جست‌وجوی استاد و پژوهشگر

Emtiyaz Khanمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Bayesian Statistics
• Deep Learning
• +۹ مورد دیگر

Emtiyaz Khan is a Research Professor and Team Leader of the Adaptive Bayesian Intelligence team at the RIKEN Center for Advanced Intelligence Project (AIP) in Japan. His research focuses on developing AI systems that can autonomously learn to perceive, act, and reason throughout their lives, bridging the gap between how living beings and computers learn. Dr. Khan's research interests span multiple areas of machine learning including: Approximate inference and Bayesian statistics Deep learning and neural network optimization Reinforcement learning and active learning Information geometry and signal processing Online learning and continual adaptation His recent work has focused on developing theoretically grounded Bayesian methods for deep learning, with applications in computer vision and decision-making systems. Dr. Khan has made significant contributions to variational inference, Bayesian optimization, and continual learning frameworks that enable AI systems to retain knowledge while learning new tasks. Dr. Khan has received substantial research funding including: (2021-2026, USD 2.23 Million) JST-CREST and French-ANR's grant, The Bayes-Duality Project (2020-2023, USD 167,000) KAKENHI Grant-in-Aid for scientific Research (B), Life-Long Deep Learning using Bayesian Principles (2020-2023, USD 11,000) KAKENHI Grant-in-Aid for Chellenging Research (Exploratory), Linear algebra for continuous learning of large neural networks (2019-2022, USD 237,000) External funding through companies for several Bayes related projects Dr. Khan is actively involved in the machine learning community, having served as General Chair for AISTATS 2026 and given keynotes at major conferences including Bayes Comp 2025 and the 1st EurIPS conference. His work has resulted in multiple papers accepted at top-tier conferences like NeurIPS, with some receiving spotlight presentations.

Felipe Werndl Trevizanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Automated Planning and Scheduling
• Reasoning under Uncertainty
• +۹ مورد دیگر

Felipe Werndl Trevizan is a Senior Lecturer in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of Artificial Intelligence, Operations Research, and Machine Learning. His work focuses on automated planning and scheduling, reasoning under uncertainty, heuristic search, and machine learning for planning. He is actively involved in research projects related to uncertainty-centric pilot assistance systems and probabilistic planning. His research interests include: Automated Planning and Scheduling Probabilistic and Multi-Objective Planning Heuristic Search and Admissible Heuristics Deep Learning for Generalized Planning Constraint-Based and Risk-Aware Planning Learning Heuristics from Data His recent publications demonstrate a strong trend toward integrating machine learning with classical planning techniques, particularly in learning reliable, domain-independent heuristics using both deep and classical machine learning methods. He has developed novel algorithms for constrained and stochastic shortest path problems, multi-objective probabilistic planning, and generalized planning using neural networks. His scientific awards include: Best Paper Award at ICAPS 2016 Best Paper Award at ICAPS 2017 2016 Kikuchi-Karlaftis Best Paper Award from the Transport Research Board Felipe Werndl Trevizan is registered to supervise research students and has advised several successful projects in AI planning. He has secured research grants for projects such as 'Integrated Planning for Uncertainty-Centric Pilot Assistance Systems' and 'Chance-Constrained POMDP for Pilot Assistance Systems' at ANU. He is also organizing key community events including the 'Reliability In Planning and Learning (RIPL)' workshop and a tutorial on 'Learning for Planning' at ICAPS 2025. He is affiliated with the School of Computing at ANU and previously worked as a Senior Research Scientist at NICTA and Data61/CSIRO. His research is supported by active collaborations with leading researchers such as Sylvie Thiébaux, Hanna Kurniawati, and others.

