium oxide materials, quantum cascade lasers, and type-II superlattices for advanced photodetection applications, with a strong focus on practical implementations for telecommunications, imaging, and sensing. Analysis of Professor Razeghi's extensive publication record (over 1,000 papers) reveals a consistent trajectory of innovation in semiconductor photonics. Her recent work demonstrates a strategic progression toward developing high-power, efficient devices capable of room-temperature operation across challenging spectral regions. There is a clear emphasis on gallium oxide materials and type-II superlattices to overcome traditional limitations in infrared detection and terahertz generation. Her research increasingly bridges fundamental materials science with practical device engineering, resulting in technologies with significant commercial and scientific applications. Professor Razeghi has received numerous prestigious honors and awards throughout her distinguished career: Benjamin Franklin Medal in Electrical Engineering (2018) Elected Lifetime Fellow of IEEE (2017) Jan Czochralski Gold Medal (2016) IBM Faculty Award (2013) Elected Lifetime Fellow of Materials Research Society (2008) Multiple fellowships including APS, IOP, OSA, SPIE, and SWE Society of Women Engineers Achievement Award (1995) IBM Europe Science and Technology Prize (1987) As an academic mentor, Professor Razeghi has supervised 51 PhD dissertations and 20 MS theses at Northwestern University, and currently oversees approximately 15 PhD students, post-doctoral researchers, and visiting faculty. She created the Graduate and Undergraduate Programs in Solid State Engineering in the ECE Department at Northwestern, establishing a comprehensive 12-course curriculum. Her professional service includes chairing international conferences and serving on numerous editorial boards for leading journals in physics and engineering. She has also provided expertise to international organizations including the United Nations and the European Research Council, demonstrating her global impact on semiconductor science and technology. Professor Razeghi directs the Center for Quantum Devices (CQD) at Northwestern University, a world-class research facility focused on semiconductor materials and devices. Under her leadership, CQD has become a hub for innovation in infrared and terahertz technologies, with research spanning fundamental materials science to practical device applications. The center maintains strong connections with industry and government laboratories, facilitating the translation of basic research into real-world technologies. Current research directions at CQD include advanced quantum cascade lasers, novel infrared detectors, and next-generation semiconductor materials for optoelectronic applications.

Masana Castrillo, MarcView profile

Researcher

• Deep Learning
• Continual Learning
• Computer Vision
• +3 more

Herr Marc Masana Castrillo is a Researcher at the Institute of Computer Graphics and Vision within Graz University of Technology (College of Engineering). Holding a PhD in Computer Vision (cum laude) from Universitat Autònoma de Barcelona (2020), he specializes in Deep Learning , Continual Learning , and Neural Network Compression . His work addresses catastrophic forgetting in sequential tasks, out-of-distribution detection, and domain adaptation. PhD Thesis: "Lifelong Learning of Neural Networks: Detecting Novelty and Adapting to New Domains without Forgetting" MSc in Computer Vision (with Honours, 2015) BSc in Mathematics and Computer Science (2014) His research spans continual learning frameworks , feature disentanglement , and multimodal translation . Publications in top-tier venues like TPAMI , BMVC , and ICCV highlight his contributions. He co-developed Avalanche , an open-source PyTorch-based library for reproducible continual learning research. Scientific recognition includes the Best Master Thesis Project at UAB (2015) and the Business Track Award at Accenture Datathon (2016). His email addresses are mmasana@tugraz.at and marc.masana@icg.tugraz.at . He has reviewed for journals like TPAMI and conferences including CVPR and ICCV .

• Computer Science
• Operating Systems
• Computational Thinking
• +7 more

Peter Denning is a Distinguished Professor of Computer Science at the Naval Postgraduate School (NPS) in Monterey, CA, serving as Department Chair. He has held prominent roles including Professor and Department Chair at George Mason University, Director of NASA's Research Institute for Advanced Computer Science (RIACS), and faculty positions at Purdue University, Princeton University, and MIT. His research focuses on computational thinking, great principles of computing, operating systems, security, performance modeling, and innovation adoption. Denning has contributed to foundational work in virtual memory, working set theory, and queueing networks. He has authored influential books such as The Innovator's Way and Computational Thinking . His honors include IEEE Fellow (1982), ACM Distinguished Service Award (1989), ACM Karlstrom Educator Award (1996), and the IEEE Computer Society Pioneer Award (2021). He has received eight best paper awards across his career. Denning has led NSF-funded initiatives like the 'Resparking Innovation' project and pioneered interdisciplinary education in computing principles. His work bridges theoretical computing foundations with real-world applications in education and innovation leadership.

