جست‌وجوی استاد و پژوهشگر

Daniel Krutzمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Software Engineering Education
• +۵ مورد دیگر

Daniel Krutz is an Associate Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT) , with a secondary appointment as Courtesy Associate Professor at the University of Florida's Department of Computer & Information Science & Engineering. He directs the AWARE Lab , focusing on Autonomous Systems, Warfare Applications, and Software Engineering , supported by $6M+ in federal grants from NSF, NSA, DOD, and AFRL. His research spans AI/ML, Self-Adaptive Systems, Decision Support Systems, and Accessibility in Computing Education . He holds a PhD from Nova Southeastern University and has taught courses including Software Engineering Freshman Seminar and Capstone Research Project . He is an NSF CAREER Award recipient (2022) and former AFRL Fellow (2018). Educations: BS, St. John Fisher College MS, Rochester Institute of Technology PhD, Nova Southeastern University Labs: AWARE Lab (Autonomy, Warfare, and Engineering) Awards: NSF CAREER Award (2022) AFRL Research Faculty Fellowship (2018) Teaching: Undergraduate/Graduate courses in software engineering, accessibility, and AI/ML Research Interests: Focus on experiential learning in computing education, neuroevolutionary algorithms, and AI ethics. Recent projects address empathy-building interventions for inclusive software development and context-aware decision support systems . Grants & Funding: Over $6M secured since 2018 from NSF, DOD, and NSA for projects in AI/ML, cybersecurity, and adaptive systems. Current work explores AI-driven stock trading frameworks and accessibility education modules.

Olga Sorkine Hornungمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Geometry Processing
• +۳ مورد دیگر

Prof. Olga Sorkine Hornung is a Full Professor of Computer Science at ETH Zürich, leading the Interactive Geometry Lab. She holds a BSc and PhD from Tel Aviv University (2000 and 2006) and conducted postdoctoral research at Technical University Berlin. Her research focuses on computer graphics, geometric modeling, and geometry processing, with applications in shape editing, digital fabrication, and animation. She has received numerous accolades, including the ACM Fellowship (2020), ERC Consolidator Grant (2020), and the Golden Owl Teaching Award (2021). Her work bridges theoretical foundations and practical algorithms, addressing challenges in parameterization, surface compression, and interactive design tools. Her research interests span: Computer Graphics & Visualization Geometric Modeling & Processing 3D Content Creation & Digital Fabrication Garment Design & Simulation Human Motion Analysis & Animation Awards and grants include: 2024: Best Paper Honorable Mention (EUROGRAPHICS) 2023: Member of Swiss Academy of Engineering Sciences (SATW) 2020: ERC Consolidator Grant 2017: Rössler Prize (ETH Zurich) Her lab focuses on developing novel methods for interactive geometry processing, with recent advancements in garment modeling (e.g., AIpparel, Rags2Riches) and motion retargeting systems like WalkTheDog. She actively collaborates on interdisciplinary projects, including biomedical applications and sustainable fashion technology.

LEONG Tze Yunمشاهده پروفایل

استاد

• Responsible AI
• Dynamic Decision-Making
• Neurocognitive Modeling
• +۳ مورد دیگر

LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.

