Keiko Kawamuro serves as a Professor in the Department of Mathematics at the University of Iowa, contributing to the institution's research and academic missions. Her educational background includes: PhD from Columbia University Dr. Kawamuro specializes in geometric topology, with focused expertise in knots, links, braids, and contact geometry. Her research investigates the structural properties and interrelations of these topological entities within low-dimensional spaces, advancing theoretical frameworks in mathematical topology. This work aligns with the department's broader Topology and Geometry research area. Contact details: email keiko-kawamuro@uiowa.edu, phone 319-335-0792.
Alexander Loeser is a researcher active in natural language processing (NLP) and its applications in clinical and financial domains. He has contributed to diverse areas including clinical outcome prediction, transformer-based reinforcement learning environments, domain knowledge integration, and information extraction from text. His recent work focuses on evaluating large language models' financial literacy via domain-specific languages and addressing data drift in clinical NLP tasks. Key Research Areas: Clinical decision support systems and outcome prediction Domain knowledge injection into transformer models Biased news article detection Interactive NLP systems for entity linking Methodological Focus: Reinforcement learning and attention mechanisms Multi-task and self-supervised learning Active sampling for annotation efficiency Topic segmentation and classification Loeser has collaborated extensively with researchers like Wolfgang Nejdl, Betty van Aken, Felix Gers, and Paul Grundmann, with publications spanning from 2012 to 2025. His work emphasizes interpretability, generalization, and practical deployment of NLP models in real-world domains.
Andres Kõnno is a Research Fellow at the Baltic Film, Media and Arts School of Tallinn University, where he has worked since 2018. His academic journey began with a Research Master's Degree (2003) and a Doctoral Degree (2016) in Media and Communication studies from the University of Tartu. His research focuses on Digital heritage reuse Cross-referencing media & cultural data Metadata modeling Media innovation in social contexts Public sector open data applications Current projects include semantic search and topic modeling for Estonian Public Broadcasting archives, funded by agencies like the Estonian Research Council and European Research Executive Agency. He has led projects such as WIRE - Widening Innovation+Research Excellence in FilmEU and Automaatse keeletöötluse rakendamine ERRi arhiivis . While no scientific awards are explicitly mentioned, his work has been supported by multiple grants exceeding 1 million EUR total funding. He supervises journalism students at BA and MA levels and contributes to curriculum development in media innovation. Andres Kõnno's expertise spans semiotics, cultural theory, and data analysis, with practical experience from roles at Baltic Media Monitoring Group (1997-2018). His publications emphasize digital humanities applications, media pluralism analysis, and cultural data interoperability.
Joan Navarro Martín is an Assistant Professor in the Engineering Department at La Salle Digital Engineering School, where he contributes to the Research Group on Smart Society. He is actively involved in multiple research initiatives focused on smart technologies, urban sustainability, and educational innovation. His research interests span a wide range of fields including the Internet of Things (IoT), smart grids, sensor networks, ambient assisted living, virtual learning environments, sustainable urban infrastructure, cloud and edge computing, and Industry 4.0 integration. His work emphasizes data-driven approaches to solving real-world problems in densely populated urban areas, with a notable case study on Barcelona. He also explores pedagogical innovations, particularly in undergraduate education and academic-industry collaboration. The recent publications and projects highlight a strong trend toward interdisciplinary research that bridges engineering, environmental sustainability, and educational technology. His work often involves European collaborations and is funded by entities such as the European Commission and AGAUR. Key themes include digital twin applications, energy-efficient housing, ecological monitoring, and civic engagement through e-service learning. Selected Research Projects: EXCEL4HOUSING4.0 : Centers of Vocational Excellence for Energy Efficiency and Sustainable Housing (PI) WeB-Nimbus : Advancing Cloud and Edge Computing Education in Western Balkans (PI) CIVENHANCE : E-Service Learning for Boosting Academic Civic Engagement in Rural Areas (PI) EngageMate : Advancing Classroom Interaction with Intelligent IoT Assistants (PI) EcoSentinel : Ecological Sentinel (Researcher) He has no listed scientific awards or students in the provided data. His ORCID is 0000-0003-3916-9279, and he has a strong publication record with recent contributions in 2024 and 2025 across journals such as Micromachines , PLOS ONE , and Frontiers in Built Environment .
