جست‌وجوی استاد و پژوهشگر

Andrea Carronمشاهده پروفایل

مدرس ارشد

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +۷ مورد دیگر

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

• Probabilistic Machine Learning
• AI in Drug Discovery
• Bayesian Methods
• +۳ مورد دیگر

Arslan Masood is a Doctoral Researcher at the Department of Computer Science, School of Science, Aalto University. His research focuses on Probabilistic Machine Learning and its applications in Drug Discovery . University: Aalto University Department: Department of Computer Science Academic Rank: Researcher His work emphasizes data-efficient approaches using BERT-based models and Bayesian active learning , particularly for molecular property prediction and toxicity modeling. Recent publications explore imbalanced data handling in drug safety prediction and deep Bayesian optimization for experimental design.

• Machine Learning
• Bayesian Deep Learning
• Gaussian Process Models
• +۷ مورد دیگر

Rui Li is a Doctoral Researcher in the Department of Computer Science at Aalto University, Finland. Their research focuses on Bayesian deep learning, Gaussian processes, and robust machine learning methods. They collaborate closely with Professor Arno Solin and colleagues on problems involving uncertainty quantification, model generalization, and sensor fusion applications in robotics. Recent work highlights advancements in few-shot classification generalization, scalable Bayesian deep learning predictions, and hyperparameter optimization for Gaussian process models. Publications span conferences like WACV, ICLR, ICML, and ICCVW, addressing theoretical and applied challenges in machine learning and computer vision. Rui Li contributes to interdisciplinary research groups tackling problems in semantic segmentation robustness and visual-inertial SLAM optimization. Their collaborative efforts include participation in the Robust Semantic Segmentation UNCV2023 Challenge with a multi-institutional team.

• Spatio-temporal Data Mining
• Graph Neural Networks
• Causal Discovery
• +۳ مورد دیگر

Guojun Liang is a Postdoctoral Researcher at Halmstad University's School of Information Technology, actively contributing to the Cardiovascular health care project at the CAISR Health Center. He earned his Ph.D. in Artificial Intelligence from Macau University of Science and Technology in 2023, achieving the highest GPA in the Faculty of Computer Science and Engineering. His educational background includes doctoral studies at Macau University of Science and Technology (2023), where he received the Herry Fox Scholarship (2021) for academic excellence. His research career began with participation in China's National Artificial Intelligence and Industrial Robot Competition, where he secured a silver medal. Dr. Liang's research specializes in spatio-temporal data mining, with core expertise in Graph Neural Networks, causal discovery frameworks, diffusion modeling techniques, and time series analysis including prediction and imputation. His work bridges theoretical machine learning with practical applications in healthcare monitoring, transportation systems, and remote sensing. Recent publications demonstrate his focus on physics-informed architectures and causal inference mechanisms for spatio-temporal modeling. Analysis of his eight most recent publications (2021-2025) reveals a strong trend toward integrating physical constraints with deep learning architectures, particularly using graph-based approaches for spatio-temporal forecasting and imputation. His work increasingly incorporates causal discovery techniques and diffusion models to enhance model interpretability and probabilistic reasoning in time-series applications across healthcare and transportation domains. Herry Fox Scholarship (2021) Silver medal in National Artificial Intelligence and Industrial Robot Competition (China) As an educator, Dr. Liang has taught Analog/Digital Electronic Technology and Microcontroller Systems in China, and currently serves as Teaching Assistant for Artificial Intelligence, Applied Machine Learning, and Big Data Processing courses at Halmstad University. He maintains active research collaboration with the CAISR Health Center, focusing on cardiovascular health applications through advanced spatio-temporal data analysis.

• Process Mining
• Process Modelling and Simulation
• Distributed Computing
• +۵ مورد دیگر

Claudio di Ciccio is an Associate Professor at the Department of Information and Computing Science within the Faculty of Science at Utrecht University, Netherlands. Previously, he worked with the Department of Computer Science of Sapienza University of Rome (Italy) and the Institute for Information Business of the Vienna University of Economics and Business (WU Vienna), Austria. He received his PhD in Computer Science and Engineering in 2013 from Sapienza University. His research interests span across Process Mining, Formal Methods, Automated Reasoning, and Blockchain & Distributed Ledger Technologies. He has developed expertise in Process Modelling and Simulation, Distributed Computing, Logic, and Artificial Intelligence. His work bridges theoretical formal methods with practical applications in business process management and decentralized systems. His recent publications focus on advancing Process Mining techniques while addressing critical challenges in data privacy and security, particularly in decentralized environments. His research explores the intersection of visual analytics and process mining, developing innovative approaches for multi-faceted process information analysis through time and space. He has made significant contributions to formal methods for process specification verification and constraint satisfaction. Member of the Steering Committee of the IEEE Task Force on Process Mining General Chair of the Conference on Process Mining (ICPM) in 2023 PC chair of ICPM in 2021 PC chair of the Conference on Business Process Management (BPM) in 2022 PC chair of the Blockchain Forum at BPM in 2019 and 2024 Di Ciccio has been actively involved in numerous significant research projects related to process mining, blockchain applications, and formal methods. His work demonstrates a consistent focus on developing practical tools and frameworks that address real-world challenges in business process management while maintaining strong theoretical foundations. His research group appears to focus on the intersection of process science with emerging technologies, particularly in secure and decentralized environments.

