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.
Abraham D. Stroock is an Assistant Professor at the School of Chemical and Biomolecular Engineering, Cornell University, since 2003. He holds a B.A. in Physics (Cornell, 1995), M.S. in Solid State Physics (University of Paris, 1997), and Ph.D. in Chemical Engineering (Harvard, 2002). His research bridges microfluidics, biophysics, and sustainable energy. Education: B.A., Physics, Cornell University (1995) M.S., Solid State Physics, University of Paris VI/XI (1997) Ph.D., Chemical Engineering, Harvard University (2002) The Stroock Lab explores micrometer-scale chemical processes inspired by plant biology, focusing on liquid manipulation, negative-pressure water properties, vascular development in tissue engineering, and fluid mechanics in microsystems. Key technologies include microtensiometers and nanoporous membranes . His recent work (2025-2024) spans optical phenotyping using soft robotics, hydromechanical signaling in plants, tissue scaffolds for regenerative medicine, and advanced models for transpiration control. These studies integrate bioengineering, nanotechnology, and environmental science. Scientific Awards: Van Ness Lectureship (2010) Camille Dreyfus Teacher Scholar Award (2009) NSF CAREER Award (2008) MIT Technology Review TR35 (2007) ONR Young Investigator Award (2004) 3M Non-Tenured Faculty Award (2006) Beckman Young Investigator Award (2006) Dreyfus New Faculty Award (2003) He has led projects on superheated loop heat pipes , phosphorescent oxygen sensors , and synthetic tree-on-a-chip systems. His teaching includes advanced biomolecular engineering (ChemE 7770), and he contributes to policy through the Chemistry and Chemical Biology (CBE) Policy Committee.
Keunhyun (Keun) Park is an Assistant Professor of Urban Forestry at the University of British Columbia (UBC), affiliated with the Department of Forest Resources Management . He also holds an Adjunct Professor position at Utah State University in the Department of Landscape Architecture and Environmental Planning. Education: BSc and MSc in Landscape Architecture from Seoul National University; PhD in Urban Planning and Design from the University of Utah Research Lab: Faculty lead of the Urban Nature Design Research Lab ( under_lab ) His research focuses on designing healthy, just, and resilient cities through urban nature , with particular emphasis on: Environmental justice and equitable access to urban green spaces Human behavior in public spaces using drone/sensor/VR technology Smart growth urban design impacts on public health and ecological systems Recent publications demonstrate expertise in GIS applications , pedestrian behavior analysis , and urban planning across 20+ studies from 2013-2025. Collaborations include the Vancouver Park Board , Metro Vancouver , and Wasatch Front Regional Council .
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.
Fabio Zanini is an Associate Professor at the University of New South Wales (UNSW) , leading a research group focused on computational biology , single-cell approaches , and transcriptomic analysis across diseases like severe dengue , neonatal lung disease , cancer , and marine biology . He previously conducted postdoctoral research at Stanford University (2016-2019) and earned a PhD in Bioinformatics from the Max Planck Institute for Developmental Biology and the University of Tuebingen (2015). Current Affiliation: Group leader, UNSW Previous Training: Postdoc (Stanford), PhD (Max Planck/University of Tuebingen) His research spans single-cell RNA sequencing , computational virology , developmental cell biology , and bioinformatics tool development , with recent work on: Severe dengue progression (viral-host interactions, immune signatures) Lung development (endothelial cell diversity, hyperoxia-induced injury) Cancer genomics (mutant HSC clones, AZA therapy response) Marine biology (plankton transcriptomics, evolutionary analysis) Bioinformatics (HTSeq 2.0, northstar algorithm) Recent scientific awards include grants from the Chan Zuckerberg Initiative ($270,000), NIH R01 (multiple), ARC Discovery Grant , and NHMRC Ideas Grant . Notable contributions include: Northstar - Cell classification algorithm SpectralSeq - Hyperspectral-transcriptomic integration Tabula Muris - Mouse aging atlas He has supervised research into hematopoietic stem cell regulation , lung vascular development , and autophagy in viral infections , with collaborations across Stanford , University of Sydney , and Harvard .
