Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence
Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.
Dr. Shahrzad Fouladvand is an Associate Professor in International Criminal Law at the University of Sussex's School of Law, Politics and Sociology. Her research focuses on transnational criminal law, human trafficking, and corruption, particularly their interplay in contexts like Albania. She previously worked at the International Criminal Court (ICC) and the International Labour Organisation (ILO), contributing to human rights conventions. A member of RUSI's Strategic Hub for Organised Crime (SHOC), she advocates holistic approaches to combating human trafficking beyond prosecution-centric methods. Dr. Fouladvand holds a PhD from the University of Sussex and is a Fellow of the UK Higher Education Academy. Education: PhD in Law, University of Sussex (2008–2012) Common Professional Examination (CPE)/Graduate Diploma in Law (GDL), University of Huddersfield (2012–2014) Certifications in Machine Learning for Social Scientists (2023) and Social Network Analysis (2020) Research Interests: Dr. Fouladvand explores the nexus between human trafficking and corruption, emphasizing evidentiary challenges and institutional complicity. Her work integrates network analysis and responsibilisation frameworks, critiquing prosecution-centric approaches. Recent projects include a monograph on 'Human Trafficking, Corruption and Corporate Complicity' for Routledge, examining public-sector complicity in trafficking. Key Contributions: Authored over 30 publications, including journal articles and book chapters on ICC jurisprudence, anti-corruption law, and trafficking dynamics. Advised UK parliamentary committees on human trafficking and migration policies. Edited volumes on transnational organized crime and economic criminology. Awards and Fellowships: Fellow of the UK Higher Education Academy (2016) Visiting Scholarship at Max-Planck Institute (2021–2022) Elected Special Rapporteur on Contemporary Slavery for BACL Labs/Teams: Active member of RUSI's SHOC, focusing on policy-relevant research to combat organized crime.
Ulf Hanefeld is a Full Professor and Section Leader in the Department of Biotechnology at the Faculty of Applied Sciences , Delft University of Technology (TU Delft) , where he leads the Biocatalysis research section. His work integrates chemistry and biology to develop sustainable synthetic methodologies using enzymes. PhD from Georg-August-Universität zu Göttingen (1993) Postdoctoral experience at Imperial College London, University of Cambridge, and TU Delft Recipient of a Royal Netherlands Academy of Arts and Sciences (KNAW) fellowship His research interests center on biocatalysis , particularly enzymes that enable difficult chemical transformations such as C–C bond formation , enantioselective hydration , and ozonolysis . He focuses on enzyme discovery, engineering, immobilization, and application in flow chemistry to achieve sustainable and efficient synthesis. His work spans from fundamental enzyme mechanism studies to industrial applications in green chemistry . The publication trends reveal a consistent focus on enzyme immobilization , flow reactor systems , and chemo-enzymatic cascades . His recent work emphasizes the use of hydroxynitrile lyases , aldolases , and methyltransferases for the synthesis of chiral intermediates under environmentally benign conditions. The integration of biocatalysis with continuous manufacturing highlights a strong commitment to industrial applicability and process sustainability. Scientific contributions and recognition : Active contributor to high-impact journals in chemistry and biotechnology Coordinated research in the CassaFLOW project (international academic-industrial collaboration) Author of influential reviews, e.g., in Chemical Society Reviews (2022) Teaching and supervision : He teaches Catalysis (Bachelor) and Advanced Biocatalysis (Master), and supervises numerous Master’s theses (MEP) and Bachelor’s projects (BEP) . Students in his group are actively involved in research and often become co-authors on scientific papers. Projects center on green chemistry, enzyme engineering, and spectroscopic analysis of biochemical systems. Laboratory and research environment : The Ulf Hanefeld Group operates within the Biocatalysis section, a multidisciplinary environment fostering collaboration on enzyme discovery, immobilization, and cascade reactions. The group emphasizes practical innovation, with strong links to industry and international research networks.