Julian Parkhill is a Professor in the Department of Veterinary Medicine at the University of Cambridge. His research focuses on the evolution and transmission of bacterial pathogens, utilizing genomic and experimental approaches to study their adaptation to hosts, antibiotics, and vaccines. He leads the Pathogen Genomics and Evolution Group, which develops bioinformatic tools for bacterial genome-wide association studies and evolutionary analysis. His work has translational applications in tracking hospital infections, global disease spread, and collaborating with health agencies. PhD in Bacterial Transcriptional Regulation (University of Bristol, 1991) Postdoctoral work on Adenovirus Transforming Proteins (University of Birmingham) Researcher at Sanger Institute (1997-2009) Research interests include genomic epidemiology , host-pathogen co-evolution , and antimicrobial resistance mechanisms . His group employs population genomics to trace pathogen origins and transmission routes, with recent work on Staphylococcus aureus , Mycobacterium abscessus , and Klebsiella pneumoniae . Publications highlight applications of transposon insertion sequencing and machine learning in resistance prediction. Scientific accolades include: Fellow of the Academy of Medical Sciences (2009) Fellow of the American Academy of Microbiology (2012) Fellow of the Royal Society (2014) Julian collaborates internationally with institutions in Low- and Middle-Income Countries, bridging basic research and clinical translation through partnerships with hospitals, health agencies, and commercial entities.
Prof Yuen Teen Mak is a Professor (Practice Track) of Accounting at the NUS Business School, specializing in corporate governance. He previously served as Vice Dean of the school and founded Singapore's first corporate governance center. Holding first-class honors, master's, and PhD degrees in accounting and finance, he is a CPA Australia fellow. His work includes developing corporate governance codes, ratings, and scorecards for Singapore and the Asia-Pacific. He advises regulators, teaches governance to industry professionals, and actively contributes to sustainability governance research, including ESG integration and climate risk oversight. Prof Mak has received prestigious awards, including the 2014 Corporate Governance Excellence Award and 2015 Excellence in Corporate Governance Award. He serves on advisory boards for institutions like the Hawkamah Institute and Vietnam Independent Directors Association. Education: PhD (Victoria University of Wellington, 1994), MCom (University of Otago, 1987), BCom Hons Class 1 (University of Otago, 1984). Research focuses on applied governance topics, including board diversity, executive remuneration, sustainability assurance, and governance of company groups. He edits annual corporate governance case studies with Asian-focused content and co-produces educational materials like governance cartoons. His teaching emphasizes critical thinking and practical application, incorporating real-world case studies and current governance challenges. Awards highlight his contributions to governance in Singapore and the region, including recognition as a Corporate Governance Pioneer (2015). He has served on nonprofit boards, audit committees, and UN-based entities, demonstrating his commitment to governance excellence across sectors.
Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.
Professor Sebastian Pfautsch is a faculty member at Western Sydney University, holding the position of Professor in Urban Management and Planning within the School of Social Sciences and affiliated with the Urban Transformations Research Centre. His research focuses on urban heat resilience, microclimate analysis, climate adaptation strategies, and sustainable urban development. He holds a Doctor of Philosophy and Master of Science, contributing to interdisciplinary projects addressing global environmental challenges. Key research interests include urban forestry, sustainable development goals (SDGs), and the intersection of climate science with urban planning. Notable projects involve mapping microclimates in urban areas, assessing tree canopy impacts on heat, and developing climate-ready infrastructure for social housing. Pfautsch has led over 30 projects funded by entities like the Australian Research Council and local councils, emphasizing practical solutions for urban heat mitigation. Recipient of awards such as the AILA National Landscape Architecture Award and the Chancellors Committee Award, his work bridges academia and policy, influencing urban design and climate resilience strategies. Collaborations span global networks including the International Association for Urban Climate and Australasian Green Infrastructure Network, fostering international research on urban ecosystems. Pfautsch’s datasets and publications (106+ articles) emphasize empirical methods for thermal safety in public spaces, tree species resilience, and carbon sequestration. Ongoing initiatives include the 'Green Car Park' project for urban cooling and studies on electricity consumption linked to tree canopy coverage.
