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Ousmane SeidouView profile

Professor

• Hydrological Modeling
• Climate Change Impacts
• Water Resources Management
• +4 more

Dr. Ousmane Seidou is a Full Professor in the Department of Civil Engineering at the University of Ottawa, where he has served since 2007. He leads the Hydraulics Lab and holds affiliations with the Faculty of Engineering's Centre for Indigenous Community Infrastructure. His academic journey includes a PhD from École Polytechnique de Montréal (2002) and postdoctoral research at the Institut National de la Recherche Scientifique, Quebec. Dr. Seidou specializes in climate change impacts on water resources, hydrological modeling, and transboundary water management. Education: Ph.D. in Civil Engineering (Hydraulics), École Polytechnique de Montréal (2002) M.Sc. in Water Resources Engineering, École Polytechnique de Montréal (2002) Postgraduate Diploma in Hydroinformatics, École Inter-États des Ingénieurs de l'Équipement Rural (1998) Undergraduate Degree in Civil Engineering, École Mohammadia d'Ingénieurs (1996) Research Focus: Dr. Seidou’s work integrates hydrological modeling with climate adaptation strategies, emphasizing Africa’s water security. Key areas include: Climate change impacts on river basins (e.g., Niger, Congo) Development of flood early warning systems in West Africa Water-Energy-Food-Environment (WEFE) nexus frameworks Environmental flow estimation in wetland ecosystems He leads international projects involving multi-country collaborations and agencies like the United Nations Development Programme and the World Bank. Recent Projects: Principal Investigator for the Niger Basin Authority’s WEFE Nexus initiative Technical leadership in the Dutch-funded BAM-GIRE project (Mali/Guinea wetlands) Development of flood early warning systems for Niamey and Gaya (Niger) Teaching: Courses include Climate Change Impacts on Water Resources, Advanced Hydrological Modeling, and Water Resources Management. He mentors PhD students in hydrology and climate adaptation. Grants & Funding: Secured multi-million-dollar projects with organizations like Wetlands International and the UAE-BELEM Programme. Recent funding includes $135,000 for hydrological modeling tools in the Niger Basin. Labs/Teams: Director of the Hydraulics Lab at the University of Ottawa. Active in global initiatives like the Global Goal on Adaptation (GGA) indicator development under the Paris Agreement.

• Machine Learning
• Data Mining
• Bioinformatics
• +11 more

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

Bertrand ClarkeView profile

Professor

• Data Mining
• Machine Learning
• Prediction
• +4 more

Bertrand Clarke is a Professor in the Department of Statistics at the University of Nebraska-Lincoln, within the College of Agriculture & Natural Resources. He holds a PhD in Statistics from the University of Illinois (1989) and has held academic positions at Purdue University, the University of British Columbia, the University of Miami (Medical School), and served as Chair of the Department of Statistics at UNL. His research focuses on prediction, model uncertainty, and statistical methods for complex/high-dimensional data, including genomic data and machine learning applications. Education: PhD in Statistics from University of Illinois (1989), with early work recognized by the Browder J. Thompson Award. His career includes sabbaticals at University College London, Duke University (SAMSI), and the Newton Institute at Cambridge. He pioneered biostatistics programs at the University of Miami and authored a Springer textbook on data mining/machine learning. Research Interests: Prediction theory, model bias/uncertainty, ensemble methods, Bayesian approaches, and applications in genomics. He emphasizes statistical principles like variance-bias tradeoff and robustness in complex data analysis. Awards: ASA Fellow (2014), Browder J. Thompson Award (1989). Editorial roles in four journals and service on the Savage Award Committee.

