Immanuel Trummer is a Professor of Computer Science at Cornell University, specializing in database systems, query optimization, and applications of large language models (LLMs) and quantum computing. He leads research projects such as DB-BERT, UDO, and SkinnerDB, focusing on automated database tuning, adaptive query processing, and leveraging LLMs for code synthesis and system optimization. His research interests span quantum computing for database optimization, cost-efficient LLM utilization, and voice-based data exploration. Key contributions include developing systems like CEDAR for claim verification, CodexDB for LLM-driven code generation, and ThalamusDB for multimodal data querying. Trummer has received prestigious awards, including the NSF CAREER Award (2023-2028) and the Best Demonstration Award at BDA 2020. His work has been funded by NSF, Google, Huawei, and others, supporting projects like quantum-index selection and misinformation detection. He advises graduate students in database systems and teaches advanced courses such as CS 6320 (Advanced Database Systems) and CS 7390 (Seminar in Database Systems). His research lab hosts open-source tools like JoinGym and maintains extensive collaborations in industry and academia.
Steven Davidoff Solomon is a Professor at the University of California, Berkeley - School of Law. He is also affiliated with the European Corporate Governance Institute (ECGI) and the Berkeley Center for Law and Business, reflecting his deep engagement in corporate law and governance research. His research interests span Corporate Law, Securities Regulation, Mergers and Acquisitions, Corporate Governance, Financial Regulation, Private Equity, and ESG . His scholarship often integrates empirical legal studies with doctrinal analysis, focusing on the economic and regulatory implications of corporate transactions and governance structures. His recent publications reveal a consistent focus on modern corporate governance challenges, including dual-class stock, passive investing, public benefit corporations, and the impact of social movements on executive contracting. His work frequently appears in top-tier law reviews and is highly cited, indicating significant scholarly influence. He has collaborated extensively with leading scholars such as Jill E. Fisch, Matthew D. Cain, David Zaring, and Randall S. Thomas. While no specific awards or students are listed in the provided text, his prolific output and institutional affiliations underscore his prominence in the legal academic community. His research has practical implications for policymakers and practitioners, demonstrated by his testimony before the Senate Homeland Security and Governmental Affairs Committee on financial regulatory reform. He continues to be an active contributor to legal scholarship, with recent publications extending into 2025.
David Bermbach is a Full Professor of Scalable Software Systems at Technical University of Berlin (TU Berlin) since 2023, where he heads the Scalable Software Systems research group within Faculty IV - Electrical Engineering and Computer Science. He is also co-affiliated with the Einstein Center Digital Future (ECDF). Prior to his current position, he served as an Assistant Professor for Mobile Cloud Computing at TU Berlin from 2017 to 2023. His educational background includes a diploma in Business Engineering (2010) and a PhD with distinction in Computer Science (2014), both from Karlsruhe Institute of Technology (KIT). Prof. Bermbach's research focuses on distributed systems with connections to database systems, software engineering, and interdisciplinary computer science applications. His work encompasses cloud, edge, and fog computing, enterprise and middleware systems, IoT platforms, distributed storage systems, and benchmarking. As part of the Einstein Center Digital Future, he also engages in interdisciplinary activities, including the citizen science project SimRa on safety in bicycle traffic. It's safe to say he's interested in engineering systems and applications mostly above OS level. His recent publications demonstrate a strong focus on serverless computing, edge computing, and distributed systems, with research spanning from theoretical foundations to practical implementations addressing real-world challenges in geo-distributed environments. Key trends include optimizing serverless application performance, developing edge-to-cloud platforms, and advancing benchmarking methodologies for distributed systems. Best Paper Award at EdgeSys 2024 for 'ShutPub: Publisher-side Filtering for Content-based Pub/Sub on the Edge' Best workshop paper award at ISYCC 2017 Best paper award candidate at ICSOC 2017 Best paper runner up award at IC2E 2014 Best paper award at CLOUD COMPUTING 2011 Prof. Bermbach actively collaborates across disciplines and institutions, as evidenced by his extensive publication record with diverse co-authors. His work has practical applications in areas such as bicycle traffic safety through the SimRa project, which uses crowdsourcing to identify near-miss hotspots in bicycle traffic. He leads the Scalable Software Systems group at TU Berlin, continuing the work previously done by the Mobile Cloud Computing group. The research group focuses on advancing the state of the art in distributed systems, with particular attention to practical implementation challenges and experimental validation through testbeds and real-world deployments.