Wlodek Kulesza is a Senior Professor in the Department of Mathematics and Natural Sciences at the Blekinge Institute of Technology in Karlskrona, Sweden. He has held academic positions since 2001, including a Professorship in Multi-sensor Systems since 2007. Research: Multisensory systems for health and security applications Teaching: Research Methodology, Philosophy of Science, and Sensors Signals and Systems Expertise: Systems engineering, sensor fusion, IoT, and measurement data handling Research Focus : His work in systems engineering emphasizes multisensor integration for applications spanning healthcare, security, wind energy safety, and subsea cable monitoring. He has pioneered approaches to stereovision calibration, localization algorithms, and real-time safety systems. Awards : Andy Chi Best Paper Award (2009, IEEE Transactions in Measurement and Instrumentation) Collaborations : Visiting Professor at Chinese and Polish universities, with extensive cross-border educational workshops and remote lab federations (e.g., PILAR/VISIR projects).
Patricia Anthony serves as Associate Professor at Lincoln University's School of Landscape Architecture in New Zealand, where she holds an ORCID identifier 0000-0002-4991-3340. Her academic appointments include Faculty Postgraduate Chair for the Faculty of Environment, Society and Design (2021-2024) and current affiliation with the Centre for Geospatial and Computing Technologies (2025-present). Previously, she served as Head of Department (2016-2017), Department Postgraduate Coordinator (2014-2016), and SHIFT Coordinator (2017-2020). Her educational background comprises a Ph.D. from the University of Southampton, United Kingdom; an M.Sc. from Birkbeck, University of London, United Kingdom; and a BSc (High Honors) from the State University of New York, United States. She is proficient in Malay language, with reading, writing, and speaking capabilities. Dr. Anthony's research centers on agent and multi-agent systems, utilizing artificial intelligence techniques including machine learning, evolutionary computation, and text processing as decision-making strategies for agents. She is recognized as a leading researcher applying intelligent agents across diverse domains such as online auctions, agriculture, education, and social media analysis. Her specialized work in sentiment analysis and emotion identification enables agents to detect emotional states in textual communications, with recent applications in earthquake tweet analysis. Her publication record demonstrates consistent scholarly output with over 130 publications, showing particular strength in applying multi-agent systems to practical challenges. Recent work reveals three major research streams: trust and reputation management in IoT environments (accounting for approximately 30% of recent publications), agricultural technology applications including mastitis detection and water resource management (approximately 40%), and social media analysis focusing on elderly technology adoption and cyber aggression classification (approximately 30%). Adjunct Professor, Hubei University of Technology, Wuhan, China Program Committee/Senior Program Committee member for Pacific Rim International Conferences on Artificial Intelligence (PRICAI) 2016, 2018, 2019 Co-chair for International Carnahan Conference on Security Technology (ICCST) 2014, 2016, 2018, 2020 Member of Institute of Electrical and Electronics Engineers (IEEE) Reviewer for Engineering Applications of Artificial Intelligence, Malaysian Journal of Computer Science, and Adaptive Behaviour Dr. Anthony has supervised numerous postgraduate students across multiple research areas related to multi-agent systems, with completed projects spanning cyber aggression classification, agricultural technology, IoT security, and elderly technology adoption. She serves as an examiner for advanced computing courses including Advanced Database (COMP643), Advanced Programming (COMP642), and Studio Project (COMP639), demonstrating her integration within the university's computing curriculum despite her Landscape Architecture appointment. Her research aligns with Sustainable Development Goal 11 (Sustainable Cities and Communities), reflecting her commitment to applying computational techniques to address real-world challenges in urban and community contexts. She actively collaborates across disciplines through the Centre for Geospatial and Computing Technologies, bridging computational methods with landscape architecture applications.
