جست‌وجوی استاد و پژوهشگر

Jane Arnfieldمشاهده پروفایل

دانشیار

• Biographical Theatre
• Memory Studies
• Performance and Testimony
• +۴ مورد دیگر

Jane Arnfield is an Associate Professor at Northumbria University, specializing in Design Arts and Creative Industries . Her work bridges (auto)biographical theatre practice with ethnographic and archival studies in the social sciences and humanities. She holds a BA (Hons) in Drama from Dartington College of Arts (1988). Her research focuses on memory discourses , biographical narrative interviewing methods , and performance methodologies . She developed the Living Memorial Theatre Methodology (LMTM) , which explores how historical narratives can be transformed into ensemble, choral, or solo performances staged at culturally significant sites. This work emphasizes participatory engagement, enabling audiences to 'inhabit' testimonies and reinterpret the past through present perspectives. Arnfield’s collaborations include projects with the Marek Edelman Dialogue Centre (Lodz, Poland) and the Defiant Requiem Foundation (Washington, DC). As a Leverhulme Trust International Academic Fellow (2018–2019), she advanced LMTM research at the University of Lodz. Her work contributes to UN Sustainable Development Goals related to education and social justice. She actively supervises PhD students on topics like performance and testimony, biographical monodrama, and cultural memory. Her recent research includes YouWalk-YouReclaim , a co-assessment approach for accessible university environments, and Adapting Testimonies , exploring legal-drama intersections in mass atrocity contexts.

Roanna Mitchellمشاهده پروفایل

مدرس

• Psychophysical performer training
• Interdisciplinary rehearsal methodologies
• Performing arts and mental health
• +۳ مورد دیگر

Dr Roanna Mitchell is a Lecturer in Drama and Theatre at the University of Kent, serving as Programme Lead for the MA Physical Acting. She co-directs The Chekhov Collective UK , a practice-research centre exploring Michael Chekhov's technique, and leads the 'Inappropriate' Anger project addressing mental health support through creative tools. Her work bridges theory/practice in psychophysical training, body politics, and feminist performance. She also serves as Associate Editor for Training Grounds in Theatre Dance and Performance Training journal. Research Interests: Psychophysical performer training intersections, performance-mental health connections, body politics in art/business/self interfaces, feminist contributions to theatre canon, and body activism's impact on performance. Current projects include developing the MACRO Map evaluation tool and collaborations with Medway Council and Porchlight for mental health initiatives. Key Collaborations: Co-founded Endangered Bodies global initiative with AnyBody UK, artistic collaborations with Richard Schechner and Platform 7, and leadership roles in Michael Chekhov Europe networks. Her 2014 TEDx talk on body image struggles highlights her advocacy work. Teaching & Supervision: Leads MA Physical Acting and teaches physical theatre, ensemble performance, and body politics. Supervises postgraduate research in performer training applications, social health contexts, and body politics. Curriculum development informed by her MACRO Map tool and clinical performance training research. Grants & Projects: Shaped Shape Your Culture project (Rosa UK funding), New Pathways multi-institutional research, and Marlowe Theatre's Shakespeare Nation evaluation work. Active in community-engaged projects like Margate Wellbeing College collaborations.

Stéphane BORDASمشاهده پروفایل

استاد

• Computational Mechanics
• Biomechanics
• Finite Element Methods
• +۶ مورد دیگر

Stéphane BORDAS is a Full Professor in Computational Mechanics at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), leading the Computational Mechanics (Legato) research group. His work focuses on free boundary problems, method development for complex geometries, and applications in fracture mechanics, biomechanics, and computational engineering. He previously held roles at Cardiff University and the Swiss Federal Institute of Technology in Lausanne (EPFL). His research integrates computational methods like XFEM, isogeometric analysis, and meshfree techniques to address challenges in engineering and medicine. Education: Ph.D. in Theoretical and Applied Mechanics, Northwestern University (2003) M.Sc. in Civil Engineering, École Spéciale des Travaux Publics and Northwestern University (1999) Research Interests: Computational Mechanics, Biomechanics, Finite Element Methods, Fracture Mechanics, High-Performance Computing, Isogeometric Analysis. Grants & Projects: ERC Starting Grant (RealTCut) for surgical simulation and material cutting FP7 ITN INSIST for meshless methods Labs/Teams: Computational Mechanics (Legato) Group at the University of Luxembourg. His work bridges academia and industry, with applications in aerospace, biomedical engineering, and materials science. He is active in open-source software development, including codes for XFEM, isogeometric analysis, and meshfree methods.