Ilgin Gulerمشاهده پروفایل

دانشیار

• Transportation Engineering
• Intelligent Transportation Systems
• Traffic Signal Control
• +۸ مورد دیگر

Ilgin Guler is an Associate Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University’s College of Engineering. Her research focuses on intelligent transportation systems, traffic operations, infrastructure sustainability, and equitable mobility solutions. She contributes to interdisciplinary projects such as the Equitable Optimization of Transportation Systems and Services addressing climate and sustainability challenges. Her research interests span Transportation Engineering , Intelligent Transportation Systems , Traffic Signal Control , Connected Vehicles , Pavement Management , and Transit Signal Priority . She applies advanced techniques including deep learning and optimization to improve urban and rural transportation systems. Her work emphasizes safety, equity, and sustainability in infrastructure planning and operations. The recent publications highlight a strong trend in integrating machine learning with classical traffic theory, enhancing infrastructure resilience through data-driven maintenance, and evaluating shared mobility impacts on safety and equity. Her research bridges civil engineering with data science, focusing on real-world applications in Pennsylvania and beyond. Scientific Awards: No awards explicitly mentioned in the provided text. She advises graduate students in transportation engineering and has been involved in multiple research projects, including seed-funded initiatives from the Institute for Energy and the Environment (IEE). While specific grant amounts and student names are not listed, her collaborative work with faculty like Vikash Gayah and Anik Sengupta indicates active mentorship and team leadership. Her research is associated with initiatives such as the Equitable Communities and the Built Environment and leverages tools from connected vehicle technology, impact-echo testing, and generative AI for infrastructure assessment.

• Artificial Intelligence for Science
• Explainable AI
• Trustworthy AI
• +۷ مورد دیگر

Wei (Celia) Xu serves as Research Professor in the Computer Science Department at Stony Brook University and Computational Scientist/Trustworthy AI (TAI) Group Lead at Brookhaven National Laboratory's Computational Science Initiative, driving innovation in AI for scientific discovery across multiple domains. Her educational foundation includes a Ph.D. in Computer Science from Stony Brook University and dual M.S. degrees in Computer Science from Zhejiang University, establishing expertise in computational methods and visualization. Dr. Xu's research centers on developing explainable and trustworthy AI frameworks for scientific applications, with notable contributions in digital twins for simulation workflows, performance evaluation of quantum/classical computing systems, and visual analytics for X-ray imaging and climate science. Her work integrates GPU acceleration and virtual reality to enhance scientific data interpretation, emphasizing model interpretability and reliability in high-stakes domains. Analysis of her 15 most recent publications (2019-2025) reveals a strategic evolution toward trustworthy AI systems, with increasing focus on counterfactual explanations for medical diagnostics, digital twin implementations for ensemble simulations, and quantum state visualization—demonstrating cross-disciplinary impact in materials science, climate modeling, and high-energy physics. Her exceptional contributions have been recognized with prestigious awards: Best Paper Award, PacificVis (2025) Best Paper Award, IEEE SC/ISAV (2020) Honorable Mention Award, IEEE VIS (2018) Women@Energy Recognition (2014) Best Paper Award, Fully3D/HPIR (2009) Dr. Xu actively mentors through her TAI research group while securing sustained funding from DOE's Biological and Environmental Research (BER) program and SciDAC initiatives, complemented by Brookhaven National Laboratory internal projects (LDRD and NSLSII DSSI). She serves on program committees for SC, VIS, and AAAI conferences and has organized workshops including NYSDS and Fully3D, demonstrating leadership in advancing trustworthy AI methodologies for scientific communities.

Dana Rezazadeganمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Dana Rezazadegan is a Senior Lecturer in Computer Science and Software Engineering at Swinburne University of Technology, affiliated with the School of Science, Computing and Emerging Technologies. Her work bridges artificial intelligence, computer vision, and healthcare applications, with a strong focus on human-robot collaboration and digital health solutions. Research Interests: Her primary research areas include deep learning, computer vision, LLMs, health informatics, continual learning, and AI for inclusive education. She investigates how intelligent systems can support human activities in healthcare and assistive robotics. The recent publications highlight a consistent focus on applying AI to healthcare challenges—ranging from clinical NLP and patient decision support to automated severity assessment in diseases like COVID-19. She also contributes significantly to foundational AI methods, including uncertainty quantification, speech summarization, and real-time 3D pose estimation for embedded and mobile platforms. Superstar of STEM, Science and Technology Australia, 2021 Teaching Award, Swinburne University of Technology, 2020 Early Career Marie Curie Fellowship, 2014 Top 20 in Iran's Mathematics Olympiad, 2000 Dana actively supervises PhD students on topics such as continual learning, AI tutoring, electricity price forecasting, and 3D pose estimation. She has secured multiple research grants, including projects on inclusive AI for dyslexic learners and AI for energy forecasting. Her collaborative work spans institutions like Macquarie University and CSIRO, and she engages in public outreach through media contributions on AI ethics and privacy. She leads and contributes to interdisciplinary teams focused on AI in healthcare and education, often involving co-design with clinicians and end-users. Her labs and research groups emphasize real-world applicability, ethical considerations, and human-centered AI design.