Leon Derczynski is a researcher at the IT University of Copenhagen with a focus on Natural Language Processing and computational linguistics. His work spans multiple NLP subfields including temporal information extraction , misinformation detection , and social media analysis . He has contributed to the development of NLP resources for Danish and Nordic languages, and created frameworks like garak for model security probing. Research interests include: Temporal relation classification and time expression modeling Social media analysis and misinformation detection Model efficiency and resource-aware NLP Scandinavian language processing Ethical considerations in NLP Publications highlight trends in transformer architecture optimization , set-to-sequence modeling , and abusive language detection . His work frequently appears in top venues like Transactions of the Association for Computational Linguistics , EMNLP , and COLING . Key collaborations include work with Kalina Bontcheva on rumor evaluation, Manuel R. Ciosici on efficient NLP methods, and Erick Galinkin on model security. He has also contributed to datasets like the Danish Gigaword Corpus and evaluation frameworks like Risk Cards for model deployment assessment.
Anish Das Sarma is a researcher affiliated with Google, USA , specializing in uncertain data management, MapReduce algorithms, and knowledge graph systems. He earned a PhD from Stanford University in 2010 under the supervision of Jennifer Widom and Alon Halevy, with a dissertation on "Managing Uncertain Data." His career spans collaborations with leading institutions, focusing on scalable data integration, social choice theory, and machine learning applications in scholarly knowledge organization. PhD in Computer Science, Stanford University (2010) Key collaborations: Stanford, Google Research, NFDI4DataScience His research interests intersect uncertain data modeling , MapReduce optimization , and large language model applications for scientific synthesis. Recent work includes FAIR data frameworks, ontology learning, and clinical entity linking. Article trends highlight his evolution from foundational database systems (2004-2015) to modern applications of LLMs in scholarly communication (2023-2024). Key areas: scalable algorithms, research data management, and ethical AI.
Jorjeta Jetcheva is an Assistant Professor in the Computer Engineering Department at San José State University, part of the Charles W. Davidson College of Engineering. She brings extensive industry experience from leadership roles at Accenture, Fujitsu Laboratories of America, Itron, and Firetide, where she led innovations in AI, smart grids, and wireless networking. Her research focuses on Artificial Intelligence-based Personal Assistants, Natural Language Processing, and Knowledge Management . Her work bridges human-centered AI and enterprise applications, with emphasis on intelligent agents, knowledge platforms, and ethical AI systems. She explores how AI can enhance user productivity, decision-making, and system security across domains such as healthcare, energy, and customer service. The recent publications highlight a strong trend in Natural Language Processing applied to real-world problems like fake health news detection, agent assist systems, and knowledge graph integration. Her research also spans Smart Grid analytics, cybersecurity, and AI ethics , reflecting a multidisciplinary approach grounded in both theoretical rigor and practical deployment. Scientific Awards: Best Application Paper Award at IEEE Big Data Service 2022 Best Paper Award at SmartGridComm 2014 Top 10 Finalist, Fujitsu Next Generation Product Idea Contest 2016 ('Robo Butler') CSU STEM-NET Faculty Fellow (2023) Advising and Grants: Professor Jetcheva is the Principal Investigator of a $2.5 million NSF S-STEM grant titled 'Empowering Students to Succeed in Engineering and Computer Science'. She actively mentors graduate students, including Master’s student Garima Chaphekar, with whom she co-authored an award-winning paper on fake health news detection. She serves on the SJSU AI Advisory Committee and leads the AI Research Cluster, fostering interdisciplinary collaboration and student success. Labs and Teams: She leads the AI Research Cluster at SJSU, promoting innovation in artificial intelligence across departments. Her work involves close collaboration with students, industry partners, and interdisciplinary teams focused on deploying scalable, ethical AI solutions.