Stefan Köhlerمشاهده پروفایل

استاد

• Human Memory
• Cognitive Neuroscience
• Human Brain Mapping
• +۳ مورد دیگر

Dr. Stefan Köhler is a Professor and Department Chair of Behavioral and Cognitive Neuroscience at Western University's Department of Psychology. He also serves as a Principle Investigator at the Western Institute for Neuroscience and holds affiliations with the Rotman Research Institute and Rotman Institute of Philosophy. His research focuses on the neural and cognitive mechanisms of human memory, particularly in aging, neurological, and psychiatric disorders. Dr. Köhler holds a Diplom (MSc) from the University of Bielefeld and a PhD from the University of Toronto (1995). He completed postdoctoral training at the Montreal Neurological Institute under Dr. Brenda Milner. His work employs techniques like fMRI, TMS, and behavioral experiments to study memory impairments and their neural substrates. Research Interests: The lab explores episodic memory's functional and anatomical organization, including interactions between memory systems and visual processing, neural correlates of novelty detection, and hippocampal contributions to pattern separation. Key themes include how brain regions differentiate memory types and support perceptual discrimination. Labs/Teams: Active in the Western Institute for Neuroscience and collaborates with the Rotman Research Institute. His lab integrates clinical neuroscience with advanced neuroimaging methods. Grants/Advising: Not explicitly listed, but his extensive publication record suggests sustained funding for studies on memory disorders and neuroimaging techniques. Advises graduate students in neuroscience and psychology programs.

Dimitrios Diochnosمشاهده پروفایل

استادیار

• Supervised Learning
• Semi-Supervised Learning
• Adversarial Learning
• +۴ مورد دیگر

Dr. Dimitrios Diochnos is an Assistant Professor in the School of Computer Science at the University of Oklahoma (OU) . His research focuses on theoretical and practical aspects of machine learning, including adversarial learning, semi-supervised learning, and imbalanced data classification. Prior to OU, he was a Hobby Postdoctoral Research Fellow at the University of Virginia and a Research Associate at the University of Edinburgh. He holds a PhD in Mathematics from the University of Illinois at Chicago, an MS in Mathematics from the University of Athens, and a BS in Informatics and Telecommunications from the University of Athens. Research Interests: Dr. Diochnos works on foundational aspects of machine learning, particularly under adversarial conditions. His current projects include developing semi-supervised learning techniques, analyzing online/streaming learning algorithms, and exploring theoretical guarantees for imbalanced classification. He is also involved in the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), focusing on trustworthy AI applications in environmental science. Awards and Recognition: He has received NeurIPS Top Reviewer awards (2023, 2019), NSF reviewer status, and the Teaching Award (2009). His postdoctoral fellowship at UVA was supported by the Hobby Endowment. He has held fellowships from the Zossima Brothers Foundation and the University of Illinois at Chicago. Service and Editorial Work: Dr. Diochnos serves as Managing Associate Editor of the Annals of Mathematics and Artificial Intelligence and has been a program committee member for major conferences like NeurIPS, AAAI, and IJCAI. He has organized events such as the Symposium on AI & ML at OU and served on the Scientific Committee for the International Olympiad in Informatics. Grants and Collaborations: His work is supported by NSF grants, including leadership in AI2ES. He collaborates with researchers in meteorology, oceanography, and climate science to address challenges in trustworthy AI for environmental systems. Labs and Affiliations: Dr. Diochnos is affiliated with the OU School of Computer Science and contributes to interdisciplinary initiatives like AI2ES. His research lab focuses on advancing machine learning theory with practical applications in adversarial robustness and environmental forecasting.

K. Dawn Grapesمشاهده پروفایل

دانشیار

• Early Modern English Music
• Music History Pedagogy
• Historical Musicology
• +۳ مورد دیگر

K. Dawn Grapes is an Associate Professor of Music History at Colorado State University, holding academic roles including Director of Graduate Studies and Musicology Area Coordinator within the School of Music, Theatre, and Dance. She earned her Ph.D. in Historical Musicology from the University of Colorado at Boulder. Her research focuses on Early Modern English music, particularly John Dowland and funeral elegies, alongside innovations in music history pedagogy. Dr. Grapes has authored notable works such as *With Mornefull Musique: Funeral Elegies in Early Modern England* (2018), *John Dowland: A Research and Information Guide* (2020), and the upcoming *Dowland* (2024). She has contributed peer-reviewed articles and book chapters on topics ranging from Elizabethan court music to modern pedagogical practices. Her work has been recognized with awards including the 2022 Eva O’Meara Award and the 2020 Claude V. Palisca Fellowship. Active in professional organizations, she serves as Vice-President of the North American British Music Studies Association and is a member of the American Musicological Society and other scholarly groups. Beyond academia, Dr. Grapes performs on the flute and writes program notes for the Colorado Bach Ensemble and Fort Collins Symphony, hosting a *Composer Talks* series.

