Antonio Rodríguez-Sánchez is a theoretical particle physicist at the Instituto de Física Corpuscular (IFIC), University of Valencia, specializing in high-precision Standard-Model tests, especially the muon anomalous magnetic moment, hadronic light-by-light scattering, tau-lepton physics, and effective field theories. After his PhD at Valencia and postdocs at SISSA Trieste and IJCLab Orsay, he returned to IFIC in 2024 as a permanent research faculty member. Research Interests: Muon g-2 and its hadronic contributions Hadronic light-by-light scattering and short-distance constraints Standard Model Effective Field Theory (SMEFT) and top-quark physics Tau-lepton decays and flavor physics CP violation and weak-basis invariants His work combines perturbative QCD, dispersion relations, and lattice QCD input to reduce theoretical uncertainties in precision observables. He has co-authored flagship reviews on the Standard-Model prediction of the muon g-2 and is active in global SMEFT analyses. Selected Scientific Output: 42 peer-reviewed articles (2020-2025) with >300 citations on INSPIRE Co-leader of short-distance constraints program for hadronic light-by-light contributions to muon g-2 Contributor to Snowmass 2021 white papers on g-2 and strong coupling extraction No graduate students, grants, or major awards are listed in the available sources; his primary role is research faculty at IFIC.
Dr. Jörg Lewandowski is a Privatdozent (Private Lecturer) at the Geographical Institute of Humboldt University Berlin and a full-time scientist and working group leader in the Department of Ecohydrology and Biogeochemistry at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin. He has been affiliated with IGB since 2006 and with Humboldt University since 2015, where he leads research on groundwater-surface water interactions and related biogeochemical processes. Dr. Lewandowski's research focuses on hydrodynamic transport and biogeochemical turnover processes at critical aquatic interfaces. His primary research areas include groundwater-river interactions in hyporheic zones and floodplain aquifers, groundwater-lake interactions, retention and transformation of organic trace substances in hyporheic zones, effects of bioturbation on transport processes in lake sediments, phosphorus retention in limnic sediments, and lake restoration techniques. His work combines field measurements, laboratory experiments, and modeling approaches to understand these complex ecohydrological systems. Analysis of Dr. Lewandowski's recent publications (2023-2025) reveals a strong focus on advanced methodological approaches for studying groundwater-surface water interactions. His research increasingly employs electrical conductivity as a tracer, develops sophisticated modeling tools for hyporheic exchange, and investigates the impacts of dynamic flow conditions on biogeochemical processes. Recent work also examines the role of biological activity in enhancing ecohydrological interface functions and develops standardized methods for characterizing groundwater-lake interactions. Dr. Lewandowski leads or has led several significant research projects including the long-term 'Hyporheic Zone' project (2006-2029), 'Groundwater – the disregarded component in lake water and nutrient budgets' (2010-2028), 'HypoTRAIN' (2015-2018), and the current 'Dynamic hyporheic zone' project (2023-2026). His work has been supported by various funding agencies including DFG (German Research Foundation) and has contributed to practical applications in water management and lake restoration. His laboratory and research group at IGB specialize in advanced techniques for studying groundwater-surface water interfaces, including distributed temperature sensing, electrical conductivity monitoring, novel sampling approaches for pore water, and development of integrated modeling frameworks. The group collaborates extensively with national and international partners across multiple disciplines to advance understanding of critical zone processes in freshwater systems.
Carlos A. Peres is a Professor of Tropical Conservation Ecology at the School of Environmental Sciences, University of East Anglia (UK), and the lifetime Science Director of Instituto Juruá, a non-profit conservation NGO in the Amazon. He currently serves as the Edward Bass Distinguished Visiting Environmental Scholar at Yale University until July 2026, splitting his time between Norfolk, UK and Amazonian forest sites while advising the Brazilian government on Amazon conservation issues. His educational background includes: PhD in Wildlife Research from University of Cambridge (1987-1991) MSc in Forest Ecology & Conservation from University of Florida (1984-1986) BSc in Biology from Universidade Federal do Pará, Brazil (1980-1983) Peres has dedicated over 40 years to studying wildlife community ecology in Amazonian forests, population ecology of neotropical forest resources, biological criteria for nature reserve design, and community-based conservation-development frameworks. His research spans multiple neotropical biomes including the Amazon, Atlantic Forest, Caatinga, Cerrado, and Pantanal wetlands. He currently leads the largest multi-taxa biodiversity inventory program in any tropical forest region, working across over 120 forest sites in six Amazonian countries. His recent publications reveal strong research trends across tropical ecology, conservation biology, and environmental science, with particular focus on Amazonian biodiversity patterns, deforestation impacts, habitat fragmentation consequences, species conservation strategies, ecosystem service valuation, and climate change effects on tropical ecosystems. His work consistently integrates field ecology with conservation policy applications. Notable scientific awards include: Biodiversity Conservation Leadership Prize Award from Bay and Paul Foundation (1995) Environmentalist Leader for the New Millennium by Time Magazine and CNN Network (2000) Inaugural Frontiers Planet Prize as International Champion (2023) Peres has supervised over 140 postgraduate students from 22 countries across South America, Africa, Southeast Asia, Europe, and North America. He currently co-directs four major ecology and conservation research programs throughout Amazonian forests, focusing on timber/non-timber forest resources, vertebrate assemblage structure, fragmented landscape dynamics, and land-use change impacts. His research is supported by significant grants including the Frontiers Planet Prize which recognizes his global conservation leadership. His laboratory and research team infrastructure includes extensive field operations across Amazonia, with particular strength in multi-taxa biodiversity monitoring, community-based conservation initiatives, and policy-relevant conservation science. The Instituto Juruá serves as a key operational base for his Amazonian research activities, facilitating long-term ecological studies and community engagement programs.
