جست‌وجوی استاد و پژوهشگر

Jenni Ilomakiمشاهده پروفایل

دانشیار

• Clinical Pharmacy
• Pharmacoepidemiology
• Public Health
• +۳ مورد دیگر

Associate Professor Jenni Ilomaki holds a position at Monash University's Centre for Medicine Use and Safety. With expertise in clinical pharmacy, epidemiology, and public health, she leads a research group analyzing administrative claims data. Her work spans collaborations with governmental and non-governmental organizations globally, yielding over 150 peer-reviewed publications and $4 million in grants. She previously served as Chair of ASCEPT's Pharmacoepidemiology Special Interest Group and currently serves as Science Lead for the Monash Addiction Research Centre and Executive Editor of the British Journal of Clinical Pharmacology. Education includes a Bachelor of Science (Pharmacy) from the University of Kuopio (1999) and Master of Science (Pharmacy) from the University of Kuopio (2004). She completed a PhD in alcohol epidemiology at the University of Eastern Finland (2011) and a postdoc at the University of South Australia (2011-2014). Notable recognitions include Young Epidemiologist of the Year (2011) and the Ronald D. Mann Best Paper Award (2021). Key research focuses on quality use of medicines, medicine safety, large population studies, and innovative epidemiological methods. Major projects include developing clinical decision support tools for cardiovascular prevention, analyzing preventable hospitalizations in aged care, and investigating psychotropic medication trends in youth. Her work contributes to UN Sustainable Development Goals related to health equity and non-communicable disease reduction. Recent publications emphasize drug repurposing (e.g., SGLT2 inhibitors), opioid prescribing patterns, hip fracture outcomes, and dementia detection algorithms. She chairs multinational studies on stroke and myocardial infarction cost burdens, demonstrating interdisciplinary impact across pharmacology, epidemiology, and health economics.

• Industrial Automation
• Control Systems
• Machine Learning Applications
• +۵ مورد دیگر

Valeriy Vyatkin is a Professor at the Department of Electrical Engineering and Automation, Aalto University. His research focuses on advancing industrial automation, control systems, and their integration with emerging technologies like machine learning and digital twins. He specializes in standards such as IEC 61499, addressing interoperability, formal verification, and performance optimization in distributed automation systems. Key research interests include: Physics-informed machine learning for industrial processes (e.g., steel rolling, reservoir engineering) Formal methods for control system validation and safety-critical applications Development of adaptive automation frameworks for Industry 5.0 challenges, including human-robot collaboration and energy systems Interoperability between legacy and modern industrial standards (OPAS, OPC UA) Recent work emphasizes real-time simulation, FPGA-based control prototyping, and AI-driven solutions for energy efficiency and sustainability in manufacturing, horticulture, and process industries. Publications span topics like robotic walker design, probabilistic model checking, and decentralized learning management systems. He collaborates on EU and industry-funded projects, focusing on digital twin implementation, edge computing, and virtual commissioning. His team develops tools for automated code generation, system migration, and anomaly detection in complex industrial settings.

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +۷ مورد دیگر

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework

Guido Perboliمشاهده پروفایل

استاد

• Operations Research
• Logistics
• Last-mile Delivery
• +۹ مورد دیگر

Guido Perboli is a Full Professor in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin, where he also serves as Logistics Coordinator and Project Coordinator for activities supporting relationships with government bodies. He is a member of the Interdepartmental Center CARS@PoliTO (Center for Automotive Research and Sustainable Mobility) and serves as Director of the ICT for City Logistics and Enterprises (ICElab@Polito) research center, which he founded in 2016. His research interests span a broad range of topics including Operations Research, Logistics, Last-mile Delivery, Sustainable Logistics, Combinatorial Optimization, Stochastic Programming, Business Development, and Lean Business methodologies. His work particularly focuses on City Logistics, Green Logistics, and the application of emerging technologies like Blockchain and AI in supply chain management. He has developed GUEST, a Lean Business methodology for innovation processes from early idea definition to implementation. Professor Perboli's recent publications demonstrate a strong focus on urban logistics, last-mile delivery optimization, blockchain applications in supply chains, and the integration of AI techniques in transportation systems. His work shows an increasing trend toward interdisciplinary research that combines optimization methods with emerging technologies to address sustainable urban mobility challenges. Professional Recognition: CASE Best Paper award from IEEE Conference on Automation Science and Engineering (2011) Effective member of INFORMS (2019-present) Effective member of EURO (1995-present) Effective member of AIRO (1995-present) Associate Editor for Journal of Applied Research and Technology (2020-present) Associate Editor for Sustainability (2018-present) Professor Perboli actively advises PhD students and has supervised numerous research projects, including EU-funded initiatives like SINFONICA, HESTER, and 5G-LOGINNOV. He serves as Scientific Director for multiple commercial research projects focused on blockchain, IoT, and AI applications in logistics. Beyond academia, he is Chief Scientific Officer of Arisk S.p.A., a fintech company specializing in business crisis prediction using AI and machine learning. His research group, ICElab@Polito, focuses on two main areas supporting urban growth: logistics and enterprises. The center collaborates with numerous companies including Amazon, DHL, and FCA, addressing real-world challenges in urban logistics and supply chain management through innovative research approaches.

