جست‌وجوی استاد و پژوهشگر

Wendy Robinsonمشاهده پروفایل

استاد

• Medical Genetics
• Epigenetics
• Fetal Development
• +۴ مورد دیگر

Wendy P. Robinson is a Professor in the Department of Medical Genetics at the University of British Columbia Faculty of Medicine , and a Senior Scientist at the BC Children’s Hospital Research Institute . She holds the CIHR Sex and Gender Science Chair . Research Interests: Genetics and epigenetics of early human development, placental function in pregnancy complications (fetal growth restriction, preterm birth), DNA methylation, non-coding RNA, sex differences, and polymorphisms. Her lab employs genomic and bioinformatic tools to study placental health and its impact on newborn outcomes. Recent Publications (2025-2024) focus on X-chromosome inactivation patterns in placenta, cell-type specific DNA methylation, maternal socioeconomic effects on placental epigenetics, and modeling placental development with organoids. Key themes include sex-specific epigenetic regulation , maternal-fetal interactions , and human placental methylome . Awards: UBC Faculty of Medicine Distinguished Achievement Award (2018), with trainees receiving the James Miller Memorial Prize and Mary-Jane Carroll Trainee Award. Students & Collaborations: Supervised PhD/MSc students include Li Qing Wang, Icíar Fernández Boyano, Giulia Del Gobbo, Victor Yuan, and Magda Price. Collaborators span the Alex Beristain Lab and University of Toronto institutions. Laboratory Activities: Regular team-building events like mountain hikes, climbing outings, and kayaking trips, alongside providing open access to epigenetic tools (e.g., Bisearch, SeqDoc) for the research community.

• Development Economics
• Macroeconomics
• Economic History
• +۶ مورد دیگر

Sambit Bhattacharyya is a Professor of Economics and Head of the Department of Economics at the University of Sussex Business School. He holds a PhD in Economics from the Australian National University (2007) and specializes in Development Economics, Macroeconomics, and Economic History. His research explores themes like the resource curse, commodity price volatility, and global economic systems, with a focus on historical and political dimensions. He is the author of seminal works such as *A History of Global Capitalism: Feuding Elites and Imperial Expansion* and *Growth Miracles and Growth Debacles: Exploring Root Causes*. Bhattacharyya serves as an Associate Editor of the *Review of Development Economics* and actively engages in media commentary on global economic issues. His work bridges theoretical frameworks with empirical analysis, addressing contemporary challenges like China’s economic stimulus, G7 sanctions, and copper market dynamics. He has supervised several PhD students and contributes to teaching advanced macroeconomic theories at both undergraduate and graduate levels. Education: PhD in Economics, Australian National University (2007) Affiliations: University of Sussex Business School, Review of Development Economics (Associate Editor) Media Engagement: Regular contributor to *The Conversation*, *Daily Express*, and international broadcasts on economic policy and global crises. His research interests extend to political economy, with studies on natural resources and conflict in Africa, the impact of foreign aid diversification, and the political implications of resource discoveries. Bhattacharyya’s interdisciplinary approach combines historical analysis with modern econometric techniques to address long-term economic development challenges. Recent articles highlight his engagement with contemporary issues such as commodity market dynamics, geopolitical economic strategies, and the legacy of colonial expansion on current global systems. Despite no explicit mention of awards, his extensive publication record and editorial roles underscore his scholarly influence in development economics and macroeconomic history.

Mahsa Salehiمشاهده پروفایل

مدرس ارشد

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

Mahsa Salehi is a Senior Lecturer in the Department of Data Science & AI at Monash University’s Faculty of Information Technology. She holds a PhD in Computer Science from the University of Melbourne and previously served as a postdoctoral researcher at IBM Research Australia. Her research focuses on data mining, machine learning, and time series analysis, with applications in healthcare, cybersecurity, and smart grids. Education: PhD in Computer Science, University of Melbourne (2016) MSc in Software Engineering, Amirkabir University of Technology (2009) BSc in Information Technology & Computer Engineering, Amirkabir University of Technology (2008/2006) Her key research interests include multi-dimensional time series analysis, anomaly detection, brain-inspired machine learning, and non-stationary data learning. She has led or contributed to over 40 research outputs, including high-impact papers on anomaly detection frameworks (e.g., CARLA) and EEG representation learning (EEG2Rep). Her work bridges theoretical advancements with practical applications, such as detecting urinary anomalies in seniors and securing smart grid systems against cyberattacks. Dr. Salehi has secured significant grants, including AU$246K from ARENA (2019–2021) and AU$30K from Emotiv Research (2022–2024). She is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data and has been recognized with awards like the ICDM 2022 Best Paper Runner-Up and IBM’s Manager’s Choice Award (2016). Grants & Projects: Privacy-Preserving Machine Learning (CSIRO Next Gen, 2023–2027) AI for Clean Energy & Sustainability (Monash, 2023–2027) Deep Learning for Brain EEG Analysis (PhD Top-Up, 2022–2025) Her contributions extend to editorial and patent activities, including roles at IBM Research and collaborative projects with industry partners like Emotiv.