Kaize DingView profile

Assistant Professor

• Data Mining
• Machine Learning
• Large Foundation Models
• +7 more

Kaize Ding is an Assistant Professor in Statistics and Data Science at Northwestern University, leading the REAL Lab and affiliated with the IDEAL Institute. He holds a Ph.D. in Computer Science from Arizona State University (2023) under Prof. Huan Liu, with prior degrees from Beijing University of Posts and Telecommunications. His research focuses on reliable AI systems for autonomous decision-making, knowledge-guided algorithms using GNNs/LLMs, and applications in healthcare, environmental science, and cybersecurity. Collaborations include Google Brain, Microsoft Research, and Amazon Alexa AI. Education: Ph.D. in Computer Science, Arizona State University (2023) M.S. and B.S., Beijing University of Posts and Telecommunications Research Interests: Developing robust AI for decision-making under uncertainty Graph-based machine learning for anomaly detection and network analysis Large language models (LLMs) integrated with domain-specific knowledge Cross-domain applications in healthcare diagnostics, environmental monitoring, and cybersecurity Recent Activities: Received Amazon Research Award and Google Research Grant NeurIPS 2025 Area Chair and ARR Area Chair Postdoc opening in AI4Health for swallowing disorder research Recent publications at AAAI, NeurIPS, EMNLP, and KDD Lab & Team: The REAL Lab focuses on advancing AI through interdisciplinary projects. Current students include Ruiyao Xu (PhD), Qingcheng Zeng (co-advised), and over 10 master's/undergraduate researchers. Alumni have moved to top PhD programs at UVa, UIC, and JHU.

• Data Mining for Business
• Data Visualization
• Large-Scale Experimental Design
• +3 more

Ben Collier is an Assistant Teaching Professor of Business Analytics at the Tepper School of Business , Carnegie Mellon University. He holds a PhD in Information Systems and Organizational Behavior from Carnegie Mellon and has extensive experience in data science leadership roles in industry. PhD, Information Systems and Organizational Behavior (2012), Carnegie Mellon University MS, Information Systems and Organizational Behavior (2009), Carnegie Mellon University MBA, Information Systems (2007), University of Wisconsin-Madison BBA, Management Computer Systems and Mathematics (2004), University of Wisconsin-Whitewater His research focuses on data mining for business , data visualization , and large-scale experimental design , with applications in healthcare analytics, online community dynamics, and gender equity in technology. His recent work includes monetization data science for Duolingo's $6.5 billion IPO and developing UPMC's CognitiveRx analytics engine. Ben's publications span topics including gender gaps in Wikipedia , leadership in open collaboration communities , and conflict resolution in crowdsourced platforms . He has served on CMU committees for curriculum review and summer summit planning, and actively advises MSBA capstone projects.

Joshua MarshallView profile

Professor

• Field Robotics
• Autonomous Vehicles
• Control Systems Engineering
• +7 more

Joshua Marshall is a Professor of Electrical & Computer Engineering at Queen’s University, Canada, and Director of the Offroad Robotics research group. He holds a PhD from the University of Toronto and has cross-appointments in Mechanical & Materials Engineering and the Robert M. Buchan Department of Mining. His expertise spans field robotics, autonomous systems, control engineering, and harsh-environment applications in mining, space, and marine domains. He led the Ingenuity Labs Research Institute (2018–2024) and served as a Visiting Professor at Örebro University (2016–17). Dr. Marshall’s work focuses on autonomous vehicle navigation, robotic excavation, and spatiotemporal mapping. He has received the 2025 OPEA Engineering Medal and has commercialized technologies through partnerships with companies like Epiroc and RockMass Technologies. Education: PhD, Electrical & Computer Engineering, University of Toronto (2005) MSc(Eng), Mechanical Engineering, Queen’s University (2001) BSc (Hons), Engineering, (details not specified) Research Interests: Autonomous robotics in mining, space, and marine environments Data-driven control systems and model predictive control Proprioceptive sensing and terrain classification Multi-robot coordination and task planning Underground navigation and SLAM Professional Activities: Senior Member, IEEE Editorial roles: International Journal of Robotics Research , IEEE Transactions on Mechatronics Co-founded the NSERC Canadian Robotics Network (NCRN) Contributions to the IEEE Medal for Environmental & Safety Technologies Committee Labs/Teams: Offroad Robotics Group (Queen’s University) Ingenuity Labs Research Institute (founding Director) Advisor to Queen’s AutoDrive Challenge II Team and aQuatonomous ASV Design Club

