جست‌وجوی استاد و پژوهشگر

Pingali Keshavمشاهده پروفایل

استاد

• Programming Languages
• Parallel Computing
• High-Performance Computing
• +۴ مورد دیگر

Dr. Keshav Pingali is the W.A. 'Tex' Moncrief Chair of Computing and Professor of Computer Science at The University of Texas at Austin, where he also directs the Center for Grid and Distributed Computing at the Oden Institute. Previously, he held the India Chair of Computer Science at Cornell University (2003–2006) and served as a professor in Computer Science and Electrical Engineering at Cornell (1999–2006). He completed his B.Tech. at IIT Kanpur (1978), followed by S.M., E.E., and Sc.D. degrees from MIT (1983, 1986). Research Interests: Focuses on programming languages, compilers, and runtime systems for parallel computing, with emphasis on optimizing irregular applications in domains like graphics and data mining. His work addresses challenges in high-performance computing and multicore processor programming methodologies. Awards & Honors: ACM SIGPLAN Programming Languages Achievement Award (2024) ACM/IEEE CS Ken Kennedy Award (2023) IEEE Charles Babbage Award (2023) Foreign Member of Academia Europaea (2020) Fellow of ACM (2012), AAAS (2010), and IEEE (2010) NSF Presidential Young Investigator Award (1989–1994) President’s Gold Medal, IIT Kanpur (1978) Professional Activities: Keynote speaker at major conferences like ACM PACT (2018), HPCA/PPoPP/CGO (2016), and HIPEAC (2012). Served as editor-in-chief of ACM TOPLAS (2007–2010) and on NSF advisory committees. Currently teaches courses on compiler optimization and performance programming.

Bernhard Kerblمشاهده پروفایل

پژوهشگر

• Real-time graphics
• High-performance rendering
• Virtual Reality (VR)
• +۳ مورد دیگر

Bernhard Kerbl is a Researcher and Teaching Assistant at the Institute of Computer Graphics and Vision, Technical University of Graz (TU Graz). He holds a Master’s degree in Software Engineering and Economics. His research focuses on parallel and distributed processing, with special emphasis on adaptive rendering and GPU optimization under Prof. Dieter Schmalstieg and Dr. Markus Steinberger. His interests include real-time graphics, high-performance rendering, and Virtual Reality (VR). He contributes to teaching by organizing lectures on computer graphics. His recent work involves publications in areas like adaptive radiance caching and distributed rendering systems. Collaborations include projects with industry and academic partners, though specific grants or lab affiliations are not detailed in the provided texts.

• Bioinformatics
• Computational Biology
• Phylogenetics
• +۳ مورد دیگر

Prof. Manuela Schmidt is a faculty member at the University of Vienna, affiliated with the Faculty of Life Sciences and the Department of Pharmaceutical Sciences. Her research focuses on modern systems biology pain research and biomedical applications, primarily conducted in her laboratory. She supervises Master's theses with limited availability, requiring applications between January 1–31, 2025, and coordinates both experimental and literature-based thesis opportunities. Her work spans bioinformatics, computational biology, and phylogenetics, with significant contributions to maximum likelihood phylogenetic methods and software development (e.g., IQ-TREE). Research trends include evolutionary biology, molecular biology, and applications of machine learning in phylogenetic analysis. Additional interests involve environmental microbiology and developmental genetics. For contact, email manuela_schmidt@univie.ac.at. Her lab is located at UZA II, Josef-Holaubek-Platz 2, A-1090 Vienna, Austria (phone: +43-1-4277-55301).

• Optimizing Compilers
• Program Analysis
• Embedded Systems
• +۴ مورد دیگر

Jens Knoop is a Full Professor at TU Wien, leading the Department of Information Systems Engineering (E194) and the Research Unit Compilers and Languages (E194-05). His roles include Head of Institute for Information Systems Engineering and membership in the Curriculum Commission for Informatics. He specializes in compiler design, program analysis, and embedded systems, with a focus on worst-case execution time (WCET) analysis and formal methods. His research interests span optimizing compilers, programming languages, and real-time computing. Notable contributions include the Platin tool suite for WCET analysis and contributions to the T-CREST project on time-predictable multi-core architectures. He has authored and edited multiple influential publications, including proceedings of international conferences such as Code Generation and Optimization (CGO) and ARCS. Professor Knoop has supervised numerous theses, including works on effect systems in Haskell, mutation testing in Rust, and robotic systems. He has received the Most Influential PLDI Paper Award (2002) for foundational work in compiler optimization.

