جست‌وجوی استاد و پژوهشگر

Anthony Widjaja Linمشاهده پروفایل

استاد

• Automated Reasoning
• String Constraint Solving
• Formal Language Theory
• +۵ مورد دیگر

Anthony Widjaja Lin is a Full Professor (W3) in Theoretical Computer Science (Automated Reasoning) and Max-Planck Fellow at University of Kaiserslautern-Landau, Germany. Previously, he was an Associate Professor in Programming Languages at Oxford University Department of Computer Science and Governing Body Fellow at Kellogg College (2016-2019), and an Assistant Professor at Yale-NUS, Singapore (2014-2016). He completed his PhD in Informatics at University of Edinburgh in 2010 under Leonid Libkin (supervisor) and Richard Mayr (co-advisor). Dr. Lin's educational background includes: PhD in Informatics, University of Edinburgh (2010) MSc, University of Toronto BSc (Honours), Melbourne University Dr. Lin's research focuses on automated reasoning, particularly over strings, formal language theory, learning/synthesis, and foundations of machine learning. His work has significant applications in software verification, program synthesis, querying graph databases, and computer security. He leads the development of the OSTRICH string solver, which won the QF_S (Single Query Track) in SMT-COMP 2023. His research has evolved from foundational work on string constraint solving to applications in verification of string-manipulating programs and more recently to connections with machine learning models like transformers. Dr. Lin has received numerous prestigious awards including an ERC Consolidator Grant (2023), Amazon Research Award (2021), ERC Starting Grant (2017), Google Faculty Award (2017), and the LICS Kleene Award (2010). Dr. Lin has advised several PhD students to completion, including Pascal Bergsträßer, Chih-Duo Hong, and Xuan-Bach Le, who have gone on to become Assistant Professors at institutions like National Chingchi University and Nanyang Technical University. He currently supervises multiple PhD students and postdocs working on string solving, automated reasoning, and verification. Dr. Lin leads the AV-SMP project (Algorithmic Verification of String-Manipulating Programs), which was supported by an ERC Starting Grant (2017-2022) and an Amazon Research Award (2021). His research group develops tools like OSTRICH, SLOTH, and CertiStr for string constraint solving and verification.

• Learning Analytics
• Educational Technology
• Artificial Intelligence in Education
• +۳ مورد دیگر

Rafael Ferreira Leite de Mello is an academic affiliated with the Federal Rural University of Pernambuco in Brazil. His research focuses on Learning Analytics, Educational Technology, and Artificial Intelligence applications in education. He has contributed to the development of AI-driven tools for automated feedback, essay scoring, and equity in educational systems. His work often addresses challenges in under-resourced regions and emphasizes explainable AI, fairness, and human-AI collaboration. Dr. Mello has co-authored numerous publications in top-tier conferences like AIED, EC-TEL, and LAK, and has edited conference proceedings (e.g., EC-TEL 2024). His research spans topics such as automated essay evaluation, rhetorical structure analysis, and the impact of generative AI on educational practices. He collaborates extensively with institutions globally, advocating for inclusive technology solutions in education. His efforts include designing systems like MathAIde for underserved regions and frameworks like MMALA to guide Learning Analytics adoption in institutions. He actively engages in initiatives to bridge the digital divide and improve accessibility through technology.

• Machine Learning
• Pattern Recognition
• Computer Vision
• +۵ مورد دیگر

David M. J. Tax is a researcher affiliated with Delft University of Technology, Netherlands. His work spans machine learning, pattern recognition, and computer vision, with a focus on neural networks, anomaly detection, and medical image analysis. He has collaborated extensively with researchers like Marco Loog, Robert P. W. Duin, and Marcel J. T. Reinders on topics including multiple instance learning, dissimilarity-based methods, and physics-informed neural networks. Key research contributions include advancements in incremental learning for neural networks, personalized anomaly detection in biomedical signals, and frameworks for evaluating time-series anomalies. His recent work (2023–2025) emphasizes physics-informed neural networks, stochastic scheduling algorithms, and proximity-aware evaluation techniques. Tax’s publications reflect a strong emphasis on interdisciplinary applications, integrating computer science with biomedical engineering and operations research. His methodologies often address challenges in scalability, robustness, and interpretability, with applications ranging from healthcare monitoring to automated project scheduling. Tax maintains an active presence in top-tier conferences like AAAI, ICPR, and NeurIPS, contributing to both theoretical foundations and practical implementations of machine learning systems.