Jason W. Griffinمشاهده پروفایل

استادیار

• Autism Spectrum Disorders
• Social Cognition
• Face Processing
• +۱۲ مورد دیگر

Jason W. Griffin, Ph.D., is an Assistant Professor in the Department of Psychology at the University of Houston, within the College of Liberal Arts and Social Sciences. He is the Director of the Griffin Laboratory and holds affiliations with the Texas Center for Learning Disorders and the Texas Institute for Measurement, Evaluation, and Statistics. His research focuses on the cognitive and clinical neuroscience of autism, particularly social perception and face processing in children and adolescents. Dr. Griffin earned his Ph.D. from Pennsylvania State University and completed postdoctoral training at Yale University as a Hilibrand Postdoctoral Fellow. His work integrates neuroscience, clinical psychology, and quantitative methods to enhance the translational impact of autism research. His research interests include autism spectrum disorders, face processing, social cognition, electrophysiology, eye-tracking, and advanced statistical modeling. He employs tools such as EEG and intervention science to study neural and behavioral mechanisms in autism. His recent publications emphasize meta-analytic approaches, naturalistic neuroscience, and innovative methodologies for capturing social perception in real-world contexts. Dr. Griffin has received significant recognition, including the NOMIS and Science Young Explorer Award and the INSAR Slifka/Ritvo Innovation in Autism Research Award. He is funded by the National Institute of Mental Health (NIMH), Autism Science Foundation, Hilibrand Foundation, and others. Alumni Association Dissertation Award Distinguished Scholar Medal INSAR Slifka/Ritvo Innovation in Autism Research Award NOMIS and Science Young Explorer Award He mentors several doctoral students, including Samuel Turner, Ayesha Sultana, and Callie Istre, and leads a team of research assistants. His lab is supported by multiple grants aimed at understanding and improving social perception in autism. He also serves on the Early Career Committee for APA Division 3 and the editorial board of Autism . The Griffin Laboratory develops novel eye-tracking and EEG methods to study naturalistic neural responses in autistic children, supported by foundations such as the Alan B. Slifka Foundation and INSAR.

Tomislav Henglمشاهده پروفایل

پژوهشگر

• Machine Learning
• Environmental Data Analysis
• Soil Science
• +۹ مورد دیگر

Dr. Tomislav Hengl is a leading researcher and Technical Director at OpenGeoHub Foundation and Envirometrix BV, specializing in spatial statistics, machine learning, and environmental data science. With over 20 years of experience in predictive soil mapping and geostatistics, he has pioneered open source frameworks for automated global environmental mapping. Co-founder of OpenGeoHub Foundation Initiator of OpenGeoHub Summer Schools (running since 2007) Project leader of OpenLandMap system Recipient of Clarivate Highly Cited Researcher (2021) His research focuses on: Machine learning for spatial/spatiotemporal data Environmental data cube systems Global soil and vegetation mapping Open source geospatial software development Spatio-temporal predictive modeling Cloud computing for Earth observation data Recent research trends include: Development of high-resolution global terrain models Analysis of vegetation productivity using satellite time-series Ensemble machine learning for environmental mapping Integration of multi-source geospatial datasets Applications in climate change impact assessment Advancing open data infrastructures Scientific contributions include: Clarivate Highly Cited Researcher (2021) Over 60 journal publications Founding Vice-Chair of the International Society for Geomorphometry (2011-2015) Development of open source R packages for geospatial analysis

