Find Academic Supervisors

• Econometrics
• Time Series Analysis
• Factor Models
• +3 more

Philipp Gersing is a researcher at the Department of Statistics and Operations Research, University of Vienna. His academic work focuses on econometrics, time series analysis, and statistical modeling with applications in macroeconomic and financial data. He teaches courses in mathematics and econometrics at both undergraduate and graduate levels. Current courses: Mathematics 1, Introductory Econometrics (MA) Advanced courses: Applied Econometrics 1 & 2, Seminar in Empirical Finance and Financial Econometrics (MA) His research specifically addresses challenges in generalized dynamic factor models, including identifiability conditions, distributed lag approaches, and the resolution of rotational indeterminacy issues. Recent work explores the canonical decomposition of factor models and the prevalence of weak factors in empirical applications. Publications demonstrate consistent contributions to factor modeling theory, with applications spanning macroeconometric analysis and financial data processing. Key themes include mixed-frequency data integration, non-stationary time series handling, and structural identification in multivariate systems.

Maria-Esther VidalView profile

Professor

• Data Science
• Semantic Web
• Knowledge Graphs
• +7 more

Prof. Dr. Maria-Esther Vidal is the head of the Scientific Data Management Research Group at Technische Informationsbibliothek (TIB) , a member of the L3S Research Centre at Leibniz Universität Hannover, and a full professor (on-leave) at Universidad Simón Bolívar, Venezuela. She lectures Master courses at the Faculty of Electrical Engineering and Computer Science, Leibniz Universität Hannover.

Anastasios GounarisView profile

Professor

• Distributed Databases
• Autonomous data processing
• Big Data Management
• +7 more

Anastasios Gounaris is a Professor at the Department of Informatics, Aristotle University of Thessaloniki, where he has been a faculty member since April 2008. He previously served as a Visiting Lecturer at the University of Cyprus (2007-2008) and held research positions at the University of Manchester and CERTH. His academic journey includes a PhD from the University of Manchester (2005), an MPhil from UMIST (2002), and a degree in Electrical and Computer Engineering from Aristotle University of Thessaloniki (1999). His research spans Distributed Databases, Autonomous Data Processing, Big Data Management, Workflow Optimization, and Data Mining . He has made significant contributions to large-scale data management systems, massive parallelism techniques, and business process analytics. His work bridges theoretical computer science with practical industrial applications, particularly in predictive maintenance and edge computing environments. His recent publications (2024-2025) demonstrate a strong focus on process mining, anomaly detection, predictive maintenance systems, and edge analytics . These works address critical challenges in handling evolving data streams, optimizing task allocation in resource-constrained environments, and developing parameter-free algorithms for real-world applications. His research shows consistent progression from foundational database systems work to cutting-edge applications in industrial IoT and healthcare analytics. Dr. Gounaris has successfully supervised numerous PhD students to completion, including Athanasios Naskos (2017), Georgia Kougka (2017), Christos Bellas (2022), Theodoros Toliopoulos (2022), Anna-Valentini Michailidou (2023), Ioannis Mavroudopoulos (2024), and Konstantinos Varvoutas (2025). His research has been supported by multiple national and European projects including DataflowOpt, PRECognition, NavGreen, CUREX, Rainbow, Lifechamps, and Trineflex. He is an active member of Datalab (formerly Delab) and has contributed to industry applications through collaborations with companies such as Comidor, Gnomon, Istognosis, Atlantis, AFS, Follow-Apps, Sboing, and Upcom. His work demonstrates a strong commitment to transferring research results to real-world applications while maintaining academic rigor.