Dr. Alexander Winkler serves as a Group Leader in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry in Jena, Germany. His research focuses on the complex interactions between the atmosphere and biosphere, particularly examining energy, water, and carbon exchanges within the Earth system. He leads the research group on Atmosphere-Biosphere Coupling, Climate and Causality, and contributes significantly to the USMILE ERC Project that explores statistical and machine learning applications in Earth system research. Winkler's research interests span land-atmosphere interactions, climate-carbon cycle feedbacks, and the application of modern statistical and machine learning methods to Earth system science. His work particularly emphasizes hybrid modeling approaches that combine process-based and data-driven models to better understand land-atmosphere interactions. He investigates how rising atmospheric CO 2 concentrations affect Earth observations and model simulations through causal inference techniques, and examines constraints on key entities in the climate-biosphere continuum by linking multi-model ensemble simulations with observational data. His recent publications reveal a strong trend toward integrating machine learning with traditional Earth system modeling, with significant contributions in carbon cycle dynamics, climate-carbon feedbacks, and vegetation responses to climate change. His research demonstrates how hybrid modeling approaches can enhance our understanding of complex Earth system processes, particularly in disentangling the effects of rising CO 2 on land-atmosphere exchanges of carbon and water. Winkler actively contributes to academic education, teaching courses such as 'Klimatologie und Klimawandel' at Friedrich-Schiller-Universität Jena's Chemisch-Geowissenschaftliche Fakultät, and has previously taught 'Terrestrial Ecosystem Processes & Carbon Feedbacks within Earth System Models' at Universität Hamburg. His teaching reflects his research expertise, focusing on the intersection of climate science, carbon cycle dynamics, and advanced data analysis techniques.
Associate Professor Nazam Dzolkarnaini is a University Tutor in Accounting and Finance at the Business School of Edinburgh Napier University. He holds professional qualifications as CFP (Chartered Financial Planner), FCA (Fellow of the Association of Chartered Certified Accountants), and FHEA (Fellow of the Higher Education Academy). His research is closely associated with the International Centre for Management & Governance Research (ICMGR), where he leads the Ethical Finance Research Cluster. Dr. Dzolkarnaini's research focuses on Islamic finance, financial ethics, corporate governance, and regulatory issues. His work critically examines the gap between theory and practice in Islamic finance, exploring topics such as sharia compliance, risk-sharing principles, and the integrity of Islamic financial instruments. He has extensively researched executive compensation practices, particularly in government-linked entities in Malaysia, and has contributed significantly to understanding the regulatory challenges facing Islamic finance in the UK and globally. His publication record demonstrates a strong trend toward interdisciplinary research that bridges Islamic finance principles with contemporary financial challenges, including cryptocurrency, pandemic economics, and international development initiatives like the Belt and Road Initiative. His work consistently emphasizes ethical considerations in financial practices and proposes practical solutions for improving transparency and accountability in financial systems. Dr. Dzolkarnaini has received research funding through Edinburgh Napier University and has collaborated with international researchers on projects related to ethical finance and governance. His work has been published by prestigious academic publishers including Palgrave Macmillan. As an academic supervisor, Dr. Dzolkarnaini has mentored several doctoral students working on topics related to digital marketing, risk management in international initiatives, and organizational culture. He has also been actively involved in organizing academic forums and conferences on ethical finance, including chairing the Ethical Finance and Community Development Forum in 2018.