• High-Performance Computing
• Parallel Programming
• Task Parallelism
• +۶ مورد دیگر

Stephen L. Olivier is a prominent researcher in high-performance computing at Sandia National Laboratories, with a distinguished publication record spanning nearly two decades. His work focuses on parallel programming models, performance optimization, and energy-efficient computing across diverse architectures including CPUs, GPUs, and FPGAs. Olivier has made significant contributions to OpenMP standards and Kokkos programming model development, collaborating extensively with Department of Energy national laboratories and international research teams. Olivier's research interests center on task parallelism, memory management in distributed systems, and performance portability across heterogeneous architectures. His work addresses critical challenges in exascale computing, including efficient task scheduling for unbalanced workloads, power management in large-scale systems, and optimization of communication patterns. More recently, he has expanded his research into medical imaging applications, applying high-performance computing techniques to tuberculosis detection in rural healthcare settings. Analysis of Olivier's recent publications (2021-2024) reveals a strong focus on practical performance engineering for next-generation computing platforms. His work spans traditional HPC domains while increasingly incorporating data science applications and medical imaging analysis. The research demonstrates consistent innovation in parallel programming models, particularly around OpenMP tasking and Kokkos abstractions, with growing emphasis on energy efficiency and hardware-specific optimizations for emerging architectures. Olivier has maintained a prolific research output with numerous publications in top-tier conferences including SC, IPDPS, and IWOMP. His collaborative work extends across multiple Department of Energy laboratories and international institutions, reflecting the interdisciplinary nature of modern high-performance computing research. While specific grant information isn't detailed in the publication record, his work on DOE systems suggests significant involvement in national supercomputing initiatives.

• Human-Robot Interaction
• Affective Computing
• E-commerce Search Algorithms
• +۴ مورد دیگر

Mingming Li is an active professor at the Chinese Academy of Sciences with extensive research contributions across multiple domains including human-robot interaction, e-commerce systems, and wireless communications. With over 110 publications spanning from 2007 to 2025, Dr. Li demonstrates consistent scholarly productivity and leadership in interdisciplinary research. Dr. Li's research interests span Human-Robot Interaction , Affective Computing , E-commerce Search Algorithms , Machine Learning , and Wireless Communications . Recent work focuses on understanding how humanoid robots' multimodal characteristics (appearance, voice, touch) affect users' emotional responses and cognitive processing, employing physiological measures like ECG and fNIRS. Simultaneously, Dr. Li leads research in e-commerce search optimization, developing novel deep learning architectures for improved recommendation systems. The publication record reveals significant trends toward increasingly sophisticated multimodal analysis in HRI and more efficient neural architectures for practical applications. Dr. Li's work bridges theoretical advancements in machine learning with practical implementations in human-centered systems. Dr. Li has advised numerous graduate students including Jiahao Chen, Aung Naing Win, and Songlin Wang, with whom collaborative publications demonstrate strong mentorship. Research has been supported by substantial funding reflected in the volume and quality of publications across top venues. Current research activities include leading projects on digital twin-based thermal error compensation, multimodal affective robot design, and advanced search algorithms for e-commerce platforms, indicating an active laboratory environment with multiple ongoing research threads.

Sebastian Peitzمشاهده پروفایل

استاد

• Multiobjective Optimization
• Optimal Control
• Machine Learning
• +۳ مورد دیگر

Sebastian Peitz is Professor (previously Assistant Professor) at Paderborn University's Department of Computer Science, leading the Data Science for Engineering group. He obtained his PhD in Multiobjective Optimization from Paderborn University and MSc in Mechanical Engineering from RWTH Aachen. His research develops computational methods for multiobjective optimization, optimal control, and machine learning with applications in fluid dynamics, autonomous systems, and industrial processes. He leads the BMBF-funded Multicriteria Machine Learning group. Research trends show consistent focus on Koopman operator theory, reinforcement learning applications in control systems, and physics-informed machine learning across publications. Achievements include the 2019 PRECEDE Best Paper Award and leadership in international optimization conferences.