Canan Atilganمشاهده پروفایل

استاد

• Biophysics
• Protein dynamics
• Computational biology
• +۵ مورد دیگر

Canan Atilgan is a Professor at the Faculty of Engineering and Natural Sciences, Sabancı University, Turkey since 2007. She holds a Ph.D. and M.S. in Chemical Engineering from Boğaziçi University (1996) and has been instrumental in biophysics research, particularly in protein dynamics and antibiotic resistance mechanisms. Ph.D. Chemical Engineering, Boğaziçi University (1996) B.S. Chemical Engineering, Boğaziçi University (1991) Her research spans conformational multiplicity, molecular evolution, biophysics, and computational modeling of protein dynamics. Her recent work focuses on allosteric regulation, antibiotic resistance evolution, and efflux pump dynamics, with over 86 archival publications. She has received prestigious awards including EMBO membership (2023), Academia Europaea membership (2023), and TÜBA-GEBİP (2004). She leads the MIDST Lab, supervises numerous graduate students, and actively promotes computational biophysics education in Turkey through workshops and outreach.

Palmroth Minnaمشاهده پروفایل

استاد

• Space Physics
• High-performance Computing
• Space Plasma Simulations
• +۲ مورد دیگر

Minna Palmroth is a Professor in Computational Space Physics at the University of Helsinki (since 2017) and Director of the Finnish Centre of Excellence in Research of Sustainable Space (2018–2025). Previously, she held roles including Research Professor at the Finnish Meteorological Institute (FMI) (2013–2016), Head of Earth Observation Department at FMI (2011–2016), and Academy Research Fellow (2008–2013). She earned her PhD in Physics from the University of Helsinki in 2003 after doctoral studies at FMI. Research Interests : Focused on space weather dynamics, solar wind interactions, and large-scale numerical simulations like the Vlasiator model. Her work combines observations from spacecraft and ground instruments with high-performance computing, addressing plasma turbulence and space weather impacts. She pioneered kinetic plasma simulations in 6-dimensional space, supported by extensive computational resources (e.g., 168 million CPU hours from the Finnish Centre of Scientific Computing and 190 million from PRACE). Awards & Grants : Awarded the ERC Starting (2008) and Consolidator Grants (2016), Väisälä Prize (2016), and the J.V. Snellman Prize (2022). Recognized as a member of multiple prestigious academies, including the Finnish Academy of Science and Letters (2018). Leadership & Infrastructure : Led the Earth Observation Department at FMI and developed Vlasiator, enabling breakthroughs in space plasma dynamics. Active in securing large-scale computing resources critical for her simulations. Engages in interdisciplinary collaborations and public outreach, bridging computational science with space exploration challenges.

Veljko Milutinovicمشاهده پروفایل

استاد

• Microprocessor architecture
• VLSI design
• Data mining
• +۹ مورد دیگر

Veljko Milutinovic is a Full Professor at the School of Electrical Engineering, University of Belgrade since 1990. He has also held academic positions at Purdue University (1982-1989) and consulted globally for institutions like IEEE and ACM. Current role: Computer Area Director, teaching VLSI, Data Mining, and E-Business Previous roles: Tenure Track Assistant Professor at Purdue University International collaborations: EU FP7 projects, Raiffeisen Bank, Wall Street Journal His research spans microprocessor architecture , VLSI design , data mining , and semantic web , focusing on energy-efficient architectures and e-business infrastructure. He pioneered DARPA's 200MHz GaAs RISC microprocessor and 4096-node systolic arrays. Recent work trends include: Technical and semantic interoperability in e-government Customer satisfaction accelerators in e-commerce 3D semantic web visualization Data mining for inverse engineering Academic-industry co-design methods Global university collaboration frameworks Scientific awards include: IEEE Life Fellow (2003) Foreign Member, Montenegrin Academy (2018) IPSI Awards (2007-2010) for eGov projects Best Method papers ranked #1 in Google search Supervised PhD students at Purdue, Belgrade, and Valencia Universities, with significant contributions to cache coherence, GaAs processors, and mobile network security. His publications (over 100 SCI papers) have been cited over 4000 times on Google Scholar.