Michael Rubinstein is the Aleksandar S. Vesic Distinguished Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University. He also holds professorships in Physics, Biomedical Engineering, and Chemistry. His research spans polymer theory, computer simulations, and the application of these principles to biological systems, particularly mucus biophysics. Dr. Rubinstein earned his Ph.D. from Harvard University in 1983. His educational background in polymer physics has formed the foundation for his extensive research career spanning several decades. Dr. Rubinstein's research focuses on developing simple physical models of soft matter and biological systems ranging from polymeric elastomers and gels to extracellular matrix and mucus in human lungs. His work encompasses several key areas: Mucus Research: Investigating airway surface layer properties and their relationship to respiratory diseases like cystic fibrosis Polymer Entanglements: Studying the dynamics of entangled polymers including ring-linear blends and bottle-brush polymers Reversible Networks: Developing theories for interpenetrating elastomers and gels with both permanent and reversible components Charged Polymers: Extending scaling theory to describe complexes of oppositely charged polymers Analysis of Dr. Rubinstein's recent publications (2023-2025) reveals a strong focus on advanced polymer systems with applications in biomedicine and materials science. His work bridges fundamental polymer physics with practical applications, particularly in understanding mucus biophysics for respiratory diseases and developing novel polymer networks with self-strengthening and adaptive properties. Key themes include chromatin organization, hydrogel mechanics, fracture behavior in polymer networks, and topological constraints in ring polymers. Dr. Rubinstein has received several notable awards including the Nelson W. Taylor Award from Penn State University (2022), a University Distinguished Professorship from Duke University (2020), and recognition from the Royal Society of Chemistry (2019). Dr. Rubinstein leads an active research group (the Rubinstein Lab) that extensively collaborates with experimental, computational, and theoretical groups at Duke and worldwide. His lab combines theoretical modeling, computer simulations, and experimental validation to advance understanding of soft matter systems. While specific grant information isn't detailed in the provided text, his numerous high-impact publications suggest substantial research funding supporting his work. The Rubinstein Lab focuses on several interconnected research thrusts including mucus biophysics, self-assembly of amphiphilic systems, reversible networks and gels, polymer entanglements, and charged polymer systems. The lab employs a multi-pronged approach combining theoretical modeling, computer simulations, and experimental collaborations to develop fundamental understanding of soft matter systems with applications to biomedical challenges.
Jason Ostanek is an Assistant Professor at Purdue University's School of Engineering Technology and Environmental and Ecological Engineering. He directs the Applied Thermofluids Laboratory and Powertrain Technology Laboratory, focusing on battery safety and thermal management systems. Ph.D. in Mechanical Engineering from Penn State M.S. in Mechanical Engineering from Penn State B.S. in Mechanical Engineering from Virginia Tech His research explores energy storage systems, thermal runaway phenomena, heat transfer mechanisms in Li-ion batteries, fluid dynamics, and internal combustion engine thermal management. He has developed analytical models for battery degradation, thermal abuse simulations, and innovative cooling strategies for large-scale energy systems. Key publication trends show expertise in: Li-ion battery thermal runaway modeling Heat transfer in confined geometries Thermal management for energy storage systems Renewable energy forecasting Computational fluid dynamics applications Scientific awards include: 2020 Purdue Teaching Academy's Award for Exceptional Teaching and Instructional Support during the COVID-19 Pandemic 2020 SOET Outstanding Faculty in Engagement 2019 SOET Outstanding Faculty in Discovery 2015 NAVSEA Commander’s Award for Innovation 2013 ASME IGTI Young Engineer Travel Award 2007 DOD SMART Fellowship Recipient As director of Purdue's Applied Thermofluids Laboratory, he leads research on battery safety mechanisms, combustion dynamics, and thermal systems optimization. His work spans fundamental and applied research with industrial collaborators.
Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.
Dr. Kibret Mequanint is a full Professor at Western University's Department of Chemical and Biochemical Engineering, with cross-appointments in Biomedical Engineering. Holding a PhD from University of Stellenbosch and postdoctoral experience at Technical University of Darmstadt and McMaster University, his research bridges polymer science, materials engineering, and life sciences with applications in Biomaterials , Tissue Engineering , and Regenerative Medicine . His work spans both fundamental and translational research in cell-material interactions , polymer biomaterial design , and therapeutic radiation dosimeters , with technologies transferred to commercial applications. Leading scholar and educator with awards from NSERC, CIHR, and Western University Fellow of: American Institute for Medical and Biological Engineering (AIMBE), Ethiopian Academy of Sciences, International Union of Societies for Biomaterials Science and Engineering, Canadian Academy of Engineering Extensive editorial and panel service for NSERC, CIHR, and international journals His research program has produced over 170 refereed publications, focusing on conductive hydrogels , bioadhesives , and vascular tissue engineering . Recent work on endoscopy-deliverable bioadhesives and snake venom-derived hemostatic gels has attracted global media attention. He has served in leadership roles at the Canadian Biomaterials Society and university governance bodies including Senate and Board of Governors.