• Systems Neuroscience
• Neurophysiology
• Sensory Systems
• +9 more

Brice Bathellier is a Research Director and team leader at the Institut Pasteur in Paris, where he heads the Neural Code in the Auditory System group within the Hearing Institute – Pasteur Institute Center . His research integrates advanced experimental and computational approaches to investigate auditory perception and neural coding in the mammalian brain. He is also a co-organizer of the international Pasteur Course Hearing: from mechanisms to restoration technologies (HeaR) , aimed at Master’s and PhD students, clinicians, and hearing professionals. His research interests lie at the intersection of systems neuroscience , neurophysiology , and sensory systems , with a primary focus on the auditory system. His team uses two-photon calcium imaging , multichannel electrophysiology , optogenetics , and behavioral analysis to study how sound representations are formed, manipulated, and integrated with other sensory modalities in the brain. Key projects include decoding nonlinear operations in auditory perception, generating artificial auditory percepts via optogenetic stimulation, modeling reinforcement learning in sensory discrimination, and exploring multisensory interactions between auditory, visual, tactile, and olfactory systems. The team's recent publications reveal a strong interdisciplinary trend, combining in vivo neuroscience , computational modeling , and behavioral neuroscience . Themes include neural coding of sensory identity, cortical dynamics during learning and memory, cross-modal integration, and the impact of network architecture on perception. The work spans molecular, cellular, and systems levels, with applications in understanding brain function and potential neuroprosthetic technologies. Brice Bathellier supervises several PhD students and postdoctoral researchers, indicating an active mentoring role. His team is part of broader institutional initiatives on brain connectivity and neurodegenerative diseases, suggesting involvement in collaborative, cross-functional research projects. While specific grant details are not mentioned, the scope and technical sophistication of the work imply significant research funding. The team is embedded within the Hearing Institute, a multidisciplinary center focused on progressive sensory deficits, pathophysiology, and therapy.

Annette Quinto RomaniView profile

Associate Professor

• Social inequality in education and health
• Econometric methods
• Causal effects analysis
• +6 more

Annette Quinto Romani is an Associate Professor at Aalborg University's Department of Sociology and Social Work within the Faculty of Social Sciences and Humanities. She leads the Share-PBL SocMap research group and focuses on social inequality in education and health using econometric methods to uncover causal effects. Her teaching emphasizes quantitative methods, regression analysis, education inequality, rational choice theories, climate/environmental sociology, and mixed methods. Education: PhD in Sociology Cand. Oecon (Candidate of Economics) Lektor in Sociology Her research integrates sociology, economics, and health studies. Recent work explores socioeconomic influences on university students' body image and attachment styles, nature access in Danish schools, and parental employment impacts on child development. Publications span Current Psychology , Journal of Adventure Education and Outdoor Learning , and edited anthologies. Scientific awards include: Årets underviser på sociologi (2024) Uglens kald – Bogpris (2018) 2017 Conference Prize for healthy snack interventions Active in public discourse, Romani contributes to media discussions on urban-rural disparities, youth behavior, and stress coping mechanisms. She coordinates internship guidance for sociology undergraduates and participates in national research networks like RECWOWE.

• Seismology
• Earth's Interior
• Inverse Theory
• +5 more

Professor Jeannot Trampert is a leading seismologist at Utrecht University's Faculty of Geosciences, where he serves as Professor of Seismology within the Department of Earth Sciences. His research group focuses on extracting information from ground motion recorded at global and regional seismic networks to produce three-dimensional models of the Earth's interior and seismic sources, primarily using high-performance computing approaches. With over 250 publications spanning decades, Trampert maintains an active research program with recent works published through 2025. Trampert's research expertise encompasses several critical areas of modern seismology: Theoretical and computational seismology Earthquake physics and analysis Earth's deep interior structure Inverse theory applications Machine learning in geophysical data analysis Mineral physics and Earth composition His recent publications demonstrate an increasing integration of machine learning techniques with traditional seismological methods, particularly for analyzing seismic data from the Groningen gas field in the Netherlands. Trampert's work bridges theoretical seismology with practical applications in monitoring induced seismicity, pore pressure changes, and temperature effects on seismic wave propagation. His research group actively collaborates with international partners on projects involving full-waveform inversion, mantle structure analysis, and seismic monitoring systems. Professor Trampert has supervised numerous students throughout his career, with university records indicating 29 supervised works. His research receives consistent funding through multiple grants, supporting both fundamental research and applied projects related to geohazards and resource extraction. He maintains active collaborations with institutions across Europe and participates in major research initiatives like EPOS-NL, the Dutch solid earth science infrastructure for georesources and geohazards research.