• Embedded Systems
• Hardware-Software Co-Design
• Invasive Computing
• +۷ مورد دیگر

Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.

• Microfluidics
• BioMEMS
• Lab-on-a-Chip Technology
• +۱۱ مورد دیگر

Prof. Kwang W. Oh is a tenured Professor at the Department of Electrical Engineering and Department of Biomedical Engineering within the School of Engineering and Applied Sciences at University at Buffalo (SUNY at Buffalo) . He serves as the Director of Graduate Studies in Electrical Engineering and Director of SMALL (Sensors and MicroActuators Learning Lab) . His academic journey includes PhD and MS in Electrical and Computer Engineering from University of Cincinnati (2001, 1997) and BS in Physics from Chonbuk National University (1995). Prof. Oh's research expertise lies at the intersection of microfluidics , BioMEMS , and lab-on-a-chip technologies. His lab has pioneered vacuum-driven microfluidic devices , PDMS-based systems , droplet manipulation , and chemical-free fabrication techniques . His work enables point-of-care diagnostics , single cell analysis , and wearable medical sensors , with significant contributions to sample-to-answer nanosystems and world-to-chip interfacing . The scientific awards section highlights his excellence in teaching and research: SUNY Chancellor's Award for Excellence in Teaching (2020) Meyerson Award for Undergraduate Teaching (2019) Qualcomm Faculty Award (2019) Senior Teacher of the Year (2017) Royal Society of Chemistry's Emerging Investigators (2013) Samsung Electronics' CEO Honor (2003) His lab has produced numerous PhD and MS students including Dr. Anyang Wang (2020), Dr. Nikhila Nyayapathi (2020), Mr. Liam Christie (2021), and Dr. Domin Koh (2019). As a conference chair , he has organized symposia at NanoTech (2012-2026) and served as editorial board member for Sensors , Micromachines , and Biomedical Engineering Letters .

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

Huy T Tranمشاهده پروفایل

استادیار

• Autonomy
• Reinforcement Learning
• Artificial Intelligence
• +۵ مورد دیگر

Huy T Tran is an Assistant Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign's College of Engineering, with additional appointments at the Applied Research Institute. His research focuses on the intersection of robotics, artificial intelligence, and multi-agent systems, with applications spanning autonomous navigation, critical infrastructure resilience, and intelligent transportation. Dr. Tran earned his Ph.D. in Aerospace Engineering from Georgia Institute of Technology in 2015, following advanced degrees from Georgia Tech and University of Wisconsin-Madison. His academic journey includes research assistant professor positions before achieving his current assistant professor role in 2021. He previously worked as a Senior Multi-Disciplinary Systems Engineer at The MITRE Corporation and served as a Visiting Scholar at the Air Force Institute of Technology. His research interests encompass Autonomy, Reinforcement Learning, Artificial Intelligence, Machine Learning, Robotics, Multiagent Systems, Intelligent Transportation Systems, and Critical Infrastructure Resilience. As director of the Lab for Intelligent Robots and Agents (LIRA), he leads cutting-edge research in autonomous systems that interact with humans and other robots. His work has evolved from foundational resilience modeling in aerospace systems toward increasingly sophisticated AI applications in multi-robot coordination and explainable decision-making. Dr. Tran's publication record demonstrates a clear trajectory toward explainable AI and human-AI collaboration, with recent work focusing on generating explanations for reinforcement learning policies, coordination in ad hoc teams, and neuro-symbolic approaches to robot policy interpretation. His research bridges theoretical advances with practical applications in air traffic control, field robotics, and critical infrastructure management. Best Paper Award: Theoretical (2016 Complex Adaptive Systems Conference) Selected for oral presentation at IROS 2023 Workshop 27% full paper acceptance rate at AAMAS 2022 44% acceptance rate at ICRA 2020 As an educator, Dr. Tran teaches core aerospace courses including Computational Systems Engineering, Aerospace Numerical Methods, and Reinforcement Learning. He has secured significant research funding from NASA's Transformational Tools and Technologies program, ARL A2I2 program, ONR Science of AI program, and DARPA. His current projects span ad hoc teaming in multi-robot systems, collective autonomous air mobility, hierarchical reinforcement learning, and interpretable AI agents.

