جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Bioinformatics
• Materials Science
• +۷ مورد دیگر

Pengyu Hong is a Professor of Computer Science at Brandeis University's Michtom School of Computer Science and an affiliated faculty member at the Benjamin and Mae Volen National Center for Complex Systems. His expertise spans Machine Learning, Bioinformatics, Materials Science, and FinTech, with a focus on interdisciplinary applications in healthcare, molecular biology, and complex systems analysis. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign M.E. in Computer Science, Tsinghua University B.Eng. in Computer Science, Tsinghua University Research Interests: Hong's lab develops advanced machine learning techniques for analyzing heterogeneous data (images, text, financial data), with notable contributions in glycomaterials analysis, clinical outcome prediction, and active nematics modeling. His work bridges computational methods with biomedical and material science challenges, including NMR spectroscopy analysis and molecular property prediction. Publications: Recent work focuses on machine learning applications in glycan sequencing, fairness analysis in medical algorithms, and optical flow techniques for fluid dynamics. The lab also maintains benchmark datasets like GlycoNMR for carbohydrate analysis. Labs & Teams: Hong leads research at the Volen National Center for Complex Systems, integrating computational approaches with experimental systems biology and materials science.

Vladimir Okhmatovskiمشاهده پروفایل

استاد

• Electromagnetics
• Computational Electromagnetics
• High-Performance Computing
• +۴ مورد دیگر

Dr. Vladimir Okhmatovski is a Full Professor in the Department of Electrical and Computer Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. His research focuses on fast algorithms in electromagnetics, quantum computing, and high-performance computing. He holds a Ph.D. from the Moscow Power Engineering Institute and has held academic and industry roles globally. He has received prestigious awards including the 2017 Intel Outstanding Researcher Award. Education: Ph.D., Antennas and Microwave Circuits, Moscow Power Engineering Institute, 1997 M.S., Radiophysics and Electronics, Moscow Power Engineering Institute, 1996 Research Interests: Dr. Okhmatovski develops advanced computational methods for electromagnetics, including tensor train decompositions and quantum algorithms. His work addresses challenges in signal integrity, layered media analysis, and inverse scattering. He leads efforts in applying quantum computing to solve matrix equations and has pioneered fast direct solvers using H-matrices. Awards: 2017 Intel Outstanding Researcher Award 1996 Best Young Scientist Report (VI International Conference on Mathematical Methods in Electromagnetic Theory) 2007 Outstanding ACES Journal Paper Award Advising & Grants: He mentors students in climate change research through collaborations with the Centre for Earth Observation Science. His funded projects include quantum computing applications and Arctic sea ice remote sensing. He chairs technical committees for IEEE MTT-S and AP-S. Labs/Teams: His research group focuses on electromagnetic modeling, quantum algorithms, and layered media analysis. Collaborations include the Churchill Marine Observatory for Arctic studies.

• Recommender Systems
• Context-aware Recommendation
• Explainable AI
• +۵ مورد دیگر

Markus Zanker is a Professor in the Department of Knowledge Engineering within the Faculty of Computer Science at Free University of Bozen-Bolzano, Italy. With an extensive publication record spanning over two decades, he has established himself as a leading expert in recommender systems research, with particular expertise in context-aware, group, and knowledge-based recommendation approaches. His work bridges theoretical foundations with practical applications across diverse domains including tourism, healthcare, and e-commerce. Zanker's research interests center on advancing the theoretical underpinnings of recommender systems while addressing practical challenges in real-world deployments. His recent work has focused on causal decision-making frameworks for recommendation, intent-aware systems, and the integration of generative AI in group recommendation scenarios. He has made significant contributions to explainable recommendation systems, medical recommendation applications, and tourism recommendation systems, demonstrating both theoretical rigor and practical impact. His recent publication portfolio reveals a strong trend toward addressing fundamental challenges in recommendation science, including the growing emphasis on causal reasoning to move beyond correlation-based approaches, the integration of generative AI capabilities for more sophisticated group decision support, and the development of frameworks for ethical and socially beneficial recommendation systems. His work increasingly bridges the gap between traditional recommendation algorithms and emerging AI paradigms while maintaining focus on real-world applicability. Zanker is actively involved in the academic community as a workshop organizer, having co-chaired the Knowledge-aware and Conversational Recommender Systems (KaRS) workshop and the Recommenders in Tourism (RecTour) workshop for multiple consecutive years at major conferences including RecSys. His research has been supported through collaborations with numerous international researchers and institutions, focusing on both theoretical advances and practical implementations of recommendation technology across multiple application domains.