Michael Baldeaمشاهده پروفایل

دانشیار

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +۳ مورد دیگر

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

Danica Kragicمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Danica Kragic is a Professor of Computer Science at the School of Electrical Engineering and Computer Science at the Royal Institute of Technology (KTH) in Stockholm, Sweden. She serves as the Director of the Centre for Autonomous Systems and leads the Robotics, Perception and Learning Lab at KTH. Her research focuses on advancing robotics capabilities through computer vision and machine learning approaches. MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia (1995) PhD in Computer Science from KTH (2001) Professor Kragic's research primarily centers on robotics, computer vision, and machine learning, with particular emphasis on robotic manipulation, grasp planning, and human-robot interaction. Her work bridges theoretical foundations with practical applications, exploring how robots can understand and interact with objects in complex environments. She investigates how visual and tactile sensing can be integrated to improve robotic perception and manipulation capabilities, with applications ranging from industrial automation to assistive robotics. Her recent publications demonstrate a strong focus on advanced grasp planning techniques, tactile sensing for manipulation, and mathematical representations for robotic control. Kragic's research shows increasing integration of machine learning approaches with traditional robotics frameworks, particularly in the areas of grasp synthesis, object recognition, and human-robot collaboration. Her work spans theoretical contributions in mathematical representations of grasps to practical implementations of robotic systems capable of adapting to novel objects and situations. 2007 IEEE Robotics and Automation Society Early Academic Career Award IEEE Fellow ERC Starting Grant (2012) Member of The Royal Swedish Academy of Sciences Member of The Royal Swedish Academy of Engineering Sciences Honorary Doctorate from Lappeenranta University of Technology Professor Kragic's research has been supported by major funding bodies including the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, and Swedish Research Council. While specific student names aren't listed in the provided information, her publication record suggests extensive mentorship of PhD students and postdoctoral researchers in robotics and computer vision. Her lab, the Robotics, Perception and Learning Lab, serves as a hub for interdisciplinary research connecting computer science, engineering, and cognitive science perspectives on robotic systems. As Director of the Centre for Autonomous Systems at KTH, Kragic oversees a major research initiative focused on advancing autonomous technologies. Her Robotics, Perception and Learning Lab brings together researchers working on visual perception, machine learning, and robotic manipulation, with particular emphasis on developing systems that can understand and interact with objects in unstructured environments. The lab's work spans theoretical foundations of robotic manipulation to practical implementations of systems capable of learning from experience.

Emily Wallمشاهده پروفایل

استادیار

• Data Visualization
• Visual Analytics
• Cognitive Psychology
• +۳ مورد دیگر

Dr. Emily Wall is an Assistant Professor in the Department of Computer Science at Emory University, leading the CAV Lab (Cognition and Visualization). She earned her Ph.D. in Computer Science from Georgia Tech (2020) and completed a postdoctoral fellowship at Northwestern University before joining Emory. Ph.D., Computer Science, Georgia Tech (2020) Postdoctoral Researcher, Northwestern University Current: Assistant Professor, Emory University Her research focuses on enhancing human decision-making through data visualization and visual analytics by addressing cognitive limitations and biases. Key areas include metacognition promotion, computational strategies for bias mitigation, and human-AI collaboration in data analysis. She combines cognitive psychology principles with technical implementation using tools like D3.js and Tableau. Recent publications highlight her work on LLM applications in visual analytics, cognitive bias interventions, and trust calibration in AI systems. Current students like Mengyu Chen, Shiyao Li, and Zhongzheng Xu are presenting at top conferences such as CHI and IEEE VIS. She also explores educational applications through interactive visualization pedagogy and innovative assessment methods. Dr. Wall actively engages in academic service through graduate mentorship, conference judging, and diversity initiatives. Her lab emphasizes both technical proficiency and social justice implications in visualization design, reflected in course projects that merge artistic expression with data-driven storytelling.

Sean F. Reardonمشاهده پروفایل

استاد

• Educational Inequality
• School Segregation
• Socioeconomic Disparities
• +۶ مورد دیگر

Sean F. Reardon is the Professor of Poverty and Inequality in Education and a Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR) at Stanford University . He also holds a Professor (by courtesy) of Sociology . His work focuses on social and educational inequality, including residential and school segregation, racial/ethnic and socioeconomic disparities in academic achievement, and causal inference methods. Education: Ed.D., Harvard University, Educational Administration, Planning, and Social Policy (1997) M.Ed., Harvard University, Educational Administration, Planning and Social Policy (1992) M.A., University of Notre Dame, International Peace Studies (1991) B.A., University of Notre Dame, Program of Liberal Studies; Minor in Honors Mathematics (1986) Research Interests center on the causes and patterns of educational and racial inequality, the effects of school integration policies, and income inequality's social consequences. His Stanford Educational Opportunity Research Lab analyzes achievement gaps and segregation trends across U.S. school districts using large-scale datasets. Recent Publications highlight cross-national achievement gap comparisons, pandemic recovery disparities, and methodological advancements in segregation measurement. His work often integrates Data Sciences and Sociology to address policy-relevant questions. Awards Elected Member, American Academy of Arts and Sciences Elected Member, National Academy of Education Advising includes mentoring Doctoral Students like Tyler McDaniel and Postdoctoral Researchers like Farah Mallah. His Stanford Interdisciplinary Doctoral Training Program in Quantitative Education Policy Analysis (2009–2021) trained future scholars in rigorous policy methods.

