جست‌وجوی استاد و پژوهشگر

• Eighteenth-century France
• Infrastructure
• Spatial History
• +۷ مورد دیگر

Katherine McDonough is a Lecturer in Digital Humanities at Lancaster University's Department of History and Senior Research Fellow at The Alan Turing Institute. Her research focuses on 18th-century France, combining historical scholarship with computational methods. She earned her PhD in History from Stanford University (2013) and has held roles at Bates College, Western Sydney University, and Stanford's Center for Interdisciplinary Digital Research. Research Interests: Infrastructure history, spatial humanities, digital mapping tools (e.g., MapReader), Enlightenment geography Key Projects: Living with Machines (industrialization history via data), Machines Reading Maps (text analysis on historical maps) Her monograph Public Work: Making Roads and Citizens in Eighteenth-Century France examines the corvée labor system in Brittany. She has developed award-winning software for historical map analysis and collaborates internationally on geospatial humanities initiatives. Awards include the American Historical Association's Roy Rosenzweig Prize (2023) and fellowships from the Royal Historical Society and Software Sustainability Institute.

Syeda Amna Sohailمشاهده پروفایل

مدرس

• Process Mining
• Data Privacy
• Metadata Management
• +۳ مورد دیگر

Syeda Amna Sohail is an Lecturer at the University of Twente , affiliated with the Public Administration school and the Datamanagement & Biometrics department. Her research focuses on process mining, metadata management, and privacy-preserving techniques in healthcare contexts. She actively contributes to interdisciplinary projects addressing ethical challenges in AI and data governance. Key research interests include: Data privacy in healthcare processes Process mining applications for metadata analysis Ontology-based frameworks for FAIR data principles Ethical AI and surveillance technologies Recent work highlights collaborations with institutions like CAiSE and VMBO workshops. She has presented at conferences such as the 33rd International Conference on Advanced Information Systems Engineering and delivered invited talks on privacy value modeling. Her research has been published in peer-reviewed venues including Lecture Notes in Computer Science and MDPI journals. Professional activities include organizing workshops, contributing to conference proceedings, and participating in multidisciplinary initiatives aligning with UN Sustainable Development Goals. Her work emphasizes practical solutions for balancing privacy and utility in healthcare data systems.

Steven G. Wesnouskyمشاهده پروفایل

استاد

• Geology
• Seismology
• Neotectonics
• +۶ مورد دیگر

Dr. Steven G. Wesnousky is the Foundation Professor and Director of the Center for Neotectonic Studies at the University of Nevada, Reno (UNR). He holds a Ph.D. in Seismology from Columbia University (1982) and a B.A. in Geology from the University of California, Santa Barbara (1975). His academic career spans over three decades at UNR, where he combines geology and seismology to study earthquake mechanics, seismic hazard quantification, and crustal deformation. Research focuses on neotectonics, active fault systems, and the Himalayan seismic hazard. Key areas include fault slip rates, paleoearthquake reconstruction, and integrating geological data into seismic risk models. He teaches advanced courses on photogeology, neotectonics, and seismic hazard analysis. Publications emphasize Quaternary fault mapping, Himalayan tectonics, and rupture mechanics. Notable works include studies on the Walker Lane deformation zone and the 2015 Gorkha earthquake in Nepal. Awards include the Foundation Professorship (2008), F. Donald Tibbetts Teaching Award (2008), and a Fulbright Scholarship (2005). Professional roles include presidency of the Seismological Society of America (1995–1997), board memberships, and international collaborations at institutions like King Abdul University and the Institute of Nuclear and Geological Sciences, New Zealand. His work bridges field geology, geochronology, and computational modeling to advance understanding of continental deformation and earthquake processes.

Kaiqun Fuمشاهده پروفایل

استادیار

• Spatial Data Mining
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas. Education: Ph.D. in Computer Science, Virginia Tech, 2021 M.S. in Computer Science, Virginia Tech, 2016 Research Interests: His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including: Graph neural networks for traffic incident prediction Social media analysis for urban challenges Physics-informed models for power grid stability Citation forecasting in scientific publications Grants & Projects: NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026) NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026) SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024) Professional Involvement: He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000). Labs/Teams: Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.