• Big Data
• Data Mining
• Machine Learning
• +2 more

Xiang Zhang is an Associate Professor at the College of Information Sciences and Technology, The Pennsylvania State University, focusing on big data, data mining, machine learning, and biomedical informatics. His work develops algorithms for analyzing large-scale social, biological, and medical datasets using graph-based methods. Education: Ph.D. in Computer Science (2011), University of North Carolina at Chapel Hill Research Interests: Xiang Zhang's research spans graph neural networks, dynamic graph analysis, and biomedical data mining. He specializes in explainable AI, robust learning, and topology-aware models for imbalanced data. His group investigates security in graph models, synthetic data generation, and cross-domain adaptation. Publications Trends: Recent work emphasizes explainable dynamic graph neural networks, adversarial attacks on LLMs, and robust contrastive learning. His team explores topology-level imbalances, prompt attacks, and causal relation modeling in temporal systems. Scientific Awards: Best Research Paper Award at SIGKDD 2008 Best Student Paper Award at ICDE 2008 Best Paper Candidate at ICDM 2018 and ICDM 2012 SIGKDD Dissertation Award Honorable Mention (2012) Advising: He mentors a group of Ph.D. students including Zongyu Wu, Minghua Lin, and Junjie Xu, with alumni securing faculty positions at Florida International University and North Carolina State University.

• Statistical Mechanics
• Telecommunications
• Nanomaterials
• +6 more

Dr. Mario Castro Ponce is a Professor at the Escuela Técnica Superior de Ingeniería (ICAI) , Universidad Pontificia Comillas, where he has worked for 28 years. He holds a Ph.D. in Physics from Universidad Complutense de Madrid and serves as a Visiting Professor at the University of Leeds since 2016-2017. His research bridges Statistical Mechanics with applications in Complex Systems , Biophysics , and Epidemiology , with a focus on modeling experimental data through analytical and computational methods. Education: Ph.D. in Physics, Universidad Complutense de Madrid B.S. in Physics, Universidad Complutense de Madrid Research Themes: Statistical mechanics of complex systems Ion-beam nanopatterning Theoretical immunology Wildfire dynamics Biofluid microrheology Machine learning in social science Publications (89+ peer-reviewed) span interdisciplinary topics, including: SARS-CoV-2 geometry and T-cell receptor dynamics Non-Newtonian blood flow modeling Wildfire risk assessment via Bayesian networks Viscoelastic thermosyphon systems Complex pattern formation in biological and physical systems Funding includes 6 principal investigator projects for Spain's Ministry of Science and participation in 3 EU Horizon 2020/Marie Skłodowska-Curie actions. His work has been cited over 2000 times.

• Design Computing
• Design Cybernetics
• Design Geometry
• +4 more

Thomas Fischer is a Professor at the School of Design, Southern University of Science and Technology (SUSTech), with over 25 years of international academic experience. He leads the Design Cybernetics Group at SUSTech and serves as Chief Design Officer of anabrid technology company. Previously, he held teaching positions at The Hong Kong Polytechnic University (Interactive Systems Design & Industrial Design, 10+ years) and Xi’an Jiaotong-Liverpool University (Architecture & Industrial Design, 10 years), along with visiting positions at National Cheng Kung University and Humboldt University. Double PhD: Education (University of Kassel), Architecture & Design (RMIT University) Certification: Graduate Certificate in Cybersecurity (Harvard University) Research Focus: Design cybernetics, computational design theory, autopoietic systems, and digital media. His work bridges cybernetics with architectural and product design, emphasizing indeterminism, circular causality, and epistemic processes. Academic Contributions: Over 100 peer-reviewed publications with notable works on non-trivial machine design, BIM adoption models, and cybernetic design epistemology. Key journals include Kybernetes , International Journal of Architectural Computing , and Frontiers of Architectural Research . Fellow, Design Research Society Fellow, Cybernetics Society Certified Talent, International Academy for Systems & Cybernetic Sciences Recipient, Warren McCulloch Award (American Society for Cybernetics) Collaborations: Active partnerships with Christiane M. Herr, Guillermo Sánchez Sotés, and other systemic design researchers. His recent publications appear in Constructivist Foundations and Materials & Design , focusing on autopoietic architecture and cross-morphological performance analysis.