• Security Engineering
• Science of Security
• Human-Computer Interaction
• +۴ مورد دیگر

Janne Lindqvist is an Associate Professor at the Department of Computer Science, Aalto University. His research bridges security engineering, human-computer interaction (HCI), and privacy, with a focus on making security systems usable and user-centric. University: Aalto University Department: Department of Computer Science Rank: Associate Professor Email: janne.lindqvist@aalto.fi Lindqvist’s work spans security engineering , privacy systems , and user research , emphasizing practical authentication methods, password management, and human behavior in security contexts. He explores how users interact with systems like TPM APIs, gesture passwords, and mobile authentication mechanisms. Recent publications highlight trends in authentication systems , ubiquitous computing security , and mobile user behavior . Key themes include biometric authentication, gesture-based security, and balancing usability with cryptographic robustness. CHI'25 Honorable Mention Award CHI'24 Best Paper Award His research integrates empirical studies with technical implementations, such as analyzing password forgetting patterns and developing acoustic sensing for vehicle detection (e.g., Auto++, BO-Ear). Collaborative efforts span machine learning, psychology, and embedded systems.

Marc V Fuccilloمشاهده پروفایل

دانشیار

• Synaptic transmission
• striatal circuits
• inhibition
• +۹ مورد دیگر

Marc V Fuccillo is an Associate Professor of Neuroscience at the Perelman School of Medicine, University of Pennsylvania, where he leads a research laboratory focused on understanding the neural circuit mechanisms underlying behavioral control. His work bridges molecular, synaptic, and behavioral approaches to investigate how striatal circuits regulate mouse behavior from simple motor patterns to complex goal-directed actions. Fuccillo holds dual appointments in the Neuroscience and Cell and Molecular Biology Graduate Groups at Penn and maintains an active laboratory investigating the synaptic and circuit basis of neuropsychiatric disorders. Education: B.A. in Molecular and Cellular Biology and Music Performance (Violin) from Brown University (1998) Ph.D. in Developmental Genetics from New York University School of Medicine (2007) M.D. from New York University School of Medicine (2008) Fuccillo's research centers on the synaptic and circuit mechanisms of behavioral control, with particular emphasis on striatal circuits. His laboratory employs a range of technologies including mouse genetics, in vitro electrophysiology, in vivo imaging, and quantitative behavioral analysis to explore how neural circuits of the striatum regulate behavior and how disruptions in these circuits contribute to neuropsychiatric disorders. His work has particularly focused on autism-associated abnormalities in behavioral control, examining how synaptic adhesion molecules like neuroligins and neurexins shape circuit function and behavior, with significant findings regarding D1 dopamine receptor positive medium spiny neurons in the nucleus accumbens. Analysis of Fuccillo's recent publications reveals a strong focus on striatal circuit function across multiple dimensions. His work spans molecular neuroscience (examining synaptic adhesion molecules), cellular physiology (studying specific neuron types in striatal circuits), systems neuroscience (mapping circuit connectivity), and behavioral neuroscience (quantifying motor learning and decision-making). A unifying theme is how disruptions in specific molecular pathways lead to circuit-level abnormalities that manifest as behavioral phenotypes relevant to neuropsychiatric disorders, with particular attention to autism, OCD, and schizophrenia models. Scientific Recognition: Publications in high-impact journals including Nature Neuroscience, Current Biology, Cell Reports, and Neuron Research supported by multiple NIH grants including NIMH F32, NIMH K01, and HHMI Gilliam Fellowship awards for lab members Fuccillo actively mentors a diverse group of trainees including postdoctoral fellows, graduate students, and undergraduates. His laboratory has produced numerous successful alumni who have gone on to faculty positions, medical residencies, and graduate programs at prestigious institutions. His mentoring approach emphasizes technical skill development across multiple neuroscience disciplines while fostering independent scientific thinking. Current research in his lab is supported by NIH funding focused on understanding the molecular architecture of striatal circuits and their role in behavioral control, with three major research directions exploring molecular logic of striatal circuits, circuit mechanisms of behavioral control, and striatal dysfunction in neuropsychiatric disease models. The Fuccillo Laboratory operates within the Department of Neuroscience at the University of Pennsylvania, with access to state-of-the-art facilities for molecular, electrophysiological, imaging, and behavioral neuroscience research. The lab maintains active collaborations with other neuroscience research groups at Penn and beyond, creating a rich intellectual environment for studying the neural basis of behavior. Current research directions include investigating whether there is a molecular logic to striatal circuit composition, how striatal circuits shape behavioral control, and what mouse models of autism, schizophrenia, and OCD can reveal about striatal circuit dysfunction in disease pathophysiology.

