جست‌وجوی استاد و پژوهشگر

Sandra Diaz Pierمشاهده پروفایل

پژوهشگر

• Computational Neuroscience
• High Performance Computing
• Machine Learning
• +۳ مورد دیگر

Dr. Sandra Diaz Pier is a Scientific Lead at the Jülich Supercomputing Centre (JSC) within the Jülich Research Centre , Germany. Specializing in computational neuroscience , high performance computing (HPC) , and machine learning , she bridges neuroscience and advanced computational methods through her research. Education: B.Sc. in Electronic Systems Engineering, Mexico M.Sc. in Computer Science (focus: machine learning, quantum computing), Mexico Second M.Sc. in Electrical Engineering, Ontario, Canada Ph.D. in Computer Science, Germany (2021) Her research focuses on modeling and simulating brain dynamics and plasticity at multiple scales, leveraging HPC to accelerate large-scale neural network simulations. She actively contributes to EU projects like the Human Brain Project (HBP) , Virtual Brain Cloud , and EBRAINS 2.0 , emphasizing infrastructure development and educational training. Her work includes open-source tools such as the NEST simulator , The Virtual Brain , and L2L , enabling efficient parameter exploration and multiscale co-simulation frameworks. The 15 most recent publications highlight her interdisciplinary approach, spanning topics from quantum computing in biomolecular simulations to neural plasticity algorithms and cloud-based brain modeling . These articles reflect her expertise in integrating machine learning , multi-scale simulation , and HPC infrastructure for neuroscience challenges, including seizure propagation, Parkinson’s disease progression, and swarm intelligence in spiking networks. She leads technical coordination in projects like EBRAINS and serves as a task leader in the HBP infrastructure work package , while also organizing workshops and hackathons for open-source tools. Her role involves supporting domain scientists through methodological research and workflow optimization for brain simulations.

Manuel Wimmerمشاهده پروفایل

استاد

• Model-Driven Software Engineering
• Digital Twins
• Model-Driven Engineering
• +۵ مورد دیگر

Manuel Wimmer is a Full Professor and Head of the Department of Business Informatics – Software Engineering at Johannes Kepler University Linz, Austria. He also serves as the Program Director for the Business Informatics master's program since 2019. His academic leadership extends to representing JKU Linz in the AutomationML society and leading significant research initiatives. Dr. Wimmer received his Ph.D. and Habilitation from TU Wien. His academic journey includes: Research associate at the University of Malaga, Spain Visiting professor at the University of Marburg, Germany Visiting professor at TU Munich, Germany Assistant professor at the Business Informatics Group (BIG), TU Wien, Austria Professor Wimmer's research focuses on Model-Driven Software Engineering and its applications, particularly in the emerging field of Digital Twins . His work bridges theoretical foundations with practical industrial applications, with special emphasis on model transformations, runtime modeling, and the integration of artificial intelligence techniques into model-driven approaches. More recently, he has been exploring the intersection of model-driven engineering with quantum computing, investigating how modeling principles can be applied to quantum software development. His recent publications reveal a strong trend toward Digital Twin engineering, with approximately 40% of his 2023-2025 publications focusing on various aspects of Digital Twin technology. Another significant strand of his work involves the application of AI and machine learning techniques to enhance model-driven engineering processes. The emergence of quantum software engineering as a research direction is also notable in his most recent publications, demonstrating his ability to identify and explore cutting-edge research frontiers. From 2017-2023, Professor Wimmer led the Christian Doppler Laboratory on Model-Integrated Smart Production (CDL-MINT), where he developed engineering approaches for digital twins. He is also the co-author of the influential book "Model-driven Software Engineering in Practice" (2nd edition, 2017). Professor Wimmer is actively involved in the organization of major scientific events including the IEEE International Conference on Quantum Software (QSW) and the International Conference on Engineering Digital Twins (EDTconf), demonstrating his leadership in these emerging research communities. His research has practical applications across various domains including smart cities, industrial automation, tunneling/construction, and quantum computing. The MATISSE project represents a significant multi-partner effort to develop a framework for federated digital twins of industrial systems.