Madhur Tulsianiمشاهده پروفایل

استاد

• Theoretical Computer Science
• Complexity Theory
• Algorithm Design
• +۳ مورد دیگر

Madhur Tulsiani is a Professor at the University of Chicago's Department of Computer Science and a researcher at the Toyota Technological Institute at Chicago (TTIC). His research focuses on theoretical computer science, particularly complexity theory and algorithm design, with applications in coding theory and information theory. He has been supported by NSF grants 1254044, 1816372, and 2326685. Education: Bachelor’s in Computer Science, IIT Kanpur (2001-2005) Ph.D. in Computer Science, UC Berkeley (2005-2009), advised by Luca Trevisan Postdoctoral fellowships at the Institute for Advanced Study (IAS) and Princeton University Research Interests: Mathematical foundations of computation Complexity theory and algorithm design Coding theory and error-correcting codes Sum-of-Squares hierarchies and approximation algorithms Recent Contributions: Pioneering work on list decodable codes and expander-based constructions Advances in approximation algorithms for high-dimensional expanders Lower bounds for Sum-of-Squares algorithms using high-dimensional expanders Teaching: Information and Coding Theory Mathematical Toolkit (linear algebra/probability) Summer REU programs in theoretical computer science Students: Advised PhD students including Fernando Granha Jeronimo, Goutham Rajendran, and Shashank Srivastava Co-advised students with Sasha Razborov, Janos Simon, and others Labs/Groups: Member of the Theoretical Computer Science Group at TTIC and UChicago, contributing to cross-disciplinary research in algorithms and complexity.

Saleh Ashkboosمشاهده پروفایل

پژوهشگر

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Saleh Ashkboos is a Ph.D. student in the Computer Science Department at ETH Zurich, advised by Professors Torsten Hoefler and Dan Alistarh. He is also a Research Assistant at the Scalable Parallel Computing Lab and an affiliated doctoral student of the ETH AI Center. His research focuses on accelerating deep neural network training and developing systems for large-scale graph processing. Prior to ETH Zurich, he earned his Master's degree in Computer Science from Sharif University of Technology, advised by Professor Amir Daneshgar. His work has led to notable contributions, including the best paper award at SC22 for 'ProbGraph.' Recent research emphasizes efficient LLM training and quantization techniques, with publications on topics like 4-bit inference, quantization-aware training frameworks, and scalable meteorological modeling. He has interned at Apple and Microsoft, and his work is accessible via Google Scholar and GitHub. Key projects include GPTQ (post-training quantization for transformers), SliceGPT (LLM compression), and ProbGraph (high-performance graph mining). His technical contributions span distributed systems, neural network optimization, and climate-related machine learning.

Kai Salomaaمشاهده پروفایل

استاد

• Theory of Computation
• Automata Theory
• Formal Languages
• +۵ مورد دیگر

Kai Salomaa is a Professor and Graduate Chair in the School of Computing at Queen’s University, Canada. He holds a Ph.D. from the University of Turku (1989). His research focuses on theoretical computer science, particularly automata theory, formal languages, and their applications. Key areas include descriptional complexity, cellular automata, and quantum computing innovations. Affiliations: Queen’s University, School of Computing. Education: Ph.D. in Computer Science from the University of Turku (1989). Research Interests: Prof. Salomaa explores foundational topics like automata state complexity, nondeterminism measures, and computational models. His work bridges classical theory with modern applications in quantum computing, vehicular networks, and algorithmic resource optimization. Notable contributions include studies on input-driven pushdown automata and the integration of quantum algorithms into practical systems. Publications: His recent work spans quantum-enhanced optimization (e.g., vehicle platooning), fair matching algorithms, and complexity analysis of automata. These studies emphasize innovative solutions for computational challenges in dynamic systems and distributed networks. Grants & Labs: Leads the Formal Languages and Automata Theory Research Group, actively organizing conferences like CIAA and DCFS. His work often addresses practical applications of theoretical computer science in areas like sensor networks and metaverse resource management.