Frank R. Kschischangمشاهده پروفایل

استاد

• Digital Communications
• Coding Theory
• Information Theory
• +۴ مورد دیگر

Frank R. Kschischang is a Distinguished Professor of Digital Communication in the Department of Electrical and Computer Engineering at the University of Toronto. He holds the Canada Research Chair in Communications Algorithms and has been a faculty member since 1991. His research focuses on coding theory, digital communications, and information theory, with applications in optical, wireless, and fiber-optic systems. He is a Hans Fischer Senior Fellow at the Institute for Advanced Study (TUM-IAS), Technische Universität München, collaborating on advancing fiber-optic communication capabilities. Education: B.A.Sc. (University of British Columbia, 1985), M.A.Sc. and Ph.D. (University of Toronto, 1988 and 1991). He has held visiting roles at MIT (1997–1998) and ETH Zurich (2005). Academic roles include IEEE Information Theory Society President (2010) and editorial roles for the IEEE Transactions on Information Theory. Research emphasizes error-correcting codes, fiber-optic channel modeling, and nonlinear Fourier transform-based communication. Notable contributions include staircase codes for 100 Gb/s optical networks and rank-metric codes for network coding. His work has led to practical advancements in high-speed optical systems and improved channel capacity understanding. Awards include IEEE Fellow (2006), Killam Research Fellowship (2010), and multiple teaching and service awards. He advises numerous graduate students and collaborates internationally on projects like spectral modulation in fiber optics and machine learning for signal processing. Labs and teams: Leads the Coding and Information Theory group at UofT, focusing on cutting-edge research in communications algorithms and hardware implementation. Collaborates with industry partners like the Communications Research Centre Canada and Inphi Corp.

Jelena Vuckovicمشاهده پروفایل

استاد

• Nanophotonics
• Quantum Optics
• Nonlinear Optics
• +۳ مورد دیگر

Jelena Vuckovic is a Professor in the Department of Electrical Engineering at Stanford University, leading the Nanoscale and Quantum Photonics Lab. She holds a Hans Fischer Senior Fellowship at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). Her academic journey includes roles from postdoctoral scholar to full professor at Stanford, with a Humboldt Prize (2010) and numerous awards in photonics and quantum research. Education: PhD in Electrical Engineering (Caltech, 2002), M.Sc. (Caltech), and Diploma in Engineering (University of Nis, Serbia). Her research focuses on nanophotonics, quantum optics, and optoelectronics, with applications in quantum computing and sensing. Key contributions include photonic crystal nanocavities, quantum dot-cavity systems, and integrated nonlinear optics. Research highlights include cavity quantum electrodynamics (CQED), nonlinear frequency conversion, and ultra-low-threshold nanocavity lasers. Awards span the Marko V. Jaric Award (2012), PECASE (2007), and DARPA Young Faculty Award (2008). She has authored over 140 journal articles, 12 book chapters, and 11 patents, with >10,000 citations. Publications emphasize quantum photonics platforms, including diamond color centers, silicon carbide integration, and hybrid photonic systems. Her work bridges theory and experiment, addressing scalability in quantum technologies. Collaborations with institutions like TUM-IAS and Humboldt University Berlin further highlight her interdisciplinary impact.

• Model-Driven Engineering
• Software Product Lines
• Variability Management
• +۵ مورد دیگر

Jean-Marc Jézéquel is a prominent professor and researcher at IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires) in Rennes, France, a joint research unit of INRIA, CNRS, and the University of Rennes. His extensive publication record spanning over two decades demonstrates his significant contributions to software engineering, particularly in model-driven engineering and software product lines. His research expertise encompasses several critical areas in contemporary software engineering. Jézéquel has pioneered work on managing uncertainty in model-based development, developing frameworks like DataTime for temporal reasoning in models. He has made substantial contributions to deep variability management across multiple layers of software systems, investigating how compile-time and run-time configuration options interact. His recent work increasingly integrates machine learning techniques with traditional software engineering approaches, creating innovative hybrid methodologies for software development and analysis. Jézéquel's publication trends reveal a clear evolution in his research focus, moving from foundational model-driven engineering concepts toward addressing uncertainty, incorporating temporal aspects into models, and leveraging machine learning for software analysis and development. His work spans both theoretical foundations and practical industrial applications, with notable collaborations including projects with Airbus on specialized software product line engineering. The interdisciplinary nature of his research connects software engineering with artificial intelligence, data science, and systems engineering. His significant contributions to the field include foundational work on model transformation, software language engineering, and variability management. Jézéquel has helped advance model-driven engineering from what he describes as 'craft' to a more systematic engineering discipline, as evidenced in his publications spanning from early 2000s to the present. As an academic leader, Jézéquel has supervised numerous PhD students and maintained extensive international collaborations. His work demonstrates a consistent commitment to bridging the gap between academic research and industrial software development practices, with applications ranging from aerospace systems to urban transportation solutions.

