Dr. Pieter van Oel is an Associate Professor in the Water Resources Management group at Wageningen University & Research, focusing on sociohydrology and addressing water crises in South America and Africa. His expertise spans civil/hydraulic engineering, environmental management, hydrology, irrigation systems, and drought diagnosis. He teaches courses like Design in Land and Water Management and supervises PhD students such as Louise Cavalcante and Daniel Wiegant. His research emphasizes societal dimensions of water challenges, including drought impacts, agent-based modeling, and serious gaming for community engagement. Projects include analyzing drought policies in Brazil, improving water governance in Kenya, and evaluating groundwater vulnerability in Ethiopia. He collaborates on initiatives like the Diagnosing Drought project and Socio-hydrological Simulation tool development. Key contributions include critiques of drought monitoring systems' alignment with local realities and frameworks for sustainable agroforestry management. His work bridges science and policy, advocating for interdisciplinary solutions to water scarcity and climate resilience.
John Byabazaire is a Research Fellow at the School of Computer Science, University College Dublin (UCD). He holds a PhD in Computer Science from UCD (2024), following a BSc (Gulu University, 2013) and MSc (Waterford Institute of Technology, 2018). His research focuses on IoT systems for data collection, remote sensing, AI-driven end-to-end system management, and fog analytics. He has held academic roles including Assistant Lecturer at Gulu University (2018–2019) and teaching roles at UCD since 2019, including Occasional Lecturer and Senior Teaching Assistant. His research spans smart agriculture, data quality in IoT, and education technology. Notable contributions include frameworks for yield mapping in precision agriculture, trust-based data validation in IoT, and machine learning approaches for livestock health monitoring. He has secured grants like the National ICT Initiatives Support Program (Uganda Government, 2019–2020). Teaching includes courses on cloud computing, web development, and distributed systems. His articles emphasize IoT data quality, agricultural analytics, and educational technology innovation. He actively promotes technology adoption in African education and agriculture sectors through collaborative projects.
Dan Sheldon is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, holding a Five College joint faculty position with Mount Holyoke College. His research focuses on developing algorithms to address environmental challenges using large datasets, emphasizing computational sustainability. Key areas include spatial optimization for endangered species conservation, continent-scale bird migration modeling, and interpreting weather radar data for ecological insights. Methodologically, his work leverages probabilistic inference, network modeling, and machine learning. Sheldon earned a PhD in Computer Science from Cornell University and an AB in Mathematics from Dartmouth College. His postdoctoral training at Oregon State University was supported by an NSF Bioinformatics Fellowship. He co-leads the BirdCast project, an NSF-funded initiative applying novel machine learning to avian migration studies. His research affiliations include the Center for Data Science and the Computational Social Science Institute. Research interests span computational biology, machine learning, and data privacy. Notable contributions include algorithms for ecological decision-making, differentially private synthetic data techniques, and Gaussian process applications in environmental forecasting. Awards include an NSF Fellowship in Bioinformatics. Current projects integrate radar data analysis, biodiversity tracking, and privacy-preserving statistical methods. Grants include the BirdCast NSF grant and collaborations in computational sustainability. His work bridges theoretical computer science with applied ecological challenges, emphasizing interdisciplinary approaches to global-scale environmental problems.
Dr. Amy Turitz is an Associate Professor of Obstetrics, Gynecology & Reproductive Sciences at Yale School of Medicine. She specializes in maternal-fetal medicine, focusing on high-risk pregnancies at Greenwich Hospital. Her research centers on preterm birth, fetal growth restriction, and obstetric complications. Dr. Turitz earned her MD from the Mount Sinai School of Medicine, completed her residency at the Hospital of the University of Pennsylvania, and her fellowship in Maternal-Fetal Medicine at Columbia University Irving Medical Center. She has been honored with multiple teaching awards for her mentorship of medical students, residents, and fellows. Education: BS in History, Wesleyan University (2005) MD, Mount Sinai School of Medicine (2009) Residency: Obstetrics & Gynecology, Hospital of the University of Pennsylvania (2013) Fellowship: Maternal-Fetal Medicine, Columbia University Irving Medical Center (2016) Research Interests: Dr. Turitz’s work explores mechanisms and interventions for preterm birth prevention, fetal growth restriction, and managing high-risk pregnancies. Her studies often involve collaborations on topics like cerclage timing, progesterone therapy, and maternal-fetal outcomes during pandemics. She emphasizes clinical translation to improve patient care. Awards: Sreedhar Gaddipati Maternal-Fetal Medicine Teaching Award (2023) APGO Excellence in Teaching Award (2020) Resident Teaching Award (2013) Labs/Teams: She co-founded the Cervix Research Working Group and was part of the Preterm Birth Prevention Center at Columbia University. Her current affiliations include the Maternal Fetal Medicine Unit at Yale.
