Find Academic Supervisors

• Business Process Management
• Process Mining
• Process Analytics
• +4 more

Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.

Vedhus HoskereView profile

Assistant Professor

• Image Analysis
• Machine Learning
• Artificial Intelligence
• +6 more

Vedhus Hoskere is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Houston. His research focuses on interdisciplinary areas at the intersection of civil engineering, computer science, and robotics, emphasizing automated infrastructure inspection, machine learning, and AI-driven solutions for structural health monitoring. He holds the Liu Huixian Earthquake Engineering Scholarship (2018) for his work on automated post-earthquake building inspections. Key research areas include image analysis, machine learning, robotics, scientific computing, and visualization. His work integrates computer vision with civil infrastructure challenges, such as flood modeling, structural damage assessment, and autonomous inspection systems using drones and robotics. Notable projects include developing digital twin frameworks for bridges, synthetic environment testing for inspection algorithms, and semi-supervised learning for disaster damage analysis. His recent publications highlight advancements in transformer networks for instance segmentation, physics-informed generative models for damage assessment, and AI-driven hurricane resilience strategies. Hoskere collaborates on platforms like InstaDam for automated damage segmentation and explores Bayesian neural networks for quantifying uncertainties in infrastructure monitoring. Key Awards: Liu Huixian Earthquake Engineering Scholarship (2018) Advising & Labs: While no specific student names or grant details are provided, his research group likely focuses on robotics, computer vision, and AI applications in civil engineering. He contributes to open-source tools like the InstaDam platform and collaborates with institutions like the University of Illinois and USACE.

• Art Education
• Digital Art
• Art Didactics
• +3 more

Lena Eriksson is a Lecturer at the Lucerne School of Design, Film and Art, part of Lucerne University of Applied Sciences and Arts. She holds a Master of Arts (MA) in Art. Her research focuses on art education and didactics, digital art, and interactive media projects. She contributed to the Kompetenzzentrum für Kunstvermittlung- und Didaktik (Competence Center for Art Education and Didactics), emphasizing innovative approaches in art dissemination and teaching methods. A key work is the 2015 web project www.parallelspur.com , exploring digital storytelling and online art curation. She also presented on this work at a 2016 event in Baselstrasse. No scientific awards are listed, and no advising/grants details are provided. She collaborates with interdisciplinary teams in art and technology projects.

Yasmeen GeorgeView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Medical Image Analysis
• +6 more

Yasmeen George is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology. She holds a PhD in Medical Image Processing from the University of Melbourne (2018) and a Master's in Computer Science from the University of Ain Shams (2013). Her research focuses on AI applications in healthcare, particularly medical image analysis for conditions like cancer, glaucoma, and psoriasis. She co-founded the AIM for Health Lab at Monash IT and is a research affiliate at the Victorian Institute of Forensic Medicine. Education: PhD (Medical Image Processing), University of Melbourne (2018); Master of Computer Science (Medical Image Analytics), University of Ain Shams (2013). Research interests include AI-driven medical image analysis, machine learning, and cross-domain healthcare analytics in radiology, dermatology, and ophthalmology. Her work addresses challenges in automated disease detection, lesion segmentation, and severity assessment across modalities like 2D/3D imaging and text data. Recent articles focus on kidney segmentation, glaucoma detection, federated learning for breast cancer, and AI frameworks for hazardous waste policy. Her research has led to patents with IBM and grants from MRFF and NHMRC. Awards: Recipient of the 2023 Heidelberg Laureate Foundation Alumni Award. Collaborations include projects on clean energy sustainability and hazardous waste management through advanced AI techniques. She actively advises PhD students and engages in interdisciplinary research teams across Monash and industry partners. Labs/Teams: Co-founder of the AIM for Health Lab, affiliated with the Victorian Institute of Forensic Medicine.