Omar Rifki is an Associate Professor (Maître de Conférences) specializing in combinatorial optimization and artificial intelligence applications. His research bridges theoretical computer science with practical logistics challenges, focusing on routing problems, process mining, and machine learning integration for complex decision systems. His core research interests include phase transitions in NP-hard problems, vehicle routing optimization under time constraints, and healthcare process modeling. Rifki's work demonstrates a consistent pattern of integrating reinforcement learning with traditional optimization techniques to solve large-scale real-world problems in transportation and logistics, with particular emphasis on spatio-temporal data effects and collaborative systems. Analysis of his 15 publications (2019-2025) reveals three dominant research thrusts: (1) Fundamental studies of combinatorial problem hardness using phase transition frameworks, (2) Practical applications of deep reinforcement learning in vehicle routing and taxi assignment, and (3) Healthcare process optimization through advanced process mining techniques. His work consistently addresses scalability challenges in real-world implementations while maintaining theoretical rigor. No scientific awards were documented in the provided materials. His collaborative work with researchers like Christine Solnon and Thierry Garaix indicates active participation in European operations research communities, though specific grant details remain unreported. Rifki's research shows increasing integration of graph theory and machine learning in transportation applications, particularly evident in his Lyon City case studies on autonomous ride-sharing systems.
Fred Feinberg is the Joseph and Sally Handleman Professor of Marketing and Professor of Statistics (by courtesy) at the University of Michigan, where he is also an Affiliated Faculty member of the Center for the Study of Complex Systems. His work integrates advanced Bayesian methods with large-scale marketing data to illuminate how people make choices under uncertainty. Education Ph.D., Sloan School of Management, Massachusetts Institute of Technology (1989) Doctoral program in Mathematics, Cornell University (1983–84) S.B. Mathematics & S.B. Philosophy, Massachusetts Institute of Technology (1983) Research Focus Feinberg’s scholarship centers on discrete choice models that leverage real-world decisions to infer latent attributes such as demographics, product appeal, and socioeconomic status. Methodologically, he employs Hierarchical Bayes (HB) models and cutting-edge MCMC algorithms to handle massive data sets, while theoretically he advances dyadic utility theory and optimal search under uncertainty. Applications span click-through behavior, menu-based choice, online dating preferences, spatial marketing, and consumer reactions to intangible or aesthetic product features. Recent empirical studies explore the wearout versus weariness effects of online advertising, the impact of data breaches on consumer behavior, and dynamic pricing for digital media subscriptions. Across these projects, Feinberg couples rigorous statistical innovation with actionable managerial insights, bridging marketing science, operations, and engineering. Scientific Awards & Leadership Joseph and Sally Handleman Endowed Professorship Past President, INFORMS Society for Marketing Science Departmental Editor, Production and Operations Management Former Co-Editor, Marketing Science Co-author (with T. Kinnear & J. Taylor) of the textbook Modern Marketing Research: Concepts, Methods, and Cases Grants & Collaborations While explicit grant lists are not provided, Feinberg’s prolific publication record in top-tier journals (e.g., Journal of Marketing Research , Marketing Science , Management Science ) and editorial board service imply sustained external funding and interdisciplinary partnerships, particularly with operations, engineering, and computer-science groups. Laboratories & Teams Feinberg is formally affiliated with the Center for the Study of Complex Systems (CSCS) at the University of Michigan, where he collaborates on network-based choice frameworks and large-scale behavioral data analytics. He maintains active ties to the Ross Marketing faculty and the Department of Statistics, fostering joint workshops and doctoral training initiatives.