Hui Zhang is a Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on data-driven networking systems, video streaming optimization, and network control frameworks. He has contributed to innovations in adaptive resource allocation, real-time analytics, and sustainable strategies for resource utilization. Key research themes include time-state analytics, network anomaly detection, and integrating machine learning for enhanced performance. His work addresses challenges in content delivery networks (CDNs), peer-to-peer systems, and environmental applications like waste management. Recent publications (2021–2024) highlight advancements in neural network-based prediction, timeline frameworks, and sustainable material science innovations. No scientific awards are mentioned in the provided text. His research emphasizes practical solutions for improving video quality of experience (QoE), network efficiency, and cross-disciplinary applications.
Dr. Xi Yu is a Lecturer in Chemical Engineering at the University of Southampton, affiliated with the Faculty of Engineering and the Environment. He holds a Bachelor's from Tianjin University and a Ph.D. from the University of Sheffield. His research focuses on low carbon fuels, granulation techniques, and computational fluid dynamics. He has supervised PhD students such as Jerin Jacob and is currently accepting new PhD applicants in these areas. Dr. Yu's educational background includes degrees in Chemical Engineering and prior academic roles at Aston University and the Energy and Bioproducts Research Institute (EBRI). His work spans bioenergy systems, particle technology, and multi-physics modeling. Key research projects include advancements in biomass gasification, biofuel production, and sustainable energy systems. His publications emphasize computational modeling, fluid dynamics, and biomass utilization. Recent articles explore topics like absorption chiller systems, fluidization validation, and bio-oil aging strategies. He contributes to teaching modules such as CHEG3000 and CHEG3004, reflecting his commitment to both research and education.
Matthew Price is the George W. Albee Green & Gold Professor of Psychological Science and Director of the Clinical Psychology Training Program at the University of Vermont's College of Arts and Sciences. He holds a B.A. from SUNY Binghamton (2004), an M.A. (2006), and Ph.D. (2011) from Georgia State University. His research focuses on expanding clinical care access for trauma survivors and anxiety disorder patients via technology-driven interventions. Key areas include mobile health applications, wearable sensors, and acute trauma care in Emergency Departments. His interdisciplinary approach involves collaborations with computer science, bioinformatics, and medicine. Current projects explore digital biomarkers (e.g., heart rate variability), technology adoption barriers, and culturally adapted therapies. He leads the Center for Research on Emotion, Stress, and Technology, emphasizing translational frameworks bridging basic research and clinical practice. Recent work includes randomized controlled trials evaluating mobile apps like 'Bounce Back Now' for disaster-related PTSD, and sleep-monitoring studies using wearable devices. Over 150 peer-reviewed articles highlight his focus on trauma mechanisms, symptom networks in veterans, and tech-enabled mental health innovations. His lab actively addresses global mental health disparities through mHealth solutions.
Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.
Yanhua Li is an Associate Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI), where he has served since 2021 (previously as Assistant Professor from 2015-2021). He is also an affiliated researcher at UMass Transportation Center (UMTC). His educational background includes dual PhDs: Computer Science from University of Minnesota, Twin Cities (2013) and Electrical Engineering from Beijing University of Posts and Telecommunications (2009), along with an MS (2006) and BS (2003) in Electrical Engineering from Sichuan University. Dr. Li's research focuses on artificial intelligence and spatial-temporal data science with applications in smart cities and urban intelligence. His work particularly emphasizes imitation learning and meta learning in AI for understanding and influencing urban human agents' decision-making strategies, such as taxi drivers' passenger-seeking behaviors and urban travelers' transit choices. His laboratory develops advanced computational methods for urban transportation systems, traffic prediction, and spatial-temporal data analytics. His publication record shows a strong trajectory in top AI and data science venues, with recent work bridging foundation models with urban computing, enhancing robustness in spatial-temporal representation learning, and applying generative models to urban traffic estimation. His research spans computer vision, reinforcement learning, generative modeling, and spatio-temporal data analysis with applications in transportation, environmental monitoring, and urban planning. Best Applied Data Science Paper Award at SDM 2019 NSF CAREER Award (2020) Runner-up for the 10-Year Impact Award for SIGSPATIAL Conference (2024, for 2014 paper) Dr. Li has secured significant research funding including an NSF CAREER award ($529k), multiple NSF grants totaling over $2 million, and industry collaborations with DiDi Chuxing Research. He has advised numerous PhD students who have gone on to faculty positions at institutions like San Diego State University and SUNY Binghamton University. His research group maintains active collaborations with industry partners including DiDi Chuxing, Pitney Bowes Inc., and NVIDIA. He leads several research initiatives including the CityLines project for urban transportation systems and has contributed to foundational work in spatial-temporal imitation learning. His laboratory continues to expand into new areas including applying large language models to urban dynamics prediction and developing advanced methods for environmental monitoring.
