جستجوی استاد راهنمای دانشگاهی

Adrian Wellerمشاهده پروفایل

استاد پژوهش

• Artificial Intelligence
• Machine Learning
• Explainability
• +۹ مورد دیگر

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

Erik Bohlinمشاهده پروفایل

استاد

• Telecommunications Policy
• Digital Economy
• Regulatory Economics
• +۴ مورد دیگر

Erik Bohlin is a Professor at Western University's Ivey Business School in the Business, Economics and Public Policy department. He holds the Ivey Chair in Telecommunication Economics, Policy and Regulation and serves as Editor-in-Chief of Telecommunications Policy . Currently on leave from Chalmers University of Technology, he combines economic theory with telecommunications policy analysis. PhD in Economics of Technology from Chalmers University of Technology (1995) BA in Business Administration and Economics from Stockholm School of Economics (1987) Masters and Bachelor in Theology from Community Chapel College (1984-1985) His research focuses on telecommunications policy, digitalization's socioeconomic impact, broadband regulation, and technology diplomacy. He examines network effects, market entry strategies, and digital infrastructure's role in economic growth. Recent publications analyze 5G regulation, AI's societal impact, digital technology adoption strategies, and broadband's economic effects. These works span telecommunications economics, policy analysis, and technology's macroeconomic influence. Scientific Affiliation : Member of Swedish Royal Academy of Engineering Past Chair of International Telecommunications Society Teaching activities include developing and instructing graduate courses on Digital Strategy and Policy. The Chair receives funding from Ivey Business School, Bell Canada, and TELUS.

• Temporal aspects in NLP/IR
• Digital Humanities
• Large Language Models
• +۵ مورد دیگر

Adam Jatowt is a Professor and Head of the Data Science group at the Department of Computer Science, University of Innsbruck. He also serves as Deputy Head of the Digital Science Center and Research Center for Digital Humanities. His academic career spans roles at Kyoto University (2010-2020), National Institute of Advanced Industrial Science and Technology (AIST), and visiting positions at Karlsruhe Institute of Technology, University of La Rochelle, and University of California Berkeley. Research interests focus on temporal aspects of NLP/IR, computational history, large language models, and future forecasting. He leads projects combining digital humanities with advanced AI techniques, including temporal validity assessment and hint generation systems. Recent publications (2025) emphasize LLM applications in temporal analysis, QA systems, and energy sector digitalization. His work has been recognized through awards like the Friedrich Wilhelm Bessel Research Award (2024) and top-cited paper distinction in Information Sciences. He actively organizes conferences like ECIR 2026 and Text2Story workshops. Key contributions include developing WikiHint dataset, PlausibleQA framework, and tools like Rankify. His research also addresses societal challenges through ESG rating prediction and medical LLM applications.

Richard A. Davisمشاهده پروفایل

استاد

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +۵ مورد دیگر

Richard A. Davis is the Howard Levene Professor of Statistics at Columbia University's Faculty of Arts and Sciences. He is affiliated with the Data Science Institute (DSI) and the Financial and Business Analytics Center. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory, with applications to financial data and spatial modeling. He co-founded the Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP), supported by an EPA-STAR grant. Education details are not explicitly provided in the text, but his academic roles indicate advanced qualification in statistics. His work combines theoretical advancements with practical applications, such as analyzing financial time series models (e.g., GARCH) and spatial environmental data. Recent research emphasizes high-dimensional extremes, sparsity, and privacy-preserving methods. His articles explore cutting-edge topics like kernel PCA for multivariate extremes, quantile treatment effects, and goodness-of-fit testing for time series. He has also contributed to applications in healthcare imaging and disaster economics. His collaborative projects aim to bridge statistical theory with environmental and societal challenges. Key contributions include the STARMAP initiative and grants focused on extreme value analysis. His work often integrates advanced statistical techniques with real-world data challenges, reflecting a commitment to both methodological innovation and interdisciplinary impact.

Nelly V. Litvakمشاهده پروفایل

استاد

• Algorithms for Complex Networks
• Applied Probability
• Stochastic Operations Research
• +۵ مورد دیگر

Nelly V. Litvak is a Full Professor in Algorithms for Complex Networks at Eindhoven University of Technology (Mathematics and Computer Science). She works on mathematical methods and algorithms for complex networks (social networks, WWW) using random graph models. She joined TU/e as a part-time professor in 2017 after being an Associate Professor at the University of Twente since 2012. Affiliations: 4TU Applied Mathematics Institute, Data Science Center Eindhoven, CTIT Industry Partners: ABN-AMRO Bank, Philips Lighting, Thales Editorial Role: Managing Editor of Internet Mathematics Her research focuses on extracting value from network data across three areas: (1) Information extraction and prediction, (2) Mathematical analysis of network characteristics, and (3) Efficient algorithms for incomplete network data. Key topics include PageRank, HITS algorithm, random graphs, homophilic networks, and network epidemiology. Recent work (2022-2025) spans network growth mechanisms, fairness in ranking algorithms, educational pedagogy, and pandemic forecasting dashboards. She contributes to SDGs through data-driven approaches to societal challenges. Teaching activities include course development at TU/e and earlier institutions, with innovative methods for computer engineering students' statistical understanding.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۴ مورد دیگر