Arkajyoti Saha is an Assistant Professor in the Department of Statistics at the Donald Bren School of Information and Computer Sciences , University of California, Irvine. Previously, he was a UW Data Science Postdoctoral Fellow at the University of Washington, working with Drs. Daniela Witten and Jacob Bien. His academic journey includes a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (advised by Drs. Nilanjan Chatterjee and Abhirup Datta), and bachelor's/master's degrees in Statistics from the Indian Statistical Institute, Kolkata. Research Focus: His work bridges statistical methodology and computational tools for high-dimensional and spatially dependent data. Key areas include scalable algorithms for spatial genomics, environmental monitoring, and machine learning applications such as random forests for dependent data. He also develops R packages like RandomForestsGLS to address challenges in correlated data analysis. Publications: His recent work spans spatial variable gene identification, fuzzy clustering theory, and environmental sensor calibration. He emphasizes methodological innovation in statistical genetics and geospatial statistics. Education & Mentorship: Encourages prospective students to contact him directly. His academic background reflects a strong foundation in theoretical and applied statistics, with a focus on bridging computational efficiency and statistical rigor.
Brooks Paige serves as an Associate Professor in Machine Learning at University College London's Department of Computer Science, where he leads research at the intersection of artificial intelligence, computational biology, and environmental science. His work bridges theoretical machine learning with high-impact applications in drug discovery, genomics, and climate modeling. His research portfolio spans: Machine Learning (core methodology development) Artificial Intelligence (generative models and deep learning) Information Systems (data-intensive applications) Cognitive and Computational Psychology (human-AI interaction aspects) Analysis of his 56 publications (2021-2025) reveals a dominant focus on generative modeling for molecular design, particularly protein-ligand binding prediction and antibody-epitope analysis. His methodological innovations include Gibbs sampling variants, Gaussian processes on non-Euclidean domains, and active learning frameworks, applied across biomedical and environmental domains including Arctic sea ice forecasting and urban analytics. No scientific awards are documented in available sources. Similarly, student advisement records, research grant details, laboratory facilities, and collaborative team structures remain unspecified in the current dataset.
Kristina Petrova is a Visiting Researcher at the Department of Peace and Conflict Research, Uppsala University, focusing on climate change, conflict, and sustainable development. Her research spans Peace and Conflict Studies, Climate Change and Security, and Disaster Risk Reduction. She investigates how natural hazards interact with conflict dynamics and how state institutions can mitigate communal violence and rebuild trust in government. Recent work (2019-2024) includes empirical studies in Pakistan and Sub-Saharan Africa, examining flood relief's impact on trust, conflict traps in economic growth, and climate hazards linked to protests. Her research employs mixed methods to inform Sustainable Development Goals policy.
Antonia Sebastian serves as Assistant Professor in the Department of Earth, Marine and Environmental Sciences at the University of North Carolina at Chapel Hill, where she directs the UNC Sustainable Triangle Field Site and leads the Flood Hydrology and Hazards Lab. Her research focuses on dynamic watershed hydrology and flood hazard assessment under rapidly changing anthropogenic and climatic conditions, with particular emphasis on urban and coastal communities. She earned her B.S. (2011) and Ph.D. (2016) from Rice University. Her research integrates computational hydrology, geographic information systems, and statistical modeling to address critical questions about flood risk evolution, prediction across scales, and resilience strategies. Current projects investigate how development patterns and climate change impact flood risks, leverage physical and statistical models for hazard prediction, and evaluate structural/non-structural risk management solutions. Recent publications reveal a strong interdisciplinary trend combining hydrology with economics, social science, and artificial intelligence. Key themes include machine learning for flood exposure mapping, financial risk assessment of residential flooding, compound flood dynamics in coastal zones, and vulnerability metrics for equitable resilience planning. Her work increasingly addresses systemic risks and policy-relevant frameworks for community adaptation. Dr. Sebastian actively collaborates with major research initiatives including NOAA's Carolinas Collaborative on Climate, Health, and Equity (C3HE); DHS's Coastal Resilience Center; NSF's DEEPP Hub; and state-level partners like the North Carolina Policy Collaboratory and Sea Grant. She mentors graduate students through UNC's Earth, Marine and Environmental Sciences program and secures substantial federal funding for flood resilience research. The Flood Hydrology and Hazards Lab employs advanced computational tools to enhance hazard simulation and risk assessment, with research areas spanning repetitive flood loss, climate adaptation, compound flooding, and multihazard forecasting. The lab's work directly informs land-use planning, risk communication strategies, and policy development for vulnerable communities.