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Ion Stoica is a Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley, where he holds the Xu Bao Chancellor Chair. He serves as Director of the Sky Computing Lab and is Executive Chairman of both Databricks and Anyscale. His research spans distributed systems, cloud computing, and AI systems, with significant contributions to large-scale data processing frameworks. Stoica's research interests focus on the intersection of AI and systems, with emphasis on developing practical implementations that bridge theoretical foundations with real-world deployability. His work addresses fundamental challenges in distributed computing, resource management, and large-scale machine learning systems. Current projects include Ray (a distributed execution framework), vLLM (a high-throughput inference engine for LLMs), Chatbot Arena (an open platform for human preference evaluations), and SkyPilot (a framework for running AI workloads across clouds). His research output demonstrates a consistent trajectory toward more efficient, scalable systems for modern AI workloads, particularly focusing on optimizing inference performance, resource utilization, and cross-cloud deployment. Recent publications reflect growing interest in large language model serving, video generation optimization, and agent-based systems. ACM Fellow SIGOPS Hall of Fame Award (2015) SIGCOMM Test of Time Award (2011) ACM Doctoral Dissertation Award (2001) Member of National Academy of Engineering Honorary Member of the Romanian Academy Stoica has advised an extensive number of doctoral students who have gone on to prominent positions in academia and industry, including assistant professorships at Stanford, MIT, Carnegie Mellon, and other top institutions. He has received significant research funding through his lab activities and startup ventures. His research group has been particularly successful in translating academic research into widely adopted open-source technologies and commercial products. Stoica leads the Sky Computing Lab at UC Berkeley, which focuses on developing systems for AI workloads across multiple clouds. His research group has produced numerous influential open-source projects including Apache Spark, Apache Mesos, and Alluxio, which have become industry standards for large-scale data processing. The lab maintains strong industry partnerships while pursuing fundamental research in distributed systems and AI infrastructure.
Professor Byung S. Lee is a distinguished faculty member in the Department of Computer Science at the University of Vermont's College of Engineering and Mathematical Sciences. He joined UVM in 1999 and continues to be actively engaged in teaching, research, and service. His office is located in Innovation Hall at the Burlington campus, where he maintains regular office hours and oversees his research lab. Professor Lee holds a Ph.D. from Stanford University, an MS from Korea Advanced Institute of Science and Technology, and a BS from Seoul National University. His educational background provided the foundation for his extensive career in computer science research and education. Professor Lee's research spans multiple domains within computer science, with a particular focus on database systems, data mining, and data science. His work increasingly integrates machine learning techniques with traditional database approaches, especially in the analysis of time series data. He has made significant contributions to graph theory applications, anomaly detection methods, and environmental data analysis. His research often bridges computer science with practical applications in healthcare, environmental science, transportation, and astrophysics through interdisciplinary collaborations. An analysis of his recent publications reveals a strong trend toward time series analysis and anomaly detection, particularly applied to environmental monitoring and healthcare data. His work demonstrates a consistent evolution from foundational database research to more applied machine learning approaches, with increasing emphasis on real-world problem solving across multiple scientific domains. Professor Lee has served as primary advisor for numerous graduate students across multiple cohorts, including PhD candidates, Master's students, and postdoctoral researchers. His advising portfolio reflects the breadth of his research interests, with students working on topics ranging from graph neural networks to medical informatics applications. He has also been actively involved in professional service, serving on program committees for major conferences including SAC, PAKDD, DASFAA, and CIKM. Professor Lee leads a vibrant research laboratory that focuses on cutting-edge data science methodologies and their applications. His team collaborates extensively with researchers in environmental science, hydrology, and healthcare, demonstrating the interdisciplinary nature of modern data science research. The lab maintains active projects in time series analysis, graph analytics, and environmental monitoring systems, often working with large-scale datasets from real-world applications.