Aleksei Tiulpinمشاهده پروفایل

استادیار

• Machine Learning
• Medical Imaging
• Bayesian Deep Learning
• +۴ مورد دیگر

Aleksei Tiulpin is an Assistant Professor at the University of Oulu since January 2022 and a member of the European Lab for Learning and Intelligent Systems (ELLIS). His research focuses on Intelligent Medical Systems at the intersection of machine learning and healthcare. Current affiliation: Faculty of Medicine, University of Oulu Research group: Intelligent Medical Systems (IMEDS) Academic rank: Assistant Professor Aleksei's research interests span Machine Learning , Medical Imaging , Bayesian Deep Learning , and Human-in-the-loop Machine Learning . He develops methods that: Use multimodal data Minimize labeled data requirements Quantify prediction uncertainty Enable human-AI interaction Applications target musculoskeletal disorders, breast cancer, and dementia diagnostics. Scientific recognition : 2020: Best doctoral thesis at University of Oulu 2022: Docent title in Machine Learning for Medical Imaging Academic background : PhD in Medicine (2017-2020) from University of Oulu, with advisors Prof. Simo Saarakkala, Dr. Jérôme Thevenot, and Prof. Esa Rahtu.

Rich Littleمشاهده پروفایل

پژوهشگر

• Marine Environmental Science
• Fisheries Management
• Ecological Modeling
• +۵ مورد دیگر

Dr. Rich Little is a Senior Research Scientist at CSIRO Environment in Hobart, Australia. His work focuses on integrating biophysical models with socioeconomic analyses to address challenges in marine resource management, fisheries economics, and climate adaptation. He has developed decision-support tools like ELFSim for coral reef fisheries and leads the South East Australian Marine Ecosystem Survey (SEA-MES). Additionally, he contributes to Marine Visual Technologies, which employs AI for sustainable fisheries monitoring. Certificate in Executive Management and Development (2018), Australian Graduate School of Management PhD (2000), University of Alberta MSc (1995), University of Western Ontario BSc (1989), University of Western Ontario Diploma (1993), University of Western Ontario Rich Little’s research spans marine population dynamics, tradeable permit markets, Bayesian Belief Networks for fishing behavior, and the economics of marine protected areas. His recent work explores parametric insurance for climate adaptation, deep learning applications in electronic monitoring, and resilience-building in mixed fisheries. Publications highlight interdisciplinary approaches to reconciling ecological, economic, and social objectives. His scientific accolades include the CSIRO Julius Career Development Award (2008-2011), the Early Career Research Excellence Award (2005), and an Honorary Associate position at the University of Tasmania (2007-2010). He has also contributed to transdisciplinary initiatives like the Theo Murphy Frontiers of Science meeting.

• Bayesian Inference
• Neural Networks
• Statistical Learning
• +۶ مورد دیگر

Jorge Loría is a Postdoctoral Researcher at the Department of Computer Science within Aalto University . His research focuses on Bayesian inference, neural networks, and statistical learning, particularly exploring probabilistic modeling with heavy-tailed distributions and uncertainty quantification. Recent work includes advancements in posterior inference for Bayesian neural networks with unbounded variance priors, kernel methods for deep learning, and applications of α-stable distributions in regression and neural processes. His publications span conferences like ICLR and journals such as Statistics and Computing , with a thematic emphasis on robust statistical models and causal inference frameworks. He has not been explicitly associated with teaching roles, formal student advisement, or scientific prizes. Contact details include the email jorge.loria@aalto.fi .