Gustavo Scuseriaمشاهده پروفایل

استاد

• Computational Quantum Chemistry
• Electronic Structure Theory
• Strong Correlation
• +۸ مورد دیگر

Gustavo Scuseria is the Robert A. Welch Professor of Chemistry, Professor of Physics and Astronomy, and Professor of Materials Science and NanoEngineering at Rice University . He is a leading figure in computational quantum chemistry , with seminal contributions to electronic structure theory , coupled cluster methods , and density functional theory (DFT) functionals like HSE and PBE0. His research spans strong correlation , symmetry-projection techniques , and quantum computing applications . Education: PhD in Physics (1983) from University of Buenos Aires Research: Pioneered linear scaling quantum methods , developed HSE functional for semiconductor band gaps, and advanced symmetry-projected wave function approaches Awards: Feynman Prize in Nanotechnology, Humboldt Research Award, Guggenheim Fellowship, and multiple Fellowships from ACS, APS, and RSC Software Contributions: Key developer of Gaussian suite and TURBOMOLE implementations His recent publications focus on symmetry-projected methods for spin systems, dualities in electron correlation , and quantum computing applications . Collaborations with institutions like Los Alamos National Laboratory and Max-Planck Institute have shaped his interdisciplinary approach. Scuseria's work remains foundational for quantum chemistry software and materials science research.

Pierpaolo Vivoمشاهده پروفایل

استاد

• Random Matrix Theory
• Statistical Mechanics
• Complexity Science
• +۳ مورد دیگر

Pierpaolo Vivo is a Reader in Disordered Systems at King's College London's Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD from Brunel University (2008) and conducted postdoctoral research at ICTP Trieste and LPTMS Orsay. His research focuses on Random Matrix Theory, Statistical Mechanics applied to socio-economic systems, and complexity science in legal frameworks. Key research interests include eigenvalue statistics, financial stability modeling, and legal system complexity. His work bridges physics, economics, and law, with recent projects exploring phase transitions in debt recycling and network-based legal visualization tools like Graphie. Major achievements include a UKRI Future Leaders Fellowship (2019) and over 50 peer-reviewed publications. He leads projects on legal system modeling and collaborates internationally on interdisciplinary topics.

• Geophysics
• Seismic Imaging
• Petroleum Engineering
• +۲ مورد دیگر

Egil Tjåland is an Associate Professor in the Department of Geoscience at the Norwegian University of Science and Technology (NTNU). His research focuses on geophysical methods, seismic attribute analysis, and applications in petroleum engineering and deep-sea mining technologies. His publications demonstrate expertise in seismic interpretation methods, attribute benchmarking, and geophysical data processing. Recent work explores the transfer of oil and gas technologies to deep-sea mining operations. He teaches courses in geophysics, petroleum engineering, and scientific communication. His research contributions include developing seismic analysis tools like A3Mark for attribute benchmarking and investigating elastic parameter estimation methods. No information regarding awards, supervised students, or research grants is provided.

Bart De Moorمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Optimization
• Algebraic Geometry
• +۱۱ مورد دیگر

Bart De Moor is a Full Professor at the Department of Electrical Engineering, KU Leuven, Belgium, and a guest professor at the University of Siena. He leads the STADIUS research group and has supervised 85 PhD students. His roles include chairman of Health House (2016–present), member of the Board of VIB (Biotech Institute), and former Vice-Rector for International Policy (2009–2013). Education: Master Degree in Electrical Engineering (1983), KU Leuven PhD in Engineering (1988), KU Leuven Research Interests: His work spans numerical linear algebra, optimization, algebraic geometry, systems and control theory, data-driven AI, machine learning, and applications in process industry and biomedical big data. He has contributed to subspace identification, tensor decomposition, bioinformatics, and quantum computing. Publications Trends: His publications highlight subspace identification methods, tensor decomposition, bioinformatics, and biomedical data analysis. These reflect interdisciplinary advancements in control theory, quantum physics, and mathematical engineering, with applications in industrial and healthcare domains. Scientific Awards and Honors: Leslie Fox Prize (1989) Laureate of the Belgian Royal Academy of Sciences (1992) Bi-annual Siemens Award (1994) Fellow of IEEE (since 2004) Member of the Royal Academy of Belgium for Science and Arts (since 2000) Fellow of IFAC (since 2022) Commander in the Order of King Leopold I (2020) Fellow of SIAM (since 2017) FWO Excellence Award (2010) Advising and Grants: He has led a research group of 20 PhD students and postdocs, co-founded 8 spinoff companies, and secured the ERC Advanced Grant ‘Back to the roots’ (2020–2025). He also co-holds the KU Leuven Chair on healthcare systems (2018–present). Labs and Organizations: Active in the STADIUS research group (KU Leuven), he has served on boards of the Flemish Interuniversity Institute for Biotechnology (VIB), the Alamire Foundation, and the Health Tech Experience Center Health House. His spinoffs include Trendminer, Cartagenia, and Ugentec.