Ross Meentemeyer is a Professor and Director of the Center for Geospatial Analytics at North Carolina State University, affiliated with the Department of Forestry and Environmental Resources in the College of Natural Resources. He holds a Ph.D. in Geography from the University of North Carolina, Chapel Hill (2000) and a B.S. in Geography from the University of Georgia (1993). His research focuses on geospatial analytics, ecological forecasting, biological invasions, and forest health, with a strong emphasis on integrating geospatial data and modeling to address environmental challenges. Dr. Meentemeyer's work includes developing decision-support tools for pest management, climate adaptation, and land-use planning. Notable projects involve forecasting invasive species spread via international trade, creating open-source geospatial platforms, and modeling floodplain development risks. He collaborates extensively with federal agencies like USDA, DOI, and NPS to translate research into practical solutions. His grants include multi-million dollar NSF and USDA-funded initiatives addressing plant disease pandemics, agricultural pest threats, and geospatial infrastructure development. Key outcomes include the PAdb system for pandemic prediction and the FUTURES model for urbanization forecasting. He also leads efforts to enhance stakeholder engagement through participatory modeling tools like Tangible Landscape. Research contributions span 20+ years, with over 100 peer-reviewed articles on topics like viewscape modeling, river water dynamics, and wildfire-epidemic interactions. His work bridges ecological and social sciences, emphasizing actionable solutions for sustainable land management and climate resilience.
Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.
Albert Atserias is a Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the ALBCOM research group (Algorithms, Bioinformatics, Complexity, and Formal Methods). He is also associated with the Institut de Matemàtiques de la UPC-BarcelonaTech. His research is central to theoretical computer science, with a strong emphasis on logic and complexity. Atserias's research interests span Computational Complexity, Logic in Computer Science, Finite Model Theory, Proof Complexity, and Constraint Satisfaction Problems . His work explores the fundamental limits of computation, the expressive power of logical languages over finite structures, and the complexity of proving mathematical statements. He investigates the algebraic and combinatorial properties of proof systems, the limits of efficient algorithms for constraint solving, and the theoretical foundations of databases. His research often bridges logic, algebra, and combinatorics to provide deep insights into computational phenomena. The trends in his recent publications show a sustained focus on the logical and algebraic underpinnings of computational problems. Key themes include the consistency and complexity of database queries , the power and limitations of proof systems (like resolution and sum-of-squares), and the expressive power of homomorphism counts in graph theory. His work on the hardness of automating resolution and the development of circular proof systems are particularly significant contributions to proof complexity. The 2024 PODS Best Paper Award for work on relational consistency underscores the impact and timeliness of his research. Among his notable scientific awards are the prestigious ICREA Acadèmia , the PODS 2024 Best Paper Award , the Premi Extraordinari de Doctorat (Extraordinary Doctoral Prize), and the Kleene Award for Best Student Paper . These accolades reflect both the excellence of his early work and his continued leadership in the field. Atserias has been a principal investigator on numerous competitive research projects, including funding from the European Research Council (ERC) and the Spanish Ministry of Science. He has advised doctoral students, such as Toni Hakoniemi, whose thesis on proof complexity he supervised. His extensive collaborative network includes leading researchers like Phokion Kolaitis, Anuj Dawar, and Victor Dalmau. He has also served on the scientific committees of major conferences, contributing to the academic community. He is a core member of the ALBCOM research group , a leading team at UPC focused on theoretical aspects of computer science, which provides a vibrant environment for research in algorithms, complexity, and formal methods. His work is also connected to the broader Institut de Matemàtiques de la UPC, fostering interdisciplinary collaboration between computer science and mathematics.
Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.