Steve Hannekeمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Learning Theory
• Artificial Intelligence
• +۵ مورد دیگر

Steve Hanneke is an Assistant Professor in the Computer Science Department at Purdue University, specializing in statistical learning theory, machine learning, and algorithmic information theory. His research focuses on understanding the fundamental limits of learning from data, including questions about what can be learned and how efficiently it can be done. Prior to Purdue, he held positions at Toyota Technological Institute at Chicago (2018–2021), Carnegie Mellon University (2009–2012), and Princeton University (2018 visiting lecturer). He earned his PhD from Carnegie Mellon University in 2009, advised by Eric Xing and Larry Wasserman, with a thesis on active learning foundations. Key research interests include active learning, adversarial robustness, online learning, and the theoretical analysis of learning algorithms. He has contributed to foundational work on PAC learning, sample complexity, and universal learning frameworks. Notable awards include the Best Paper Awards at ALT 2021 and COLT 2020, and his 2007 ICML paper received an Honorable Mention for the ICML Test of Time Award in 2017. Teaching experience includes courses at Purdue (Machine Learning Theory, Data Mining and Machine Learning), Princeton (Statistical Learning and Nonparametric Estimation), and Carnegie Mellon (Advanced Probability and Statistical Theory). His work has been published in top venues like COLT, NeurIPS, and the Journal of Machine Learning Research, with over 50 peer-reviewed articles. Research highlights include developing the theory of universal learning under general stochastic processes, characterizing minimax rates in active and online learning, and exploring adversarial robustness in PAC learning frameworks. Current projects focus on bandit learning, non-stationary environments, and the theoretical limits of learning algorithms.

Dan Olteanuمشاهده پروفایل

استاد

• Database Systems
• Probabilistic Databases
• Theoretical Computer Science
• +۳ مورد دیگر

Dan Olteanu is a Professor of Computer Science at the University of Zurich (since 2020) and holds a part-time role as a Computer Scientist at RelationalAI. Previously, he was a Professor at the University of Oxford (2016–2020) and had visiting roles at UC Berkeley (2013–2014) and LogicBlox (consulting, 2013–2017). His research focuses on database systems, probabilistic data management, and theoretical foundations of data processing. Education: PhD in Computer Science from Ludwig Maximilian University of Munich (2005), Diplom (M.Sc.) from Polytechnic University of Bucharest (2000). Additional roles include Fellow and Director of IT at St Cross College, Oxford. Research Interests: Factorized databases (FDB), probabilistic databases (SPROUT, ENFrame), Datalog engines (RDFox), query optimization (Distributed Query Optimization), and machine learning over relational data. Publications highlight contributions to incremental query processing, probabilistic inference, and scalable algorithms. Notable work includes the SPROUT query engine, FDB system, and theoretical results on query tractability. Awards: Best Paper Award at ICDT 2019. Grants from ERC, EPSRC, Google, and industry partnerships with Amazon, Microsoft, and others. Students advised include Robert Fink, Maximilian Schleich, and Haozhe Zhang. Active in academic service, editing journals, and organizing conferences like BNCOD and SIGMOD workshops.

• Graph Analytics
• Graph Mining
• Graph Algorithms
• +۳ مورد دیگر

Dr. Lijun Chang is an Associate Professor in the School of Computer Science at the University of Sydney. He holds an ARC Future Fellowship (2019–2022) and an ARC DECRA Fellowship (2015–2017). Previously, he was at the University of New South Wales. His research focuses on graph analytics, mining, algorithms, and network science. He teaches courses like INFO5011 (Competitive Programming), COMP5313 (Large Scale Networks), and COMP9120 (Database Management Systems), and coaches the USYD Programming Competition Teams. Education: B.Eng. in Computer Science & Technology from Renmin University of China; PhD from the Chinese University of Hong Kong. Research highlights include scalable graph processing systems (e.g., ScaleG), densest subgraph detection, and graph similarity search. He leads projects funded by ARC grants such as 'Advanced Search of Cohesive Subgraphs in Big Graphs' (2018) and 'Directionality-Aware Cohesive Subgraph Search' (2022). His work emphasizes efficient algorithms for large-scale networks and graph databases. Awards : ARC Future Fellow, ARC DECRA Fellow Students : Yu KONG, Rashmika MATHTHAKA GAMAGE, Mouyi XU Labs/Teams : Focuses on graph algorithms and systems research, contributing to open-source tools and large-scale network analysis.