Michael Quellمشاهده پروفایل

پژوهشگر

• Semiconductor Technology
• Computational Engineering
• Numerical Methods
• +۳ مورد دیگر

Michael Quell is a Researcher at the Microelectronics Research Department (E360-01) at TU Wien. His work focuses on numerical methods for semiconductor process simulation, including level-set techniques, hierarchical mesh processing, and high-performance computing. He holds a Dipl.-Ing. (Master's equivalent) and a Dr.techn. (PhD) in technical sciences. His research emphasizes parallel algorithms for material flow simulation, adaptive mesh refinement, and TCAD applications. Education: Dipl.-Ing. and Dr.techn. in Technical Sciences Research interests span computational engineering, numerical analysis, and semiconductor fabrication modeling. Key contributions include shared-memory parallel implementations of the Fast Marching Method, hierarchical re-distancing algorithms, and feature detection for topography simulations. His work often addresses challenges in nanoscale fabrication processes and multi-material wet etching. Collaborations involve projects on parallel mesh adaptation frameworks and quantum transport simulations. He has contributed to advancing computational tools for semiconductor process design and manufacturing optimization.

• Integrated Circuits
• Quantum Computing
• mm-Wave Transmission
• +۳ مورد دیگر

Harald Pretl is a Professor at the Department of Integrated Circuits within the Institute for Integrated Circuits and Quantum Computing at Johannes Kepler University Linz (JKU). Holding the title Univ.-Prof., he maintains active research leadership with current projects extending through 2029 and 107 documented research outputs including patents, articles, and conference proceedings. His research spans integrated circuits, quantum computing, mm-wave transmission, biomedical sensing, and open-source EDA tools. He specializes in high-frequency circuit design, ultra-low-power biomedical sensors, and educational applications of open-source design methodologies for analog/mixed-signal IC development. Recent work demonstrates cross-disciplinary innovation bridging engineering, neuroscience, and artistic expression. Recent publications reveal strong emphasis on practical implementation challenges: THz transmitter efficiency, brain-computer interface applications, ADC performance metrics, and open-source layout automation. These works collectively advance wireless communication systems, biomedical instrumentation, and accessible semiconductor design education through open-toolchain development. Prof. Pretl has supervised 3 students and actively promotes open-source EDA adoption through educational initiatives. His funded projects include Sub-blocks generators for NSSAR ADC (2025-2029), Open Parasitic Extraction for KLayout, and United Micro Technology collaborations, demonstrating sustained industry engagement. He co-leads the JKU LIT - SAL Intelligent Wireless Systems Lab (IWS Lab) and contributes to the High-Performance Integrated Quantum Computing project. Current activities include 42 documented presentations such as 'Using Open-Source EDA Tools in Hands-On IC Design Education' (2025) and 'Recent Developments in Ultra-Low-Power Biomedical Sensing' (2025), reflecting his dual focus on research innovation and pedagogical advancement.

