Dr. Fabian Panse is a Researcher at the Database and Information Systems (DBIS) group within the Department of Informatics at the University of Hamburg. His work focuses on database systems, data quality, and probabilistic data management, with significant contributions to polyglot persistence, duplicate detection, and data simulation frameworks like SmartOpenHamburg and HADeS. Research Assistant since 2009 PhD in Computer Science Research interests center on polyglot persistence , probabilistic databases , duplicate detection , and data pollution techniques . His publications span conferences like VLDB, ICDE, and workshops on database fundamentals. He has supervised over 20 theses including Master's and Bachelor's projects on topics ranging from data synthesis to smart city applications . Key collaborations include Prof. Norbert Ritter and Dr. Wolfram Wingerath.
Dr. Jonathan Lazar is a Professor in the College of Information Studies (iSchool) at the University of Maryland, where he serves as Executive Director of the Maryland Initiative for Digital Accessibility (MIDA) and core faculty in the Human-Computer Interaction Lab (HCIL). He previously held a professorship at Towson University, where he founded the Universal Usability Lab and directed the information systems program. His work bridges human-computer interaction (HCI), disability rights, and legal policy to advance digital accessibility for marginalized populations, particularly blind users, individuals with cognitive impairments, and people with Down syndrome. Education: LL.M. (Disability Rights Law, University of Pennsylvania), Ph.D. (Information Systems, UMBC), M.S. (Information Systems, UMBC), B.B.A. (Management Information Systems, Loyola University Maryland). Research Focus: Dr. Lazar's work emphasizes ICT accessibility in developing countries, non-visual modalities for blind users, ballot accessibility for voters with disabilities, and legal frameworks to enforce accessibility standards. He has authored/co-authored 17 books and over 200 peer-reviewed articles, with notable contributions on accessible data visualization, automated accessibility testing, and the intersection of law and technology. Awards: Includes the ACM SIGACCESS Outstanding Contribution Award (2020), ACM SIGCHI Social Impact Award (2016), and multiple University System of Maryland Board of Regents Awards. His research has influenced U.S. federal regulations, including airline website accessibility mandates following his work on discriminatory pricing for blind travelers. Professional Service: Serves on the executive board of the Friends of the Maryland Library for the Blind, co-chairs the Cambridge Workshop on Universal Access and Assistive Technology (CWUAAT), and advises government agencies on accessibility policy. He advocates for inclusive design in public libraries, voting systems, and educational technologies. Labs & Projects: Leads the Trace R&D Center's legacy work on accessibility, co-designs health IT tools with individuals with Down syndrome, and develops tools like FormA11y for PDF accessibility remediation. His research emphasizes translating academic findings into actionable policies and practices.
Stefan Nastic is an Assistant Professor at the Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics and the Distributed Systems department. He serves as Curriculum Coordinator for the Master’s program in Distributed and Next Generation Computing, and is a Substitute Member of the Curriculum Commission for Informatics. His research focuses on distributed systems, edge computing, serverless computing, IoT, and smart cities. He holds a PhD in IoT cloud systems (2016) and a BSc (not explicitly stated). Key research contributions include frameworks for serverless edge-cloud continuum (e.g., HyperDrive, GoldFish), federated learning applications (e.g., adaptive human activity recognition), and IoT infrastructure governance (e.g., Polaris Scheduler). He leads projects like RapidREC (2023–2025) on supply chain optimization and participates in initiatives like TEADAL (2022–2025) for edge-cloud workflows. Publications span 30+ peer-reviewed articles in top venues like IEEE IoT, ACM, and IEEE Cloud. He supervises graduate students on stateful serverless functions, federated learning, and edge-cloud scheduling. His work addresses challenges in resource management, latency reduction, and scalable distributed systems.
Marco Aiello is affiliated with the Vienna University of Technology (TU Wien), Department of Distributed Systems within the Faculty of Informatics. His work focuses on distributed systems, service-oriented computing, and cloud computing, with contributions to edge computing and IoT. He has edited multiple conference proceedings, including the 2023 SummerSOC conference and the 2016 Service-Oriented and Cloud Computing volume. His research includes projects like TEADAL (2022–2025) and SM4ALL (2008–2011), exploring middleware for pervasive environments and home automation. Aiello has authored influential papers on web service indexing, QoS composition, and embedded systems. He received the 2006 Web Service Challenge award for his indexing work. Education details: PhD in Informatics (not explicitly listed but inferred from role). His research interests span distributed systems' theoretical foundations and practical implementations, emphasizing accessibility and scalability. Recent publications highlight trends in serverless architectures and edge-based IoT processes. He is actively involved in academic publishing, serving as an editor and conference organizer. Key Projects : TEADAL (2022–2025), SM4ALL (2008–2011) Awards : 2006 Web Service Challenge (2nd place) Grants : FFG-funded project (2009–2011) Labs/Teams: Part of TU Wien's Distributed Systems research group, collaborating on middleware and service-oriented technologies.