Zeynep Akataمشاهده پروفایل

استاد

• Multimodal Learning
• Explainable Artificial Intelligence
• Zero-Shot Learning
• +۲ مورد دیگر

Zeynep Akata is the Liesel Beckmann Distinguished Professor of Computer Science at the Technical University of Munich (TUM) and Director of the Institute for Explainable Machine Learning at Helmholtz Munich. Previously she was a W3 Professor at the University of Tübingen (2019-2023) and held faculty and post-doctoral positions at the University of Amsterdam, UC Berkeley and the Max Planck Institute for Informatics. Her research focuses on multimodal learning and explainable artificial intelligence . Education: PhD, University of Grenoble / INRIA Rhône-Alpes, 2014 MSc, RWTH Aachen University, 2010 BSc, Trakya University, Turkey, 2008 Research Interests: Professor Akata’s group develops algorithms that learn from vision, language and other modalities simultaneously, with a strong emphasis on zero-shot, few-shot and continual learning . A central theme is making decisions interpretable, leading to work on explainable AI, concept bottleneck models, multimodal reasoning and human-aligned representation learning . Recent projects investigate large-scale multimodal language models, dataset distillation, model merging and continual knowledge editing. Publication Trends: Her 2024-2025 publications reveal a shift toward foundational large-scale models (diffusion, LLMs, vision-language transformers) while retaining the core themes of interpretability and generalization under limited supervision . Topics span dataset distillation, model merging, continual learning, fairness auditing of generative models and novel evaluation protocols for zero-shot learning systems. Scientific Awards: Lise-Meitner Award for Excellent Women in Computer Science (2014) Young Scientist Honour, Werner-von-Siemens-Ring Foundation (2019) ERC Starting Grant, European Commission (2019) DAGM German Pattern Recognition Award (2021) ECVA Young Researcher Award (2022) Alfried Krupp Award (2023) Advising & Funding: Prof. Akata currently supervises or co-supervises 25+ PhD students across TUM and the University of Tübingen via ELLIS and IMPRS-IS doctoral programs. She holds major grants including an ERC Starting Grant and DARPA Explainable AI funding, and is a frequent program chair and area chair for premier conferences (CVPR 2024, ECCV 2026, NeurIPS, ICML, etc.). Labs & Teams: She leads the Institute for Explainable Machine Learning at Helmholtz Munich and heads the Multimodal Learning and Explainable AI group at TUM. The institute collaborates closely with the ELLIS Institute Tübingen and Cyber Valley ecosystem, and maintains close ties with the Max Planck Institute for Intelligent Systems and Informatics.