Sergiy Vorobyovمشاهده پروفایل

استاد

• Optimization
• Signal Processing
• Multi-Linear Algebra
• +۹ مورد دیگر

Sergiy Vorobyov is a Professor at the Department of Signal Processing and Acoustics , Aalto University , Finland. He has held academic and research positions at multiple institutions, including the University of Alberta (Canada), Kharkiv National University of Radio Electronics (Ukraine), RIKEN (Japan), McMaster University (Canada), Duisburg-Essen University and Darmstadt University of Technology (Germany), and Heriot-Watt University (UK). His expertise spans optimization, signal processing, and multi-antenna systems. Dr. Vorobyov holds a Doctoral degree in Natural Sciences from the National Technical University Kharkiv Polytechnical Institute, awarded on January 15, 2002. His research interests focus on optimization and multi-linear algebra applied to signal processing challenges, including statistical and array signal processing, sparse signal processing, estimation and detection theory, and sampling theory. He explores multi-antenna, large-scale, cooperative, and cognitive systems, contributing to advancements in wireless communications and radar engineering. His work aligns with UN Sustainable Development Goals, emphasizing education and innovation. In recent years (2025), his publications emphasize cutting-edge advancements in wireless communications and signal processing. Topics include millimeter-wave MIMO channel estimation, optimization algorithms with momentum-based techniques, vehicular network communications, and robust covariance matrix estimation in challenging noise environments. These contributions highlight his expertise in developing efficient and adaptive methods for modern communication systems. He has received prestigious awards, including: 2004 IEEE Signal Processing Society Best Paper Award 2007 Alberta Ingenuity New Faculty Award 2011 Carl Zeiss Award for teaching and innovative methods 2012 NSERC Discovery Accelerator Award 1st Price Best Paper Award (2015) 1st Price Best Student Paper Award at CAMSAP 2015 As a researcher, Vorobyov has supervised seven theses and led multiple funded projects, such as: AI Based RAN (2023–2025): Scalable AI solutions for 5G/6G networks. MASSIVE AND SPARSE ANTENNA ARRAY PROCESSING FOR MILLIMETERWAVE COMMUNICATIONS (2019–2021): Advanced antenna design and processing techniques. M-CUBE SPA (2017–2021): EU-funded sparse antenna array research. Transmit beamspace for active compressive sensing and communication with multiple waveforms (2016–2020): Radar and MIMO system optimization. He leads the Sergiy Vorobyov Group , focusing on real-time signal processing algorithms and their applications in next-generation wireless systems. His research addresses practical challenges such as efficient channel estimation, robust detection in massive access scenarios, and improving network performance in urban environments.

• Polyhedral combinatorics
• Extended formulations
• Combinatorial optimization
• +۲ مورد دیگر

Samuel Fiorini is Associate Professor in the Department of Mathematics at the Université libre de Bruxelles (ULB) , member of the Algebra and Combinatorics group (CP 216). His research centres on polyhedral combinatorics, extended formulations, combinatorial optimisation and approximation algorithms , with frequent overlap into structural graph theory. Research in depth: Fiorini’s work explores how high-dimensional polytopes can sometimes be expressed compactly through extended formulations, proving exponential lower bounds when they cannot. He has contributed new approximation algorithms for classical problems such as vertex cover, clique transversal and odd-cycle packing, and has advanced the understanding of sorting and entropy in partially ordered sets. His papers often combine tools from graph minors, communication complexity and polyhedral theory. Scientific recognition: Best Paper Award, 44th ACM Symposium on Theory of Computing (STOC 2012) Programme committees: FOCS, IPCO, APPROX, STACS, WAOA Organiser, Sixth Cargese Workshop on Combinatorial Optimization Advising & grants: He currently supervises PhD students Carole Muller and Matthew Drescher and has mentored six completed PhDs as well as more than a dozen post-doctoral researchers. His group has been supported by an ERC starting grant and other national and international projects focusing on polyhedral approaches to hard optimisation problems. Lab & team: Fiorini leads a vibrant team within the Algebra and Combinatorics cluster at ULB, maintaining active collaborations with researchers worldwide and hosting frequent visitors working on discrete optimisation and polyhedral combinatorics.