Laks V.S. Lakshmananمشاهده پروفایل

استاد

• Data Management
• Graph Computing
• Machine Learning
• +۵ مورد دیگر

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

Stefano Martinianiمشاهده پروفایل

استادیار

• Computational and Statistical Physics
• Materials Science
• Artificial Intelligence for Science
• +۵ مورد دیگر

Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neuroscience at New York University, affiliated with the Center for Soft Matter Research and the Simons Center for Computational Physical Chemistry. His interdisciplinary research explores computational physics of complex systems, including neural circuit theories, non-equilibrium statistical mechanics, and AI-driven materials discovery. He has pioneered methods for analyzing high-dimensional energy landscapes and received prestigious awards like the NSF CAREER Award (2024) and IUPAP Early Career Prize (2023). Education: PhD in Physics (2017), University of Cambridge MPhil in Physics (2013), University of Cambridge BSc in Physics (2012), Imperial College London Research Interests: His work bridges statistical physics and artificial intelligence, focusing on: Engineering disordered materials with tailored spectral properties Quantifying entropy production in active matter Developing open science frameworks like ColabFit for machine learning interatomic potentials Neural circuit models for cortical communication Grants & Collaborations: Funded by NSF, NIH, Chan Zuckerberg Initiative, and Simons Foundation. Leads interdisciplinary teams in computational physics, AI, and materials science. Labs/Initiatives: Core member of NYU's Center for Soft Matter Research; develops software tools like FReSCo and KLIFF-Torch for computational materials science.

• Machine Learning
• Generative Modeling
• Scientific Computing
• +۳ مورد دیگر

Tanya Marwah is a Research Fellow at the Simons Foundation , collaborating with Polymathic AI . She earned her PhD from Carnegie Mellon University's Machine Learning Department, co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton, and holds a master's degree from CMU's Robotics Institute. Her research bridges Machine Learning and Scientific Computing , focusing on generative modeling , inverse problems , and building scientific agents . Her work explores theoretical and empirical foundations for applying ML to differential equations, with key contributions in neural operators , memory mechanisms , and edge embeddings in GNNs . Recent publications highlight trends in PDE solvers via LLMs , cross-modal adaptation , and implicit regularization in SGD . She has received the prestigious Siebel Scholar award and actively contributes to top ML venues (NeurIPS, ICML, ICLR, TMLR). Her collaborations span institutions including Carnegie Mellon University, Polymathic AI, and CMU's Robotics Institute.

Sven Scheweمشاهده پروفایل

استاد

• Automata Theory
• Game Theory
• Formal Verification
• +۱۲ مورد دیگر

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

• Quantum Machine Learning
• Reinforcement Learning
• Medical Informatics
• +۱۲ مورد دیگر