Anders Martin FjellView profile

Professor

• Cognitive Psychology
• Neuropsychology
• Brain Development
• +4 more

Anders Martin Fjell is a Professor and Center Manager for the Center for Lifespan Changes in Brain and Cognition (LCBC) at the Department of Psychology, University of Oslo. His research focuses on how the brain and cognitive abilities develop in childhood and change throughout the adult lifespan. Dr. Fjell's research spans multiple areas of cognitive neuroscience and neuropsychology, with particular emphasis on: Brain development and aging across the lifespan Relationships between brain structure and cognitive function Memory processes and their neural correlates Neurodegenerative processes, particularly related to Alzheimer's Disease Effects of sleep on brain health and cognitive function His publication record demonstrates strong focus on lifespan neuroscience, with recent work examining brain aging trajectories, memory systems, Alzheimer's disease biomarkers, and the relationship between sleep patterns and brain health. His research frequently employs advanced neuroimaging techniques and large-scale longitudinal datasets to investigate cognitive and neural changes across the lifespan. Among his notable achievements: Elected to the Norwegian Academy of Sciences and Letters (2016) Recipient of the University of Oslo Research Prize (2015) LCBC awarded status as one of 5 world-leading research groups at UiO (2015) Morgenbladet's top 10 list of young Norwegian researchers (2012) Recipient of Fridtjof Nansen Award for Young Scientists and His Majesty the King's Gold Medal for best doctoral thesis (2006) Dr. Fjell has secured significant research funding including an ERC Starting Grant (€1.5 million), multiple grants from the Norwegian Research Council, and funding from the National Association for Public Health. His current projects focus on biomarkers for early Alzheimer's detection, brain inflammation and atrophy in aging, and memory development and decline across the lifespan. As Center Manager for LCBC, Dr. Fjell leads one of the world's leading research groups studying lifespan changes in brain and cognition, contributing significantly to our understanding of how the brain changes from childhood through old age.

Weiyang LiuView profile

Assistant Professor

• Machine Learning
• Inductive Bias
• Generative Modeling
• +3 more

Weiyang Liu is a machine learning researcher and incoming Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK), where he will lead the Scalable Principles for Learning and Reasoning Lab (SphereLab). He has previously held postdoctoral and research roles at the Max Planck Institute for Intelligent Systems, Georgia Tech, Google Brain, Nvidia Research, and MERL. His research focuses on principled modeling of inductive bias in learning algorithms, emphasizing geometric invariance, symmetry, and formal reasoning in large language models. His work spans generative modeling (diffusion models, 3D mesh synthesis), foundation models (parameter-efficient training), and symbolic/mathematical reasoning evaluation. Publications include top-tier conferences like NeurIPS, ICML, CVPR, and ICLR, with Spotlight/Notable Top-25% designations. He mentors PhD students including Zeju Qiu, Longhui Yu, and Zhouliang Yu, with alumni transitioning to institutions like Technical University of Munich, University of Tübingen, and Caltech. His lab emphasizes curiosity-driven research , theoretical rigor, and open collaboration.