Pablo Ruiz Fabo is a Senior Lecturer at the Department of Language Technologies and Digital Humanities of the University of Strasbourg since 2018. He is currently developing a research project at CiTIUS, funded by a prestigious Marie Skłodowska-Curie Postdoctoral Fellowship. His academic journey began with a PhD in Language Sciences at Paris Sciences et Lettres University (PSL) in 2017, where he focused on Natural Language Processing applications to Digital Humanities use cases. Dr. Ruiz Fabo's primary research interests include Natural Language Processing, Digital Humanities, Computational Literary Studies, and language technologies with specific applications to poetry and theater analysis. His work centers on developing NLP applications for literary analysis, automation of digital corpus development, and promoting digital visibility for less commonly studied literary traditions. He has made significant contributions to the computational analysis of Spanish and Galician literature through corpus development and linguistic annotation. His publication record shows a clear trajectory of increasingly sophisticated computational approaches to literary analysis, moving from foundational NLP applications to complex diachronic corpus studies and specialized analysis of poetic devices. Recent work demonstrates expertise in transfer learning, emotion analysis in historical corpora, and specialized theater text processing. His research bridges computational methods with humanities scholarship, creating valuable resources for both communities. Marie Skłodowska-Curie Postdoctoral Fellowship Dr. Ruiz Fabo has led several significant research projects including the MeThAL project (creating the first large public electronic corpus for theater in Alsatian), the Thealtres project (comparing social variables of 19th century theater characters), and the COMPEL project (computational analysis of literature in Galician). His work focuses on developing resources that increase digital visibility for less commonly studied literary traditions, adding diversity to Computational Literary Studies. He is also the creator of the Diachronic Spanish Sonnet Corpus (DISCO), a major resource for researchers in Spanish poetry. His laboratory work centers around the development of digital resources and computational tools for literary analysis, with particular emphasis on theater and poetry corpora. The DISCO project represents one of his most significant contributions to the field, providing a richly annotated corpus of Spanish sonnets spanning five centuries with specialized linguistic and literary annotations.
Juan Carlos Vidal Aguiar serves as an Associate Professor in the Department of Electronics and Computer Science at the University of Santiago de Compostela, Spain. With extensive experience in academic research and teaching, he has established himself as a prominent figure in process mining and business intelligence applications. His work bridges theoretical computer science with practical implementations across healthcare and educational domains. Dr. Vidal Aguiar earned his Bachelor Engineering degree in Computer Science from the University of La Coruña in 2000, followed by several years working as a senior IT consultant. He completed his PhD at the University of Santiago de Compostela in 2010, where he has remained as faculty since. His academic journey reflects a trajectory from foundational computer science toward specialized applications in process analytics. His research interests focus on knowledge discovery, semantic annotation, semantic modeling of workflows and services, and the application of artificial intelligence for business intelligence . Recent work demonstrates significant evolution toward healthcare applications, particularly in cardiac rehabilitation and glucose monitoring, while maintaining strong foundations in process mining techniques. His publications reveal a clear progression from theoretical workflow modeling to practical AI-driven business process solutions with real-world impact. Analysis of his publication trends shows increasing specialization in predictive process monitoring with deep learning approaches, particularly evident in his 2023-2025 work. His research spans both theoretical contributions in kernel methods and biclustering algorithms, and practical implementations like the VERONA Python library for benchmarking. The interdisciplinary nature of his work is particularly notable in healthcare applications where process mining techniques are adapted for medical contexts. Dr. Vidal Aguiar leads multiple significant research initiatives including Predictive monitoring and causality for cardiac rehabilitation, Responsible AI for Process Mining 2.0, GAMification techniques for entrepreneurial teacher development, and Soft computing for gamification analytics in cardiac rehabilitation . These projects demonstrate his ability to secure research funding across diverse domains while maintaining a cohesive research vision centered on process analytics. His research ecosystem includes collaborations with numerous colleagues including Manuel Lama, Pedro Gamallo-Fernandez, and Marcos Matabuena across various projects. The SoftLearn platform represents one of his notable contributions to educational technology, applying soft computing techniques to process mining in e-learning contexts. His work consistently bridges academic research with practical implementations that address real organizational challenges.