Jose David Martin Guerrero is a Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His research is conducted within the Intelligent Data Analysis Laboratory (IDAL), focusing on the intersection of quantum computing, machine learning, and biomedical applications. His research interests span Quantum Machine Learning , Reinforcement Learning for Quantum Control , Deep Learning for Medical Image Analysis , and Long-COVID Symptom Trajectory Modeling . He applies advanced AI techniques to solve complex problems in physics, healthcare, and engineering, particularly in modeling prostate cancer, hemodialysis, and post-COVID neurological and functional impairments. The recent publications highlight a strong trend toward integrating quantum computing with classical machine learning, especially in developing quantum neural networks, quantum autoencoders, and reinforcement learning for quantum gate optimization. Simultaneously, a significant body of work uses clustering, deep learning, and Bayesian modeling to analyze longitudinal clinical data from multicenter studies like LONG-COVID-EXP, aiming to understand symptom persistence and recovery patterns in post-COVID patients. Dr. Martin Guerrero earned his PhD from the Universitat de València in 2004 with a thesis on unsupervised techniques for web trend detection and collaborative filtering-based recommendation systems, supervised by Dr. Emilio Soria Olivas and Dr. Paulo Jorge Gomes Lisboa. His work demonstrates active collaboration across disciplines, particularly in clinical research involving multicenter studies, and in quantum computing applications in physics and finance. While no specific grants are mentioned, his involvement in large-scale clinical studies and quantum computing research suggests significant project leadership and funding acquisition. He leads research within the Intelligent Data Analysis Laboratory (IDAL) , which focuses on self-organizing maps, clustering, learning vector quantization, and their applications in security, healthcare, and quantum systems.

Giuseppe Vinciمشاهده پروفایل

استادیار

• Astrostatistics
• Bayesian Statistics
• Computational Neuroscience
• +۸ مورد دیگر

Giuseppe Vinci is an Assistant Professor at the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. His research focuses on probabilistic graphical models, particularly in neuroscience and genomics applications. He holds a Ph.D. in Statistics from Carnegie Mellon University (2017), and completed postdoctoral research and lecturing at Rice University (2017–2020). Vinci’s expertise spans astrostatistics, forensic science, and geometric data analysis. Education: Ph.D. in Statistics, Carnegie Mellon University (2017) M.Sc. in Statistics, Carnegie Mellon University (2013) M.Sc. in Economics and Social Sciences, Bocconi University (2012) B.Sc. in Economics, University of Catania (2009) Research Interests: High-dimensional statistical theory of graphical models, matrix completion, astrostatistics, neuroscience, genomics, forensic science, and geometric data analysis. His work addresses challenges in neuronal functional connectivity, genomic networks, and climate science. Awards: NeuroNex Postdoctoral Trainee (NSF) Rice Academy Postdoctoral Fellow Three-minute thesis competition (Top-10, Carnegie Mellon University) Advising & Grants: Vinci mentors multiple Ph.D., MSc, and undergraduate students in projects spanning forensic statistics, genomics, and astrostatistics. He has secured funding for undergraduate research programs and participated in NIH-funded interdisciplinary training. Labs/Teams: Involved in collaborative projects with institutions like Baylor College of Medicine and Rice University, focusing on neurotheory and statistical methods in neuroscience.

Michael Millerمشاهده پروفایل

استاد

• Medical Imaging
• Computational Anatomy
• Pattern Theory
• +۳ مورد دیگر

Michael I. Miller is the Bessie Darling Massey Professor and director of the Department of Biomedical Engineering at Johns Hopkins University (JHU), where he also leads the Center for Imaging Science and co-directs the Kavli Neuroscience Discovery Institute. His work focuses on computational medicine, medical imaging, and computational anatomy, particularly in neurodegenerative diseases like Alzheimer’s, Huntington’s, and schizophrenia. Miller has pioneered cloud-based methods for brain image libraries and algorithms, co-founded four startups, and holds over 200 publications and two textbooks. He earned his BS from SUNY Stony Brook (1976) and MS/PhD from JHU (1978/1983). Education: Bachelor of Science, State University of New York at Stony Brook, 1976 Master of Science, Johns Hopkins University, 1978 Doctor of Philosophy, Johns Hopkins University, 1983 Research Interests: Miller’s research integrates advanced imaging technologies to study brain structure and function in health/disease. Key areas include: Functional and structural analysis of neurodegenerative diseases Development of computational tools for early disease detection Multiscale data integration (molecular to tissue) Translational applications in clinical diagnostics Awards: IEEE Biomedical Engineering Thesis Award (1982) Johns Hopkins Paul Ehrlich Thesis Award (1983) NSF Presidential Young Investigator Award (1986) Johns Hopkins Gilman Scholar (2011) Fellow, American Institute for Medical and Biological Engineering Labs/Teams: His lab at JHU develops cloud-based imaging tools and collaborates on translational research. Projects include the xIV-LDDMM Toolkit and STalign spatial transcriptomics alignment software.