Tiziana Di MatteoView profile

Professor

• Econophysics
• Complex Systems
• Complex Networks
• +3 more

Professor Tiziana Di Matteo is the world's first Professor of Econophysics at King's College London (KCL), appointed in 2014. She is affiliated with the Department of Mathematics within the Faculty of Natural & Mathematical Sciences. Additionally, she serves as External Faculty at the Complexity Science Hub in Vienna and is a member of the UCL Centre for Blockchain Technologies. Her roles include Enterprise and Engagement Lead at KCL and Knowledge Exchange Champion for the UK Knowledge Exchange Hub for Mathematical Sciences. Di Matteo holds a BSc (Hons) in Physics (1994) and a PhD in Physics (1999) from the Università di Salerno, Italy. Her research focuses on interdisciplinary applications of physics to economics and finance, spanning: Econophysics : Analyzing financial markets using statistical physics. Complex Systems & Networks : Studying interdependencies in financial and social systems. Data Science : Developing tensor-based methods for multidimensional forecasting. Her 120+ publications predominantly explore volatility modeling, network theory, and machine learning applications in economics. Recent work emphasizes multiscaling analysis, blockchain impacts, and pandemic-related economic resilience. Scientific Awards: Elsevier’s Most Cited Article Award (2010) QEII Fellowship (2003-2008) INFM/CNR Fellowships (1994-1995) Erasmus Fellowship (1992) She leads PhD students (names unspecified) and collaborates globally. Editorial responsibilities include serving as Main Editor for Physica A and Editor for Chaos, Solitons & Fractals .

Matthias JanetschekView profile

Associate Professor

• Computer Science
• Cybersecurity
• Distributed Systems
• +5 more

Matthias Janetschek holds the position of Associate Professor at the Management Center Innsbruck (MCI), Austria, since September 2020. Previously, he served as a Lecturer at MCI (2018–2020) and was a Co-Founder/Senior Developer at Felix Solution UG (2013–2015). He earned a PhD in Computer Science from the University of Innsbruck (2011–2019), alongside earlier degrees in Computer Science (BSc 2002–2007, MSc 2007–2011). His research focuses on distributed systems, workflow management, cybersecurity, and IoT , with notable contributions to scientific workflow execution on cloud/manycore architectures and AR/VR applications in education. He has supervised over 18 bachelor/master theses addressing topics like IoT security, reinforcement learning, and edge computing. Teaching responsibilities include courses such as Distributed Systems , Virtual Reality , and Computer Architecture at MCI. He has also completed further training in online education tools (e.g., Sakai, Adobe Connect) and corporate communication. His publications span journals like Multimodal Technologies and Interaction and conference proceedings on workflow systems, cloud computing, and AR usability. Current work emphasizes large-scale IoT security assessments and edge computing optimizations.

• Distributed Systems
• Computer Science
• Networking
• +3 more

Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Markus Peschl is a full professor affiliated with the Institute of Distributed Systems at TU Wien. His research focuses on distributed systems, networking, and related computational paradigms. He holds academic titles including Austrian University Professor (Ao.Univ.Prof.) and Doctor of Technical Sciences (Dr.techn.) . Contact details include his university email and TISS profile.

• High Performance Computing
• MPI Programming
• Parallel Algorithms
• +2 more

Ioannis Vardas is a Researcher in Parallel Computing at TU Wien's Faculty of Informatics. His work focuses on optimizing high-performance computing systems through advanced process mapping, MPI programming, and performance analysis tools. Key research areas: Topology-aware process mapping for HPC MPI library development and optimization Performance profiling of parallel applications Resource allocation in hierarchical architectures Energy-efficient parallel computing He contributes to projects improving MPI performance tools and develops methods for efficient resource utilization in supercomputing environments.