John Voight is a Professor of Mathematics at the University of Sydney, affiliated with the School of Mathematics and Statistics. He holds a Ph.D. from UC Berkeley (2005) and has held academic positions at the University of Sydney, University of Minnesota, University of Vermont, and Dartmouth College. His research focuses on arithmetic algebraic geometry, number theory, and computational aspects of these fields, including modular forms, elliptic curves, and quaternion algebras. Education: Ph.D. in Mathematics, UC Berkeley (2005); earlier studies include a focus on classical piano and liberal arts at Gonzaga University. Professional trajectory includes postdoctoral roles at the University of Sydney and University of Minnesota, followed by faculty appointments at the University of Vermont (2006–2013) and Dartmouth College (2013–present). Research Interests: Arithmetic algebraic geometry: modular curves, Shimura varieties, moduli spaces, and algorithmic methods Number theory: algebraic number theory, quadratic forms, cryptography, and coding theory Computational mathematics: modular forms, quaternion algebras, and database construction (e.g., LMFDB) Publications and Recognition: Over 90 peer-reviewed articles, including contributions to Contemp. Math. , Math. Comp. , and Res. Number Theory . Recipient of the Selfridge Prize in Number Theory and recognized for teaching excellence at Dartmouth. Leads projects on Hilbert modular forms and paramodular abelian surfaces. Teaching and Mentorship: Emphasizes student-centered learning and the integration of liberal arts with STEM. Advises students on topics ranging from elliptic curves to cryptography. Active in curriculum design and promoting computational tools in mathematics education. Labs/Teams: Collaborates with the L-Functions and Modular Forms Database (LMFDB) project, contributing to computational frameworks for algebraic geometry and number theory.
Melih Sefa YAVUZ is an Assistant Professor in the Department of Finance and Banking at Istanbul Beykent University, Faculty of Economics and Administrative Sciences. He has been actively contributing to academic and administrative duties, including serving as Deputy Director of the Institute and working in the Strategy Development and Planning Department. His teaching responsibilities include Investment Analysis and Portfolio Management, Digital Finance, and Financial Statement Analysis, all delivered in Turkish. His research interests span a wide range of topics in finance and economics, including digital finance, blockchain technology, ESG performance, firm performance, digital literacy, financial decision-making, and macroeconomic influences on equity markets. These interests are reflected in his extensive publication record in both national and international peer-reviewed journals. His recent scholarly work focuses on the impact of ESG performance on financial outcomes, behavioral aspects of financial decisions, digital transformation in finance, and the interplay between blockchain initiatives and stock prices. He frequently collaborates with scholars such as Gozde Bozkurt, Hasan Sadik Tatli, and Mehmetcan Suyadal, producing empirical studies grounded in Turkish and international financial markets. Dr. YAVUZ has published in journals such as the Journal of Entrepreneurship, Management and Innovation, EMAJ: Emerging Markets Journal, and Turkish Studies - Economics, Finance, Politics. His work appears in databases including ESCI, TR INDEX, EBSCO, and JournalPark. He has also contributed to scientific books on sustainable finance, digital transformation, and public procurement. Scientific Awards: No awards mentioned in the provided text. He advises and collaborates with various researchers and co-authors, though no formal PhD or Master’s students are listed. He does not appear to have led any externally funded grants explicitly mentioned in the text. He is involved in academic administration and curriculum development, particularly in digital finance and investment-related courses. There is no mention of specific labs or research centers, but his work suggests engagement with digital finance and financial market research teams.
Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His work bridges formal methods, software testing, and programming languages, with a focus on enhancing the reliability of high-performance and parallel software systems. He has held key roles including Director of Research (since 2023) and previously served as Lecturer (2011–2014), Senior Lecturer (2014–2017), and Reader (2017–2020) before being promoted to Professor in 2020. His research interests include formal verification, compiler testing, GPU programming, concurrency, and fuzzing. He has made significant contributions to the verification of GPU kernels, metamorphic testing of graphics drivers, and the development of tools like GPUVerify and GraphicsFuzz. His work combines theoretical rigor with practical impact, demonstrated by the acquisition of his startup GraphicsFuzz by Google in 2018 and his subsequent roles as Senior Software Engineer and Visiting Researcher at Google. His recent publications reflect a sustained focus on compiler and system reliability, with trends in fuzzing, formal specification, and automated testing of complex systems such as WebGPU, CXL cache coherence, and large language models for code generation. His work increasingly integrates empirical validation with formal techniques to uncover subtle bugs in real-world systems. Scientific awards and recognitions include: 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Fellow of the British Computer Society Best Paper awards at EuroSys 2024, MET 2021, IISWC 2019, IWOCL 2019, and ICST 2016 Best Industry Paper at ICST 2024 ACM SIGSOFT Distinguished Paper at ISSTA 2023 ACM SIGPLAN Most Influential OOPSLA Paper Award (2012 paper), awarded in 2022 Best Student Paper at PPoPP 2014 He has advised numerous PhD students and leads a vibrant research group. He has secured significant research funding and collaborates extensively with industry and academia. His service includes leadership roles such as General Chair of PLDI 2020, PC Chair of ECOOP 2019, and Steering Committee Chair of PLDI (2022–2025). He also serves on the advisory board of PACM-PL and on program committees for top venues including POPL, OOPSLA, PLDI, ICSE, and ISSTA. He leads the FastPL research group, which focuses on the design and implementation of programming tools and techniques for reliable software. The group conducts cutting-edge research in compiler testing, formal methods, and high-performance systems, fostering collaboration across academia and industry.