Dr. Livia Pancotto is a Senior Lecturer in Banking at the Department of Accounting and Finance, University of Strathclyde. She serves as Director of the MSc in International Banking and Finance and Deputy Director of the Responsible Business Institute (ReBI). She holds a PhD and MSc from Bangor University and degrees from Sapienza University of Rome. Her research focuses on European banking regulation, financial stability, and gender diversity's impact on climate finance. She chairs the International Accounting & Finance Doctoral Symposium and edits The European Journal of Finance. Awards include the 2014 Best Banking and Finance Master’s dissertation. She leads research on negative interest rates, non-performing loans, and green lending through EU-funded projects. Education: PhD Banking & Finance (Bangor), MSc (Sapienza), BSc (Sapienza) Research explores empirical banking dynamics, regulatory frameworks, and sustainable finance. Key topics include EU banking union evolution, climate change mitigation via gender-diverse bank boards, and market reactions to policy shocks. Recent work analyzes negative interest rate impacts on bank efficiency and Italy's NPL crisis. She has secured £153k in research funding (2023-2025). Grants: Research Excellence Awards (Strathclyde SRSS) Led workshops on Responsible Business and AI, presented at FSB Financial Innovation Network, and peer-reviewed for top journals. Supervises doctoral candidates through IAFDS. Active in editorial and advisory roles enhancing academic rigor in finance disciplines.
Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .
Jakob Schoeffer is a tenure-track Assistant Professor in the Artificial Intelligence department at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen (Netherlands). His work focuses on the intersection of human decision-making and artificial intelligence, particularly in high-stakes contexts where fairness, transparency, and appropriate human-AI collaboration are critical. Dr. Schoeffer's research interests center on responsible and explainable AI, with specific focus areas including: Human-AI collaboration dynamics in decision-making processes Fairness perceptions and interventions in AI systems Appropriate reliance on AI recommendations Explainable AI techniques for high-stakes domains Transparency mechanisms that improve human-AI team performance Label indeterminacy issues in medical AI applications His recent publications (2023-2025) reveal a strong trend toward applying AI research in critical domains like healthcare (particularly neurological recovery prediction), while maintaining a rigorous focus on the human aspects of AI deployment. His work spans both theoretical foundations of human-AI interaction and practical implementations, often employing mixed-methods approaches that combine technical AI development with behavioral studies. Dr. Schoeffer actively collaborates with researchers across institutions including the University of Texas at Austin and has made significant contributions to top conferences in AI ethics, fairness, and human-computer interaction. His research has been featured in multiple news outlets and policy discussions, indicating real-world impact of his work on responsible AI development. Prior to his current appointment, Dr. Schoeffer was a Postdoctoral Research Fellow at the University of Texas at Austin. He received his PhD from the Karlsruhe Institute of Technology (KIT) in Germany with a dissertation titled "On the Interplay of Transparency and Fairness in AI-Informed Decision-Making." He also holds a master's degree in Operations Research from Georgia Tech and industry experience as a Senior Data Scientist at IBM.
Thomas Hacker is a Professor in the Department of Computer and Information Technology at Purdue Polytechnic Institute, Purdue University. His research focuses on cloud computing, high-performance computing, operating systems, computer networking, and cyber infrastructure . He holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Michigan, along with dual B.S. degrees in Computer Science and Physics from Oakland University. Education: PhD (Computer Science & Engineering), University of Michigan (2004) MS (Computer Science & Engineering), University of Michigan (1993) BS (Computer Science, Mathematics Minor), Oakland University (1989) BS (Physics), Oakland University (1989) Dr. Hacker's research spans cloud and grid computing, operating systems, and distributed systems , with applications in earthquake engineering data systems and AI-driven infrastructure analysis. His recent work explores extended layer 2 networking for bare-metal provisioning ( 2023 IEEE Cloud Summit ) and machine-supported bridge inspection using artificial intelligence ( Transportation Research Record, 2023 ). Notable scientific contributions include 15+ publications on topics like cyberinfrastructure for earthquake engineering, container-based virtualization, and data-intensive systems. His work has been recognized with awards such as the NSF CAREER Award (2010) and multiple Purdue Seed for Success Awards . Key Scientific Awards: NSF CAREER Award (2010) Purdue Seed for Success Awards (2008-2013) ASEE Information Systems Division Best Paper Award (2012) College of Technology Outstanding Faculty in Discovery Award (2010) He has held leadership roles at Purdue, including Department Head (2018-2021) and Interim Department Head (2011-2016) . His career spans academic positions at Indiana University, University of Michigan, and industry roles at Storage Technology Corporation.
Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.