Sean GallenView profile

Associate Professor

• Landscape Evolution
• Tectonics
• Natural Hazards
• +5 more

Prof. Sean Gallen is an Associate Professor in the Department of Geosciences at Colorado State University (CSU), USA. He holds a Senior Humboldt Research Fellowship at the GFZ German Research Centre for Geosciences, where he collaborates with Prof. Jean Braun's team in the Earth Surface Process Modeling section. His research integrates fieldwork, numerical modeling, geochronology, and GIS to study landscape evolution, tectonics, and natural hazards. Gallen earned his Master's (2008) from Western Washington University and PhD (2013) from North Carolina State University, followed by a Turner Postdoctoral Fellowship at the University of Michigan. Research Focus: His work examines climate-landscape interactions, subduction zone dynamics, and extreme event impacts on erosion. Notable projects include studying Colorado Front Range topography using cosmogenic nuclides and modeling climate effects on volcanic island landscapes. His interdisciplinary approach addresses topics like tectonic uplift quantification, silicate weathering feedbacks, and landscape-fish coevolution. Awards: Recipient of the NSF CAREER Award (2021) and Senior Humboldt Fellowship (2024). His research has been supported by NSF grants focusing on critical zone dynamics and subduction zone hazards. Current Projects: Investigating climatic controls on river incision in hotspot volcanoes and quantifying glacial erosion impacts on fault slip in the Sangre de Cristo Mountains. Collaborates internationally, including with Tribhuvan University (Nepal) on coseismic landslide studies.

Amy De'AthView profile

Senior Lecturer

• Contemporary Poetry
• Poetics
• Feminist Cultural Studies
• +3 more

Dr. Amy De'Ath is a Senior Lecturer in Contemporary Literature, Culture, and Theory at King’s College London, based in the Department of English within the Faculty of Arts & Humanities. She holds a PhD from Simon Fraser University (2017), an MA from University College London, and a BA from the University of East Anglia. Her research focuses on late 20th and early 21st-century poetry and poetics, with a forthcoming book, Behind Our Backs (Stanford UP, 2026). She supervises PhD students exploring related topics. Teaching includes undergraduate and postgraduate modules such as 'Thinking the Contemporary: Literature and Theory After Postmodernism' and 'Post-45: Literature, Culture, Theory.' She has co-edited Toward. Some. Air.: Remarks on Poetics (2015) with Fred Wah and published poetry in Not a Force of Nature (2024). Her work engages public audiences through symposia on social reproduction and poetry’s relation to labor practices, alongside invited talks in the UK, US, Canada, France, and the Netherlands. Her academic contributions span feminist cultural studies, postmodern literary criticism, and interdisciplinary explorations of contemporary poetics. She actively participates in global poetry communities and advocates for first-generation students through initiatives like the 'First-Gen Staff-Student Q&A' event.

Avery E. BroderickView profile

Associate Professor

• Theoretical Astrophysics
• Black Hole Physics
• General Relativity
• +3 more

Avery E. Broderick is an Associate Professor in the Department of Physics & Astronomy at the University of Waterloo and an Associate Faculty Member at the Perimeter Institute for Theoretical Physics. His research focuses on theoretical astrophysics, particularly studying compact objects like black holes and testing general relativity through astronomical observations. He is a key member of the Event Horizon Telescope (EHT) collaboration, which produced the first direct images of black hole horizons in M87* and Sagittarius A*. Broderick’s work emphasizes relativistic astrophysical phenomena such as accretion flows, jet formation, and polarization signatures. He collaborates extensively with observational astronomers and computational physicists to model black hole environments using general relativistic magnetohydrodynamic simulations. His recent research includes analyzing EHT data to constrain black hole spin, test spacetime metrics, and study photon ring dynamics. He also explores next-generation EHT (ngEHT) capabilities for higher-resolution imaging and multi-wavelength studies. Broderick has delivered invited lectures globally, including at Harvard-Smithsonian CfA, MIT, and the Aspen Center for Physics, reflecting his leadership in the field. Broderick’s contributions span over 135 refereed publications and conference proceedings, with a focus on black hole astrophysics, VLBI techniques, and relativistic plasma physics. His work bridges theoretical predictions with observational data, advancing our understanding of extreme gravitational regimes in the universe.

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +3 more

Dr Tianning Li is a Lecturer in Computing at the University of Southern Queensland's School of Mathematics, Physics and Computing. Affiliated with the School of Agriculture and Environmental Science, their research focuses on biomedical engineering, signal processing, and machine learning applications in clinical settings. Dr Li holds a PhD from USQ, an MAccFin from Adelaide, and a BISM from Nanjing. Core research interests include EEG signal analysis for anesthesia monitoring and epilepsy detection, with emphasis on developing novel signal processing techniques like spectral entropy analysis, synchroextracting transforms, and federated learning approaches. Their work bridges machine learning innovations with clinical diagnostics, addressing challenges in real-time medical signal interpretation and healthcare data efficiency. Recent publications (2021-2025) emphasize advancements in anesthesia depth assessment algorithms, seizure prediction methodologies, and lossless signal compression. Research trends reflect a strong focus on integrating neural networks (CNN-LSTM, 1D CNN) with traditional signal analysis frameworks to enhance clinical decision-making accuracy. No scientific awards are listed, but Dr Li maintains active research collaborations through affiliations with multiple departments. Supervision activities are not detailed in available records, though their work likely involves student contributions to biomedical computing projects. ORCID: 0000-0001-5142-8654 .