• Computational Social Science
• Conversational AI
• Discourse and Pragmatics
• +۴ مورد دیگر

Maarten Sap is an Assistant Professor at Carnegie Mellon University's Language Technologies Institute with a courtesy appointment in the Human-Computer Interaction Institute. He also holds a part-time research scientist position at the Allen Institute for AI (AI2) as an AI safety lead. Current affiliations: CMU (2022–present), AI2 (2022–present) Prior: Postdoctoral Researcher at AI2 (2021–2022), Research Intern at AI2 (2018–2019) and Microsoft (2019) His research focuses on enhancing AI systems with social intelligence and addressing social biases in language technology. Key themes include: Ethical AI and Human-Centric Design Narrative Dynamics and Social Context Analysis AI Agents and Social Intelligence Toxic Language Detection and Cultural Bias Mitigation Recent publications examine: AI safety frameworks like HAICOSYSTEM Clinical reasoning alignment (ALFA) Multilingual moderation (PolyGuard) Cultural sensitivity in non-verbal AI (Mind the Gesture) Personality shaping in LLMs (BIG5-CHAT) Scientific Recognition: 2025 Okawa Research Grant Best Paper Runner Up - NAACL 2025 Outstanding Paper - EMNLP 2023 Best Paper - FAccT 2023 Best Paper - WeCNLP 2020 He advises a diverse group of PhD students across CMU and MIT, and has served on multiple program committees including ACL, EMNLP, and FAccT. His work appears in top venues like Nature Machine Intelligence, PNAS, and ACL.

Ziming Zhangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Ziming Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI) , with additional affiliations in Data Science and Robotics Engineering. He previously held research roles at Mitsubishi Electric Research Laboratories (MERL) and Boston University. PhD in Computing (2013) from Oxford Brookes University , UK MS in Computing Science (2010) from Simon Fraser University , CA BS in Computer Science and Technology (2005) from Northeastern University , China Research interests span computer vision , machine learning , and their applications in point cloud processing , medical imaging , autonomous driving , and IoT . He leads the Vision, Intelligence, and System Laboratory (VISLab) at WPI. Recent publications focus on 3D reconstruction , hyperbolic learning , and robust classifiers . Awards include the R&D100 Award 2018 and NSF funding for data-efficient deep learning. PhD Students: Yecheng Lyu (co-supervised), Guojun Wu (co-supervised), Hangrui Zhang, Xuechu Yu Master's Students: Yun Yue, Yuping Shao Visiting Scholars: Fangzhou Lin His lab partners with industry and academic institutions, focusing on autonomous systems , robotics , and scientific imaging projects.

• Geometallurgy
• Compositional Data Analysis
• Geostatistics
• +۳ مورد دیگر

Dr. Raimon Tolosana Delgado is a Research Fellow at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), affiliated with the Helmholtz Institute Freiberg for Resource Technology. He leads research in predictive geometallurgy and statistical analysis of mineral resources, focusing on translating geological data into processing insights. His research integrates geostatistics , compositional data analysis (CoDa) , and machine learning to model ore behavior and resource potential. Key areas include: Predictive geometallurgy for forecasting ore/waste behavior Bayesian statistics for parameter estimation and uncertainty analysis Development of R-based tools (e.g., compositions and gmGeostats packages) for mineral data analysis Particle-based process modelling for mineral separation optimization Recent publications emphasize machine learning integration (e.g., neural networks for geophysical tensor fields), tailings reprocessing (3D geostatistical assessment of resource potential), and advanced statistical methods for compositional data. A consistent trend involves enhancing predictive accuracy in mineral processing through multi-source data fusion. Dr. Tolosana Delgado coordinates the development of technology platforms for geometallurgical data analysis, including databases and interfaces for industrial applications. His work bridges ore geology, mineral processing, and metallurgy to optimize resource efficiency.