Andreas MaierView profile

Researcher

• Drug Repurposing
• Network Medicine
• Bioinformatics
• +3 more

Andreas Maier is a Researcher at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, affiliated with the Computational Systems Biology department. He began his PhD in May 2021 with Cosy.Bio (Center for Systems Biology) at UHH, focusing on drug repurposing projects such as REPO-TRIAL. Previously, he completed a Bioinformatics master's thesis at TUM (Technical University of Munich), developing a web application for analyzing molecular disease networks. His research interests emphasize network medicine, drug repurposing, and computational tools for biomedical discovery. He has contributed to platforms like NeDRex-Web, Drugst.One, and BioCypher, which democratize access to systems medicine workflows. His work bridges heterogeneous data integration, federated learning for rare diseases, and quantum computing applications in genetics. Maier's publications highlight innovations in knowledge graph-based drug discovery, privacy-preserving federated learning, and single-cell network analysis. He actively develops open-source bioinformatics tools to address challenges in disease module identification and patient stratification. His projects align with the REPO4EU consortium and other collaborative initiatives in translational bioinformatics.

Wei DingView profile

Professor

• Knowledge Discovery
• Data Mining
• Machine Learning
• +7 more

Wei Ding is a Professor in the Department of Computer Science at the University of Massachusetts Boston (UMass Boston). She earned her Ph.D. in Computer Science from the University of Houston in 2008. From 2019 to 2023, she served as a Program Director at the National Science Foundation's Division of Information and Intelligent Systems (IIS), overseeing programs in Information Integration, Smart Health, Deep Learning Foundations, and Scalable Systems. Her research integrates knowledge discovery, data mining, and machine learning with applications spanning health sciences, astronomy, geosciences, and environmental sciences. She employs advanced techniques like spatio-temporal modeling, deep neural networks, and semantic analysis to address complex real-world problems such as disease subtyping, physical activity prediction, and environmental forecasting. Her work emphasizes interdisciplinary collaboration and societal impact. Analysis of her recent publications reveals a focus on AI-driven healthcare solutions (e.g., neuroimaging biomarkers, disorder diagnosis), fundamental ML advancements (e.g., generalization, GAN stability), and cross-domain applications (e.g., climate forecasting, animal behavior analysis). Recurring themes include low-data learning, interpretability, and scalable algorithms. Awards & Honors: IEEE Fellow (2023) NSF Director's Award (2022) WISAY Distinguished Woman in Science Award, Yale University (2019) AI for Earth Award (2018) Best Paper Awards (ICTAI 2011, ICCI 2010) Advising & Grants: She mentors PhD and Master’s students in the Knowledge Discovery Lab (KDLab), with alumni at institutions like Facebook, Google, and McKinsey. Her research is funded by NSF, NIH, NASA, and DOE, including: NIH R01: Predicting youth physical activity (2016) NSF EAGER: Machine learning for cancer subtyping (2017) NIH R01: Accelerometer/gyroscope data for activity estimation (2022) Leadership: She directs the KDLab and co-founded the Women in Sciences Club (WINS). She serves as Associate Editor for ACM TKDD, TIST, and KAIS journals.

Michael GallaugherView profile

Assistant Professor

• Clustering and Classification
• Machine Learning
• Matrix and Tensor Variate Data Analysis
• +5 more

Dr. Michael Gallaugher is an Assistant Professor of Statistical Science at Baylor University. He holds a Ph.D., M.S., and B.S. in Statistics from McMaster University. His research focuses on clustering and classification methodologies, particularly in matrix/tensor variate data, mixed-type data, clickstream analysis, and outlier detection. He has been recognized with prestigious awards including the Vanier Canada Graduate Scholarship and the Banting Postdoctoral Fellowship. Education: Ph.D., Statistics, McMaster University (2020) M.S., Statistics, McMaster University (2017) B.S., Statistics, McMaster University (2015) Research Interests: Dr. Gallaugher's work emphasizes advanced clustering techniques for complex data structures, including high-dimensional datasets, clickstream behavior analysis, and skewed distribution modeling. His contributions span statistical methodology development and applications in fields like bioinformatics and sports science. His recent work explores hidden Markov models for time series and robust co-clustering algorithms. Publications Trends: His publications reflect a strong focus on matrix-variate distributions, skewed data modeling, and algorithmic innovation in clustering. Recent work (2022-2025) highlights advancements in contaminated normal mixtures, co-clustering for high-dimensional data, and spatial regression models. Awards: Vanier Canada Graduate Scholarship Banting Postdoctoral Fellowship Advising & Grants: While no advisees are listed, his research has been supported by grants from the Natural Sciences and Engineering Research Council of Canada. He contributes to statistical consulting services at Baylor and collaborates internationally on methodological projects. Labs & Teams: He is affiliated with Baylor's Department of Statistical Science and collaborates with research groups focused on machine learning and statistical computing.