Tong Zhang is a Professor in the Electrical, Computer and Systems Engineering Department at Rensselaer Polytechnic Institute (RPI). He joined RPI in 2002 as an assistant professor, advancing to associate professor in 2008 and full professor in 2013. His research focuses on computer systems, particularly memory and data storage across software and hardware stacks, with interdisciplinary applications in computer architecture, VLSI signal processing, and error correction coding. He holds a B.S. and M.S. from Xian Jiaotong University (China) and a Ph.D. from the University of Minnesota. His work emphasizes energy-efficient storage solutions, transparent compression, and hardware-software co-design. Notable contributions include innovations in SSD arrays, computational storage drives, and database systems. He is an IEEE Fellow and maintains affiliations with RPI's Computer Science programs. Research interests span memory systems optimization, storage architectures, and emerging technologies like CXL-based AI acceleration. His publications highlight advancements in reducing energy consumption, improving data deduplication, and enhancing B+-tree performance on modern storage hardware. Awards: IEEE Fellow (2023) Grants/Advising: Extensive grant-funded research in storage systems; no student advisees explicitly listed. Labs/Teams: Engaged in interdisciplinary collaborations within RPI's computational storage and memory research groups.
Christian Pilegaard Hansen is an Associate Professor in the Department of Food and Resource Economics at the University of Copenhagen, specializing in the political ecology and political economy of natural resources in developing countries. His work primarily focuses on forests and forestry, with particular emphasis on timber and charcoal commodity chains. Hansen's research centers on the relationship between economic and political processes and natural resources, with extensive fieldwork in Ghana. He leads two major research programs: Access and Exclusion along the charcoal commodity chain in Ghana (AX) and Access-Authority Nexus in Farmer-Herder Conflicts in Ghana (AAN). His work examines benefit sharing, illegal logging, and the implications of the FLEGT (Forest Law Enforcement, Governance and Trade) and VPAs (Voluntary Partnership Agreements). Hansen's recent publications reveal a strong focus on Ghanaian resource governance, with particular attention to farmer-herder conflicts, charcoal production systems, and forest conservation history. His work spans multiple disciplines including political ecology, environmental history, and natural resource economics, with field research extending across multiple African countries and Southeast Asia. Royal Veterinary and Agricultural University Award for excellent MSc degree (1992) Chairman of the Danish Steering Committee of the Growth & Employment Platform under the Building Stronger Universities in Developing Countries initiative (2011-2013) Danish Representative in European Tropical Forestry Research Network (ETFRN) Hansen supervises four PhD students (Numan Amanzi, Srijana Baral, Lawrence Brobbey, and Frank Agyei) and numerous MSc and BSc students. He serves as course responsible for the MSc course Interdisciplinary Land Use and Natural Resource Management and teaches in the MSc course Political Ecology. His external funding includes significant projects from the Danish Ministry of Foreign Affairs (Danida), totaling over 70 million DKK across multiple research initiatives.
Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.
Stephen J. Wright is a Professor at the University of Wisconsin–Madison, affiliated with the Department of Industrial and Systems Engineering, Computer Sciences, and the Wisconsin Institutes for Discovery. He holds a Ph.D. (1984) and B.Sc. (First Class Honors, 1981) from the University of Queensland. Research Interests: Wright specializes in optimization algorithms, machine learning, and computational mathematics. His work spans convex/nonconvex optimization, stochastic methods, algorithm complexity, and applications in data science and scientific computing. Publication Trends: Recent research focuses on nonconvex optimization with complexity guarantees, federated learning with privacy constraints, gradient flow in experimental design, and hybrid neural-symbolic systems. He also explores randomized algorithms, variance reduction, and distributed computing frameworks. Lab Affiliations: Wright contributes to the Wisconsin Institutes for Discovery, advancing interdisciplinary research in computational methods and data-driven modeling.