David Abbinkمشاهده پروفایل

استاد

• Human-Robot Interaction
• Haptics
• Shared Control
• +۷ مورد دیگر

Professor David Abbink is a Full Professor of Haptic Human-Robot Interaction at Delft University of Technology, holding a joint appointment between the Department of Cognitive Robotics in the Faculty of Mechanical Engineering and Industrial Design Engineering since November 2023. He founded the Delft Haptics Lab and co-founded the Cognitive Robotics Department in 2017. Abbink leads the transdisciplinary research and innovation centre FRAIM, which was awarded the prestigious NWO Stevin Premie (Dutch Nobel Prize equivalent) in June 2024. Trained as a mechanical engineer specializing in biomechanics, Abbink's research focuses on human behavior adaptations when interacting with autonomous systems. He has published over a hundred scientific articles on human-robot interaction, haptics, shared control, tele-operation, driver assistance systems, and sensorimotor control. His research has been funded by industry partners (Nissan, Boeing, Renault), RVO (Brightsky project 2022-2026), and the Dutch Science Foundation NWO through personal grants (VENI 2010-2014, VIDI 2015-2019). Abbink's recent work centers on worker-robot relations as an academic focus, collaborating with organizations like Erasmus Medical Centre for nursing work, Schiphol and KLM for baggage handling, and KLM Engine Repair Services for maintenance work. He also serves as scientific director for the Centre for Meaningful Human Control, launched in October 2024. His work bridges engineering, social sciences, and practical applications to responsibly shape the future of work with emerging robotic capabilities. NWO Stevin Premie (2024) Best IEEE SMC journal paper on Cybernetics (2019) Top 25 scientific talents according to New Scientist (2015) Best teacher of Faculty 3mE (2013, 2014) Best teacher of Department of BioMechanical Engineering (seven consecutive years) Abbink has supervised over 110 MSc students and 11 PhD students. His educational contributions include developing the Master Programme in Robotics at TU Delft and receiving international recognition for his course 'The Human Controller.' He is also a prominent science communicator, featured on national television, radio, and major Dutch newspapers, and has delivered lectures at venues like The Royal Institution and Lowlands Festival. Despite his academic commitments, Abbink maintains a drummer persona, having recorded four albums and performed over 400 shows across three continents between 1999-2014.

Emily Crossمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Social Neuroscience
• Embodied Cognition
• +۵ مورد دیگر

Emily Cross is a Full Professor at the Department of Humanities, Social and Political Sciences at ETH Zurich, leading the Social Brain Sciences Professorship since spring 2023. She previously held professorships at Bangor University (Wales), University of Glasgow (Scotland), Macquarie University (Australia), and Western Sydney University's MARCS Institute (Australia). Her research centers on how embodied experience shapes social learning and perception across diverse contexts. Key contributions include identifying neural signatures of embodied expertise using dancers, developing embodied neuroaesthetics theory, uncovering neurocognitive foundations of visual learning across lifespans, and pioneering paradigms for human-robot social engagement. Her interdisciplinary approach bridges technology, performing/visual arts, and social sciences to explore experience-dependent plasticity at brain and behavioral levels. Recent publications (2024-2025) demonstrate intense focus on human-robot interaction dynamics, aesthetic movement perception, and context-dependent social cognition. Work increasingly examines self-disclosure mechanisms to robots, cultural influences on robot acceptance, and neural correlates of movement synchrony, reflecting her expanding influence at the neuroscience-robotics intersection. Scientific awards include: Philip Leverhulme Prize for Psychology Jacob Bronowski Award from British Science Foundation Young Talent Award from Dutch Neuroscience Society RoboHub and Insight Analytics top women in robotics listings Australia’s Superstars of STEM (2022) Cross passionately trains next-generation scientists with emphasis on research ethics. Her work attracts major funding from ERC, NIH, Fulbright Commission, ESRC, EPSRC, and UK Ministry of Defence. She serves on UNESCO’s International Bioethics Committee (co-rapporteur for neurotechnology ethics report) and as Associate Editor for International Journal of Social Robotics. She leads ETH Zurich's dynamic Social Brain Sciences group, which embraces interdisciplinarity through research paradigms bridging technology, performing/visual arts, and biological/social sciences, while maintaining active roles in editorial boards and conference committees including Intelligent Virtual Agents and Affective Computing meetings.