Feng ChenView profile

Associate Professor

• AI Safety
• Ethical AI
• Machine Learning
• +7 more

Feng Chen is an Associate Professor in the Department of Computer Science at The University of Texas at Dallas (UT Dallas), part of the Erik Jonsson School of Engineering & Computer Science. He directs the AI Safety Laboratory and holds tenure. His research focuses on AI safety, ethical machine learning, uncertainty quantification, and fair algorithms. He earned a Ph.D. from Virginia Tech (2012), M.S. from Beijing University of Aeronautics & Astronautics (2004), and B.S. from Hunan University (2001). His research spans AI safety frameworks, resilient AI systems, fairness in healthcare and finance, cybersecurity, and environmental AI applications. Key contributions include uncertainty-aware deep learning, causal representation learning, and adversarial vulnerability analysis. He has authored over 150 peer-reviewed publications, including top venues like NeurIPS, ICML, KDD, and ICDM. Grants: $4.2M+ in funding from NSF, Army Research Office, IARPA, and industry partners. Teaching: Courses in data mining, anomaly detection, and artificial intelligence since 2014. Notable achievements include an NSF CAREER Award (2018) and leadership in projects like EMBERS (social media event prediction). His work bridges theory and practice, addressing societal challenges through ethical AI principles. Labs/Teams: AI Safety Laboratory (UT Dallas), collaborative projects with Virginia Tech, Carnegie Mellon, and industry partners on software vulnerability analysis and causal AI. Awards: Multiple best paper nominations, including 2nd Place in NAE Security Category (2009) and UT Dallas Outstanding Research (2021-2022).

Ali GhanbariView profile

Assistant Professor

• Software Engineering
• Programming Languages
• Data Science
• +4 more

Ali Ghanbari is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. His research focuses on software engineering, programming languages, and data science, with an emphasis on automated program repair, deep learning, and mutation analysis. He received his Ph.D. in Software Engineering from the University of Texas at Dallas and his M.Sc. and B.Sc. from Amirkabir University of Technology in Tehran, Iran. Education: Ph.D. Software Engineering, University of Texas at Dallas M.Sc. Software Engineering, Amirkabir University of Technology B.Sc. Software Engineering, Amirkabir University of Technology Research Interests: Dr. Ghanbari's work spans automated program repair, deep neural network analysis, and mutation-based fault localization. He explores techniques to enhance software quality through methods like patch correctness assessment, object similarity-based prioritization, and optimization of mutation testing frameworks. His contributions include frameworks such as PRF and tools like Shibboleth for hybrid patch evaluation. Publications Trends: His recent work highlights advancements in accelerating mutation analysis, improving deep learning models via modular decomposition, and refining automated repair techniques. Notable contributions include Rocq for goal clone detection and MeMu for faster mutation analysis. Awards & Grants: No specific awards or grants mentioned in the provided materials. Advising & Labs: While no advisees are listed, his research group likely focuses on program repair and deep learning applications. His work is supported by datasets like Defexts, which provides reproducible real-world bugs for JVM languages.

Alina RoitbergView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Human Activity Recognition
• +7 more

Alina Roitberg is a Junior Professor (Assistant Professor) at the University of Stuttgart , affiliated with the Faculty of Computer Science, Electrical Engineering and Information Technology . Her research focuses on advancing computer vision, machine learning, and robotics applications, particularly in human activity recognition, domain adaptation, and synthetic data generation. She explores challenges in action understanding, cross-domain generalization, and real-world deployment of AI systems in fields like healthcare, autonomous vehicles, and industrial automation. Her work emphasizes robust learning under noisy conditions, multimodal data fusion, and ethical AI applications. Recent projects include foundational studies on large language models in construction (AEC), video-based muscle group estimation, and improving driver activity recognition for autonomous vehicles. She also investigates circular factory design through uncertainty-aware process optimization and human-robot interaction. Dr. Roitberg's contributions span academic publications and industrial collaborations, addressing both theoretical advancements and practical implementations. Her research bridges computer vision techniques with real-world problems, emphasizing scalability and ethical considerations in AI deployment.