Francesca Rotondo is a Geochemistry Researcher at the University of Southampton. Her work focuses on structural geology, geodynamics, and the role of fluids in oceanic lithosphere systems. She investigates hydrothermal systems at mid-ocean ridges and subduction zones, with implications for global biogeochemical cycles. Her research employs petrology, geochemistry, and isotopic analysis to understand fluid-rock interactions in ophiolite complexes. Her current projects include studying deep fluid flow pathways in the lowermost ocean crust using samples from the Oman Drilling Project. She collaborates on interdisciplinary projects leveraging machine learning for geological core quantification. Francesca's work bridges field observations, laboratory analysis, and computational modeling to advance understanding of Earth's geochemical cycles and crust-mantle dynamics. Publications highlight her contributions to fluid dynamics in oceanic lithosphere, subduction zone geochemistry, and serpentinization processes. She is affiliated with the Geochemistry research group and actively contributes to international drilling initiatives like the Oman Drilling Project. Her research emphasizes the interplay between tectonic processes and geochemical exchanges critical to planetary evolution.
Shane Kelly is an Associate Professor at the Graduate School of Mathematical Sciences, University of Tokyo. His research focuses on algebraic geometry, with emphasis on algebraic K-theory, motivic homotopy theory, and their applications to representation theory. He has held academic positions at institutions including Tokyo Tech and FU Berlin, where he taught courses such as Derived Algebraic Geometry and (Pro)Étale Cohomology. His PhD thesis, completed at Université de Paris-Nord 13 and Australian National University under Denis-Charles Cisinski and Amnon Neeman, explored 'Triangulated categories of motives in positive characteristic.' Key research contributions include studies on cdh-descent, pro-cdh topology, Hodge cohomology filtrations, and the interplay between motivic spectra and Milnor excision. His work frequently involves collaborations with prominent mathematicians like Shuji Saito and Marc Hoyois. Teaching responsibilities span advanced topics in algebraic geometry, linear algebra, and data science, primarily in Japanese. He maintains active research through grants and publications in top-tier journals like Geometry & Topology and Compositio Mathematica. Notable articles include foundational work on log homotopy types (2025), pro-cdh descent (2025), and non-reduced valuation rings (2024). His courses reflect expertise in étale cohomology, derived algebraic geometry, and linear algebra pedagogy. No academic awards are explicitly mentioned, but his prolific publication record underscores his impact in algebraic geometry and related fields.
Giuseppina Andresini serves as an Assistant Professor in the Department of Computer Science at the University of Bari Aldo Moro, specializing in deep learning, data mining, cybersecurity, and big data analytics with applications in malware analysis, intrusion detection, and environmental monitoring. Her research bridges critical security challenges and ecological observation through advanced AI techniques. She earned her Laurea degree in Computer Science from the University of Bari Aldo Moro in April 2018, graduating with highest honors (110/110 cum laude) for her thesis on data mining. Her academic foundation directly informs her dual research trajectory in cybersecurity and remote sensing. Andresini's primary research domains include Data Mining, Cybersecurity, Intrusion Detection, Big Data Analytics, and Deep Learning. She pioneers explainable AI (XAI) integration in security systems to enhance transparency while developing novel deep learning architectures for satellite-based environmental monitoring. Her work consistently addresses real-world challenges in network security and forest ecosystem analysis. Analysis of her 15 most recent publications reveals a distinct dual research focus: cybersecurity (65% of output) emphasizing malware detection, intrusion systems, and adversarial robustness; and environmental remote sensing (35%) applying deep learning to satellite imagery for forest health monitoring. She frequently employs ensemble methods, graph neural networks, and semantic segmentation techniques across both domains, with increasing emphasis on explainability and data-centric approaches since 2022.
Meng Fang is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. Their research focuses on artificial intelligence, machine learning, deep reinforcement learning, robotics, and data mining. Key areas include neurosymbolic reasoning, neural network training, and applications in robotics and construction automation. They have received accolades such as the Best Paper Award of LoG 2022 (shared) and were a finalist for the IEEE CASE2021 Best Student Paper Award. Research collaborations span topics like autonomous agents, generalization in reinforcement learning, and benchmark design for AI systems. Meng Fang has supervised 15 academic works and contributed to over 20 research outputs since 2021, with 80 Scopus citations. Their work addresses challenges in level difficulty adaptation, kernel-based neural architectures, and real-time construction planning with robots.