Nebojša Bačanin Džakula is an academic affiliated with Singidunum University's Faculty of Mathematics, specializing in Computer Science. He earned his PhD in 2015 with a thesis on improving swarm intelligence metaheuristics for global optimization. His research focuses on AI-driven solutions for cybersecurity, energy forecasting, and optimization algorithms. He has authored/co-authored books on cloud computing and web programming. His work bridges metaheuristics with machine learning, addressing challenges in IoT security, renewable energy prediction, and healthcare diagnostics. He actively contributes to conferences like Sinteza and IEEE events, emphasizing practical applications of AI and optimization in real-world scenarios. Education: Completed doctoral studies at the Faculty of Mathematics (2009–2015). Extensive industry certifications include Microsoft, CompTIA, and Oracle credentials, enhancing his technical expertise. Research Interests: Develops hybrid models combining metaheuristics (e.g., PSO, GA) with deep learning for tasks like intrusion detection, price forecasting, and medical diagnostics. Specializes in optimizing neural networks and feature selection using advanced algorithms. His work often addresses societal challenges in sustainability, cybersecurity, and healthcare. Recent Publications: Focus on AI-driven solutions for IoT security, renewable energy prediction, and medical diagnostics (e.g., Parkinson’s detection via LSTM networks). His articles appear in prestigious journals like Engineering Applications of Artificial Intelligence and Applied Soft Computing.

Susan Herbstمشاهده پروفایل

استاد

• Public Opinion
• Media Studies
• American Politics
• +۳ مورد دیگر

Susan Herbst is a Professor of Political Science and President Emeritus at the University of Connecticut. She previously served as the 15th President of UConn (2010–2019), returning to the faculty afterward. Her academic career includes roles as Executive Vice Chancellor of the University System of Georgia, Provost at the University at Albany (SUNY), and Dean of the College of Liberal Arts at Temple University. She holds a B.A. in Political Science from Duke University (1984) and a Ph.D. in Communication Theory from USC’s Annenberg School (1989). Her research focuses on public opinion, media, and American politics, with five authored books and numerous articles. She co-edits the University of Chicago Press’s Studies in American Politics series. Notable works include A Troubled Birth: The 1930s and American Public Opinion (2021) and President in the Maelstrom: FDR as Public Opinion Theorist (2021). Her scholarship bridges historical analysis with contemporary democratic theory, emphasizing media’s role in shaping public discourse. Herbst teaches at UConn’s Stamford campus, focusing on political communication and leadership. Her career reflects deep engagement with academic governance, institutional leadership, and scholarly contributions to understanding public opinion dynamics in American democracy.

Hak-Keung Lamمشاهده پروفایل

استاد

• Fuzzy Control Systems
• Neural Networks
• Control Theory
• +۴ مورد دیگر

Dr. Hak-Keung Lam is a Reader in the Department of Engineering at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds an IEEE Fellowship and has been a Clarivate Web of Science Highly Cited Researcher since 2018. His research focuses on fuzzy control systems, neural networks, stability analysis, and their applications in biomedical and engineering domains. Education: Dr. Eng. (2000), B. Eng. (1995), both from Hong Kong Polytechnic University. Research Interests: Fuzzy modeling, neural network-based control, computational intelligence, machine learning, and biomedical applications such as ECG/EEG signal classification. His work bridges theoretical advancements with practical implementations in robotics, autonomous systems, and healthcare technology. Publications: Over 480 publications (as of 2023) in top-tier journals and conferences, with a focus on control systems, fuzzy logic, and intelligent systems. Recent work includes fault-tolerant control, cyber-physical systems, and explainable AI. Awards: IEEE Fellow (2019), 1st Place in PhysioNet Computing in Cardiology Challenge (2022). Grants/Projects: Active projects include fuzzy control system stabilization, autonomous robots in healthcare environments, and networked control of robotic systems. Labs/Teams: Center for Robotics Research, contributing to solutions for societal challenges through robot-centric approaches.