• statistics
• applied statistics
• Bayesian modelling
• +۳ مورد دیگر

Dr. Edgar Santos Fernandez is a Senior Research Fellow in the School of Mathematical Sciences within the Faculty of Science at Queensland University of Technology (QUT). His work focuses on advanced statistical methodologies with applications across various scientific domains. Dr. Santos Fernandez's primary research interests include: Statistics and applied statistical methods Bayesian modeling and computation Data science and analysis of complex datasets Anomaly detection in spatio-temporal data Environmental and ecological data analysis His research demonstrates a strong focus on developing and applying innovative statistical techniques to address real-world challenges, particularly in environmental monitoring, ecology, and health sciences. Dr. Santos Fernandez has made significant contributions to the development of Bayesian methods for spatio-temporal data analysis, with applications to stream networks, sensor data, and citizen science projects. Dr. Santos Fernandez has been actively involved in several interdisciplinary research collaborations, working with experts in ecology, environmental science, and health. His work on the Virtual Reef Diver project demonstrates his commitment to applying statistical methods to important environmental conservation efforts. He has developed statistical software tools including clusterBMA for Bayesian model averaging in clustering and SSNbayes for Bayesian spatio-temporal modeling on stream networks, contributing to the statistical computing community through R package development.

Stephane Rossignolمشاهده پروفایل

پژوهشگر

• Economics
• Speech Signal Processing
• Political Economy
• +۳ مورد دیگر

Stephane Rossignol is an active researcher with interdisciplinary contributions to economics, political science, and speech signal processing. His work spans theoretical modeling and empirical analysis. Affiliation: Unspecified (likely affiliated with French academic institutions given the context) Research Themes: Spatial competition, pandemic dynamics, speech processing algorithms Research Interests : Economics : Political economy, inequality analysis, social insurance mechanisms Political Science : Valence politics, international agreements, trust in governance Signal Processing : Speech voicing extraction, co-channel classification, analytic signal methods Recent Publications highlight his focus on: Econometric modeling of political and health policies Statistical approaches to speech signal analysis Interdisciplinary studies combining Bayesian inference and social dynamics

• Machine Translation
• Multilingual NLP
• Bias and Fairness in NLP
• +۶ مورد دیگر

Christian Hardmeier is an Associate Professor in Data Science at the IT University of Copenhagen, specializing in Natural Language Processing (NLP) and computational linguistics. His research spans machine translation, multilingual NLP, bias and fairness, coreference resolution, and discourse analysis. Key research areas include: Cross-lingual pronoun prediction and anaphora resolution Discourse structure in multilingual contexts Ethical considerations in NLP (dehumanizing language, bias statements) Low-resource language processing and dataset creation Evidential deep learning for uncertainty estimation His recent publications focus on Egyptian Arabic discourse relations, uncertainty modeling, and biomedical translation. He contributes to community efforts through the Pioneer Centre for Artificial Intelligence and SafeNet initiatives. Notable recognition includes an Outstanding Paper Award at the ML Evaluation Standards Workshop 2022. He actively participates in assessment committees and NLP community-building activities.

• Stock Assessment
• Fish Bioenergetics
• Food-Web Dynamics
• +۷ مورد دیگر

Ji Xiang He is an Adjunct Associate Professor at the Department of Fisheries and Wildlife, College of Agriculture & Natural Resources, Michigan State University. He also serves as a Research Fisheries Biologist at the Michigan Department of Natural Resources (MI DNR) at the Alpena Fisheries Research Station. His professional focus integrates fish bioenergetics, food-web dynamics, and life-history parameter analysis within the context of human-ecosystem interactions in fisheries management. Education: Undergraduate in Fisheries and Aquaculture (specializing in Chinese reservoir systems), with graduate training in Fisheries and Ecology at the State University of New York (SUNY) beginning in 1995. His research bridges ecological frameworks (natural resource geography, landscape ecology, systems ecology) with quantitative fisheries science. While his primary work centers on stock assessment and ecosystem modeling in aquatic environments, recent Google Scholar publications indicate interdisciplinary applications of computational modeling and machine learning in biomedical contexts such as arterial hypertension and aneurysm progression. Key trends in his publications reveal expertise in physics-based machine learning, patient-specific hemodynamic modeling, and longitudinal data analysis for cardiovascular diagnostics. Though seemingly divergent from his fisheries background, these computational techniques may inform ecological modeling practices through cross-disciplinary methodological innovation. He maintains affiliation with the Quantitative Fisheries Center and emphasizes functional connections between ecological systems and human management practices in his work.