Adrian Staubمشاهده پروفایل

استاد

• Psycholinguistics
• Eye movement research in reading
• Language comprehension
• +۴ مورد دیگر

Adrian Staub is a Professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst. He directs the UMass Eyetracking Laboratory and focuses on psycholinguistics, particularly the cognitive processes underlying language comprehension and production. His work frequently employs eye-tracking methodologies to investigate syntactic parsing, word recognition, and predictability effects in reading. PhD in Psychological and Brain Sciences (2008), University of Massachusetts Amherst Research Interests Staub's research spans psycholinguistic theory, eye movement analysis during reading, and methodological issues in psychological science. He studies how readers process syntactic structures, recognize words, and integrate predictability information. His recent work includes investigations of function word errors in reading, statistical power in language research, and the theoretical foundations of the replication crisis in psychology. Recent Publications His publications examine phenomena such as the 'Sentence Superiority Effect,' the effects of predictability on lexical processing, and methodological challenges in eye-tracking research. These studies span diverse topics including cross-linguistic comparisons (e.g., Italian, French, Chinese), computational modeling of reading processes, and theoretical debates in psycholinguistics. Academic Roles Since January 2023, Staub has served as Editor-in-Chief of the Journal of Memory and Language . He has also acted as guest editor for a special issue on eye movements in reading at 50. His teaching includes graduate seminars on the replication crisis and undergraduate courses on the psychology of reading. International Collaborations Staub has held visiting appointments at institutions including the Labex EFL Project in Paris, the University of Salzburg, and the University of Milan-Bicocca. These roles reflect his engagement in international research networks focused on reading cognition and eye-tracking methodologies.

Dmitry Vetrovمشاهده پروفایل

استاد

• Bayesian Deep Learning
• Diffusion Models
• Loss Landscape Analysis
• +۵ مورد دیگر

Dmitry Vetrov serves as a Professor of Computer Science at Constructor University in Bremen, where he founded and leads the Bayesian Methods Research Group. His academic foundation includes graduation from Moscow State University in 2003 and completion of his PhD in 2006, establishing a career centered on advancing probabilistic machine learning methodologies. His educational trajectory features: Undergraduate studies at Moscow State University (2003) Doctoral degree (PhD, 2006) Vetrov's research program critically bridges Bayesian statistics with deep learning architectures, with his group pioneering efficient diffusion model algorithms, loss landscape characterization in neural networks, scalable stochastic optimization tools, tensor decomposition applications for large-scale ML systems, and enhanced conditional text generation frameworks. This work manifests practical implementations across generative AI domains while maintaining theoretical rigor in probabilistic modeling. Analysis of his 2024-2025 publications reveals a concentrated research thrust toward diffusion model innovation, spanning text generation (token embedding smoothing, language model encoding properties), image synthesis (hair transfer, gesture generation), and scientific applications (protein modeling, genetic fine-mapping). Key thematic threads include sampler acceleration, theoretical property analysis of diffusion processes, and robust evaluation frameworks for generative systems. No scientific awards were documented in the source materials. Mentorship outcomes demonstrate significant impact, with three recent PhD students securing research positions at DeepMind. While specific grant details remain undisclosed, the group's prolific output across NeurIPS, ICML, and CVPR indicates sustained research funding. The Bayesian Methods Research Group operates as an integrated innovation hub within Constructor University's academic ecosystem. The research collective he directs maintains active development of Bayesian-deep learning fusion techniques, with current projects emphasizing diffusion model efficiency, theoretical foundations of optimization landscapes, and cross-domain applications in computational biology and multimodal generation.

Kevin J Liangمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• 3D Computer Vision
• +۵ مورد دیگر

Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.

Gwen Salaünمشاهده پروفایل

استاد

• Formal Methods
• Specification Languages
• Automated Verification
• +۱۰ مورد دیگر

Gwen Salaün is a Professor of Computer Science at Université Grenoble Alpes, affiliated with the CONVECS and LIG research groups, and Inria Grenoble. She holds a PhD (2003) and HDR in Computer Science. Her research spans formal methods, specification languages, automated verification, concurrent systems, and component/service-based architectures. She contributes to industrial applications, particularly in BPMN processes, IEC 61499, and IoT systems. Recent publications focus on probabilistic model checking, runtime enforcement, resource isolation testing, and automated refactoring of business processes, reflecting collaborations across Europe and funded by Région Auvergne-Rhône-Alpes. She supervises PhD students Ahang Zuo, Philippe Ledent, and Irman Faqrizal, and participates in committees like SAC'25 and FormaliSE'25. Her work emphasizes rigorous modeling, verification, and tool development for complex systems.