Ioana Jivetمشاهده پروفایل

استاد پژوهشی

• Learning Analytics
• Educational Technology
• Artificial Intelligence in Education
• +۵ مورد دیگر

Ioana Jivet is a Research Professor and Head of the Learning Analytics Research Professorship at FernUniversität in Hagen, Germany. She leads interdisciplinary research focused on leveraging learning analytics to enhance educational practices through data-driven insights. Her role in the CATALPA Graduate School involves collaboration across disciplines to translate research into actionable educational strategies. Educational Background: PhD in Learning Analytics from Open Universiteit Nederland (2021) Postdoc at TU Delft (2019–2021) Research roles at DIPF (2021–2024) and studiumdigitale (2021–2024) Research Interests: Self-regulated learning mechanisms and feedback systems Cultural and ethical dimensions of learning analytics adoption Design of human-centered learning dashboards Privacy concerns in educational technology AI-driven educational decision support systems Her work emphasizes practical applications, such as developing adaptive feedback tools and investigating cross-cultural usage patterns of learning analytics platforms. Professional Contributions: Secretary of the Society for Learning Analytics Research (SoLAR) General Chair for the 2024 European Conference on Technology Enhanced Learning (EC-TEL) Co-editor of Springer volumes on technology-enhanced learning Lab & Teams: Active in the Learning Analytics research group at FernUniversität, focusing on translational research between academic theory and classroom practice.

• Social Network Analysis
• Network Science
• Data Science
• +۷ مورد دیگر

Giacomo Fiumara is an Associate Professor at the University of Messina, Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences. He holds academic rank since October 2021. Previously, he served as a Permanent Researcher (2008–2021) and secondary school teacher (1997–2008). He earned a Doctorate in Physics (1993) and a Degree in Physics (1989), both from the University of Messina. He is an associate member of the Accademia Peloritana dei Pericolanti and qualified as an associate professor in INF/01 and ING-INF/05 sectors. His research focuses on social network analysis, network science, data science, criminal networks, knowledge representation, bioinformatics, and computational modeling. He has supervised over 170 theses and advised PhD students in Mathematics and Computational Sciences. Key collaborations include work with Prof. Pasquale De Meo on criminal networks and complex systems, and international projects with institutions in the US, UK, China, and Australia. Teaching includes courses on Algorithms, Data Structures, Bioinformatics, and Machine Learning across Computer Science, Engineering, and Medical programs since 2000. He also contributed to international programs at Lviv Polytechnic, Birzeit University, Cluj-Napoca, and Murcia. His editorial roles include Associate Editor of IEEE Access and Academic Editor of Complexity. He holds a patent for predictive analysis of criminal organizations' social structures and has received FFABR research funding. Key awards include FFABR funding (2017) and recognition in the FFABR Unime 2020 II edition. He organized conferences like Crimenet 2014 and participated in high-profile events such as the 2022 Complex Networks conference in Palermo, presenting on quantum walks for criminal network analysis.

• Digital Preservation
• Digital Libraries
• Data Curation
• +۵ مورد دیگر

Seamus Ross is an Honorary Professor at the School of Humanities, University of Glasgow, with a career spanning over two decades in digital preservation, digital libraries, and data curation. He has led and collaborated on numerous European initiatives, including the DL.org and BlogForever projects, focusing on policy interoperability, genre classification, and digital repository auditing. University of Glasgow School of Humanities His research explores the intersection of digital preservation, cultural heritage, and information management, emphasizing automated genre classification, metadata extraction, and sustainable digital libraries. He has also investigated user perspectives in digital information communication and developed frameworks for assessing digital preservation infrastructures. Recent publications highlight his contributions to digital preservation strategies, including analyses of storage formats, spam filtering, and preservation methodologies. These works reflect his expertise in digital forensics , metadata standards , and repository auditing . Seamus has collaborated extensively with researchers like Yunhyong Kim and Piero Innocenti, co-authoring studies on genre classification, digital policy, and significant properties in web archiving. While no specific awards are listed, his involvement in foundational projects like DELOS and ERPANET underscores his influence in the field.