Zhu, Jia-Jieمشاهده پروفایل

پژوهشگر ارشد

• Stochastic Analysis
• Optimization
• Machine Learning
• +۳ مورد دیگر

Zhu, Jia-Jie is a Research Fellow at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS). His research focuses on interdisciplinary areas bridging stochastic analysis, optimization, and machine learning with applications to mathematical finance and partial differential equations. He contributes to foundational theory in gradient flows, rough volatility modeling, and algorithmic game theory. Key research interests include the analysis of entropy decay in gradient systems, pricing financial derivatives under rough volatility, and developing kernel-based methods for mixed Nash equilibria. His work integrates advanced mathematical tools from functional analysis, stochastic calculus, and numerical methods. Zhu has published in top-tier venues such as the Proceedings of Machine Learning Research and WIAS preprint series. His current research explores the intersection of machine learning techniques with classical mathematical analysis, addressing challenges in quantitative finance and complex systems modeling. Notably, he collaborates on projects involving deep-signature methods for financial modeling and RKHS-based optimization frameworks. His contributions advance both theoretical understanding and practical computational approaches in applied mathematics and data science.

Martin Heidaمشاهده پروفایل

پژوهشگر

• Homogenization
• Partial Differential Equations
• Stochastic Homogenization
• +۶ مورد دیگر

Dr. Martin Heida is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, where he has been a member of the Partial Differential Equations research group since 2015. His work focuses on homogenization theory, stochastic processes, and numerical methods for complex systems. He holds a PhD from the Universities of Heidelberg and Prague (2011), and prior to WIAS, he was a PostDoc at TU Dortmund (2011–2015) and a graduate student at the Heidelberg Graduate School of Mathematical and Computational Methods (2008–2011). Education : PhD in Mathematics and Physics, cotutelle between Heidelberg University (Prof. Willi Jäger) and Charles University Prague (Prof. Josef Málek), 2011 Diploma in Physics and Mathematics from Heidelberg University, 2008 Research Interests : His research spans stochastic homogenization, partial differential equations (PDEs), numerical analysis, and material modeling. He develops methods for multiscale problems in porous media, plasticity, and fracture mechanics, with applications to battery aging, geology, and phase separation. Grants & Projects : Third-party funded projects: SPP 2256 (Fractal and Stochastic Homogenization), Math+ project on battery aging dynamics Teaching : Recent courses include Nonlinear Analysis at TU Munich, Homogenization Theory, and Analysis for Engineers. Labs & Collaborations : Leads the HighVoronoi.jl project for high-dimensional Voronoi diagram computations and collaborates with researchers in applied mathematics and computational science.

• Hydrometeorology
• Climate Modeling
• Numerical Weather Prediction
• +۳ مورد دیگر

Evgenii Churiulin is a Scientific Programmer at the Max Planck Institute for Biogeochemistry (2022–present). He holds a Ph.D. (2019) and M.Sc./B.Sc. (2016/2014) in hydrology from Lomonosov Moscow State University and Russian State Hydrometeorological University. His research focuses on hydrometeorology, improving numerical parameterization schemes for snow cover, vegetation, and fires in climate models, and developing scripts for data analysis from models like COSMO and OCN. He has held roles including Postdoctoral Fellow at the University of Kassel (2020–2022), Researcher at the Hydrometcenter of Russia (2017–2020), and Junior Researcher at the Arctic and Antarctic Research Institute (2014–2017). He lectured at Lomonosov Moscow State University (2017–2019) on hydrology and snow hydrology. His work emphasizes climate modeling innovations, snow dynamics, and interdisciplinary collaborations. Education: Ph.D. in Geographical Science (Hydrology), Lomonosov Moscow State University (2016–2019) M.Sc. in Hydrometeorology, Russian State Hydrometeorological University (2014–2016) B.Sc. in Hydrometeorology, Russian State Hydrometeorological University (2010–2014) Research Interests: Climate modeling, snow cover parameterization, vegetation-fire interactions, numerical weather prediction, and data analysis frameworks for climate models. His work bridges hydrometeorological processes with computational tools like COSMO-CLM and SnoWE models to enhance predictive accuracy in regional climate systems. Publications & Contributions: Over 15 peer-reviewed articles and technical reports, including key contributions to COSMO-Ru and SnoWE models. His work on snow water equivalent estimation and flood modeling has advanced hydrological forecasting. He also co-authored a comprehensive Arctic atlas and holds software certificates for SnoWE model development. Awards & Recognition: None explicitly listed, though his contributions to climate modeling are recognized through active participation in workshops like COST HarmoSnow. Grants & Labs: Part of the SnoWE model development team and collaborates with institutions like the Hydrometcentre of Russia. His work is supported by projects in numerical weather prediction and climate model refinement.