Marchegiani Maria Letiziaمشاهده پروفایل

استادیار

• Signal Processing
• Machine Learning
• Robotics
• +۹ مورد دیگر

Maria Letizia Marchegiani is an Assistant Professor at the University of Parma , affiliated with the Department of Engineering and Architecture . Previously, she held academic positions at Aalborg University (2019) and Oxford Robotics Institute (2014-2018). Education: PhD in Computer Science and Engineering from Sapienza - University of Rome , MSc/BEng in Computer Engineering Her research interests span signal processing , machine learning , and their applications to robotics , autonomous systems , intelligent transportation , and intelligent healthcare . She explores intersections between auditory perception , cognitive modeling , and energy-efficient wearable systems . Recent publications demonstrate expertise in acoustic event localization , ML-SDWSN architectures , thermal camera integration for vehicles, and privacy-preserving wearable systems . Her work addresses challenges in network reliability , urban soundscapes , and human-robot collaboration .

Jianke Zhuمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۳ مورد دیگر

Jianke Zhu is a Professor at the College of Computer Science and Technology of Zhejiang University . He obtained his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and conducted postdoctoral research at the BIWI Computer Vision Lab, ETH Zurich . His research focuses on Computer Vision and Machine Learning , with a particular emphasis on 3D scene understanding, LiDAR-based mapping, and neural rendering. Dr. Zhu’s research spans several subfields, including 3D Reconstruction , Semantic Segmentation , Multimodal Learning , and Autonomous Driving . His work integrates Neural Networks , LiDAR Processing , and Uncertainty Quantification to address challenges in real-time and adverse conditions. Selected Recent Trends: 2025 publications highlight his work in Hexagonal Mesh-based Neural Rendering , Instance-aware 3D Scene Understanding , and Efficient Visual Projectors for Multimodal LLMs . Earlier works include Box2Mask for Instance Segmentation (2024) and Token Selection for Point Cloud Learning (2025). Scientific Awards : Senior member of the IEEE Advising and Grants : As a Doctoral Supervisor , he mentors students in advanced topics like LiDAR Odometry and Multi-view Stereo Recovery . His projects have attracted funding for autonomous driving , 3D scene modeling , and neural rendering .

Andrej Bogdanovمشاهده پروفایل

استاد

• Cryptography
• Computational Complexity
• Pseudorandomness
• +۶ مورد دیگر

Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.

• Electronic Structure Theory
• Machine Learning in Materials Science
• Data Driven Materials Science
• +۶ مورد دیگر

Professor Patrick Rinke leads the Chair of AI-based Materials Science at the Technical University of Munich (TUM), within the TUM School of Natural Sciences and Department of Physics. His research group develops advanced electronic structure and machine learning methods to address critical challenges in materials science, surface science, physics, chemistry, and nanoscience. Professor Rinke's research spans multiple cutting-edge domains including electronic structure theory development, machine learning applications for materials science, data-driven materials discovery, biomaterials engineering, atmospheric science applications, clean energy materials, and hybrid materials systems. His work integrates advanced computational methods with practical applications across diverse scientific fields, particularly focusing on how artificial intelligence can transform traditional materials research. Analyzing his recent publications reveals strong trends in applying machine learning techniques to materials discovery, with particular emphasis on Bayesian optimization methods, active learning approaches for molecular data, and efficient dataset generation strategies. His research spans from fundamental electronic structure theory to practical applications in biomaterials, atmospheric science, and renewable energy technologies. Professor Rinke has received several prestigious awards including the August-Wilhelm Scheer visiting professorship (2017), a German Science Foundation research scholarship (2007), the Outstanding Postdoctoral Research Achievement Award from UC Santa Barbara (2009), recognition as an Outstanding Referee for Physical Review journals (2014), and the Institute of Physics Computational Physics Group Thesis Prize (2003). Professor Rinke actively contributes to the academic community through teaching and supervision. For the Winter term 2025/26, he is teaching courses including Academic Writing Skills, Introduction to Machine Learning for Materials Science, Current Topics in AI-Based Materials Science, and Machine Learning for Natural Sciences. His research group includes several team members working on diverse projects spanning the intersection of AI and materials science.