Sergei Kalininمشاهده پروفایل

استاد

• Machine Learning in Microscopy
• Atom by Atom Fabrication
• Scanning Probe Microscopy
• +۴ مورد دیگر

Sergei Kalinin is the Weston Fulton Professor at the Department of Materials Science and Engineering, University of Tennessee, Knoxville, and holds a Corporate Fellow position at Oak Ridge National Laboratory (ORNL). His academic affiliations include adjunct professorships at Sung Kyun Kwan University (South Korea) and Penn State University. He earned his M.S. summa cum laude from Moscow State University (1998) and Ph.D. from the University of Pennsylvania (2002), both in Materials Science. Kalinin's research focuses on transformative applications of machine learning and AI in materials science, particularly in: Atom-by-atom fabrication using electron beams Big data analytics in scanning probe/transmission electron microscopy Nanoscale electromechanical phenomena in functional oxides Development of novel microscopy techniques and autonomous experimentation frameworks His recent publications (2024-2025) demonstrate strong emphasis on AI-driven microscopy, autonomous labs, and computational materials design. Over 85% of his last 15 articles involve machine learning applications in experimental physics, with emerging focus on human-AI collaboration and real-time instrumentation control. Major scientific honors include: Blavatnik National Award (2018) Feynman Prize in Nanotechnology (2022) Presidential Early Career Award (2009) 4 R&D100 Awards (2008-2023) Fellowships in APS, MRS, IEEE, AVS He directs the ORNL Institute for Functional Imaging of Materials and leads research on autonomous experimentation systems integrating high-performance computing with microscopy platforms.

Christian Vogelمشاهده پروفایل

مدرس ارشد

• Signal Processing
• Analog/Digital Conversion (ADCs)
• Mixed-Signal Systems
• +۵ مورد دیگر

Christian Vogel is a Senior Lecturer affiliated with the Institute for Signal Processing and Speech Communication at TU Graz. His work focuses on advanced signal processing techniques, analog/digital conversion systems, and mixed-signal architectures. Key research areas include time-interleaved ADC error correction, digital predistortion for RF systems, and EVM estimation methodologies. Education: Holds a Dr.techn. (Doctor of Engineering) and Dipl.-Ing. (Diploma in Engineering). His research emphasizes practical applications in mixed-signal systems, including calibration techniques for high-resolution ADCs, nonlinear system compensation, and energy-efficient transmitter design. Recent work explores optimization-based quantization, crest factor reduction in envelope tracking, and adaptive filtering for digital predistortion. Publications span over 15 years, addressing topics ranging from PWM-based RF transmission to all-digital phase-locked loops. His contributions bridge theoretical signal processing with hardware implementation challenges in modern electronic systems. Teaching expertise includes analog and digital signal processing, with authorization to teach at TU Graz. Active in industry-academia collaboration, his work aims to improve system performance in telecommunication and embedded electronics.

Günther Raidlمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Heuristic Optimization
• Metaheuristics
• +۷ مورد دیگر

Günther Raidl is an Associate Professor and Head of the Algorithms and Data Structures Group at the Institute of Computer Graphics and Algorithms, Faculty of Informatics, TU Wien. He holds a Dipl.-Ing. (1992), Ph.D. (1994), and Habilitation (2003) from TU Wien. His research focuses on combinatorial optimization, heuristic methods, and hybrid optimization techniques, addressing large-scale problems in transportation, network design, and cutting/packing. He leads a group of 1 PostDoc, 8 PhD candidates, and collaborates with institutions like the Vienna Graduate School on Computational Optimization (VGSCO). Education: Dipl.-Ing. in Computer Science (1992), TU Wien Ph.D. in Computer Science (1994), TU Wien Habilitation (2003), TU Wien Research Interests: Raidl’s work combines exact and heuristic optimization techniques, including mixed-integer programming, metaheuristics, and matheuristics. Applications span transportation systems (e.g., electric vehicle routing, bike-sharing systems), network design, and bioinformatics. His group leverages high-performance computing resources, such as the Vienna Scientific Cluster (VSC). Publications & Awards: Over 115 reviewed articles in journals/conferences like INFORMS Journal on Computing and Evolutionary Computation. Notable recognition includes the EvoStar 'Old Croc' Award (2012) for contributions to evolutionary computation. Advising & Grants: Supervises 8 funded PhD students and contributes to the Vienna Graduate School’s DK funding (€20,000/year for personnel and travel). Labs/Teams: Algorithms and Data Structures Group at TU Wien, collaborating with researchers like Monika Henzinger and Nysret Musliu.