• Applied Mathematics
• Mathematical Biology
• Epidemiology
• +4 more

Paul Tupper is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He holds a Ph.D. in Scientific Computing from Stanford University (2002). His research focuses on applied mathematics with emphasis on mathematical modeling in epidemiology, speech perception, neural networks, and computational linguistics. He teaches advanced courses in probability, numerical linear algebra, and calculus for social sciences. His work bridges theoretical mathematics and real-world applications, particularly in understanding complex systems like disease transmission dynamics and cognitive processes. Recent studies include modeling the transition of pandemics to endemic states, genomic analysis of viral spread, and audio-visual perception mechanisms in speech. He actively contributes to public health policy discussions through epidemic modeling research. Professor Tupper's research has been published in high-impact journals and conferences, with notable contributions to diversity metrics in biology and geometry, stochastic differential equations, and connectionist models of linguistic phenomena. His courses reflect interdisciplinary interests, integrating mathematical rigor with practical computational methods.

Ali NazemiView profile

Associate Professor

• Hydrology
• Water Security
• Climate Change
• +3 more

Dr. Ali Nazemi is an Associate Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University, where he joined in 2015 as a Strategic Hire in Water Resources. He holds adjunct appointments at the University of Saskatchewan's School of Environment and Sustainability and is an Associate Member of the Global Institute for Water Security. His foundational education includes a PhD from the University of Birmingham (UK), an MSc from Ferdowsi University of Mashhad (Iran), and a BSc from KNT University of Technology (Iran). Dr. Nazemi's research advances methodologies for water security challenges under climate change, focusing on: Hydrological modeling and algorithm development Climate change vulnerability assessments Coupled human-water systems Hydroclimatic data diagnostics His recent publications predominantly explore climate model integration, uncertainty quantification in water systems, and hybrid modeling approaches, with consistent themes of climate adaptation and risk management across Canadian and international contexts. He has been recognized with several awards, including: Dorothy Hodgkin Postgraduate Award (UK government, 2005–2009) Best Presentation Award at IEEE World Congress on Computational Intelligence (2006) Post Graduate Teaching Award (University of Birmingham, 2004–2007) Dr. Nazemi actively supervises graduate students through his Water Security and Climate Change (WSCC) lab, with projects on climate downscaling, vulnerability assessment tools, and coupled human-water systems. He leads research collaborations with the Saskatchewan Water Security Agency, NSERC's Changing Cold Region Network, and the Global Water and Energy Cycle Experiment.

Douglas NychkaView profile

Professor

• Spatial Statistics
• Nonparametric Regression
• Computational Statistics
• +5 more

Douglas Nychka is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines since 2018. He holds an emeritus position at the National Center for Atmospheric Research (NCAR), where he previously directed the Institute for Mathematics Applied to Geosciences (IMAGe) from 2004 to 2017. Nychka earned his PhD in Statistics from the University of Wisconsin-Madison and a BA in Mathematics (with Physics emphasis) from Duke University. His research focuses on spatial statistics, nonparametric regression, and computational methods for large datasets, particularly applied to environmental and geophysical problems. He has developed influential R packages like fields and LatticeKrig , which are widely used for spatial data analysis. Nychka received prestigious awards including the Jerry Sacks Award for Multidisciplinary Research (2004) and recognition as a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. His work bridges statistical theory, computational innovation, and real-world applications in climate science and renewable energy. His academic career includes 14 years as a faculty member at North Carolina State University and roles at the National Institute of Statistical Sciences. Nychka's research emphasizes spatial statistics for climate data, statistical downscaling, and uncertainty quantification, with contributions to solar radiation modeling and extreme event analysis. He actively engages in interdisciplinary projects, collaborating with experts in climatology, environmental science, and data science. Professional service includes roles on committees for the National Research Council and leadership in statistical societies. His teaching focuses on modernizing curricula to integrate data science with applied statistics. Nychka’s work is characterized by a commitment to open-source software and reproducible research, exemplified by his R package contributions.