• Clinical Informatics
• Emergency Medicine
• Electronic Health Records
• +۵ مورد دیگر

Mark Iscoe, MD, MHS is an Assistant Professor of Emergency Medicine and Biomedical Informatics and Data Science at Yale School of Medicine. He holds fully joint appointments in both the Department of Emergency Medicine and the Department of Biomedical Informatics & Data Science, reflecting his interdisciplinary work at the critical intersection of clinical emergency care and health informatics innovation. Dr. Iscoe completed his medical degree at Johns Hopkins University School of Medicine in 2017, followed by residency training in Emergency Medicine at New York University / Bellevue Hospital in 2021. He further specialized with a Master of Health Science (MHS) in Clinical Informatics from Yale School of Medicine in 2023. He is board certified in both Emergency Medicine (2022) and Clinical Informatics (2024). His research spans several interconnected domains with a focus on optimizing the interface between emergency physicians and health information technology. Key areas include electronic health record (EHR) optimization, artificial intelligence applications in emergency settings, clinical decision support systems, and medication safety protocols. His 2024 JAMA Network Open publication 'Benchmarking Emergency Physician EHR Time per Encounter Based on Patient and Clinical Factors' represents a significant contribution to understanding the digital burden on emergency clinicians. More recently, he has pioneered work applying large language models to emergency medicine challenges, with multiple 2025 publications on AI applications for deprescribing, symptom identification, and risk stratification. His research trajectory shows a clear evolution from foundational EHR usage studies toward increasingly sophisticated AI implementations that bridge theoretical informatics with practical clinical tools in high-pressure emergency settings. YCCI Scholar Award for AI Research on Drug Reactions (2024) Dr. Iscoe has received research funding from multiple prestigious sources including the National Institute on Drug Abuse (NIDA), the American Medical Association (AMA), the National Institutes of Health, and Yale New Haven Health System. His collaborative network includes prominent researchers such as Andrew Taylor (6 joint publications), Ted Melnick (5 joint publications), and Rohit Sangal (4 joint publications), reflecting his work's multidisciplinary nature spanning clinical departments, informatics specialists, and data scientists.

John E. Taylorمشاهده پروفایل

استاد

• Smart City Digital Twins
• Urban Infrastructure Resilience
• Network Dynamics
• +۵ مورد دیگر

John E. Taylor is the Frederick Law Olmsted Professor and Associate Chair for Faculty Development and Research Innovation at the Georgia Institute of Technology's School of Civil and Environmental Engineering within the College of Engineering. His research focuses on the intersection of human and engineered networks, with particular emphasis on creating resilient infrastructure systems that serve society's needs while creating more livable communities. Taylor's research interests span multiple domains including Smart City Digital Twins , Urban Infrastructure Resilience , Network Dynamics , and Building-Occupant Interaction . His work examines how human behavior, infrastructure systems, and environmental factors interact during normal operations and extreme events. He has developed innovative approaches to understanding urban systems through the lens of network theory and computational modeling. His publication record demonstrates consistent contributions to the fields of urban analytics and infrastructure resilience, with a recent focus on digital twin technologies for urban systems. Taylor's work shows a clear trajectory toward increasingly sophisticated integration of AI, network science, and civil infrastructure engineering to address complex urban challenges. His research has particular relevance for cities facing climate change impacts and seeking to build more equitable and resilient communities. Taylor leads the Network Dynamics Lab at Georgia Tech, where he mentors PhD students and postdoctoral researchers. His lab has produced significant work on human-infrastructure interaction, particularly during disasters and extreme events. The lab's research combines computational modeling, data analytics, and field studies to understand and improve urban systems. His work has been applied to real-world challenges including river emergency response systems, urban heat exposure forecasting, and disaster response optimization. Taylor has collaborated with city officials and agencies to implement systems that have demonstrable community benefits, such as the AI-enabled camera system for drowning prevention on the Chattahoochee River and crime reduction systems using mobile cameras guided by AI algorithms.