• Artificial Intelligence
• Image Processing
• Medical Imaging
• +۳ مورد دیگر

Dr Ahmed Fetit is a Senior Lecturer and Senior Teaching Fellow at Imperial College London's Department of Computing (Faculty of Engineering), specializing in AI for Healthcare through the UKRI Centre for Doctoral Training. His research focuses on applying machine/deep learning to medical imaging, particularly in collaboration with NHS Trusts. He holds a PhD from the University of Warwick (2015) and degrees from the University of Birmingham (MSc, BEng). He is a Fellow of the Higher Education Academy (FHEA). Education: PhD: University of Warwick (2015), applications of machine learning to tumor diagnosis/prognosis via MRI MSc: University of Birmingham BEng (Hons): University of Birmingham Research Interests: Ahmed's work bridges AI and healthcare, emphasizing medical imaging applications such as MRI segmentation, adversarial robustness, fetal brain connectivity analysis, and retinal biomarker discovery. His projects often involve NHS collaborations to translate AI innovations into clinical practice. Recent Article Trends: Recent publications highlight advancements in CNN robustness (e.g., k-space artifact simulation), fetal/neonatal brain segmentation, and XAI explanations in healthcare imaging. His work also explores cross-modal MRI inference and cardiovascular risk stratification using retinal features. Awards: Fellow of the Higher Education Academy (FHEA) Advising & Grants: While specific grant details are not listed, his NHS collaborations imply involvement in funded healthcare AI projects. No advisees are explicitly mentioned. Labs/Teams: Active in the UKRI CDT in AI for Healthcare and the Department of Computing's Neuroimaging research groups.

Sheng Liمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۴ مورد دیگر

Sheng Li is an Adjunct Assistant Professor in the School of Computing at the University of Georgia (UGA). He holds roles such as Graduate Program Faculty and Courtesy Faculty in the Institute of Bioinformatics. Previously, he served as an Assistant Professor at UGA from August 2018 to July 2022. His research focuses on Machine Learning, Computer Vision, Data Mining, Natural Language Processing, Causal Inference, and User Modeling. Li earned his Ph.D. in Computer Engineering from Northeastern University in 2017, following degrees from Nanjing Institute of Post and Telecommunications in China. His work bridges theoretical advancements and practical applications, including AI solutions for healthcare (e.g., medication adherence monitoring), computer vision for wildlife tracking (fish re-identification), and causal inference methodologies. He has received notable awards like the Fred C. Davison Early Career Scholar Award (2022) and the Aharon Katzir Young Investigator Award (2020). His research is funded by grants from agencies such as the US Department of Defense and NIH, supporting projects like Knowledge-Guided Scene Graph Generation and Reasoning for Visual Understanding. Li’s publications span top venues in AI and computer science, addressing challenges in domain adaptation, trustworthy AI, and multimodal learning. He has collaborated on interdisciplinary projects, including bioinformatics and agricultural NLP. His contributions emphasize both technical innovation and societal impact.

Gyunam Parkمشاهده پروفایل

پژوهشگر

• Process Mining
• Action-Oriented Process Mining
• Object-Centric Process Analysis
• +۱۲ مورد دیگر

Gyunam Park is a Research Group Lead and Process and Data Scientist at Fraunhofer FIT and a Scientific Assistant at the Chair of Process and Data Science at RWTH Aachen University, a leading institution in computer science and engineering. He is actively involved in both research and teaching, contributing to the advancement of process mining, data science, and artificial intelligence. His work bridges academic research and industrial applications, particularly in SAP ERP systems and digital twins of organizations. Research Interests: Gyunam Park’s research focuses on Action-Oriented Process Mining (AOPM) , Object-Centric Process Analysis , and Responsible Machine Learning . He aims to transform process mining insights into actionable management decisions, ensuring transparency, fairness, and compliance. His work enables organizations to monitor operational constraints, generate corrective actions, and assess their impact using data-driven methods. Publication Trends: His recent publications emphasize object-centric approaches to process mining, predictive monitoring, constraint checking, and integration with AI planning. There is a strong trend toward preserving structural information in event logs, improving machine learning performance, and applying these techniques to real-world systems like SAP ERP and after-sales service processes. Scientific Awards: No awards are explicitly mentioned in the provided text. Advising and Grants: While no formal students are listed, Gyunam Park leads research projects and collaborates with industry partners such as Samsung Electronics and SAP. His projects involve root cause analysis, resource optimization, and educational data mining. He has developed open-source tools like ProAct and OCPA , indicating active grant or institutional support for software development and research dissemination. Labs and Teams: He is a core member of the Process and Data Science (PADS) group led by Prof. Wil van der Aalst at RWTH Aachen University and leads a research group at Fraunhofer FIT. These teams focus on cutting-edge research in process mining, data science, and AI, with strong industry collaborations and regular contributions to top conferences and journals.