• Computer Architecture
• Compilers
• Programming Languages
• +4 more

Andreas Krall is an Associate Professor in the Department of Software Technology at TU Wien's Faculty of Informatics. He holds roles as Curriculum Coordinator for the Bachelor Informatics and Bachelor Software and Information Engineering programs, and serves as a Substitute Member of the Curriculum Commission for Informatics. His research focuses on compiler design, architecture description languages (e.g., VADL), and formal methods for ensuring correctness in compiler-processor co-design. Key research areas include computer architecture, compiler verification, embedded systems, and the development of tools for processor simulation and optimization. Krall has led projects such as 'Correct Compilers for Correct Processors' (2010–2015) and contributed to the CACAO JVM project. He has published extensively on topics like instruction selection, abstract state machines (CASM), and SSA-based optimizations. Notable awards include the Heinz Zemanek Preis (1987). His work spans over 30 years, with contributions to both academic research and industry-relevant tools like the VADL architecture description framework. Krall has advised numerous students in topics ranging from compiler backends to garbage collection algorithms.

Sabrina HerbstView profile

Researcher

• Quantum Computing
• Machine Learning
• High-Performance Computing
• +3 more

Sabrina Herbst is a PreDoc Researcher at the Department of Computational Sustainability within the Faculty of Informatics at Technische Universität Wien . She works on Quantum Computing , focusing on Machine Learning integration and High-Performance Computing (HPC) augmentation for quantum algorithms. Education : Dipl.-Ing. (2023) in Informatics from TU Wien, with a thesis on quantum machine learning. Her research explores scalable Quantum Machine Learning (QML) algorithms, emphasizing theoretical foundations and robust numerical implementations. Key areas include Quantum Neural Networks (QNNs) , hyperparameter optimization, and the intersection of quantum computing with HPC and edge computing. Recent work investigates channel distinguishability in QNNs and hybrid quantum-classical systems for IoT data processing. Projects she contributes to include HPQC (High Performance integrated Quantum Computing), Themis FWF (Trustworthy and Sustainable Code Offloading), and TRITON FWF (Transprecise Edge Computing). Awards include the Andreas Dieberger–Peter Skalicky Scholarship (2025), Siemens Award for Excellence (2023), and a conference scholarship from TU Wien (2024). Scientific Awards Andreas Dieberger–Peter Skalicky Scholarship (2025) Siemens Awards for Excellence (2023) Conference Scholarship for female PhD students (2024)

• Compilers
• Programming Languages
• Computer Architecture
• +3 more

Benedikt Lukas Huber serves as a PreDoc Researcher at TU Wien's Faculty of Informatics within the Compilers and Languages research group (Institute E194-05), located at Argentinierstrasse 8, Room EA0402. His primary affiliation centers on compiler technologies and system architecture research. His research spans Compilers , Programming Languages , and Computer Architecture , with specialized focus on architecture description languages and compiler optimization techniques like software pipelining. Recent work demonstrates sustained innovation in formal system modeling and performance-critical code generation. Publication analysis reveals consistent contributions to compiler infrastructure, particularly through the Vienna Architecture Description Language (2024) and foundational software pipelining research (2008), highlighting expertise in bridging hardware constraints with high-level language design. No scientific awards or fellowships were documented in available sources. Information regarding student advising, research grants, or leadership roles remains unspecified beyond his core research group affiliation. He operates within TU Wien's Compilers and Languages research ecosystem, contributing to compiler design and system architecture initiatives under the Institute of Engineering Mathematics and Computing Science.