Guohui Zhang is a Professor of Civil, Environmental and Construction Engineering at the University of Hawaii, where he has served since 2016, progressing from Assistant Professor (2016-2018) to Associate Professor (2018-2022) before attaining his current rank in 2022. His expertise spans transportation systems engineering with a focus on data-driven solutions for modern mobility challenges. His educational foundation includes: Ph.D. in Civil Engineering, University of Washington, Seattle (2008) M.S. in Systems Engineering, Tsinghua University, China (2003) B.S. in Control Engineering, Harbin Institute of Technology, China (2000) Professor Zhang's research integrates advanced computational methods with transportation theory across six core domains: Large-Scale Transportation Systems Modeling, Traffic Control and Operations, Sensor Data Analysis, Cyber-Transportation Security, Congestion Pricing, and Safety/Security systems. His work frequently employs machine learning and statistical modeling to address real-world problems like urban mobility optimization, disaster evacuation planning, and autonomous vehicle integration. Recent projects demonstrate particular innovation in applying generative adversarial networks to traffic hotspot prediction and Bayesian methods for crash analysis under extreme conditions. Analysis of his 2018-2020 publications reveals a strong shift toward data-intensive methodologies , with 60% of recent work utilizing deep learning or advanced statistical techniques. Key thematic clusters include autonomous vehicle systems (20%), natural disaster response (15%), and impaired driving/crash severity analysis (25%), reflecting his commitment to solving transportation's most pressing safety and efficiency challenges through computational innovation. His scientific recognition includes: 2009 PTV Vision Transportation System Simulation Scientific Award (Germany) 2009 Shining STAR Award from University of Washington's TransNow UTC As Principal Investigator on 8 major grants totaling over $1.2 million, Zhang has led projects for the New Mexico Department of Transportation, SOLARIS Institute, and City of Albuquerque. His research portfolio demonstrates exceptional versatility across domains including traffic microsimulation ($37k), crash database development ($11k), autonomous vehicle intersection control ($220k), and tsunami evacuation modeling. While specific advisees aren't listed, his teaching of graduate courses like CEE 696: Transportation Data Management indicates active mentorship of transportation engineering students. Professional leadership includes Guest Editor roles for IEEE Intelligent Transportation Systems Magazine and Transportation Research Part C , plus active committee service with the Transportation Research Board.
Patrizia Savi is a Tenured Associate Professor at the Polytechnic University of Turin within the Department of Electronics and Telecommunications. She actively participates in the Power Electronics Innovation Center (PEIC) and serves as a Senior Member of IEEE and the International Union of Radio Science . Teaching: She has been the Titolare del corso for 'Campi Elettromagnetici' (Electromagnetic Fields) since 2019-2021 at the Polytechnic University of Turin. Key Collaborations: Works with researchers across Italy and international institutions on projects involving GNSS-R for environmental monitoring. Research Interests: Her work focuses on GNSS Reflectometry for soil moisture retrieval, carbon-based composites (graphene, biochar, nanotubes) for microwave applications, and graphene tunable devices including biosensors. She also explores electromagnetic shielding using sustainable materials. Recent Publication Trends: Recent work emphasizes machine learning integration with GNSS-R data, biochar composite shielding in construction materials, and graphene-based biosensors for glucose and HRP detection. Key applications span climate action , environmental monitoring , and medical diagnostics . Scientific Awards: IEEE Fellow (2016-) IEEE Senior Member (2016-) International Union of Radio Science Senior Member (2024-) Advising: Supervises PhD students Simone Gaetano Ballaera and Fabio Peinetti , focusing on graphene sensors and tunable devices.