David A. RandallView profile

Professor

• Atmospheric Science
• Climate Modeling
• Cloud Physics
• +3 more

David A. Randall is a Professor in the Department of Atmospheric Science at Colorado State University, where he leads the Randall Group research team. His work focuses on advancing climate modeling techniques, particularly through superparameterization approaches and high-resolution simulations. He maintains active research projects in atmospheric dynamics, cloud physics, and tropical meteorology. Randall's research explores fundamental atmospheric processes including convective organization, cloud-radiative interactions, and climate system predictability. His recent work emphasizes tropical phenomena like the Madden-Julian Oscillation and tropical cyclones, with significant contributions to understanding climate change impacts on these systems. The Randall Group develops and utilizes advanced modeling frameworks like EarthWorks and participates in major community modeling efforts including the Community Earth System Model (CESM). He currently advises graduate students including Andrew Feder, Ann Casey Hughes, and Michael Needham. The Randall Group operates computational resources for large-scale climate simulations and maintains collaborative relationships with national laboratories and international research initiatives.

Pierre PerronView profile

Professor

• Econometrics
• Time Series Analysis
• Structural Change Analysis
• +4 more

Pierre Perron is a Professor of Economics at Boston University, specializing in econometrics and time series analysis. He holds positions in the Department of Economics, with a focus on structural change, unit root testing, and applications in climate change and financial markets. He received his PhD from Yale University. Education: PhD in Economics, Yale University Research Interests: Development of econometric methods for time series with structural breaks and unit roots Applications to climate change, financial markets, and macroeconomic policy Advances in statistical inference for nonstationary processes Key Awards: Fellow of the Econometric Society Clarivate Citation Laureate (2020) Doctor Honoris Causa from Pontificia Universidad Católica del Perú (2023) Recipient of the Distinguished Author Award (2022) Advising & Grants: Supervised numerous PhD students in econometrics, with many contributing to theoretical and applied research globally. His work has been supported by grants and recognized through high-impact publications. Publications: Authored/co-authored over 150 articles and books, including two volumes on Time Series Econometrics (2019) and cutting-edge work on structural change and climate modeling.

Matthias SchlesewskyView profile

Professor

• Neurobiology of Language
• Cognitive Neuroscience
• Language Processing
• +3 more

Prof. Matthias Schlesewsky is a Professor of Psychology at the University of South Australia. Previously, he held positions as Professor of General Linguistics at the University of Mainz and as a Junior Professor at the University of Marburg. With a unique background in chemistry and a PhD in linguistics, he specializes in interdisciplinary research at the intersection of science and humanities. His research focuses on the neurobiological foundations of language and how language processing evolves across the lifespan. Key interests include sentence comprehension mechanisms, cross-linguistic neurocognitive models, and the interplay between auditory processing and language evolution. Prof. Schlesewsky’s work integrates experimental methods like EEG and eye-tracking to explore real-time language processing. His contributions have advanced understanding of linguistic universals and neural mechanisms underlying language.

• Theoretical Physics
• Relativistic Heavy Ion Collisions
• Monte Carlo String Models
• +4 more