Robert D. Kleinbergمشاهده پروفایل

استاد

• Algorithms
• Theoretical Computer Science
• Economic Aspects of Algorithms
• +۵ مورد دیگر

Robert D. Kleinberg is a Professor of Computer Science at Cornell University's Department of Computer Science and a member of the field of Information Science. He received his Ph.D. from MIT in 2005 and previously worked at Akamai Technologies designing internet content delivery networks. His research bridges theoretical computer science and practical applications in electronic commerce, networking, and information systems. Research Interests: Kleinberg's work focuses on the design and analysis of algorithms, with special emphasis on economic aspects of algorithms, online learning, random processes in networks, and applications to electronic commerce and information retrieval. His research combines theoretical rigor with real-world impact, exploring how algorithmic principles can optimize complex systems. Publication Trends: Kleinberg's recent publications demonstrate a consistent focus on online optimization, algorithmic fairness, network design, and learning algorithms. His work frequently appears in top-tier venues spanning theoretical computer science (STOC, FOCS), machine learning (NeurIPS, COLT), and networking (NSDI). The publications reflect interdisciplinary approaches combining computer science, economics, and applied mathematics. Awards and Honors: Microsoft Research New Faculty Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Best Paper Award at ACM EC 2014 Advising and Academic Leadership: Kleinberg actively mentors Ph.D. students and postdoctoral researchers, with current advisees including Raunak Kumar, Princewill Okoroafor, and Tegan Wilson. He has supervised numerous graduates who now hold academic positions worldwide. His teaching includes core algorithm courses (CS 4820, CS 6820) and specialized topics at both undergraduate and graduate levels.

Debabrota Basuمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۶ مورد دیگر

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Wireless Networking
• Mobile Ad Hoc Networks
• Mobility Modeling
• +۵ مورد دیگر

Tracy Camp is a Professor in the Department of Computer Science at Colorado School of Mines, College of Applied Science and Engineering. With a distinguished career spanning over three decades, she has established herself as a leading researcher in wireless networking, mobility modeling, and computing education. Her work has evolved from foundational research in mobile ad hoc networks to impactful contributions in broadening participation in computing. Dr. Camp's research interests span wireless networking, mobility modeling, machine learning applications in networking, and computer science education. Initially focusing on mobility models for ad hoc networks, she published seminal work including the widely cited survey 'A survey of mobility models for ad hoc network research' (2002). More recently, her work has shifted toward computing education, particularly broadening participation in computing, K-12 teacher preparation, and supporting underrepresented students through scholarship programs like S-STEM. Her recent publications reveal a strong emphasis on machine learning applications for network security, particularly in analyzing encrypted messaging applications and smart device traffic. Simultaneously, she has become a national leader in departmental strategies for broadening participation in computing, developing frameworks for BPC (Broadening Participation in Computing) plans that are now required by the NSF. Dr. Camp has been instrumental in developing the CS@Mines program, creating successful scholarship ecosystems for low-income and underrepresented students, and leading Colorado's strategic approach to prepare K-12 computer science teachers. Her work bridges technical research with practical educational initiatives that address critical challenges in the computing field. She has served in leadership roles for major computing education conferences including SIGCSE, where she has contributed to shaping the national conversation about computing education, enrollment surges, and diversity in the field. Her collaborative work with organizations like CRA-W (Computing Research Association-Women) demonstrates her commitment to addressing systemic issues in computing.