Professor Ralf Stanewsky leads the Stanewsky Group at the Institute of Neuro- and Behavioral Biology, University of Münster. His research focuses on the molecular mechanisms of circadian rhythms in Drosophila melanogaster , particularly how environmental cues like light and temperature reset the circadian clock. The group employs genetic, molecular, histological, and behavioral approaches to study sensory pathways and their integration in central clock neurons. Member of the Multiscale Imaging Centre (MIC) and Imaging Network – Microscopy Current lab members: Ph.D. students Anna Katharina Eick, Angelica Coculla, Maia Zabel Barroso Technical assistants Regina Hube and Ume Aiman Research Themes Light and temperature synchronization of circadian clocks Temperature compensation mechanisms in biological timing Neuronal integration of environmental signals Evolutionary aspects of circadian regulation Professor Stanewsky’s work spans molecular clock components (e.g., cryptochromes, timeless gene variants) to broader ecological implications of temporal niche choice. His lab investigates how clock gene expression responds to seasonal changes and environmental stressors, while also exploring novel synchronization pathways beyond classical photoreceptors. Publication Trends Recent articles emphasize temperature-dependent clock regulation , evolutionary capacitance via Hsp90 , and non-canonical phototransduction in circadian systems. Key subfields include nuclear transport dynamics, kinase evolution, and computational modeling of periodic patterns across species. Contact Information Institute of Neuro- and Behavioral Biology, University of Münster MIC | Röntgenstraße 16, D-48149 Münster, Germany Email: stanewsky@uni-muenster.de Phone: +49 251 8321029
Prof. Rajiv Sinha is a Professor in the Department of Earth Sciences at Indian Institute of Technology Kanpur . With a PhD from the University of Cambridge (1992), his career spans over two decades at IITK, including roles as Head of Department since 2014. Education: PhD (University of Cambridge, 1992), M.Tech (University of Roorkee, 1987), B.Sc (Patna University, 1983) Key Affiliations: Member of International Association of Sedimentologists, SEPM, Quaternary Research Association, and Indian Professional Societies Research Focus: Specializing in river science , Prof. Sinha investigates fluvial geomorphology , sedimentology , and natural hazards like Kosi floods . His work integrates remote sensing and GIS for climate change and paleoclimate reconstruction , notably studying the Ganga river system and its anthropogenic impacts . Scientific Leadership: His publications (2013-2017) reveal: Anthropocene river systems (2016) Indus Civilization paleohydrology (2017) Kosi megafan dynamics (2015) Monsoon evolution (2010, 2014) Groundwater management (2016) Awards & Recognitions: Pandit Girish Ranjan Chair Professorship (2013) National Mineral Award (2002) Alexander von Humboldt Fellowship (2000) UGC Research Fellowship (1988) University Gold Medal (1987) Collaborative Network: Partners include University of Durham , Imperial College London , and Institute du Physique de Globe, Paris . Currently leading Ganga River Basin Management studies and river science initiatives at IITK.
Jenny Huangfu Day, a Professor of History and the Francis Young Tang '61 Chair in China Studies at Skidmore College, specializes in the intellectual, diplomatic, and legal history of late imperial and modern China. She earned a PhD from the University of California San Diego (2012) and a BA from the University of Washington (2007). Education: PhD in History, University of California San Diego (2012) BA in History, University of Washington (2007) Research Interests: Her work bridges intellectual history , legal history , and diplomacy to examine how China negotiated sovereignty and political legitimacy from the Qing Dynasty to the Republican era. Current projects analyze the politicization of historical education and transnational legal negotiations . Her publications include monographs on Qing diplomatic missions and extradition law , alongside peer-reviewed articles in Law and History Review , Modern Asian Studies , and 中华文史论丛 . She has also contributed to digital humanities projects like Visualising China and public intellectual forums such as 澎湃新闻 . Scientific Awards: Outstanding Academic Title of 2019 by ACRL Choice for *Qing Travelers to the Far West* Academic Leadership: As Interim Chair of the History Department at Skidmore College (Fall 2025), she oversees curriculum development and faculty mentorship, with a focus on integrating interdisciplinary methodologies into China studies.
Andrew Zammit Mangion is an Associate Professor at the University of Wollongong , affiliated with the School of Mathematics and Applied Statistics . His research focuses on spatio-temporal statistics, computational methods, and environmental informatics, with applications in climate science and geospatial data analysis. Education : PhD in Statistics (University of Sheffield, 2012), B.Eng. (University of Malta, 2007) Research Themes : Spatio-temporal modeling, Bayesian inversion frameworks (e.g., WOMBAT v2.S), deep learning integration, and statistical software development (e.g., FRK package) Grants & Projects : ARC DECRA Fellow (2018), Chief Investigator on ARC Discovery Project (greenhouse gases), ARC Special Research Initiative (Securing Antarctica's Environmental Future), and ARC Industrial Transformation Hub (TIDE). Collaborations : University of Bristol, University of Edinburgh, ESA CCI, NASA OCO-2 data projects Scientific Awards include the prestigious Australian Research Council Discovery Early Career Researcher Award (DECRA). His work spans Antarctic ice sheet analysis, CO2 flux inversion, and scalable spatial statistical models for environmental monitoring.