Zhuang LiView profile

Lecturer

• Natural Language Processing
• Machine Learning
• Data-efficient Learning
• +5 more

Dr. Zhuang Li is a Lecturer at RMIT University's School of Computing Technologies, specializing in Natural Language Processing (NLP), machine learning, and trustworthy AI. He earned his PhD from Monash University (2023), focusing on semantic parsing in low-resource conditions, and previously worked at Microsoft on Cortana and Bing. His research bridges NLP, computational social science, and cultural alignment of AI systems, emphasizing data-efficient training and LLM safety. Education: PhD in NLP, Monash University (2019–2023) M.Comp in AI, Australian National University (2014–2015) B.Eng in Electrical Engineering, Wuhan University of Science and Technology (2009–2013) Research Interests: Data-efficient learning, culturally-grounded AI, large language models, computational social science, and ethical AI deployment. Collaborations: Active in industry partnerships and academic collaborations with Monash University, TU Darmstadt, Microsoft Research India, and Ant Group. Recent work includes ACL 2025 papers on LLM safety and peer review analysis. Supervision: Open to PhD students in low-resource languages and culturally-aligned LLMs. Collaborates with senior faculty at RMIT and Monash.

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +5 more

Amir-Hossein Karimi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo and a Faculty Affiliate at the Vector Institute. His research focuses on trustworthy human-AI collaboration, including causal inference, algorithmic recourse, and explainable AI. He holds a PhD from ETH Zurich and prior industry experience at Meta, Google Brain, and DeepMind. Education: PhD (ETH Zurich, 2023), MMath (UWaterloo, 2018), BEng (UofT, 2015) Research interests include advancing AI systems that recover from errors, evaluating AI safety/ethics, and combining human-machine strengths. His work spans causal inference, counterfactual explanations, and program synthesis. Recent articles emphasize causal perspectives in fairness, adversarial robustness, and scalable recourse mechanisms. His publications appear in top venues like NeurIPS, ICML, and AAAI. Awards: Google PhD Fellowship (2021), NSERC Scholarship (2018) Teaching: ECE 457B and ECE 657 since 2024. Advises graduate students in AI ethics, causal ML, and human-AI teaming.

Alice MitchellView profile

Assistant Professor

• Anthropological Linguistics
• Interactional Sociolinguistics
• Linguistic Anthropology
• +2 more

Alice Mitchell is Junior Professor for Anthropological Linguistics of Africa at the University of Cologne's Institute for African Studies within the Faculty of Arts and Humanities. Her research focuses on anthropological linguistics, interactional sociolinguistics, and language documentation, with particular emphasis on African languages and cultures. Her work examines linguistic practices related to kinship, social hierarchy, and cultural norms in Tanzanian Datooga communities. Recent publications investigate children's acquisition of avoidance registers, kinship categorization, and the sociolinguistic history of pastoral societies in eastern Africa. Research Methodology: Fieldwork-based language documentation Cross-cultural linguistic comparisons Quantitative and qualitative analysis of social interactions

• Statistical Data Analysis
• Computer Vision
• Digital Health
• +5 more

Mirko Birbaumer is a Professor at the Lucerne School of Engineering and Architecture (HSLU), specializing in data science and machine learning applications. He holds a PhD in computer-assisted image analysis from ETH Zurich and has completed advanced executive education at MIT in Innovation and Strategy. PhD in Systems Biology (ETH Zurich, 2010) Executive Certificate in Innovation and Strategy (MIT, 2022) Advanced Executive Certificate in Innovation, Strategy, and Technology (MIT, 2024) His research integrates statistical data analysis , computer vision , and digital health , with applications spanning industrial quality control, medical imaging, and environmental monitoring. Recent work focuses on physics-informed neural networks, interpretable anomaly detection, and causal machine learning. His projects include AI-driven solutions for medical diagnostics, predictive maintenance in manufacturing, and environmental anomaly detection. He has supervised over 20 student theses on topics like deep learning for knee osteoarthritis detection, monocular depth estimation, and AI-assisted logo similarity analysis. He leads the Data Science profile in the Master of Science and Engineering program and teaches courses on deep learning, Bayesian machine learning, and computer vision. His methodological expertise includes agent-based modeling, statistical analysis, and explainable AI.