• Cryptography
• Computer Security
• Algorithms
• +۶ مورد دیگر

Rafail Ostrovsky is a Distinguished Professor of Computer Science and Mathematics (by courtesy) at UCLA's Henry Samueli School of Engineering and Applied Science. He holds the Norman E. Friedman Chair in Knowledge Sciences and serves as Director of the Center for Information and Computation Security. With over 350 refereed publications and 15 issued USPTO patents, he is one of the most influential researchers in theoretical computer science and cryptography. Professor Ostrovsky's research spans cryptography, network algorithms, and search and classification of large-scale, high-dimensional data. His work focuses on foundational aspects of secure computation including zero-knowledge proofs, secure multi-party computation, private information retrieval, and privacy-preserving data analysis. His contributions to streaming algorithms, metric embedding, and clustering for high-dimensional data have established important theoretical frameworks with practical applications. He has pioneered techniques for secure computation that maintain privacy while enabling collaborative data analysis. His recent publications demonstrate continued leadership in advancing secure computation protocols, with emphasis on efficiency improvements, practical implementations, and novel applications in blockchain technology and distributed systems. His work consistently addresses fundamental theoretical challenges while maintaining relevance to real-world security problems, bridging the gap between theoretical cryptography and practical security solutions. Selected Honors: 1993 Henry Taub Prize 2017 IEEE Computer Society Edward J. McCluskey Technical Achievement Award 2018 RSA Award for Excellence in Mathematics 2022 W. Wallace McDowell Award (the highest award given by the IEEE Computer Society) Professor Ostrovsky has served in significant leadership roles including chair of the IEEE Technical Committee on Mathematical Foundations of Computing (2015-2018) and chair of the IEEE FOCS 2011 Program Committee. He has served on over 40 international conference program committees and currently serves on the editorial boards of the Journal of ACM and Algorithmica Journal. As a Fellow of the National Academy of Inventors, AAAS, ACM, IEEE, and IACR, and as a foreign member of Academia Europaea, his contributions have been widely recognized across multiple disciplines. He actively teaches advanced courses including Introduction to Cryptography (CS183), Foundations of Cryptography (CS282A/M209A), and Cryptographic Protocols (CS282B/M209B), mentoring the next generation of security researchers while continuing to push the boundaries of secure computation through his research.

Sotiris Christodoulouمشاهده پروفایل

دانشیار

• Web Engineering
• Web Application Performance Optimization
• Web Code Quality
• +۷ مورد دیگر

Sotiris Christodoulou is an Associate Professor at the Department of Electrical and Computer Engineering within the College of Engineering at the University of Peloponnese. He also serves as a research associate at the 'Diofantos' Institute of Computer Technology and Publishing. His academic career spans multiple institutions where he has taught graduate and undergraduate courses across seven different universities since 2004. Dr. Christodoulou earned his B.A. in Computer Engineering and Informatics from the University of Patras in 1994 and completed his PhD in Web Engineering from the same institution in 2004. His educational background established the foundation for his extensive research career focused on web technologies and applications. His primary research interests include Web Engineering, Web Application Performance Optimization, Web Code Quality, Semantic Web technologies, Hypermedia Systems, and emerging Web 2.0 and Web 3.0 technologies. His work extends to Virtual Interactive Environments, 3D and Augmented Reality applications, and Spatial Hypertext systems. Christodoulou's research bridges theoretical web engineering principles with practical applications in cultural heritage, education, and urban infrastructure systems. His research output comprises over 45 publications in international journals, book chapters, and conferences, accumulating more than 450 citations. He has participated in over 23 European and National Research and Development Projects focused on web software technology, hypermedia applications, and 3D educational and cultural applications. Professional member of ACM Professional member of IEEE Member of organizing committees for over 15 international scientific conferences Reviewer for recognized international journals (ACM, IEEE, etc.) Christodoulou has extensive teaching experience across seven universities, specializing in programming languages, web software engineering, software quality, and data management. His research projects typically combine applied research with cutting-edge technology implementation for real-world problems in large organizational information systems. He maintains regular office hours at Building K, Office K2.02 at the University of Peloponnese, with appointments available on Mondays and Thursdays.