Kathleen Fisherمشاهده پروفایل

استاد مدعو

• Programming Languages
• Domain-Specific Languages (DSL)
• Program Synthesis
• +۴ مورد دیگر

Kathleen Fisher is an Adjunct Professor in the Computer Science Department at Tufts University and currently serves as the Director of the Information Innovation Office at DARPA. She previously held roles as Professor and Department Chair at Tufts (2016-2021), Program Manager at DARPA, and Principal Member of Technical Staff at AT&T Labs Research. Her academic journey began with a PhD in Computer Science from Stanford University. Kathleen’s research focuses on advancing programming languages through domain-specific languages (DSLs), program synthesis, and formal methods. Her work addresses challenges in ad hoc data management, secure systems, and integrating machine learning with programming language design. Notable projects include the Hancock and PADS systems for data processing, Forest for filestore management, and verified parser generators. She has received prestigious accolades, including ACM Fellow, Hertz Foundation Fellow, and SIGPLAN Distinguished Service Award. Her service includes leadership roles in ACM SIGPLAN, CRA-W, and as General Chair for ICFP 2015. Kathleen has advised PhD student Matt Ahrens and led impactful DARPA programs like HACMS and PPAML. As co-founder of the Programming Language Mentoring Workshop (PLMW), she actively contributes to diversity initiatives in computer science. Her research group, TuPL, explores DSLs, program synthesis, and language-based security, maintaining projects such as Autobahn and PADS.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +۳ مورد دیگر

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.

Leopold Partsمشاهده پروفایل

استاد

• Genome engineering
• CRISPR/Cas9 technology
• Human genetics
• +۵ مورد دیگر

Dr. Leopold Parts is a Group Leader at the Wellcome Sanger Institute, where he leads the Parts Group within the Human Genetics Programme and the Generative and Synthetic Genomics Programme. His research focuses on understanding human DNA function through genome engineering approaches, combining experimental and computational methods to study how genetic variation affects cellular traits. Parts received his undergraduate education at MIT, double majoring in Computer Science and Mathematics, before earning his PhD in Molecular Biology from the University of Cambridge in 2011 under Richard Durbin. His doctoral work on sources of variation in gene expression earned him the Grand Prize of life sciences PhDs in Estonia. He then completed postdoctoral training as a Canadian Institute for Advanced Research Global Scholar at the University of Toronto with Brenda Andrews and Charles Boone, followed by a Marie Curie Fellowship at EMBL Heidelberg and Stanford University with Lars Steinmetz. His research program integrates genome engineering, high-throughput screening, and computational modeling to understand how DNA sequence variation affects cellular phenotypes. The Parts Group develops tools for genetic perturbations using CRISPR/Cas, prime editing, and recombinase systems to create cell lines for randomization, screening, and evaluation. They combine these experimental approaches with probabilistic modeling to analyze large-scale genetic screens and their outputs. The analysis of Parts' recent publications reveals a strong focus on genome engineering technologies, particularly CRISPR-based approaches. His work spans multiple applications including predicting editing outcomes, developing tools for structural variant engineering, and applying these techniques to understand human disease mechanisms, particularly in cancer. His research bridges computational biology and experimental genomics, with increasing emphasis on single-cell technologies and clinical applications. Grand Prize of life sciences PhDs in Estonia Parts leads a diverse research team including postdoctoral fellows, advanced research assistants, and PhD students, with current members including Dr. Alistair Dunham, Mr. Gareth Girling, Elin Madli Peets, and Isabelle Zane. His group collaborates extensively with other research teams at the Sanger Institute, including the Cancer Dependency Map, Cellular Generation, and Cellular Screening groups. The Parts Group is part of the broader Human Genetics Programme, which aims to understand the genetic causes and biological mechanisms of disease susceptibility. The Parts Group maintains strong collaborations with external partners including the Open Targets consortium and the Cancer Dependency Map initiative. Their work combines laboratory-based genome engineering with computational analysis to address fundamental questions about human genome function.