• Algebraic Geometry
• Differential Geometry
• Geometric Complexity Theory
• +۴ مورد دیگر

Joseph Landsberg is the Owen Professor and Professor in the Department of Mathematics at Texas A&M University , affiliated with the College of Arts & Sciences . His research focuses on Algebraic Geometry, Differential Geometry, and their applications to computational complexity, particularly matrix multiplication and tensor analysis. He has contributed to geometric complexity theory, secant varieties, and the study of symmetric tensors. Education: Ph.D., Mathematics, Duke University, 1990 Habilitation, Université de Toulouse, 1997 B.Sc. and M.Sc., Brown University, 1986 Research Interests: Landsberg explores the geometry of tensors, matrix multiplication algorithms, and complexity theory. His work bridges algebraic geometry, representation theory, and computational problems, with applications in quantum computing and cryptography. Recent studies include secant varieties, border rank analysis, and symmetry exploitation in tensor networks. Grants & Awards: AF: Small grants (2022, 2018) for complexity theory and matrix multiplication research Labs & Affiliations: Affiliated with the Institute for Applied Mathematics and Computational Science (IAMCS) at Texas A&M. His work intersects with interdisciplinary teams in computational mathematics and theoretical physics.

Manuel Sierra Castañerمشاهده پروفایل

استاد

• Planar Antennas
• Antenna Measurement Systems
• Telecommunications Engineering
• +۳ مورد دیگر

Prof. Manuel Sierra Castañer is a full professor at the Technical University of Madrid (UPM), serving as the Dean of the School of Telecommunications Engineering since May 2021. He holds a Telecommunication Engineering degree (1994) and a PhD (2000), both from UPM. His career includes roles as assistant and associate professor, research positions at Tokyo Tech and EPFL, and leadership in antenna measurement systems and international projects. He is a Senior Member of IEEE and Fellow of AMTA, with research focused on planar antennas and measurement systems. Notable achievements include the IEEE APS 2007 Schelkunoff Prize and leadership roles in EurAAP (Vice-Chair, General Chair for EuCAP 2022). He directed 7 PhD theses and led projects like FUTURE-RADIO and TERASENSE. His administrative roles include Director of International Cooperation at UPM (2010–2020) and leadership in the European School of Antennas. Key contributions span antenna design for 5G, satellite communications, and energy harvesting. He has authored 40+ journal papers and pioneered phaseless near-field techniques, robotic antenna measurement systems, and 3D-printed antenna components. His work bridges academic research with industry applications, emphasizing sustainable engineering education and global collaboration.

Wenwu Zhuمشاهده پروفایل

استاد

• Big data analysis
• Multimedia communication and networking
• Multimedia computing
• +۵ مورد دیگر

Wenwu Zhu is a Professor and Vice Chair of the Department of Computer Science and Technology at Tsinghua University. He has held prominent positions at Microsoft Research Asia, Intel Research China, and Bell Labs, establishing himself as a leading figure in multimedia computing and networking with international recognition as a FOREIGN member of the Academy of Europe (elected 2018). His educational background includes: Ph.D. in Electrical and Computer Engineering from New York University (1996) Professor Zhu's research focuses on the intersection of multimedia systems, networking, and big data. His work has pioneered advancements in internet video streaming, multimedia cloud computing, and social-aware content distribution. He has made significant contributions to understanding how multimedia content can be efficiently delivered across diverse network environments, from traditional wired networks to modern mobile and social platforms. His research bridges theoretical computer science with practical applications, with his work on social-aware video content distribution being transferred to Tencent company. His publication record shows a clear evolution from foundational work on internet video streaming in the early 2000s, through multimedia cloud computing in the early 2010s, to more recent work on social-aware multimedia and network embedding using deep learning approaches. This progression reflects the changing landscape of multimedia computing from infrastructure-focused to socially-aware and AI-driven systems. Professor Zhu has received numerous prestigious honors: AAAS Fellow (2016) SPIE Fellow (2013) IEEE Fellow (2010) Minister of Education's Natural Science Award, 1st prize (2017) Chinese Institution of Electronics's Natural Science Award, 1st prize (2015, 2012) National Natural Science Award, 2nd prize (2012) Chief Scientist for NSFC Major Project (2016) Chief Scientist for Ministry of Science and Technology's 973 Project (2014) Multiple Best Paper Awards including ACM Multimedia 2012 As Editor-in-Chief of IEEE Transactions on Multimedia since 2017 and through leadership roles as General Co-Chair for ACM CIKM 2019 and ACM Multimedia 2018, Professor Zhu has significantly shaped the multimedia research community. His research has been supported by major grants including NSFC Major Projects and Ministry of Science and Technology's 973 Projects, demonstrating both academic and national strategic importance. He has published over 300 referred papers with an H-Index of 55, including 6 Best Paper Awards and 7 books or book chapters. Professor Zhu leads a research group at Tsinghua University focused on multimedia big data computing, with strong industry connections. His team has made pioneering contributions to structural network embedding using deep learning and social contextual recommendation systems, bridging theoretical advances with practical applications in social media platforms.