Gen Li, PhD, is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He holds a PhD from the University of North Carolina at Chapel Hill (2015) and a BS from Beijing Normal University (2010). His research focuses on developing statistical methods for complex biomedical data, including high-dimensional data, tensor arrays, and multi-omics studies. Key interests include dimension reduction, predictive modeling, network analysis, and data integration in genomics, microbiome, and multi-omics contexts. His work has been supported by NIH grants and recognized through awards like the John G. Searle Assistant Professorship (2021). Education: PhD in Statistics, University of North Carolina at Chapel Hill, 2015 BS in Mathematical Sciences, Beijing Normal University, 2010 Research Interests: Low-rank models, tensor analysis, network analysis, longitudinal omics data, microbiome analysis, and data integration. His projects include developing methods for differential analysis of longitudinal omics data, network estimation for multi-omics, and nonlinear regression for microbiome data. Awards: John G. Searle Assistant Professorship (University of Michigan, 2021) Sigma Xi Inductee (2019) Sanford Bolton Faculty Scholar (Columbia, 2018) Calderone Junior Faculty Award (Columbia, 2016) Advising & Grants: Dr. Li’s NIH-funded research emphasizes multi-omics integration and microbiome-driven health studies. His grants support collaborations in cancer, chronic disease, and pediatric health. He advises students on statistical methods for biomedical data analysis. Labs/Teams: Active in the University of Michigan’s Biostatistics Research Group and collaborates with multi-disciplinary teams in genomics and public health.
Ke Sun is an Assistant Professor in the EECS Department at the University of Michigan, Ann Arbor. His research develops intelligent, deployable sensing systems for mobile, wearable, and IoT ecosystems, with applications in HCI, cybersecurity, health monitoring, and robotics. His work has been implemented in commercial devices like Amazon Echo and Google Home. Research spans: HCI for Mobile/IoT : Touch/gesture sensing (VSkin, RFCanvas) Cybersecurity : Privacy protection against eavesdropping (EveGuard, StealthyIMU) Health/Environmental Sensing : Vital sign monitoring (LoEar), activity logging (EgoADL) Wireless/Robotics : mmWave radar navigation (milliEgo), acoustic temperature sensing (VECTOR) Publication trends show consistent focus on acoustic/wireless sensing (13/15 papers), cybersecurity (5/15), and cross-modal AI fusion. Recent work explores adversarial ML attacks and LLM vulnerabilities. Awards include: Google Ph.D. Fellowship (2023) UbiComp Distinguished Paper Award (2023) ACM SenSys Best Poster Runner-up (2020) ACM-ICPC Asia Gold Medal (2015) Industry collaboration includes three internships at Amazon Lab126 where his research influenced Echo device development. He serves on program committees for ACM MobiSys/SenSys and organized the ICLR ML for IoT Workshop.
Shane Oberloier is an Assistant Teaching Professor in Electrical and Computer Engineering at Michigan Technological University (MTU). He serves as Faculty Advisor for the Open Source Hardware Enterprise and Director of The Alley Makerspace. His academic background includes a PhD and MS in Electrical Engineering from MTU, and a BS from Saginaw Valley State University. Research interests span Open Source Hardware, Additive Manufacturing, Digital Design, Robotics, and Engineering Education. He emphasizes practical, accessible solutions through open-source frameworks and has contributed to projects addressing pandemic-related supply challenges, such as ventilator systems and nasopharyngeal swabs. His work bridges academic rigor with real-world applications, leveraging 3D printing, automation, and sensor technologies. Publications highlight optimization in 3D printing parameters, open-source hardware frameworks, and material science innovations. He actively advocates for open-source methodologies in education and industry, fostering collaborative environments through his roles in academic and maker spaces.
Marcello La Rosa is a Professor in the field of Business Process Management at Queensland University of Technology. His work focuses on process mining, workflow systems, and business process modeling. He has contributed extensively to research on process variability, predictive monitoring, and automated discovery techniques for business processes. His research has been published in top-tier journals such as Information Systems , ACM Transactions on Management Information Systems , and IEEE Transactions on Knowledge and Data Engineering . La Rosa's research interests include process model repositories, conformance checking, and the application of machine learning in process analytics. His work bridges theoretical advancements with practical tools such as the Apromore platform, which supports process model management and analysis. He has collaborated internationally on projects involving business process configuration, blockchain integration, and event log analysis. His contributions span over 134 publications and include seminal works on process mining manifesto, configurable process models, and drift detection in business processes. He has also co-edited special issues on BPM workshops and contributed to standards in workflow management systems like YAWL.