Rainer Böhmeمشاهده پروفایل

استاد

• Steganography and steganalysis
• Digital forensics
• Privacy-enhancing technologies
• +۴ مورد دیگر

Professor Rainer Böhme is a prominent academic in the field of IT security and privacy at the University of Münster's School of Business and Economics, Department of Information Systems. He also holds a professorship for Security and Privacy at the University of Innsbruck's Institute of Computer Science. His research spans multiple dimensions of digital security, with particular expertise in steganography, digital forensics, privacy-enhancing technologies, and the economic aspects of cybersecurity. Dr. Böhme earned his Doctoral Degree from TU Dresden's Faculty of Computer Science in 2008, following studies in Communications, Economics and Computer Science at Technische Universität Dresden and Université Paris XII. His academic journey includes positions as Assistant Professor at the University of Münster (2010-2015), Postdoctoral Fellow at the International Computer Science Institute in Berkeley (2009-2010), and Scientific Staff at TU Dresden's Chair of Privacy and Data Security. His research interests focus on steganography and steganalysis, digital forensics, privacy-enhancing technologies, security and usability, economic and behavioral aspects of information privacy, virtual currencies, and the economics of information security. His work bridges technical security mechanisms with economic and behavioral considerations, creating a unique interdisciplinary approach to cybersecurity challenges. Analysis of his recent publications reveals a strong trend toward the economics of security, particularly regarding blockchain technologies, cryptocurrency, and privacy valuation. His work increasingly examines the intersection of technical security mechanisms with market dynamics, regulatory frameworks, and user behavior. The research spans theoretical game-theoretic models, empirical studies of user behavior, and practical applications in digital forensics and privacy protection. Program Co-Chair, Financial Cryptography and Data Security (2015) Program Co-Chair, New Security Paradigms Workshop (NSPW) (2015) General Chair, 8th International Conference on IT Security Incident Management & IT Forensics (2014) Organizer and Program Co-Chair, 1st Workshop on Bitcoin Research (2014) Associate Editor, Electronic Commerce Research (since 2013) Associate Editor, EURASIP Journal on Information Security (since 2013) Professor Böhme has supervised multiple Master's theses in Information Systems, including work on static code analysis for smart contracts, remote maintenance protocol security, and application protection requirements in retail banking. His research is supported by significant grants from the German Federal Ministry of Research, Technology and Space, the DFG, and the EU FP7 program, with projects focusing on digital forensics, privacy-enhancing technologies, and cryptocurrency crime prevention. He leads the IT Security Research Group at the University of Münster, which investigates theoretical and applied aspects of information security with particular emphasis on the economic dimensions of security decisions.

• Cyber Law
• Artificial Intelligence and Law
• Business Law
• +۷ مورد دیگر

Zaklina Spalevic is a distinguished Professor at Singidunum University, specializing in the intersection of law, technology, and business. With a strong academic background in criminal law and extensive research in cyber law, artificial intelligence applications in legal systems, and tourism law, she has established herself as a leading scholar in digital legal frameworks. Her work spans multiple disciplines, connecting traditional legal principles with emerging technological challenges. Dr. Spalevic earned her doctoral degree in Criminal Law from the University of the Academy of Economics in Novi Sad (2008-2012), following basic studies in General Law at the University of Pristina (1996-2001). Her educational foundation in Science and Mathematics from First Pristina High School (1992-1996) provided the analytical basis for her interdisciplinary approach to legal scholarship. Her research interests focus on the evolving landscape of cyber law, particularly the legal implications of artificial intelligence in judicial systems, electronic governance, and tourism industry regulation. Dr. Spalevic has pioneered work on digital evidence processing, algorithmic justice, and the integration of green law principles into sustainable business practices. Her publications reveal a consistent pattern of addressing emerging legal challenges in the digital age, with increasing emphasis on AI applications across various legal domains. Analysis of her recent publications shows a clear trend toward interdisciplinary research that bridges law, technology, and business. Her work increasingly focuses on practical applications of legal frameworks for emerging technologies, particularly in tourism, agriculture, and judicial systems. The integration of technical solutions with legal regulation represents a distinctive feature of her scholarly contributions. Dr. Spalevic has authored numerous publications including three books: 'Fundamentals of Law with Special Reference to Business and Tourism Law' (2021, 2024 editions) and 'Legal Aspects of Cyberspace' (2018). Her extensive publication record includes over 100 journal articles and conference papers spanning cyber security, electronic governance, intellectual property in digital environments, and legal frameworks for emerging technologies. She actively contributes to academic discourse through conference presentations and collaborative research projects, particularly focusing on the legal implications of artificial intelligence across various sectors. Her work demonstrates a commitment to developing practical legal frameworks that address real-world technological challenges while maintaining foundational legal principles.