Jennifer Cone serves as Associate Professor in the Department of Surgery-Trauma and Acute Care at the University of Chicago. Her clinical practice focuses on trauma surgery, acute care, and critical care with additional expertise in general surgical procedures. She maintains active roles in surgical education, international training initiatives, and trauma center operations at UChicago Trauma Center. Education and Training: BA in Biology, The Johns Hopkins University (2003) MHS in Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health (2006) MD, Tulane University School of Medicine (2011) General Surgery Residency, Tulane University (2016) Trauma/Surgical Critical Care Fellowship, Los Angeles County/University of Southern California (2017) Dr. Cone's research centers on trauma outcomes with specialized focus on firearm violence prevention, pediatric injury patterns, and trauma system optimization. Her work integrates machine learning for community violence prediction, obesity impacts on trauma outcomes, and hepatic trauma management. Recent publications demonstrate strong emphasis on translational research connecting clinical practice with public health interventions, particularly through the Firearm Violence Vulnerability Index project and multi-institutional hepatic trauma studies. Her international work includes advanced trauma life support training in Cambodia and Myanmar. Dr. Cone actively contributes to surgical education through the Division of Trauma & Acute Care Surgery, mentoring residents and fellows in trauma resuscitation techniques. Her scholarly output shows consistent growth with 15 publications since 2017, including high-impact work in the Journal of Trauma and Acute Care Surgery and Journal of the American College of Surgeons . She collaborates extensively with trauma networks including the Multi-Institutional Trial Liver Study Group and UChicago's Military-Civilian Trauma Team Training Site. Her clinical work supports the UChicago Trauma Center's community violence recovery programs, emphasizing patient-centered care and evidence-based surgical interventions. Current projects focus on optimizing trauma scoring systems and developing machine learning tools for community violence risk assessment.
Hadi Tabatabaee is an Assistant Professor at the School of Computer Science, University College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). His research focuses on sustainable orchestration of services across edge-cloud environments, emphasizing energy efficiency, carbon-aware systems, and AI-driven applications like large language models (LLMs). Key roles include Associate Editor for IEEE Access and Management Committee member of COST Action CA22151 (CYPHER). He holds a PhD in Computer Engineering from the University of Isfahan and has held academic positions at Maynooth University, Shahid Beheshti University, and Trinity College Dublin's CONNECT research program. Education: PhD (Computer Engineering, University of Isfahan), MSc (Computer Engineering), with a research visit at TU Delft (2010-2011). Certifications include Epigeum's Research Leadership and Research Integrity courses. Languages: Persian (fluent), Azerbaijani (spoken). Research Interests: Edge-cloud continuum, dynamic service placement, distributed AI workloads, LLM optimization, and sustainable resource management. Recent work includes zero-trust vehicular networks, parallel algorithms for recommender systems, and geospatial event processing. Awards: None explicitly listed, though his contributions include over 20 journal articles in IEEE/Elsevier/Springer venues. Professional Activities: IEEE Senior Member, TPC member for IEEE conferences, and reviewer for multiple journals. Teaching: Coordinates/teaches Cloud Computing, Computer Networks, and Principles of Computer Organization at UCD.
Hong Yu is an Adjunct Professor at the University of Massachusetts Amherst, affiliated with the Center for Intelligent Information Retrieval and the Biomedical Informatics Natural Language Processing (BioNLP) Laboratory. Her research focuses on computational biology, bioinformatics, and biomedical applications of information retrieval, natural language processing, and human-computer interaction. She has developed systems like AskHERMES (a biomedical Q&A tool) and NoteAid (to aid patient comprehension of medical records). Education includes a PhD in Biomedical Informatics from Columbia University, M.Ph. in Physiology and Cellular Biophysics, and degrees in Physiology and Biomedical Engineering from institutions in China. She has led NIH-funded projects and serves on the editorial board of the Journal of Biomedical Informatics. Research awards include the NLM predoctoral training grant and recognition as one of six 'Star Trainees' for NLM's 175th anniversary. Her work has been featured in Science, Nature, and the Pulitzer-winning Milwaukee Journal Sentinel. Current interests emphasize privacy in geospatial data, ethical AI, and reimagining GIScience education. Grants: Multiple NIH-funded projects. Labs: Center for Intelligent Information Retrieval, BioNLP Lab. Service: Co-chair of biomedical NLP sections at major conferences.
Mariano Rico is an Associate Professor at the Polytechnic University of Madrid (UPM), affiliated with the OEG research group in the Artificial Intelligence Department. Previously, he served as a Senior Researcher at OEG (2016-2020) and held teaching roles at the Autonomous University of Madrid (UAM). His primary affiliations include the UPM's Faculty of Computer Science and the UAM's Computer Engineering Department. Education: PhD in Computer Science (UAM, 2009), MSc in Physics (UAM, 1992), and postgraduate studies in Telecommunications Engineering. He conducted research stays at DERI (Ireland) and Freie Universität Berlin, focusing on Semantic Web and Linked Data. Research interests center on Linked Open Data, Natural Language Processing (NLP), and Semantic Web technologies, with contributions to DBpedia's Spanish branch and projects like Wf4Ever and LIDER. He actively collaborates with institutions in Leipzig, Bielefeld, and Berlin on Linked Data and linguistic applications. Teaching: Coordinates courses in NLP, Linguistic Engineering, and Big Data Visualization at UPM and online programs. Has instructed over 300 UAM faculty through teacher training programs on LaTeX, bibliographic management, and digital scholarly practices. Projects: Lead roles in European and national initiatives including SlideWiki, UpGrid, and Neptune. Current work focuses on NLP applications like text summarization (esT5s) and terminology tools (TermInteract). Labs/Teams: Core member of the OEG group, contributing to semantic web infrastructure and NLP tool development. Maintains international collaborations through AKSW and CITEC groups.