Larissa V. Bravina is a Professor of Theoretical Physics at the Department of Physics, University of Oslo (2003-present). Her career spans institutions across Russia, Norway, and Germany, including post-doctoral work at the University of Bergen and fellowships at the University of Frankfurt. Education PhD in Physics and Mathematics (1990, Moscow State University) Master Degree in Physics (1982, Moscow State University) Bravina specializes in theoretical modeling of relativistic heavy-ion collisions, focusing on Quark-Gluon Plasma formation, hydrodynamic evolution, jet quenching, and anisotropic flow. Her work integrates computational physics for simulating pp, Pb+Pb, and A+A collisions at LHC energies. Article Trends Her recent publications emphasize Monte Carlo simulations , shear viscosity , Unruh effect , and QCD phase transitions , with collaborations on laser-induced fusion and hyperon polarization . Key keywords include Nuclear Physics , Computational Modeling , and Quantum Chromodynamics . Scientific Awards Alexander von Humboldt Fellowship (1997) 3rd Prize, SINP MSU Young Scientists Competition (1989) 1st Prize (Group), SINP MSU Young Scientists Competition (1987) Advising & Collaborations She has supervised 4 doctoral students to completion (Konrad Tywoniuk, Ionut Arsene, Mads S. Nilsson, Gylnara Eyyubova) and currently mentors 2 unnamed students. Bravina organizes the annual international conference New Frontiers in Physics and collaborates with institutions like Los Alamos National Laboratory, Niels Bohr Institute, and ITEP Moscow.

Roger PenroseView profile

Professor

• General Relativity
• Cosmology
• Quantum Mechanics
• +3 more

Sir Roger Penrose is a British mathematician and theoretical physicist renowned for his contributions to general relativity and cosmology. He held the Rouse-Ball Chair of Mathematics at the University of Oxford since 1973 and served as Professor of Geometry at Gresham College (1998–2001). His work with Stephen Hawking established the singularity theorems in black hole physics. Penrose also pioneered concepts like Penrose diagrams for visualizing spacetime and Penrose tiling in geometry. Education: PhD in Algebraic Geometry from the University of Cambridge (1957). Research Interests: Black holes, cosmology, quantum mechanics, consciousness studies, and mathematical physics. His theories include conformal cyclic cosmology, proposing an eternal succession of Big Bang events. He explored consciousness in The Emperor’s New Mind and Shadows of the Mind , arguing quantum processes underpin mental phenomena. Penrose’s accolades include the Nobel Prize in Physics (2020), Copley Medal (2008), and knighthood (1994).

• Artificial Intelligence
• Pattern Recognition
• Biometrics
• +6 more

Venu Govindaraju is a SUNY Distinguished Professor of Computer Science and Engineering at the University at Buffalo, where he also serves as Vice President for Research and Economic Development. He founded the Center for Unified Biometrics and Sensors (CUBS) in 2003 and leads the National AI Institute for Exceptional Education, funded by a $20M NSF grant. His research focuses on artificial intelligence, pattern recognition, biometrics, and handwriting analysis, with notable contributions to postal automation, cybersecurity, and early screening for dyslexia/dysgraphia in children. Govindaraju holds IEEE and ACM Fellowships, and was named Person of the Year by the Council of Heritage and Arts of India in 2024. He has advised 47 doctoral students and pioneered foundational AI technologies impacting global industries. Research interests include: AI-driven education tools, handwriting recognition systems, document analysis, and cross-domain biometric technologies. His work bridges AI with humanities through projects like the Marianne Moore Digital Archive. Recent initiatives focus on leveraging AI to address speech-language pathologies and enhance K-12 education accessibility. Leadership roles include co-chair of SUNY’s AI Task Group and advisor to the SUNY-IBM AI Research Alliance. He is a vocal advocate for responsible AI policy, advising lawmakers at White House events and contributing to legislative briefings. His innovations are highlighted in Empire AI, New York’s $400M computing consortium.

• Quality Inspection and Control
• Predictive Modeling
• Smart Manufacturing Systems
• +3 more

Dr. Yu (Chelsea) Jin is an Assistant Professor in the Department of Industrial Engineering at the University at Buffalo, specializing in quality inspection, predictive modeling, and data analytics for advanced manufacturing systems. She holds a PhD in Industrial Engineering from the University of Arkansas, an ME from the University of Michigan, and dual BS degrees in Network Engineering and Finance from Jinan University. Her research focuses on integrating machine learning and physics-based models to optimize manufacturing processes, such as additive manufacturing, PCB assembly, and pharmaceutical distribution systems. She has developed frameworks like ReflowNet for reflow oven optimization and physics-informed neural networks for thermal profile prediction. Her work emphasizes both theoretical advancements and practical applications in smart manufacturing and healthcare logistics. Dr. Jin's recent publications highlight contributions to generative AI for knowledge retrieval, AGV system optimization, and multi-source transfer learning for pandemic modeling. She actively collaborates with industry partners to bridge academic research and real-world manufacturing challenges.