Martin Vechevمشاهده پروفایل

استاد

• Programming Languages
• Software Analysis
• Static Analysis
• +۴ مورد دیگر

Martin Vechev is a Professor in the Department of Computer Science at ETH Zurich, leading the Secure, Reliable, and Intelligent Systems (SRL) Lab. His research bridges programming languages, software analysis, and emerging domains like quantum computing, with significant contributions to static analysis, abstract interpretation, and machine learning for code. His primary research interests include programming languages, static analysis, abstract interpretation, and quantum computing. Vechev has pioneered scalable techniques for software verification, particularly in concurrency and security analysis, and has recently driven innovations at the intersection of programming languages and quantum software development. His work emphasizes practical applications while maintaining theoretical rigor. Analysis of Vechev's recent publications reveals a strategic evolution toward quantum programming languages and machine learning integration. His lab has shifted from traditional static analysis to developing foundational frameworks for quantum circuit synthesis (e.g., Silq, Unqomp) and robust neural network certification, addressing critical challenges in quantum resource management and AI safety. The SRL Lab under Vechev's direction focuses on building intelligent systems that are inherently secure and reliable through advanced program analysis techniques. The lab's research spans theoretical foundations to industrial-strength tools, with strong emphasis on quantum software engineering and the application of machine learning to code understanding and generation.

• Machine Learning
• Computational Materials Science
• Partial Differential Equations
• +۷ مورد دیگر

Yaohua Zang is a Professor at the Technical University of Munich , affiliated with the Department of Data-driven Materials Modeling . Their research bridges computational materials science, machine learning, and mathematical modeling, focusing on advanced methods for solving partial differential equations (PDEs), inverse problems, and optimal control in complex systems. Research Interests include: Physics-informed neural operators for PDEs Stochastic generative modeling for materials design Optimal control in robotics and dynamic systems Bayesian inference and uncertainty quantification High-dimensional numerical analysis Adversarial and weak formulation-based neural networks Recent work highlights physics-aware neural operators (DGenNO), weak adversarial networks for inverse problems, and applications in elastography and robotic assembly. Their scientific contributions span PDEs, materials science, and computational control. Labs & Teams : Part of the Chair of Data-driven Materials Modeling, located in Garching b. München, Germany.

Gil Robalo Reiمشاهده پروفایل

پژوهشگر

• Uncertainty Quantification
• Bayesian Methods
• Inverse Problems
• +۲ مورد دیگر

Gil Robalo Rei is a Research Associate at the Institute for Numerical Mechanics within the TUM School of Engineering and Design at the Technical University of Munich (TUM). He has been working at the institute since 2021, contributing to research in computational mechanics and numerical methods, and is actively involved in teaching courses related to numerical methods and computational mechanics. His educational background includes: Master of Science (M.Sc.) in Mechanical Engineering from Technical University of Munich (2021) Bachelor of Science (B.Sc.) in Mechanical Engineering from Technical University of Munich (2018) Gil's research focuses on advanced computational methods for solving complex engineering problems. His primary interests span Uncertainty Quantification , Bayesian Methods , and Inverse Problems , with applications across multiple domains including solid-state battery technology, biomedical modeling, and materials science. His work often involves developing novel computational frameworks that integrate statistical methods with physics-based simulations to address challenges where traditional approaches fall short, particularly when dealing with computationally expensive forward models. Analysis of Gil's publication record reveals a strong trend toward interdisciplinary research that bridges computational mechanics with statistical inference. His work demonstrates expertise in applying Bayesian methods to inverse problems in diverse contexts such as tumor growth modeling, solid-state battery optimization, and powder system characterization. A notable pattern is his focus on developing computationally efficient approaches for problems with expensive forward models, often leveraging Gaussian processes and active learning techniques to reduce computational costs while maintaining accuracy. Gil has actively contributed to academic mentoring through supervision of student projects: Multiple Bachelor's and Master's theses in computational mechanics and related fields Research internships focused on engineering simulations Term papers and visualization labs exploring numerical methods His collaborative approach is evident in co-supervision with other researchers like Christoph Schmidt and Jonas Nitzler, reflecting the interdisciplinary nature of his work and the research environment at TUM. As part of the research group led by Prof. Wolfgang A. Wall, Gil contributes to the QUEENS framework development and participates in the broader activities of the Institute for Numerical Mechanics. His work connects with several research teams focusing on computational mechanics applications in energy storage systems, biomedical engineering, and advanced materials, demonstrating the versatility and applicability of his methodological contributions across different scientific domains.