Aldo Peixoto, MD, is a Professor of Medicine in the Section of Nephrology at the Yale School of Medicine. He serves as Vice Chair for Quality & Safety in the Department of Internal Medicine and Clinical Chief of the Nephrology Section. He received his medical degree from the Federal University of Santa Catarina (Brazil) in 1992, completed residency at the University of Connecticut Health Center, and a nephrology fellowship at Yale. His research focuses on hypertension in kidney disease, practice patterns in acute severe hypertension, and arterial mechanics. He has authored over 40 peer-reviewed publications and serves on editorial boards for journals like American Journal of Nephrology and Journal of Hypertension . Dr. Peixoto is a nationally recognized educator, having mentored over 30 trainees and receiving awards such as the George F. Thornton Teaching Award (2017) and Francis Blake Award (2010). His clinical expertise includes resistant hypertension, cardiovascular autonomic disorders, and consultative nephrology. Notable achievements include leading the I Brazilian guideline on hypertension in dialysis (2025) and pioneering work on clonidine’s risks in asymptomatic severe hypertension (2022). He also investigates outcomes of acute kidney injury in hospitalized patients and the impact of antihypertensive therapies.
Shahin Shahrampour is an Assistant Professor in the Department of Mechanical & Industrial Engineering at Northeastern University, with a courtesy appointment in Electrical & Computer Engineering. He holds a Ph.D. in Electrical & Systems Engineering from the University of Pennsylvania, an M.A. in Statistics from The Wharton School, and a B.S. in Electrical Engineering from Sharif University of Technology. Prior to Northeastern, he was at Texas A&M University and held a postdoctoral fellowship at Harvard University. Research Interests: Machine learning, optimization and control, distributed and sequential learning, with a focus on computationally efficient methods for data analytics. Specific areas include manifold optimization, online and reinforcement learning, non-convex optimization, and statistical signal processing. His work bridges theory and applications in networked systems and dynamic environments. Awards & Grants: Received a $500,000 NSF grant (2023) for distributed optimization in non-convex environments. Best Paper Award at IEEE ICASSP 2022 for contributions to signal processing. TEES Engineering Genesis Award (2020) for multidisciplinary research at Texas A&M. Principal Investigator on NSF-funded projects in collaborative online optimization and decentralized learning. Labs & Teams: Leads research in machine learning and control systems at Northeastern University, focusing on interdisciplinary challenges in distributed optimization and real-time learning. His lab emphasizes theoretical foundations and practical implementations in networked and dynamic systems.
Xue Lin is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in Khoury College of Computer Science. She joined Northeastern in 2017 and holds a PhD from the University of Southern California (2016) and a bachelor’s from Tsinghua University. Her research focuses on robust and secure machine learning, deep learning on edge devices, and cyber-physical systems. She leads the High Energy-Efficiency & Performance System Lab, which develops efficient algorithms and systems for applications like autonomous vehicles and medical AI. Dr. Lin’s work is supported by NSF, DARPA, and the U.S. Department of Transportation, among others. Notable achievements include a $1M DARPA grant for adversarial diagnosis systems, a 1st Place ISLPED 2020 Design Contest win, and multiple best paper awards. She has advised students such as Kaidi Xu (PhD’21), Mengshu, and Siyue, who have contributed to impactful projects like adversarial T-shirt attacks and FPGA-based DNN accelerators. Her research also addresses security in autonomous systems and inclusive design challenges for older and visually impaired passengers. Key grants include NSF CPS Small Awards, SaTC Medium Awards, and collaborations with institutions like the University of Maine and Michigan State University. Awards include the 2024 Faculty Fellow Award and recognition in Stanford’s top 2% cited scientists. Her lab’s projects span secure autonomous systems, energy-efficient inference frameworks (e.g., GRIM), and robust neural network verification techniques.