Miyuki Hinoمشاهده پروفایل

استادیار

• Climate Change Adaptation
• Environmental Policy
• Disaster Risk Management
• +۳ مورد دیگر

Miyuki Hino is an Assistant Professor in the Department of City and Regional Planning and an Adjunct Assistant Professor in the Environment, Ecology, and Energy Program at the University of North Carolina at Chapel Hill. She holds a Ph.D. in Environment and Resources from Stanford University and a B.S. in Chemical Engineering from Yale University. Her research focuses on climate hazards, governance, and public policy , with emphasis on equitable adaptation to climate change. Key areas include sea level rise impacts, flood risk on property markets, and managed retreat strategies. She has conducted extensive work on floodplain development policies, household relocation programs, and community resilience frameworks. Dr. Hino's interdisciplinary approach integrates environmental science, urban planning, and social equity . She collaborates with academic and municipal partners, such as the Center for Urban and Regional Studies and Annapolis, MD local governments, to develop actionable solutions for climate adaptation. Her work bridges technical analyses (e.g., sensor networks, machine learning) with policy design to ensure both effectiveness and justice in climate responses. Recent projects emphasize preventing future 'trapped households' by analyzing zoning policies and market dynamics that drive risky development. She advocates for climate-smart growth strategies to balance economic needs with environmental safety, while addressing disparities in vulnerability across communities. Her research has been featured in Science Advances , Nature Climate Change , and interdisciplinary journals. She actively engages with policymakers to translate findings into practical measures, such as equitable buyout programs and floodplain management reforms.

Mohsen Kalantariمشاهده پروفایل

دانشیار

• Geospatial Engineering
• Construction Engineering
• Building Information Modelling (BIM)
• +۷ مورد دیگر

Associate Professor Mohsen Kalantari is a Geospatial Engineering academic at the University of New South Wales (UNSW) School of Civil and Environmental Engineering , with concurrent roles as co-founder of the startup Faramoon . His career spans roles at the University of Melbourne's Department of Infrastructure Engineering and Victorian government's land administration initiatives through DELWP. Education : PhD in Geomatics Engineering (2008, University of Melbourne), Master of GIS Engineering (2004), Bachelor of Surveying Engineering (2001) His research bridges geospatial engineering with construction automation , focusing on 3D cadastre , BIM-GIS integration , and smart cities . Recent publications show trends in underground land administration , digital twins , and LADM standard implementations . Scientific Awards : National educational recognition (2019), Victorian educational grants (2018), and prestigious fellowships (2012) As a supervisor , he guides PhD candidates in topics ranging from BIM for waste management to underground cadastral systems . His industry engagement includes partnerships with the United Nations , Open Geospatial Consortium , and Singapore Land Authority .

Sidi Wuمشاهده پروفایل

پژوهشگر

• Cartography
• Geoinformatics
• Geographic Information Systems (GIS)
• +۴ مورد دیگر

Sidi Wu is a Researcher affiliated with ETH Zürich's Institute of Cartography and Geoinformatics. Their primary role is as Staff of the Professorship for Cartography, contributing to academic research and technical operations within the department. They are based at HIL G 23.2, Stefano-Franscini-Platz 5 in Zürich, Switzerland, and can be reached at sidiwu@ethz.ch. Research interests center on advancing AI-driven cartographic methods, historical map analysis, and environmental spatial dynamics. Specific focuses include generative AI applications in map-making, semantic segmentation of historical documents, and leveraging digitized maps for ecosystem studies. They also explore steganography in image translation and cross-domain adaptation techniques for geospatial data. Recent work emphasizes innovations in automated map storytelling systems, spatio-temporal context modeling using transformers, and weakly supervised learning approaches for map segmentation. Their studies frequently bridge cartography with environmental science disciplines like hydrology and urban morphology. No scientific awards or grants are explicitly listed in the provided information. While no advisees are documented here, their research collaborations likely involve student contributions. They are part of the core team at the Institute of Cartography and Geoinformatics, contributing to cutting-edge projects in geomatics and computational cartography.