Katja Fennel is a Professor in the Department of Oceanography at Dalhousie University, Faculty of Science. Her research focuses on coupled physical-biogeochemical modeling to understand marine ecosystems, carbon and nutrient cycling, and climate change impacts. She specializes in coastal and open ocean dynamics, with expertise in hypoxia, ocean deoxygenation, and carbon sequestration. Fennel holds significant academic roles including Killam Professorship (2019-2024) and former Co-editor-in-Chief of Biogeosciences (2013-2021). She has led projects on ocean prediction, marine carbon dioxide removal (CDR), and coastal acidification. Education: PhD in Marine Biology and Diploma in Numerical Mathematics from University Rostock, Germany Affiliations: Adjunct roles at University of Maryland Center for Environmental Science, University of Maine, and Rutgers University Research Interests: Development of high-resolution coupled models to study biogeochemical processes in shelf and open ocean systems. Key areas include the impact of climate change on marine ecosystems, hypoxia formation mechanisms, and the role of ocean alkalinity enhancement in carbon removal. Fennel's work integrates observational data with advanced modeling to address ecological and climate challenges. Publications highlight contributions to understanding coastal-ocean carbon dynamics, biogeochemical forecasting, and Arctic carbon uptake. Recent focus includes expanding Argo float networks and evaluating marine CDR strategies like ocean alkalinity enhancement (OAE). Awards: AGU Fellow (2024), CMOS President's Prize (2023)
Markus König is a Professor of Informatics in Civil Engineering at Ruhr University Bochum, where he has been researching and teaching since October 2009. His work focuses on Building Information Modeling (BIM), digital construction technologies, and civil engineering informatics, with significant contributions to the development and implementation of digital methods in German construction industry. Dr. König earned his degree in civil engineering with a focus on applied computer science at Leibniz University Hannover, where he also completed his doctorate on cooperative building planning at the Institute for Building Informatics. He subsequently held a junior professorship for Theoretical Methods of Project Management at Bauhaus University Weimar before joining Ruhr University Bochum. His research spans multiple cutting-edge areas including Building Information Modeling (BIM), construction process simulation, tunneling informatics, infrastructure asset management, and the application of artificial intelligence and computer vision in civil engineering. As chair of the Building Informatics Working Group from 2012-2016, he played a key role in developing the first national BIM curriculum for German universities and serves as editor of the book 'Building Information Modeling: Technological Foundations and Industrial Practice.' Analysis of his recent publications reveals a strong trend toward semantic technologies, digital twins, automated compliance checking, and the integration of AI in construction processes. His work increasingly focuses on information containers, ontology development, and the application of large language models to infrastructure data, reflecting the evolving landscape of digital construction. Dr. König's significant contributions to digital construction have been recognized with prestigious awards: Lower Saxony-Bremen Construction Industry Award (2017) for 'services in the development and introduction of digital construction in Germany' Konrad Zuse Medal (2020) While specific details about his advising and grant activities aren't explicitly mentioned in the provided text, his extensive publication record with numerous co-authors suggests active supervision of doctoral students and research staff. His involvement in multiple collaborative research projects is evident from his publication history. At Ruhr University Bochum, Professor König leads a research group focused on civil engineering informatics, with particular emphasis on BIM, digital construction technologies, and their application across the building lifecycle. His team appears to work at the intersection of computer science and civil engineering, developing innovative solutions for construction process optimization, infrastructure management, and digital transformation of the AEC industry.