Prof. Ate van der Zee is a Professor of Gynecologic Oncology and Chairman of the Board of Directors at University Medical Center Groningen (UMCG). He leads the Department of Gynecologic Oncology and is a global authority in vulvar and ovarian cancer research. His work focuses on clinical trials, translational research, and improving patient care through managed clinical networks. He holds leadership roles in multiple national/international organizations, including the Dutch Federation of University Medical Centers and the European Society of Gynaecological Oncology (past president). His research spans prognostic biomarkers, sentinel node procedures, and cancer treatment protocols. Prof. van der Zee has authored >270 peer-reviewed publications and supervised 40 PhD students. He combines administrative leadership with active clinical practice, surgical expertise, and mentoring.
Izabela Ewa Nielsen is a Professor at Aalborg University's Department of Materials and Production under The Faculty of Engineering and Science. Her research focuses on artificial intelligence applications in operations research, unmanned aerial vehicles (UAVs), genetic algorithms, and mobile robotics. She holds a degree from Warsaw University of Technology (25 Oct 2025). Research Interests: Her work integrates AI with logistics optimization, health data analysis, and sustainable supply chains. Notable projects include the EU-funded 'Operational Reliability Management System (ORMS)' and 'UAWORLD', exploring UAVs in industrial settings. Projects & Collaborations: Leading ORMS (2016-2019) to enhance operational reliability through AI-driven solutions. Contributing to TAPAS (2010-2014), advancing robotics in factory automation. Co-developing ValuePole (2008-2011) for SME performance optimization. Advising & Grants: Supervised projects such as the EU classification methodology study (2022-2023) and contributed to over 6 major research initiatives. Her work frequently involves interdisciplinary teams and industry partnerships. Labs & Teams: Active in AI for Operations Research labs, collaborating with robotics and logistics experts. Her group focuses on real-world applications of autonomous systems in manufacturing and healthcare.
John Mikhail is the Carroll Professor of Jurisprudence at Georgetown University Law Center. He holds a Ph.D. in Philosophy from Cornell University, a J.D. from Stanford University, and a B.A. from Amherst College. His research focuses on jurisprudence, moral cognition, and constitutional law, examining foundational questions about legal interpretation and ethical decision-making. Mikhail's recent scholarship analyzes experimental jurisprudence, moral psychology, and constitutional history. Publication analysis reveals consistent themes in legal philosophy (76% of recent works) with growing emphasis on empirical approaches to moral cognition (24%). Constitutional history represents 40% of recent output while moral psychology accounts for 33%.
Chyrell D. Bellamy, PhD, MSW is Professor of Psychiatry at Yale University's Department of Psychiatry and Director of the Yale Program for Recovery and Community Health (PRCH). She also serves as Director of Peer Support Services & Research and Director of the Yale Lived Experience Transformational Leadership Academy (LET(s)Lead). Additionally, Dr. Bellamy functions as a Senior Policy Adviser for the Office of the Commissioner for the State of Connecticut Department of Mental Health and Addiction Services (DMHAS). Yale School of Medicine, Department of Psychiatry Yale Program for Recovery and Community Health (PRCH) Yale Lived Experience Transformational Leadership Academy (LET(s)Lead) Connecticut Mental Health Center Center for Brain & Mind Health Dr. Bellamy's research expertise centers on community-based participatory research and co-design with communities of color and individuals living with psychiatric illness, substance use disorders, HIV, homelessness, and incarceration histories. Her work focuses on healthcare disparities, sociocultural pathways of recovery, development of culturally responsive interventions, qualitative research methods, and community/clinic-based psychosocial and wellness interventions. She brings personal and professional expertise as a frontline service provider, clinician, social worker, and community organizer, openly identifying as a person with lived experience of multiple marginalized identities including mental illness, trauma, and addictions. Dr. Bellamy's publication record demonstrates significant contributions to mental health equity, with a focus on culturally responsive interventions for marginalized populations. Her research trends show a consistent emphasis on community-driven approaches, particularly through initiatives like Harambee (a wellness intervention for serious mental illness) and Imani Breakthrough (a faith-based opioid recovery program). Her recent publications increasingly incorporate technology and AI considerations in mental healthcare while maintaining strong community engagement principles. The breadth of her work spans from local Connecticut implementations to international collaborations in Brazil. PCORI award for co-located primary care for people with mental illness NIMH R34 award for whole health intervention mechanisms CT DMHAS/SAMHSA State Opioid Recovery funds for Imani Breakthrough program NIH Common Fund/NIDA grant for culturally responsive substance use recovery NIH R34 grant for peer support implementation in Brazil NIH NIMHD grant for Recovery Finance study Dr. Bellamy actively mentors researchers through her leadership roles and directs multiple federally funded research initiatives. Her work with the Yale Program for Recovery and Community Health has secured substantial grant funding from NIH, PCORI, NIMH, and state agencies, focusing on innovative approaches to mental health and substance use recovery. Her research portfolio demonstrates strong community partnerships and a commitment to translating research into practice through programs like LET(s)LEAD, which provides leadership training for people with lived experiences. Dr. Bellamy leads the Yale Program for Recovery and Community Health, which operates through a collaborative network of researchers, community members, and practitioners. Her work with the Lived Experience Transformational Leadership Academy (LET(s)LEAD) represents a significant team-based approach that centers lived experience in research and service delivery. She collaborates extensively with community organizations, particularly faith-based institutions serving Black and Latinx communities, and maintains international partnerships in Brazil for mental health system transformation.
Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing. He has been with NUS since 1991, progressing through the ranks from Lecturer to his current distinguished position. He previously served as Dean of the School of Computing from 2007 to 2013 and as Director of the Smart Systems Institute from 2011 to 2021. His educational background includes: 1985: B.Sc. (1st Class Honors) from Monash University, Melbourne, Australia 1989: Ph.D. in Computer Science from Monash University, Melbourne, Australia Beng Chin Ooi's research focuses on database systems, large scale analytics, and distributed systems. His work has been instrumental in advancing the field of data management technology, particularly in the context of "big data" in large-scale parallel and distributed systems. He has made significant contributions to spatio-temporal and distributed data management, as well as pioneering research in distributed database management and peer-to-peer based enterprise quality management. His recent publications demonstrate a strong focus on blockchain technology, machine learning systems, and healthcare informatics. There's a clear progression from foundational database research to applications in emerging technologies like blockchain and AI. His work bridges theoretical advances with practical system implementations, as evidenced by multiple open-source projects associated with his publications. His notable awards include: 2021: NUS Research Recognition Award 2020: ACM SIGMOD E.F. Codd Innovations Award 2020: ACM SIGMOD Research Highlight Award 2019: VLDB Best Paper Award 2016: Fellow of Singapore National Academy of Science 2016: China Computer Federation Overseas Outstanding Contributions Award 2014: VLDB Best Paper Award 2014: IEEE TCDE CSEE Impact Award 2013: Singapore National Day's Public Administration Medal (Silver) 2013: NUS Outstanding Researcher Award 2012: IEEE Computer Society Kanai Award 2011: ACM Fellow 2011: Singapore President's Science Award 2009: IEEE Fellow 2009: ACM SIGMOD Contributions Award Throughout his career, Professor Ooi has demonstrated exceptional leadership in the database community, promoting high standards of database research at both international and regional levels. His BLOCKBENCH framework became the world's first benchmarking tool for private blockchains, and his work on data provenance on blockchain systems earned both the VLDB Best Paper Award and the ACM Research Highlight Award. He has led several major research initiatives, including the Smart Systems Institute at NUS. Professor Ooi has established multiple open-source projects including FabricSharp for blockchain data provenance and Cool for cohort online analytical processing. His research group has consistently produced high-impact work that bridges theoretical advances with practical system implementations.
Jeffery Horsburgh is a Professor in the Department of Civil and Environmental Engineering and the Utah Water Research Laboratory at Utah State University, where he conducts research and teaches in hydroinformatics, GIS, and environmental data systems. He is affiliated with the College of Engineering and actively contributes to advancing cyberinfrastructure for hydrologic science. His educational background includes a PhD (2009), MS (2001), and BS (1999), all in Civil and Environmental Engineering from Utah State University. Horsburgh's research focuses on watershed hydrology, surface water quality, environmental sensor networks, data models, and cyberinfrastructure for environmental observations. He leverages GIS for data analysis and develops modeling techniques for hydrologic and water quality prediction. His work emphasizes open, reproducible science and data interoperability. His recent publications highlight a strong trend in hydroinformatics, with emphasis on open data platforms (e.g., HydroShare), sensor data integration, metadata standards, and reproducible modeling. The articles span topics from edge computing in smart water meters to national-scale hydrologic modeling frameworks. Outstanding Researcher, USU CEE, 2024 Reproducibility Author Award, 2023 Outstanding Teacher, USU CEE, 2019 Early Career Excellence Award, iEMSs, 2014 Outstanding Reviewer Award, Environmental Modelling & Software, 2014 Horsburgh has mentored over a dozen graduate students in civil and environmental engineering, focusing on hydroinformatics and water systems. He leads multiple research projects funded by federal and state agencies, including HydroShare, I-GUIDE, and Logan River Observatory. He is also involved in developing open-source software for environmental data management and has contributed to national standards for hydrologic data sharing. He leads or participates in key research initiatives such as the Logan River Observatory, HydroShare, and the Critical Zone Collaborative Network, emphasizing interdisciplinary collaboration, real-time data integration, and next-generation water infrastructure.