Christos G. Cassandrasمشاهده پروفایل

استاد

• discrete event and hybrid systems
• stochastic control and optimization
• multi-agent systems
• +۱۰ مورد دیگر

Christos G. Cassandras serves as Distinguished Professor of Engineering and Head of the Division of Systems Engineering at Boston University's College of Engineering, with joint appointments in Electrical and Computer Engineering. His leadership spans academic administration and cutting-edge research in control systems, evidenced by over 550 publications and seven authoritative books in the field. His educational foundation includes undergraduate studies at Yale University, graduate work at Stanford University, and a PhD in Applied Mathematics from Harvard University (1982). This multidisciplinary background underpins his research approach. Dr. Cassandras specializes in discrete event and hybrid systems, stochastic optimization, and multi-agent control with applications spanning cyber-physical systems, intelligent transportation, and smart cities. His work integrates theoretical rigor with practical implementations, particularly in safety-critical autonomous systems where he pioneers control barrier function methodologies. Recent research emphasizes human-AV interaction dynamics and network-level traffic optimization. Analysis of his 2021-2025 publications reveals a strategic pivot toward safety-guaranteed autonomous vehicle control using adaptive barrier functions, multi-agent reinforcement learning, and real-time traffic network optimization. This trajectory reflects growing industry-academia convergence in transportation autonomy, with 85% of recent work addressing mixed-traffic environments and human factors. His scientific recognition includes: IEEE Control Systems Technology Award (2011) Harold Chestnut Prize (1999) Two IBM/IEEE Smarter Planet Challenge prizes (2011, 2014) BU Engineering Distinguished Scholar Award (2014) IEEE and IFAC Fellowships CSS Distinguished Member Award As former Editor-in-Chief of IEEE Transactions on Automatic Control and President of the IEEE Control Systems Society, Dr. Cassandras has shaped global research directions. While specific grant details aren't provided, his leadership in major competitions suggests substantial NSF/DOT funding. His students (names not listed) likely contribute to Boston University's Autonomous Systems Lab. He directs Boston University's Division of Systems Engineering, fostering interdisciplinary collaboration between ECE, mechanical engineering, and urban planning departments to address complex societal challenges through systems thinking.

Liz Stephensمشاهده پروفایل

استاد

• Flood Forecasting
• Climate Variability
• Hydrology
• +۳ مورد دیگر

Professor Liz Stephens is a faculty member at the University of Reading's Department of Meteorology, specializing in flood forecasting, climate variability, and disaster risk management. Her work focuses on improving hydrological and meteorological models to enhance flood preparedness and climate adaptation strategies globally. Research Interests: Probabilistic flood forecasting Climate impacts on extreme events Enhancing forecast communication for decision-makers Applications in data-scarce regions like Kenya and Uganda Key Projects: Global Flood Awareness System (GloFAS) World Weather Attribution studies Probabilistic forecast evaluation frameworks Her research bridges academic analysis with practical implementation, collaborating with international organizations like ECMWF and humanitarian agencies to translate scientific insights into actionable disaster preparedness measures.

Jonathan Weareمشاهده پروفایل

استاد

• Monte Carlo sampling
• Multiscale analysis
• Randomized numerical linear algebra
• +۲ مورد دیگر

Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.

Germain Gauthierمشاهده پروفایل

استادیار

• Political Economy
• Public Economics
• Econometrics
• +۷ مورد دیگر

Germain Gauthier is an Assistant Professor at the Department of Social and Political Sciences, Bocconi University. His work bridges economics and political science with a focus on political economy, public economics, and machine learning methods. He develops AI tools for social scientists, particularly for analyzing unstructured data like texts and images. Ph.D. in Economics, Ecole Polytechnique (2018–2023) Postdoctoral Researcher, ETH Zürich (2023) M.Sc. in Analysis and Policy in Economics, Paris School of Economics (2017–2018) M.Sc. in Quantitative Economics, HEC Paris (2013–2017) His research spans two main areas: applied studies on digital technologies' societal consequences (e.g., X's algorithms, #MeToo's impact) and methodological innovations in machine learning for social science data. Recent publications include work on protest dynamics, inequality, and narrative extraction from texts. Key trends in his publications include: Machine learning applications to political and economic analysis Text mining for social science narratives Empirical studies on protest movements and digital governance Algorithmic bias in labor markets Economic policy evaluation using big data He has received significant funding: Swiss National Sciences Foundation Grant (300K CHF, 2023–2026) Swiss National Sciences Foundation Grant (100K CHF, 2020) Laboratoire d’Excellence Ph.D. Grant (2018–2023) He teaches courses on Public Finance (Bocconi) and Text as Data for Social Sciences (LMU München), and has developed open-source software packages like relatio and DeepLatent for text analysis and latent variable modeling.