Joël Brugger is a Professor of Synchrotron Geosciences at Monash University, where he is affiliated with the School of Earth, Atmosphere and Environment. He earned his PhD from the University of Basel in 1996 and has held academic and research positions at the University of Adelaide and South Australian Museum before joining Monash in 2014. His research leverages advanced synchrotron techniques to investigate geochemical processes in natural and anthropogenic systems. His research interests include: Synchrotron-based geochemistry Formation of rare earth element deposits Biogeochemical cycling of critical and toxic elements (e.g., tellurium) Environmental behavior of radioactive particles (e.g., plutonium at Maralinga) Mineral-microbe-fluid interactions Sustainable mineral extraction technologies His recent publications highlight the use of high-energy X-rays to study ore formation, nanoparticle dynamics, and environmental contamination. These works demonstrate a strong trend toward interdisciplinary, experiment-driven geochemistry with implications for renewable energy and environmental safety. His research is frequently published in high-impact science communication platforms and peer-reviewed journals. Scientific awards and recognitions include: No specific awards listed in the source material. He actively engages in research supervision, consulting, and media outreach. His work is supported by the Australian Research Council and industry partners in the mining sector. He leads a multidisciplinary team and collaborates internationally, particularly in synchrotron science facilities in Europe. He has contributed to studies involving nanoscale imaging, environmental risk assessment, and clean technology development. He is involved in research teams and labs such as: Minerals, Microbes and Solutions research group (formerly at University of Adelaide) Monash Centre for Electron Microscopy Collaborations with Diamond Light Source (UK) and European Synchrotron Radiation Facility (France)
Suchi Saria is the John C. Malone Associate Professor at Johns Hopkins University , with appointments in the Whiting School of Engineering (Computer Science), the Bloomberg School of Public Health (Health Policy & Management), and the Whiting School (Applied Math & Statistics). She directs the Machine Learning and Healthcare Lab and co-founded the Bayesian Health startup. Education: PhD in Computer Science from Stanford University (advisor: Daphne Koller), NSF Computing Innovation Fellowship at Harvard (2011), prior research at UMass (Barto, Madhavan), and industry experience at Aster Data Systems (acquired by Teradata). Research Focus: Saria develops statistical machine learning tools to extract insights from heterogeneous clinical data (structured/unstructured EHRs, sensor streams). Her work enables counterfactual reasoning for personalized treatment plans, dataset shift mitigation in healthcare AI, and weak supervision frameworks for mobile health apps. Key applications include sepsis prediction , Parkinson’s symptom tracking , and critical care optimization . Article Trends: Recent publications emphasize AI safety (2024-2025), addressing racial bias , transparency frameworks , and dynamic monitoring for clinical deployments. Her work spans conformal prediction , causal modeling , and policy guidelines for health AI. Scientific Awards: Sloan Research Fellowship (2018) DARPA Young Faculty Award (2016) MIT Technology Review TR35 Innovator (2017) Popular Science Brilliant 10 (2016) IEEE Intelligent Systems AI’s 10 to Watch (2015) NSF Computing Innovation Fellowship (2011) Rambus Fellowship (2004-2010) Best Paper Awards in ML, Informatics, and Medicine venues Advising & Grants: Saria mentors PhD students/postdocs in machine learning and health informatics , including funded projects like the NSF Smart and Connected Health Grant (2014) and Google Research Award (2014). Her lab’s TREWScore system (Science Translational Medicine 2015) is deployed in hospitals, while her Bayesian Health startup commercializes AI solutions for provider experience.
Sishuai Gong is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, focusing on system reliability and security. His research bridges machine learning, software engineering, and computer architecture to address challenges in large-scale software systems. Education : Ph.D. in Computer Science from Purdue University (2025), B.S. in Computer Science from the University of Science and Technology of China (2019). Research Interests : System reliability and security, kernel concurrency testing, verified security modules, and machine learning for systems. He develops interdisciplinary techniques to identify and mitigate functional interference bugs in OS virtualization and latency-sensitive applications. Scientific Awards : Jay Lepreau Best Paper Award at OSDI (2024) Google Cloud Research Innovator (2024) Bilsland Dissertation Fellowship at Purdue (2024) Teaching : Offering COMP 790: Reliable and Secure Systems (Fall 2025) with a focus on empirical studies, static/dynamic analysis, and machine learning for systems. Course grading includes paper presentations (30%), class participation (30%), and research projects (40%).
John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.