• Operations Management
• Supply Chain Management
• Industry 4.0/5.0
• +۳ مورد دیگر

Dr. Albert Aidoo-Anderson is a Lecturer in Management at the Salford Business School, University of Salford. He holds a PhD in Operations Management (Supply Chain, Logistics, and Project Management), an MSc in Procurement, Logistics, and Supply Chain Management, and an MSc in Finance. He is a Chartered Member of the Institute of Procurement and Supply (MCIPS) and a Fellow of the Higher Education Academy (FHEA). His research focuses on Operations Management, Supply Chains, and Industry 4.0/5.0 applications in sectors like Pharmaceutical, Mining, Healthcare, and Oil & Gas. Education: PhD in Operations Management, University of Salford MSc in Procurement, Logistics, and Supply Chain Management, University of Salford MSc in Finance, University of Dundee Postgraduate Certificate in Academic Practice (PGCAP) Research interests include demand forecasting for SMEs, Industry 4.0 integration in supply chains, and smart logistics. He led the OPEN SME project, a £1M initiative funded by the Greater Manchester Combined Authority and ERDF, collaborating with Manchester University, Manchester Metropolitan University, and Bolton University to develop an Online Leadership Programme for SMEs post-COVID. His teaching spans undergraduate and postgraduate modules such as Operations Management, Supply Chain Management, Project Management, and Research Methods. Scientific Awards and Recognition: Fellow of the Higher Education Academy (FHEA) Chartered Member of the Institute of Procurement and Supply (MCIPS) Grants and Advising: Supervised MSc/PhD dissertations and contributed to the OPEN SME project. His professional experience spans Mining, Logistics, Banking, Finance, and NHS Health sectors. Labs/Teams: Led a multidisciplinary team in the OPEN SME initiative, emphasizing SME resilience and digital transformation in Greater Manchester.

• Social Media Analysis
• Sentiment Analysis
• Computational Journalism
• +۶ مورد دیگر

Fabrício Benevenuto is an Associate Professor in the Computer Science Department at Federal University of Minas Gerais (UFMG), where he conducts interdisciplinary research at the intersection of social media analysis, data science, and computational journalism. His work spans complex networks, machine learning, and natural language processing with strong societal impact. His research focuses on social media dynamics, particularly in Brazilian contexts, with major contributions to hate speech detection, fake news analysis, and political discourse monitoring. He leads large-scale projects against misinformation, including development of systems like WhatsApp Monitor, Media Bias Monitor, and Purple Feed. His work combines technical innovation with real-world applications for election transparency and public discourse integrity. Benevenuto's recent publications demonstrate strong trends in multilingual NLP for social media analysis, with emphasis on Brazilian Portuguese contexts. His team produces both theoretical contributions and practical systems addressing hate speech, misinformation, and media bias. Notable methodological approaches include combining network analysis with linguistic features, developing culturally-aware detection systems, and creating large annotated datasets for understudied languages. CAPES award for best Brazilian computer science thesis (2010) Humboldt Foundation scholarship recipient (2017-2018) Member of TikTok Safety Advisory Council WWW'20 Best Paper Nominee & CNIL-INRIA Privacy Protection Prize winner Multiple best paper awards at CEAS, WBC, and ICWSM conferences Test-of-Time Award at ICWSM'20 Benevenuto actively mentors PhD and MSc students, with numerous advisees securing academic positions at Brazilian universities and research roles at institutions like Max Planck Institute. His projects often receive funding supporting interdisciplinary collaborations across computer science and social sciences. Current work includes large-scale analysis of Telegram political groups, real-time election monitoring systems, and developing culturally-aware NLP tools for Portuguese. He leads research teams working on social media analysis systems with societal impact, particularly focused on Brazilian digital ecosystems. Projects involve cross-institutional collaborations with researchers from MPI-SWS, Max Planck Institute, and various Brazilian universities, emphasizing practical applications for public discourse integrity.