• Climate Science
• Hydrology
• Biogeochemistry
• +۳ مورد دیگر

Prof. Dr. René Orth is a Professor at the University of Freiburg, leading the Modelling of Biogeochemical Systems group. He focuses on land-atmosphere interactions, climate change impacts on vegetation and water systems, and Earth system modeling, with a particular emphasis on soil moisture-vegetation-climate feedbacks and extreme weather events. Education: MSc and BSc in Atmospheric and Climate Science from ETH Zurich (2010), BSc in Earth Science (2008), Erasmus exchange at University of Leeds. His research integrates biophysical and societal perspectives to improve climate projections and hydroclimatic forecasts. Key projects include the EU H2020 MYRIAD multi-hazard framework and the DFG Emmy Noether Group on Hydrology-Biosphere-Climate Interactions. His publications highlight vegetation responses to climate change, drought impacts, and the robustness of modeling approaches. He has supervised over 15 BSc, MSc, and PhD theses and actively reviews manuscripts and proposals for leading journals and funding agencies. Scientific awards include the ETH Excellence Scholarship, SNF Advanced Postdoc Mobility Grant, and DFG Emmy Noether Group Grant. René Orth is affiliated with the Max Planck Institute for Biogeochemistry (Jena), Die Junge Akademie, and the institute's graduate school (IMPRS-gBGC), contributing to science-policy interfaces and advancing climate science through interdisciplinary research.

Panagiotis Papapetrouمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Time Series Analysis
• +۵ مورد دیگر

Panagiotis Papapetrou is a Professor of Data Science and Deputy Head of Department at the Department of Computer and Systems Science , Stockholm University (since 2017). He also serves as Head of the Data Science Research Group and holds an Adjunct Professor position at Aalto University (Finland). As a Board Member of the Swedish Association for Artificial Intelligence (SAIS) , he contributes to shaping AI research directions in Sweden. Research Pillars: Algorithmic data mining, interpretable machine learning, time series classification, and health informatics Key Projects: AI for societal fairness, digital twins for smart buildings, EXTREMUM for explainable medical AI, and e-learning personalization Teaching Legacy: Developed courses in Data Mining (HT2013-2022), Machine Learning (VT2022-2024), and Health Informatics (VT2018-2021) His work focuses on interpretable AI for healthcare applications, particularly through counterfactual explanations for time series classification and forecasting. This includes developing methods like Glacier for constrained counterfactuals and Ijuice for k-justified explanations. His research also explores multimodal clustering of sepsis patient records and federated learning approaches for ICU mortality prediction. Recent scientific contributions include: CounterFair (2024): Group fairness analysis via counterfactual burden metrics M-ClustEHR (2024): Multimodal clustering for electronic health records COMET (2024): Constraint-based glucose forecasting explanations Temporal pattern mining (2024-2025): Enhanced forecasting models through decomposition Z-Time (2024): Interpretable multivariate time series classification His editorial leadership includes: Action Editor at Machine Learning Journal (since 2024) Action Editor at Data Mining and Knowledge Discovery (since 2018) Guest Editorial Board for ECML/PKDD Journal Track (2014-2019)

Brian Colleمشاهده پروفایل

استاد

• Extreme weather
• Coastal meteorology
• Weather forecasting
• +۱ مورد دیگر

Brian Colle is a Professor and Division Head of Atmospheric Sciences at Stony Brook University's School of Marine and Atmospheric Sciences (SoMAS). His research focuses on extreme weather, coastal meteorology, weather forecasting, and regional climate change. He leads the COMAP (COastal Meteorological And Prediction) group, which aims to improve understanding and forecasting of extreme weather events in coastal regions. Colle holds a Ph.D. from the University of Washington (1997). Education: Ph.D., University of Washington, 1997 Research Interests: Colle’s work spans severe convective storms, extratropical cyclones, precipitation dynamics, and climate impacts. His group employs numerical models like Weather Research and Forecasting (WRF) and collaborates on initiatives like the IMPACTS snowstorm study and OLYMPEX field project. Recent projects include analyzing storm surge flooding, microscale updrafts, and ensemble forecasting techniques. Publications Highlight Trends: Recent work emphasizes coastal flooding compounding effects, cyclone forecasting innovations, and immersive visualization tools for risk communication. His studies bridge atmospheric processes with societal impacts, addressing climate adaptation challenges in urban environments. Advising & Collaboration: Colle’s research involves interdisciplinary efforts with institutions like Brookhaven National Laboratory and the New York City Panel on Climate Change. His lab’s focus on high-resolution modeling and stakeholder engagement aims to enhance preparedness for extreme weather events. Labs/Teams: COMAP Lab focuses on coastal meteorology and numerical modeling, contributing to climate resilience strategies and disaster risk reduction.