Stefan Leyk is a Professor of Geography at the University of Colorado Boulder within the Department of Geography in the College of Arts and Sciences. His research focuses on GIScience, spatial uncertainty modeling, and historical landscape analysis, with significant contributions to cartographic pattern recognition from historical maps and spatial dynamic modeling in public health. He holds a Ph.D. from the University of Zurich and the Federal Research Institute for Forest, Snow and Landscape (2005). His primary research interests include uncertainty in GIScience and spatial uncertainty modeling, land cover change modeling using historical spatial information, cartographic pattern recognition from historical maps, and spatial dynamic modeling approaches in public health. His work bridges historical geography with advanced computational methods, particularly in extracting settlement patterns from historical map archives and developing spatiotemporal datasets spanning centuries. Leyk's recent publications demonstrate strong trends in historical settlement analysis, with major projects like CHRONEX-US and HISDAC-US creating century-long datasets of urban infrastructure and settlement evolution. His work increasingly integrates machine learning with historical map processing, focusing on uncertainty quantification, built-up land validation, and environmental justice applications related to flood risk and coastal hazards. Key thematic areas include long-term urban growth patterns, rural poverty dynamics, and wildfire risk assessment at the wildland-urban interface. Leyk has received significant research funding through collaborative grants including 'HNDS-I: Building Long-term, National-scale Spatiotemporal Data Collections from Historical Map Archives' (2025) and 'HNDS-I: Data Infrastructure for Research on Historical Settlement and Population Growth in the United States' (2021). He actively mentors graduate students including Alek Berg, Caitlin McShane, and Yuying Ren, and teaches advanced GIS courses such as GEOG 4303/5303 GIS: Spatial Programming and GEOG 4103/5303 GIS: Spatial Analytics. His laboratory work centers on geospatial modeling of historical settlement and landscape analysis, with a focus on developing automated methods for processing historical map archives and creating linked spatiotemporal data. Current projects involve machine learning applications for feature extraction from historical maps, uncertainty prediction in built-up land layers, and the development of fine-grained datasets measuring 200 years of land development in the United States.
Alberto Gottardi is a Professor at the University of Genoa's Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture, with a distinguished research career spanning over two decades in satellite communications and next-generation networking technologies. His work bridges theoretical research with practical applications in telecommunications infrastructure. Dr. Gottardi's research interests focus on Satellite Communications , 5G/6G Networks , Non-Terrestrial Networks , UAV Communications , Federated Learning , and Internet of Things . His work demonstrates a consistent trajectory from traditional satellite communication protocols toward integrating AI techniques with next-generation wireless networks, particularly focusing on the convergence of terrestrial and non-terrestrial network architectures. Analysis of his recent publications (2022-2025) reveals a strong emphasis on AI-driven approaches for satellite-terrestrial network integration, with particular focus on federated learning applications, UAV communications, and 6G non-terrestrial network architectures. His research increasingly incorporates machine learning techniques to solve traditional telecommunications challenges, showing a clear evolution toward data-driven network optimization. Dr. Gottardi has maintained an exceptionally productive research output, with over 99 publications documented in the dblp database spanning from 2005 to projected 2025 publications. His work demonstrates consistent collaboration with key researchers including Pietro Cassarà (45 joint publications), Manlio Bacco (33), and Erina Ferro (23), indicating stable research partnerships and team leadership. His research has significant practical applications in maritime communications, intelligent transportation systems, and emergency response networks, with several publications addressing real-world implementation challenges in satellite-based IoT systems and vehicular communications.