Lu Suمشاهده پروفایل

دانشیار

• Internet of Things
• Cyber-physical Systems
• Wireless and Mobile Sensing
• +۷ مورد دیگر

Lu Su is an Associate Professor at the School of Electrical and Computer Engineering , Purdue University , with prior appointments at SUNY Buffalo . His research spans Internet of Things , cyber-physical systems , mmWave sensing , and crowd-sourced data validation , focusing on quality-of-information aware distributed sensing and security in autonomous systems . Ph.D. in Computer Science (2013) and M.S. in Statistics (2012) from University of Illinois at Urbana-Champaign M.E. and B.E. from Harbin Institute of Technology Research Interests: IoT , cyber-physical systems , crowd sensing , security and privacy , and machine learning for sensor networks. His work addresses quality-aware information integration , adversarial attacks in autonomous vehicles , and privacy-preserving crowd-sourced systems . Recent publications focus on mmWave-based sensing (e.g., 3D pose reconstruction), federated learning (driver monitoring), and data poisoning attacks in crowd-sourced systems. His research also extends to traffic optimization and human activity recognition using wireless networks. Professional Roles: Workshop Chair (INFOCOM 2023, 2022) TPC Vice Chair (INFOCOM 2021) Program Committee Member for top conferences Editorial Board, ACM Transactions on Sensor Networks Teaching: Courses on Embedded Systems , Internet of Things , and Network Concepts at both undergraduate and graduate levels.

Wei Pangمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۷ مورد دیگر

Wei Pang is a Professor of Computer Science and Bicentennial Research Leader at the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh. He leads the BCML Lab and is affiliated with the Edinburgh Centre for Robotics and National Robotarium. His expertise spans bio-inspired computing, machine learning, and AI applications in healthcare, robotics, and sustainability. Pang holds a PhD in Computing Science from the University of Aberdeen, with prior roles including Senior Lecturer at the University of Aberdeen and research fellowships in systems biology. Affiliations: Heriot-Watt University, Edinburgh Centre for Robotics, National Robotarium Education: PhD in Computing Science (2009), MEng (by research), BSc (Jilin University, China) Research Interests: Bio-inspired computing (e.g., artificial immune systems, swarm intelligence), machine learning (deep learning, explainable AI), healthcare applications (medical imaging, disease detection), and interdisciplinary projects in robotics and environmental science. His work addresses challenges in robust AI, fairness, and accountable machine learning. Recent Projects: EPSRC-funded RAIns and MI projects, CRUK-funded Endo.AI, and PRIME project on minority ethnic communities' digital experiences. His research has secured over £10M in grants, including £3.5M institutional funding. Awards: Scottish Crucible Award (2015), ADMA Best Paper Runner-Up (2016), EPSRC PRIME Award (2024) Grants/Advising: Supervised 12 PhD completions; contributed to £10M+ external funding. Labs/Teams: BCML Lab (focusing on bio-inspired AI), collaborations with Oxford, Cambridge, and industrial partners like Weather2 and Data2Text.

Yuntian Dengمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Yuntian Deng is an Assistant Professor at the University of Waterloo and a Visiting Professor at NVIDIA. He holds affiliations with Harvard SEAS as an Associate and the Vector Institute as a Faculty Affiliate. He completed his PhD in Computer Science at Harvard under Professors Alexander Rush and Stuart Shieber, followed by a postdoc under Yejin Choi. His research focuses on Natural Language Processing and Machine Learning, with notable contributions in chatbot interaction analysis (WildChat), implicit reasoning models, and markup-to-image generation. He has developed influential tools like OpenNMT and WildVis, and his work has been featured in outlets like the Washington Post and used by OpenAI and Anthropic. Education: PhD in CS (Harvard), Postdoctoral Research (University of Washington). Key achievements include the ACM Gordon Bell Prize for GenSLMs, Best Demo Runner-up at ACL 2017, and Best Paper at DAC 2020. His research emphasizes scalable datasets, efficient reasoning techniques, and real-world applications of AI models. Research interests span NLP, machine learning algorithms, and their applications in areas like dialogue systems, generative models, and ethical AI evaluation. Notable projects include WildChat (1M ChatGPT interactions), implicit chain-of-thought reasoning, and neural steganography for text-based information hiding. His articles explore topics ranging from knowledge distillation to diffusion models, with a focus on bridging theoretical advancements and practical implementations. He actively collaborates with industry partners like NVIDIA and maintains open-source tools to advance AI research accessibility.