Orkun Furatمشاهده پروفایل

مدرس

• Machine Learning
• Stochastic Modeling
• Spatial Statistics
• +۵ مورد دیگر

Dr. Orkun Furat is a Lecturer at the Institute of Stochastics, University of Ulm, Germany, where he conducts research at the intersection of machine learning, stochastic modeling, and image analysis for materials science applications. His work focuses on developing advanced computational methods to characterize and reconstruct 3D microstructures from 2D image data, with significant contributions to battery materials and particle systems. His primary research interests include generative adversarial networks (GANs) and spatial stochastic models for tomographic image analysis of functional materials. He has pioneered techniques for super-resolving microscopy images, quantifying electrode degradation in batteries, and modeling particle morphology/separation processes in mineral processing. His interdisciplinary approach bridges statistics, computer science, and materials engineering through rigorous mathematical frameworks. Recent publications (2024-2025) reveal a concentrated focus on lithium-ion and all-solid-state battery technologies, particularly analyzing how operating conditions (charge rate, temperature, cycling) induce electrode degradation. Simultaneously, his particle systems research employs multidimensional stochastic models to optimize mineral beneficiation processes like flotation, using copula-based approaches for particle property distributions. Dr. Furat actively supervises seminar students in generative machine learning and spatial stochastic modeling while teaching core courses including Point Processes and Advanced Statistics. His research impact is evidenced by numerous invited talks at premier venues like the Dagstuhl Seminar (2025) and European Congress for Stereology (2025), where he presents as a plenary speaker on AI-driven microstructure reconstruction. Collaborating with interdisciplinary teams across materials science and engineering, his work on digital twins for battery electrodes and virtual materials testing has been featured in University of Ulm press reports (2024) highlighting applications in efficient battery recycling and sustainable material design. Current projects integrate generative AI with stochastic geometry to solve industrial-scale challenges in energy storage and mineral processing.

• Computational Structural Design
• Embodied Carbon Analysis
• Sustainable Building Structures
• +۷ مورد دیگر

Bernardino D'Amico is an Associate Professor at Edinburgh Napier University's School of Computing Engineering and the Built Environment. With a background in architecture and computational structural design, he focuses on sustainable building practices with particular expertise in embodied carbon analysis, timber engineering, and innovative construction systems. His research bridges the gap between computational methods and practical sustainable construction solutions. Laurea Magistrale (Master's) in Architecture, University of Naples Federico II (2010) PhD in Computer-Aided Methods for Free-Form Grid-Shell Structural Systems, Edinburgh Napier University D'Amico's research centers on computational structural design and optimization for sustainable buildings. His work explores embodied carbon quantification, material efficiency in structural systems, and innovative use of sustainable materials like timber and bamboo. He investigates how computational approaches can reduce environmental impacts while maintaining structural integrity and functionality. His research has significant implications for decarbonizing the construction sector through data-driven design methodologies. His publication record shows a strong focus on embodied carbon assessment methodologies, sustainable material applications, and whole-life carbon analysis of buildings. Recent work includes probabilistic approaches to carbon prediction, bamboo-based construction systems, and critical analyses of net zero building frameworks. His research increasingly incorporates machine learning techniques to improve accuracy in early-stage design carbon estimation. D'Amico has received significant research funding from diverse sources including the Royal Academy of Engineering, Construction Industry Council of Hong Kong, Innovate UK, and the Scottish Government for projects addressing sustainable construction challenges. His work spans from material-level innovations to system-wide analyses of building stock carbon emissions. He actively supervises numerous PhD students working on cutting-edge sustainability topics including carbon neutrality verification, material substitution with hemp insulation, bamboo-timber composites, and embodied carbon responsibility frameworks. His supervision portfolio demonstrates his commitment to developing the next generation of sustainability-focused researchers in the built environment sector. D'Amico leads or contributes to several research groups focused on sustainable construction, embodied carbon quantification, and innovative structural systems. His work connects computational design, environmental assessment, and practical construction applications, creating a unique interdisciplinary approach to reducing the environmental impact of the built environment.

Masoud Jasbiمشاهده پروفایل

استادیار

• Semantics
• Pragmatics
• Language Acquisition
• +۴ مورد دیگر

Masoud Jasbi is an Assistant Professor in the Department of Linguistics at the University of California, Davis, specializing in experimental and theoretical approaches to natural language semantics and pragmatics. His research focuses on: Semantic and pragmatic interpretation of logical connectives (negation, conjunction, disjunction) Cross-linguistic studies across English, Persian, Mandarin Chinese, Spanish, and Hungarian Language acquisition in child and adult populations Integration of generative linguistics with artificial intelligence Experimental methodologies for testing linguistic hypotheses Recent publications demonstrate consistent emphasis on scalar implicature, context effects, and frequency-dependent processing, with increasing exploration of AI-linguistics intersections. His work bridges formal theory with empirical validation through experimental paradigms, examining how prior beliefs and contextual factors shape linguistic meaning across diverse populations. No scientific awards are documented in available sources. Information regarding student advising, research grants, laboratory facilities, or collaborative teams is not publicly specified in current materials.