David Kepplingerمشاهده پروفایل

استادیار

• Robust statistics for high-dimensional data
• Regularized estimation and feature selection
• Computational statistics
• +۴ مورد دیگر

David Kepplinger is an Assistant Professor in the Department of Statistics at George Mason University's School of Computing. His academic expertise spans robust statistical methods for high-dimensional data, computational statistics, and applications in biomedical sciences and environmental modeling. Education: PhD in Statistics, University of British Columbia, 2020 Master of Science in Statistics, Vienna University of Technology (Austria) Dr. Kepplinger's research primarily focuses on robust estimation in high-dimensional settings, with particular interest in the robustness of feature selection in the presence of arbitrary contamination and countering the effects of contamination on predictive models. His work addresses critical challenges in statistical analysis where outliers and unusual values can severely impact results, especially in settings with many variables. He develops methods that maintain reliable performance even when data contains contamination in both response variables and explanatory features. His recent publications demonstrate a strong interdisciplinary approach, spanning computational statistics, biomedical applications, environmental modeling, and clinical research. The research shows a consistent theme of developing robust statistical methodologies applicable to complex real-world problems across multiple domains, from protein biomarker identification to phenological modeling of cherry blossom predictions. His work frequently involves developing computational algorithms that can handle non-convex optimization problems common in robust statistics. Dr. Kepplinger serves as a co-organizer for the statistics seminar series and is the PR & communications contact for the Department of Statistics at George Mason University. He mentors PhD students Yang Long and Siqi Wei, who focus on improving computational methods for robust regularized regression estimators. He teaches several courses including STAT 665 (Categorical Data Analysis) and STAT 778 (Statistical Computing). He leads several notable projects including the First International Cherry Blossom Prediction Competition, which has been featured in prominent media outlets like The Weather Network, CBC Radio, and Public Radio's The World. This citizen science initiative aims to improve phenological modeling through crowd-sourced predictions of cherry blossom peak bloom dates in Washington, D.C., Vancouver, Kyoto, and Liestal-Weideli, Switzerland. He also develops multiple R packages available on CRAN and Bioconductor, including pense for robust regression estimation and examinr for creating online exams from R markdown documents.

Christina E. Bannierمشاهده پروفایل

استاد

• Sustainability
• Corporate Governance
• Compliance
• +۵ مورد دیگر

Professor Christina E. Bannier serves as Head of the Chair of Banking & Finance (BWL VI) at Justus Liebig University Giessen's Faculty of Law, Business and Economics. She concurrently holds positions as Pro-Dean of the department, managing director of the Giessen Graduate Centre for Social Sciences, Business, Economics and Law (GGS), and spokesperson for the 'Behavioral and Social Finance & Accounting' research network. Her institutional affiliations include membership on Clearstream Banking AG's Supervisory Board since 2018 and previous service on advisory boards for Germany's Federal Ministry for Economic Cooperation and Development (2011-2013) and Hessian Ministry of Economics (2012-2015). Her research spans sustainability finance, corporate governance, compliance mechanisms, and financial education, with particular focus on ESG investing dynamics, anti-money laundering frameworks, and corporate ethics programs. Current projects include analyzing sustainability-linked loans, shareholder engagement at annual meetings, climate scenario analysis tools, and corporate ethics program effectiveness. Her work demonstrates consistent interdisciplinary integration of behavioral finance principles with practical regulatory applications. Professor Bannier serves as co-editor of the Journal of Business Economics and provides peer review services for top-tier publications including the American Economic Review and Review of Financial Studies. Her supervisory responsibilities encompass multiple external PhD candidates and regular Bachelor's/Master's thesis guidance. She leads a research team including doctoral candidates and student assistants focused on experimental finance and sustainable investment behavior, operating within the university's structured academic framework that emphasizes rigorous empirical methods and practical case application.