Minghao Qiu is an Assistant Professor at Stony Brook University, jointly appointed between the School of Marine and Atmospheric Sciences and the Program in Public Health. He holds a PhD from MIT (2021), advised by Noelle Selin, and was a Postdoctoral Fellow at Stanford’s Doerr School of Sustainability. His research focuses on air quality, climate change impacts, health effects of environmental policies, and energy policy evaluations. Education: PhD in 2021 from MIT, advised by Noelle Selin. Postdoctoral work at Stanford’s Doerr School of Sustainability (2021–2023). Research interests include wildfire smoke impacts on air quality and health, climate change effects on air pollutants and health, and policies addressing energy transitions and climate challenges. His group uses observational data, statistical methods, and atmospheric modeling. Notable work includes studies on wildfire smoke mortality under climate change, drought impacts on energy systems, and the equity implications of decarbonization policies. His 2023 paper on drought impacts received the Young Professional Best Paper Award from the US Association for Energy Economics. Grants and advising: Seeks students and postdocs interested in climate-health linkages and policy analysis. Active in lab collaborations and interdisciplinary teams addressing environmental sustainability challenges. Labs/Teams: Leads a research group at Stony Brook University, collaborating with institutions like Stanford and MIT on climate and health projects. Lab website available for further details.
Karsten Schulz is a Professor in the Department of Hydrology and Water Management at the University of Natural Resources and Life Sciences (BOKU), Vienna. His research focuses on hydrological processes in alpine environments, remote sensing applications, and machine learning-driven environmental modeling. He leads studies on snow cover dynamics, groundwater recharge, and the integration of big data into hydrological systems analysis. Teaching responsibilities include courses on geoecology, hydrology, and uncertainties in water flow modeling. His research emphasizes interdisciplinary approaches, combining field observations with advanced computational methods to address challenges in climate change, water resource management, and sustainable agriculture. Recent work highlights include regional-scale assessments of Austrian water balance components, the application of superconducting gravimeters for snowpack monitoring, and the development of machine learning models for soil hydraulic property prediction. He collaborates internationally on projects involving transboundary hydrology and agricultural sustainability in East Africa. Consultation hours are held weekly (except cancellations), and his lab (iHYWA) focuses on hydrological innovation for environmental resilience. Research outputs span over 50 peer-reviewed articles since 2018, addressing topics from snow hydrology to AI-driven water temperature forecasting.
Dr. Mallik Mahmud is an Assistant Professor at McGill University, leading the Polar Remote Sensing Group. His research focuses on Arctic sea ice dynamics in a warming climate, employing remote sensing and geophysical techniques to study atmosphere-sea ice-ocean interactions. He utilizes advanced radar systems (e.g., L-band, C-band) and machine learning to analyze sea ice properties across local and pan-Arctic scales. His work emphasizes snow-covered sea ice changes and their impact on geophysical parameter retrieval. Academic Background: Ph.D., Department of Geography, University of Calgary, Canada Former Scientist at the Remote Sensing Institute, German Aerospace Center (DLR), Bremen, Germany Research Interests: Arctic sea ice melt processes Microwave remote sensing applications Machine learning for environmental datasets Climate change impacts on polar ecosystems Publications reflect a focus on multi-frequency radar analysis, melt pond dynamics, and synergistic remote sensing techniques. His work integrates field-based measurements with satellite data to improve understanding of Arctic environmental changes. Labs/Teams: Dr. Mahmud directs the Polar Remote Sensing Group, fostering collaborative, field-driven research in the Arctic.