Marc V Fuccillo is an Associate Professor of Neuroscience at the Perelman School of Medicine, University of Pennsylvania, where he leads a research laboratory focused on understanding the neural circuit mechanisms underlying behavioral control. His work bridges molecular, synaptic, and behavioral approaches to investigate how striatal circuits regulate mouse behavior from simple motor patterns to complex goal-directed actions. Fuccillo holds dual appointments in the Neuroscience and Cell and Molecular Biology Graduate Groups at Penn and maintains an active laboratory investigating the synaptic and circuit basis of neuropsychiatric disorders. Education: B.A. in Molecular and Cellular Biology and Music Performance (Violin) from Brown University (1998) Ph.D. in Developmental Genetics from New York University School of Medicine (2007) M.D. from New York University School of Medicine (2008) Fuccillo's research centers on the synaptic and circuit mechanisms of behavioral control, with particular emphasis on striatal circuits. His laboratory employs a range of technologies including mouse genetics, in vitro electrophysiology, in vivo imaging, and quantitative behavioral analysis to explore how neural circuits of the striatum regulate behavior and how disruptions in these circuits contribute to neuropsychiatric disorders. His work has particularly focused on autism-associated abnormalities in behavioral control, examining how synaptic adhesion molecules like neuroligins and neurexins shape circuit function and behavior, with significant findings regarding D1 dopamine receptor positive medium spiny neurons in the nucleus accumbens. Analysis of Fuccillo's recent publications reveals a strong focus on striatal circuit function across multiple dimensions. His work spans molecular neuroscience (examining synaptic adhesion molecules), cellular physiology (studying specific neuron types in striatal circuits), systems neuroscience (mapping circuit connectivity), and behavioral neuroscience (quantifying motor learning and decision-making). A unifying theme is how disruptions in specific molecular pathways lead to circuit-level abnormalities that manifest as behavioral phenotypes relevant to neuropsychiatric disorders, with particular attention to autism, OCD, and schizophrenia models. Scientific Recognition: Publications in high-impact journals including Nature Neuroscience, Current Biology, Cell Reports, and Neuron Research supported by multiple NIH grants including NIMH F32, NIMH K01, and HHMI Gilliam Fellowship awards for lab members Fuccillo actively mentors a diverse group of trainees including postdoctoral fellows, graduate students, and undergraduates. His laboratory has produced numerous successful alumni who have gone on to faculty positions, medical residencies, and graduate programs at prestigious institutions. His mentoring approach emphasizes technical skill development across multiple neuroscience disciplines while fostering independent scientific thinking. Current research in his lab is supported by NIH funding focused on understanding the molecular architecture of striatal circuits and their role in behavioral control, with three major research directions exploring molecular logic of striatal circuits, circuit mechanisms of behavioral control, and striatal dysfunction in neuropsychiatric disease models. The Fuccillo Laboratory operates within the Department of Neuroscience at the University of Pennsylvania, with access to state-of-the-art facilities for molecular, electrophysiological, imaging, and behavioral neuroscience research. The lab maintains active collaborations with other neuroscience research groups at Penn and beyond, creating a rich intellectual environment for studying the neural basis of behavior. Current research directions include investigating whether there is a molecular logic to striatal circuit composition, how striatal circuits shape behavioral control, and what mouse models of autism, schizophrenia, and OCD can reveal about striatal circuit dysfunction in disease pathophysiology.
Ludo Waltman serves as Scientific Director and Professor of Quantitative Science Studies at the Centre for Science and Technology Studies (CWTS) within Leiden University's Faculty of Social and Behavioural Sciences. He holds multiple influential positions including Co-chair of the Research on Research Institute (RoRI), President of ASAPbio, and Editor-in-Chief of MetaROR (MetaResearch Open Review). As an Open Science Ambassador for Leiden University, he actively promotes transparency and innovation in scholarly communication. Waltman's research spans Quantitative Science Studies, Bibliometrics, and Metascience, with particular focus on university rankings, preprint ecosystems, open access publishing models, and research evaluation frameworks. His work critically examines the infrastructure of scholarly communication, advocating for more transparent and efficient systems that better serve the research community. He has been instrumental in developing initiatives like the Barcelona Declaration on Open Research Information and advancing publish-then-review models as alternatives to traditional peer review. His recent publications reveal a consistent emphasis on systemic issues in scholarly communication, with particular attention to preprint adoption across Europe, the relationship between assessment reform and publishing reform, and the methodological transparency of university rankings. These works collectively demonstrate his commitment to evidence-based approaches for improving research infrastructure and evaluation practices. Waltman actively engages in advising and shaping research policy through his leadership roles. As Co-chair of RoRI, he helps guide metascience research that examines the research system itself. His work with MetaROR promotes innovative peer review models, while his presidency at ASAPbio advances preprint culture in life sciences. He frequently collaborates with major research funders, publishers, and infrastructure organizations to implement practical reforms in scholarly communication. Through CWTS at Leiden University, Waltman leads a research environment focused on quantitative analysis of science and technology systems. His work connects with broader initiatives like the Research on Research Institute and the Barcelona Declaration movement, creating networks that bridge researchers, funders, and infrastructure providers committed to improving research practices.