Angelina Wang is an incoming Assistant Professor at Cornell Tech and the Department of Information Science at Cornell University, starting Fall 2025. Her research focuses on responsible AI, particularly machine learning fairness and algorithmic bias. She holds a Ph.D. in Computer Science from Princeton University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Current postdoctoral work at Stanford’s HAI and RegLab explores sociotechnical challenges in AI deployment. Her research addresses fairness evaluation in generative AI, societal impacts of AI systems, and ethical trade-offs in algorithm design. Notable awards include the NSF GRFP, Siebel Scholarship, and Microsoft AI & Society Fellowship. Her work bridges technical and social dimensions of AI, emphasizing human-centered evaluation and interdisciplinary collaboration. Recent publications span medical AI applications (e.g., Alzheimer’s subphenotypes, corticosteroid treatment efficacy) and foundational fairness research. She advocates for proactive ethical considerations in technical work, citing examples like surveillance risks in facial recognition and dataset biases in computer vision. Angelina advises prospective PhD students in Cornell’s Information Science program and collaborates on projects like SciDaSynth for scientific knowledge synthesis. Her advocacy includes challenging fairness impossibility theorems and promoting algorithmic pluralism in auditing practices.
Shahram Rahimi is a Professor and Department Head in the Department of Computer Science at the University of Alabama, College of Engineering. He concurrently holds an Adjunct Professor position at Mississippi State University. His research spans computational intelligence, machine learning, healthcare AI, cybersecurity, and quantum computing. He leads the PATENT Lab, focusing on predictive analytics, decision support systems, and AI-driven healthcare solutions. His educational background includes a Ph.D. in Computer Science. Key research areas include multi-agent systems, generative models, and predictive maintenance. He has served as an editor for journals like Scalable Computing: Practice and Experience and Informatica . Rahimi’s recent work emphasizes secure MLOps, quantum algorithms, and patient-centric medical systems. His publications address challenges in explainable AI, anomaly detection, and healthcare informatics. He actively contributes to conferences and journals in AI, cybersecurity, and computational intelligence. Editorial Roles: Scalable Computing, Engineering Letters, Informatica Labs: Predictive Analytics & Technology Integration (PATENT) Lab Key Focus Areas: Healthcare AI, Quantum Computing, Cybersecurity, Explainable Machine Learning
Asad Abdi is a Lecturer in Computer Science at the College of Science and Engineering. His research focuses on deep learning, data mining, and artificial intelligence , with applications in traffic analysis, educational technology, and maritime logistics. He has published extensively on topics like social media-based traffic forecasting, fake news detection, and vessel arrival prediction. Abdi’s work bridges theoretical advancements in machine learning with practical challenges in domains such as transportation systems and education. He has explored hybrid approaches combining deep learning models with linguistic knowledge and knowledge graphs to address real-world problems. Notable contributions include frameworks for feedback analysis in hybrid classrooms and fusion-based prediction models for vessel arrival times. His recent articles highlight trends in leveraging large language models and multi-feature fusion techniques for tasks like opinion summarization and aspect extraction. Abdi’s research often emphasizes interdisciplinary collaboration, integrating insights from computer science, transportation engineering, and educational psychology. While no formal awards or grants are explicitly listed, his publication record demonstrates sustained contributions to applied AI and data-driven solutions across multiple sectors.
John Rowan is a Professor of Physical Geography and Director of the UNESCO Centre for Water Law, Policy & Science at the University of Dundee. His roles include advancing sustainable development, particularly SDG6 (water and sanitation), and leading interdisciplinary initiatives. Previously, he served as Vice Principal for Research and inaugural Dean of the School of Social Sciences. His research focuses on environmental change, climate impacts on water resources, and policy-driven solutions, with global collaborations in Bangladesh, India, and Kenya. He has held advisory roles in Scottish Government’s Centres of Expertise and chairs UNESCO’s UK International Hydrological Committee. Research Interests: Environmental change, climate-water-food nexus, river basin management, sediment dynamics, and policy integration. His work bridges academic and applied domains, emphasizing governance and sustainable practices. Key projects include enhancing water security under climate uncertainty, developing risk frameworks for drinking water, and addressing the water-food-energy nexus. Publications: Over 111 articles, focusing on climate adaptation, water resource vulnerabilities, and ecohydrological modeling. Recent highlights include studies on the Karakoram Anomaly’s hydrological tipping points and frameworks for climate-resilient water safety plans. Awards: Gold Engage Watermark Award (2020), United Nations Risk Award (2019). Projects span international collaborations and policy advisory roles. He has supervised 12 students and led over 33 research projects, including the Hydro Nation Scholars Programme. Labs/Teams: UNESCO Centre for Water Law, Policy & Science; Centre for Environmental Change and Human Resilience (CECHR), collaborating with the James Hutton Institute. Active in global forums like the UNFCCC Climate Assembly and World Water Forum.