Ricardo Baeza-YatesView profile

Professor

• Algorithms
• Responsible AI
• Web Search and Mining
• +7 more

Ricardo Baeza-Yates serves as Director of Research at the Institute for Experiential AI at Northeastern University's Silicon Valley campus. He also maintains part-time professor positions at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. Previously, he was VP of Research at Yahoo Labs (2006-2016) and CTO of NTENT, a semantic search technology company. His academic career spans over three decades with significant contributions to both industry research and academia. Dr. Baeza-Yates earned his Ph.D. in Computer Science from the University of Waterloo, Canada, in 1989. His academic journey includes professorships at Universidad de Chile (1989-2004), ICREA Professor at Universitat Pompeu Fabra (2004-2005), and ongoing part-time appointments at both institutions since 2004 and 2006 respectively. His research spans algorithms, information retrieval, web search and mining, data science, with increasing emphasis on responsible AI and ethical considerations in technology. He is particularly renowned for his work on bias in search systems, algorithmic fairness, and the societal impacts of AI. His expertise uniquely bridges technical aspects of search and data mining with ethical and societal implications, making him a leading voice in the responsible AI movement globally. His recent scholarly output demonstrates a strong focus on bias detection, fairness metrics, responsible technology development, and the societal impacts of algorithms. The work spans technical machine learning and information retrieval while increasingly addressing ethical and social dimensions across diverse application areas from finance to healthcare to social media. Dr. Baeza-Yates has received numerous prestigious awards throughout his distinguished career: 2019 Corresponding Member of the Brazilian Academy of Sciences 2019 Award Salvà i Campillo to the Person of the Year 2018 National Research Award for applied research and technology transfer 2012 ASIS&T Best Information Science Book Award for Modern Information Retrieval 2011 IEEE Fellow for contributions to computer science development 2009 ACM Fellow for contributions to information retrieval algorithms 2002 Corresponding member Chilean Academy of Sciences As an educator and mentor, Dr. Baeza-Yates has advised numerous students throughout his academic career at multiple institutions. His textbook 'Modern Information Retrieval' has become a standard reference in the field, influencing generations of computer scientists. He has secured significant research funding through his leadership roles at Yahoo Labs and academic grants, demonstrating his ability to bridge theoretical research with practical applications. Dr. Baeza-Yates is actively involved with the DATA Lab at Northeastern University's Khoury College of Computer Sciences and previously founded the Web Science and Social Computing Research Group at Universitat Pompeu Fabra. He serves on multiple high-impact advisory boards including the Global AI Ethics Consortium, Global Partnership on AI, and IADB's fAIr LAC Initiative for Latin America and the Caribbean, reflecting his global influence in shaping responsible AI practices.

Petter E. BjørstadView profile

Professor

• Numerical analysis
• Domain decomposition
• Elliptic PDE
• +4 more

Petter E. Bjørstad is a Professor of Computer Science and Mathematics at the University of Bergen, Norway, where he has served as Head of the Department of Informatics since 2010. Previously, he was Director of the Bergen Center for Computational Science (2000-2010) and Professor in both the Department of Mathematics (2006-2010) and Department of Informatics (1985-2005). He also held a position as Professor of Mathematics at the University of Minnesota during 1996-1997 on unpaid leave from Bergen. Dr. Bjørstad earned his PhD in Computer Science from Stanford University in 1980, following which he completed a postdoctoral fellowship at the Courant Institute of Mathematical Sciences at New York University. Before entering academia, he worked as a Principal Engineer at Det Norske Veritas from 1981-1985. Dr. Bjørstad's research focuses on numerical analysis and high-performance computing, with particular expertise in domain decomposition methods, parallel algorithms for elliptic partial differential equations, and scientific computing. His work bridges theoretical mathematics with practical computational challenges, especially in industrial applications. He has led significant research projects including 'Multiscale Domain Decomposition: Algorithms and Analysis' (2010-2014), funded by the Research Council of Norway and the University of Bergen. His publication record demonstrates consistent contributions to the field of numerical methods and parallel computing over several decades. The research trends show a strong focus on developing efficient algorithms for solving partial differential equations through domain decomposition techniques, with applications spanning structural analysis, reservoir simulation, semiconductor device modeling, and other industrial contexts. His work increasingly emphasizes practical implementation on modern parallel architectures including SIMD and MIMD systems. Large number of research grants from Norway and the European Union Dr. Bjørstad has been actively involved in mentoring students and collaborating with industry through projects like the Europort effort, where industrial codes were ported to parallel computing platforms. His laboratory for parallel computing (Parallab) has been instrumental in advancing practical applications of high-performance computing since its establishment in 1985 with Europe's first 64-processor Intel hypercube. The lab has evolved to include multiple MIMD machines including an Intel Paragon, Parsytec GC/Power-Plus, and DEC α-cluster. As Head of the Department of Informatics, Dr. Bjørstad leads one of Norway's premier computing research units with expertise spanning theoretical computer science, numerical methods, and practical applications in various scientific domains. His leadership has helped establish Bergen as a significant center for computational science in Europe.