• Experimental Particle Physics
• High-Energy Physics
• Higgs Boson Physics
• +۱۰ مورد دیگر

Emmerich Kneringer is an Associate Professor at the Institute for Astro- and Particle Physics, Faculty of Mathematics, Computer Science and Physics, University of Innsbruck. He is a key member of the Experimental Particle Physics research group and actively contributes to the ATLAS collaboration at CERN. His research focuses on experimental high-energy physics, particularly Higgs boson physics, top quark physics, electroweak interactions, and searches for physics beyond the Standard Model, including supersymmetry, dark matter, and exotic particles. He also works on advanced data analysis methods such as neural simulation-based inference and machine learning applications in particle physics. Dr. Kneringer's recent publications (2023–2025) show a strong trend in precision measurements (e.g., Higgs and top properties), combination of search results, and innovative analysis techniques. His work spans detector performance, cross-section measurements, and searches for new phenomena in proton-proton and heavy-ion collisions. He is actively involved in public outreach, delivering lectures on astronomy, cosmic radiation, and particle physics to schools and the public, including events like Masterclasses and the Long Night of Research. Experimental Particle Physics Higgs and Top Quark Physics Machine Learning in Physics Beyond Standard Model Searches Heavy-Ion Physics Dr. Kneringer has no listed scientific awards in the provided text. He advises no named students in the material. His research is conducted within the ATLAS collaboration, a large international team at CERN, and he contributes to both physics analysis and detector performance studies. He also participates in educational initiatives, including public lectures and training programs.

• Recommender Systems
• Multimodal Learning
• Music Emotion Recognition
• +۳ مورد دیگر

Marta Moscati works at the Institute of Computational Perception at Johannes Kepler University Linz , focusing on advanced recommendation systems and multimodal learning. Her research spans emotion-based music recommendation, privacy-preserving machine learning, and graph neural networks. Recent work includes: Developing multimodal single-branch architectures for cold-start scenarios Creating preference obfuscation techniques in implicit feedback systems Advancing music emotion recognition with semi-supervised graph networks Contributing to the FAME Challenge for multilingual face-voice association She has published extensively in top AI venues while maintaining technical expertise in both deep learning and theoretical physics , with early work on lepton universality violation. At JKU, she contributes to: Recommendation algorithms development Multimodal representation learning research Musical affective computing applications Privacy-preserving AI frameworks

• Algorithms
• Graph Theory
• Dynamic Algorithms
• +۴ مورد دیگر

Kathrin Hanauer is an Assistant Professor at the University of Vienna, where she is affiliated with the Research Group Theory and Applications of Algorithms and the Research Network Data Science. She conducts research in the design, analysis, and experimental evaluation of fast algorithms, with a focus on Algorithm Engineering connecting theoretical foundations with practical implementations. Her research interests include: Algorithm Engineering for practical algorithm implementation Dynamic algorithms for efficiently handling changing data Graph algorithms and network analysis Reachability problems on directed graphs Ranking problems, particularly the NP-hard Feedback Arc Set problem Network analysis, motif search, and subgraph counting Dr. Hanauer's recent publications demonstrate a strong focus on dynamic graph algorithms, with significant contributions to reachability queries, subgraph counting, and datacenter network optimization. Her work spans both theoretical algorithm design and practical implementation, often with C++ software projects. Notable contributions include the O'Reach algorithm for faster reachability queries in large graphs and several dynamic algorithms for subgraph counting and network analysis. Her scientific contributions: O'Reach: A novel approach to reachability queries in large graphs that outperforms previous methods Dynamic algorithms for four-vertex subgraph counting with efficient update operations Work on demand-aware link scheduling for reconfigurable datacenters Interdisciplinary research on normative reasoning with Aristotelian diagrams Dr. Hanauer actively supervises student research, with numerous completed theses focusing on dynamic graph algorithms, geometric algorithms, and reachability problems. Her lab maintains several software projects related to graph algorithms, including a modular algorithms library for dynamic graphs written in C++ and specialized implementations for reachability queries and subgraph counting.