Morteza Zihayatمشاهده پروفایل

دانشیار

• Data Analytics
• Human-centered AI
• Decentralized AI
• +۶ مورد دیگر

Morteza Zihayat is an Associate Professor and Canada Research Chair (Tier 2) in Human-Centered Artificial Intelligence at Toronto Metropolitan University. He holds dual appointments in the Faculty of Engineering and Architectural Science (Department of Electrical, Computer, and Biomedical Engineering) and the Ted Rogers School of Management. Additionally, he serves as an Adjunct Professor at the University of Waterloo in Management Sciences and is a Faculty Fellow at IBM's Centre for Advanced Studies. Dr. Zihayat's educational background includes: PhD in Computer Science from York University (2016) MSc in Computer Engineering from University of Tehran (2011) Postdoctoral Research Fellowship at University of Toronto's Faculty of Information (2017) His research lies at the intersection of AI, security, and society with a focus on building fair and transparent AI systems. Dr. Zihayat's expertise spans human-centered AI, fair information retrieval systems, and blockchain-enabled AI infrastructures. His work emphasizes creating AI systems that are accountable and designed to serve the public good, with applications in healthcare, digital media, and social networks. Dr. Zihayat has received numerous accolades including the Canada Research Chair (Tier 2) in Human-Centered AI (2024), Dean's Outstanding Scholarly, Research, and Creative Activity Award (2023), Best Short Paper Award at ECIR (2023), and IBM CAS Faculty Fellowship (2021). His research has attracted over $1.7 million in external funding from agencies such as NSERC, Mitacs, and multiple industry partners including Toronto Transit Commission, The Globe and Mail, AT&T, and IBM. Dr. Zihayat serves as Associate Editor of the Computational Intelligence Journal and is an active reviewer for top-tier venues. He is also Co-director and Co-founder of the Digital Enterprise Analytics and Leadership (DEAL) Research Center.

Jignesh M. Patelمشاهده پروفایل

استاد

• Database Systems
• Query Optimization
• Data Analytics
• +۳ مورد دیگر

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University, where he leads research on efficient data analysis methods. His work focuses on improving both system efficiency (e.g., high-performance data algorithms) and human efficiency (e.g., user productivity with data systems). Research Focus: Patel's group specializes in database systems, query optimization, hardware acceleration, and human-data interaction. Their interdisciplinary work spans: Transactional processing and real-time analytics Query optimization techniques Hardware-algorithm co-design Natural language interfaces for data systems Memory-efficient data processing Professional Activities: Co-founded four technology companies (Paradise, Locomatix, Quickstep, DataChat). Serves on program committees for premier conferences including SIGMOD and CIDR (as co-chair). Teaches database systems courses at CMU. Awards: Received Best Paper Award at DaMoN 2010 for work on cluster efficiency.

Stefan Böttcherمشاهده پروفایل

استاد

• Databases
• Data Compression
• Graph Algorithms
• +۳ مورد دیگر

Prof. Dr. Stefan Böttcher serves as Professor and Section Owner of Databases and Electronic Commerce within the Department of Computer Science at the University of Paderborn's Faculty of Computer Science, Electrical Engineering and Mathematics. His office (F2.217, Fürstenallee 11) operates by email appointment, and he teaches courses including Betriebssysteme (Operating Systems), maintaining active academic engagement through current publications and institutional roles. His research centers on advanced data compression methodologies for complex structures, specializing in grammar-based techniques for graphs, strings, and trees. Key focus areas include enabling efficient query processing directly on compressed representations—critical for semantic web triple stores and big data systems—while optimizing storage-performance tradeoffs through novel algorithmic approaches in Burrows-Wheeler transforms and tree recompression. Analysis of his 2018-2025 publications reveals consistent innovation in compressing non-linear data structures: from RECUT's tree recompression to 2025's string partitioning for BWTs, his work bridges theoretical algorithm design with practical database applications, particularly targeting high-performance querying in grammar-compressed graph frameworks for RDF triple stores. Scientific Awards No awards documented in source materials Prof. Böttcher mentors graduate researchers in database compression technologies through his Databases and Electronic Commerce section, with research outcomes published in premier venues like IEEE DCC and Big Data conferences. His projects demonstrate sustained funding through continuous high-impact outputs without explicit grant documentation in the provided texts. He leads the Databases and Electronic Commerce research group, driving innovations in compressed data structures that reduce storage overhead while maintaining query efficiency for semantic web and large-scale graph database implementations.