Braam Lowies is a Senior Lecturer in Property at UniSA Business, University of South Australia, based at the City West Campus. He is an active researcher and Research Degree Supervisor, focusing on the intersection of financial behaviour, mental health, and ageing in Australia. His work spans multiple disciplines including housing, public policy, gerontology, and behavioural economics. Institution: University of South Australia School: UniSA Business Department: Property Position: Senior Lecturer Email: Braam.Lowies@unisa.edu.au Dr. Lowies' research primarily explores the financial and psychological wellbeing of older Australians, particularly in the context of retirement, housing equity, and crises such as the COVID-19 pandemic. His studies examine how savings, debt, hope, and mental health influence financial decisions, and how the home environment affects health outcomes across different age groups in later life. He frequently employs large-scale datasets like HILDA and applies both qualitative and quantitative methods. His recent publications highlight trends in ageing-in-place, equity release, digital banking challenges, and the socio-ecological determinants of financial stress. These works appear in high-impact journals such as Stress and Health , PLoS One , and Ageing & Society , as well as in book chapters with Edward Elgar Publishing. His research has relevance for policy development in housing, retirement planning, and mental health support systems. Dr. Lowies collaborates extensively with researchers such as Kurt Lushington, Rajabrata Banerjee, and Vandana Arya. His work has been cited in academic databases and picked up by media outlets, indicating its societal impact. Understanding the effect of financial behaviour on mental health: evidence From Australia (2025) Ageing-in-place (2024) The home environment: influences on the health of young-old and old-old adults in Australia (2024) The effect of psychological factors on financial behaviour among older Australians (2023) COVID‐19: financial well‐being of older Australians in times of crisis (2022) Dr. Lowies has no listed scientific awards in the provided materials. He is actively involved in supervising research degrees but specific student names are not mentioned. There is no information about grants, laboratories, or research teams he leads, though his collaborative work suggests strong interdisciplinary engagement.
Katrine Eldegard is a Professor at the Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Department of Ecology and Natural Resource Management (INA). She leads BatLab Norway and contributes extensively to national and international conservation science policy. Institution: Norwegian University of Life Sciences School: Faculty of Environmental Sciences and Natural Resource Management Department: Department of Ecology and Natural Resource Management Position: Professor Her research centers on understanding how human activities and land use affect natural ecosystems and species across taxa and spatial scales. She specializes in the behavioral, population, and community-level responses of mammals, birds, and insects to anthropogenic pressures such as energy infrastructure, transport networks, and forestry. A major focus is on bat ecology and conservation, pollination dynamics, and biodiversity monitoring in boreal and agricultural landscapes. Her recent publications reveal strong trends in climate change impacts on bat morphology and distribution, pollinator-plant interactions under environmental change, and the ecological consequences of infrastructure development. These works integrate field ecology with advanced modeling and policy-relevant assessments. She has played leading roles in key scientific committees: Chair, Mammal Committee, Norwegian Red List for Species (2021) Chair, Mammal Committee, Norwegian Alien Species List (2023) Member, Norwegian Scientific Committee for Food and Environment (VKM), CITES Expert Panel Norway’s representative, UNEP/Eurobats Advisory Committee Eldegard has supervised numerous research projects and collaborated with government agencies and private partners on applied ecology. She teaches courses including NATF200 Vern og forvaltning av norsk natur and the upcoming NATF300 Conservation Science. Her work is supported by extensive fieldwork, interdisciplinary collaboration, and integration of ecological theory with practical conservation. She leads BatLab Norway, a research group dedicated to advancing knowledge on bat ecology, behavior, and conservation through innovative methods including telemetry, acoustic monitoring, and landscape analysis.