• Digital Signal Processing
• Acoustic Feedback Cancellation
• Hearing Aid Technology
• +۵ مورد دیگر

Akinori Nishihara is a prominent researcher with a prolific publication record spanning over 37 years from 1986 to 2023. His work primarily focuses on digital signal processing with significant contributions to hearing aid technology, particularly in acoustic feedback cancellation systems. He has published extensively in prestigious journals such as IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences and at major conferences including ICASSP and ISCAS. Dr. Nishihara's research interests encompass digital signal processing, acoustic feedback cancellation for hearing aids, filter design, wavelet analysis, image processing, and speech enhancement. His work demonstrates both theoretical depth and practical applications, particularly in developing algorithms for hearing aid technology that addresses the challenging problem of acoustic feedback. His research has evolved from fundamental filter design in the 1980s-1990s to more specialized applications in hearing aids and multimedia systems in recent years. Analysis of his publication trends shows a consistent focus on signal processing fundamentals while adapting to emerging application areas. In the 2010s and 2020s, his work increasingly centered on practical hearing aid implementations, with numerous papers co-authored with Muhammad Tahir Akhtar on adaptive filtering techniques for acoustic feedback cancellation. His research demonstrates a progression from theoretical filter design to real-world implementations addressing specific challenges in audio processing. Dr. Nishihara has maintained long-term collaborations with several researchers including Muhammad Tahir Akhtar (10 publications), Todor Cooklev (10 publications), and Saed Samadi (10 publications), indicating established research partnerships that have yielded significant scholarly output over multiple decades.

• Internet of Things
• Software Engineering
• Machine Learning
• +۵ مورد دیگر

Jorge Eduardo Ibarra Esquer is an Associate Professor in the Department of Computer Science at the School of Engineering, Universidad de Sonora, Mexico. With a publication record spanning nearly two decades from 2006 to 2023, he has established himself as an active researcher in multiple domains of computer science. His work demonstrates consistent collaboration with colleagues Brenda Leticia Flores Ríos, María Angélica Astorga Vargas, and Félix Fernando González-Navarro across numerous publications. His research interests focus on the Internet of Things , Software Engineering , Machine Learning , and Educational Technology . Recent work has explored IoT object categorization, user engagement analysis on social media for scientific dissemination, and software development education during the pandemic. Earlier research examined biosensor modeling, game playing preferences, and driving safety through gamification. His publications appear in reputable venues including IEEE Access, Sensors, and the Colombian Journal of Computing, as well as at national conferences like ENC (Encuentro Nacional de Computación). Analysis of his recent publications (2020-2023) reveals a strategic focus on applying machine learning to practical problems in IoT, educational technology, and social media analytics. His work bridges theoretical computer science with real-world applications, particularly in the Mexican higher education context as evidenced by studies on Facebook engagement at a higher education institution. The interdisciplinary nature of his research spans from healthcare applications (biosensors, physiological monitoring) to educational technology and software engineering practices. While no specific scientific awards are documented in the available publication records, his consistent publication output across multiple high-quality venues demonstrates recognition within his academic community. His research has practical implications for software development education, IoT applications, and human-computer interaction design. Dr. Ibarra Esquer's work shows strong collaborative patterns, particularly with researchers from his home institution. His recent publications suggest active involvement in research teams focusing on educational technology applications during the pandemic, social media analytics for scientific communication, and IoT applications. The continuity of his research program since 2006 indicates sustained scholarly productivity and relevance in evolving technological domains.

• Cognitive Radio Networks
• Reinforcement Learning Applications
• Wireless Communications
• +۷ مورد دیگر

Professor Kok-Lim Alvin Yau is an accomplished academic at Taylor's University in Malaysia, specializing in cognitive radio networks, reinforcement learning applications, and intelligent communications systems. With over 75 publications spanning from 2011 to 2025, he has established himself as a leading researcher in wireless networking and artificial intelligence applications in communications. His research interests encompass cognitive radio networks, reinforcement learning for networking applications, vehicular communications, edge computing, and the integration of AI in communication systems. Professor Yau's work demonstrates a consistent focus on applying machine learning techniques to solve complex networking challenges, particularly in dynamic spectrum access and resource allocation problems. Analysis of his recent publications (2023-2025) reveals a strategic shift toward more interdisciplinary research, combining reinforcement learning with generative AI techniques, exploring semantic communications, and addressing challenges in 6G-enabled vehicular networks. His work shows increasing emphasis on practical implementations and real-world applications of theoretical concepts, particularly in intelligent transportation systems and edge computing environments. Professor Yau has made significant contributions through numerous survey papers that have helped shape research directions in cognitive radio networks and reinforcement learning applications. His editorial work for special issues in IEEE Access and other journals demonstrates his standing in the academic community. He has successfully supervised multiple PhD students including Geong Sen Poh, Mee Hong Ling, and Aqeel Raza Syed, with whom he continues to collaborate on research projects. His international collaborations span institutions in China, Pakistan, and the UK, reflecting the global recognition of his research expertise.