Arpan Gujaratiمشاهده پروفایل

استادیار

• Real-Time Systems
• Distributed Systems
• Fault Tolerance
• +۵ مورد دیگر

Arpan Gujarati is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. His research focuses on real-time systems, distributed systems, fault tolerance, and reliability analysis in cloud and cyber-physical systems domains. He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and has postdoctoral and research experience at MPI-SWS and UBC. Education: PhD in Real-Time Systems (MPI-SWS/TU Kaiserslautern, 2020), Postdoctoral Researcher at MPI-SWS (2020), Research Associate at UBC (2022–2023), B.Sc. from Birla Institute of Technology and Science (BITS Pilani, India). Research interests include distributed real-time systems, reliability analysis of cloud applications, fault-tolerant machine learning, and scheduling algorithms. His work bridges theory and practice, addressing challenges in ultra-reliable CPS and resilient distributed systems. Awards: Best Dissertation Award (SIGBED, 2020), Best Paper Awards (RTSS 2022, ECRTS 2018), Distinguished Artifact Award (OSDI 2020). Advising: Supervises PhD and undergraduate students in distributed systems and resilience engineering. Active in UBC’s Computer Science Graduate Program, teaching courses like CPSC 416 (Distributed Systems) and CPSC 538G (Topics in Computer Systems). Labs/Teams: Leads the Systopia Lab, collaborating with industry partners on projects like robotic arm datasets and self-driving lab tools (RABIT). Involved in research groups focused on machine learning resilience and real-time systems.

• Computational Optimization
• Global Optimization
• Mixed-Integer Nonlinear Programming
• +۹ مورد دیگر

Dr. Ruth Misener is a Professor in the Department of Computing at Imperial College London and holds the BASF/RAEng Research Chair in Data-Driven Optimization (2022-27). Her research focuses on computational optimization, including global optimization of mixed-integer nonlinear programs, optimization under uncertainty, and integration with machine learning. She leads the Computational Optimization Group, which develops open-source tools like GALINI, and collaborates with industry partners such as Royal Mail for logistics optimization. Her research interests span optimization challenges in engineering, bioprocess design, and healthcare, including bioreactor optimization and disease trajectory modeling for leukemia. She has been awarded the Royal Academy of Engineering Research Fellowship, supporting her work on hybrid computational/experimental platforms for biomedical applications. Recent projects include scheduling optimization under uncertainty, Bayesian methods for experimental design, and robust optimization for heat recovery networks. Her work emphasizes explainable optimization and safety-critical decision-making, with applications in energy systems, pharmaceuticals, and manufacturing. Ruth’s contributions include over 50 publications, with a focus on algorithm development and industrial impact. She actively participates in conferences and chairs workshops on optimization, such as the Mixed Integer Programming Workshop and the British-French-German Conference on Optimization.

Alberto L. Sangiovanni-Vincentelliمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Design Automation
• Embedded Systems
• +۷ مورد دیگر

Alberto L. Sangiovanni-Vincentelli holds the Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a pioneer in Electronic Design Automation (EDA) and co-founder of Cadence and Synopsys. His research focuses on Cyber-Physical Systems (CPS), embedded systems, hybrid systems, and formal methods for AI. He has authored over 800 papers and 17 books, with major contributions to design automation and methodologies. Education: Dr. Ing., EECS, Politecnico di Milano (1971) Research interests include design methodologies, CPS, and AI integration. He has received numerous awards, including the IEEE James Clerk Maxwell Medal (2008) and ACM/IEEE A. Richard Newton Technical Impact Award (2009). He serves on multiple corporate boards and advisory councils, including the Strategic Committee of the Italian Strategic Fund and the Executive Committee of the Italian Institute of Technology. His work spans academia, industry, and policy, with a focus on innovation ecosystems and CPS design automation. Awards: Over 20 major honors, including Fellowships from IEEE and ACM.

Eric D. Kolaczykمشاهده پروفایل

استاد

• Statistics
• Network Analysis
• Machine Learning
• +۶ مورد دیگر

Eric D. Kolaczyk is a Professor of Statistics in the Department of Mathematics and Statistics at McGill University, where he also serves as the inaugural director of the Computational and Data Systems Initiative (CDSI). Additionally, he is an associate academic member at Mila, The Quebec AI Institute, positioning him at the forefront of statistics, data science, and artificial intelligence research. Dr. Kolaczyk's research focuses on statistical and machine learning theory and methods that support human endeavors enabled by computing and engineered systems, frequently from a network-based perspective of systems science. His work spans multiple domains including: Network analysis and modeling Computational biology and neuroscience AI-assisted chemistry and materials science Data privacy and security Statistical methodology for complex systems His recent publications demonstrate a strong emphasis on network science, with particular attention to dynamic networks, privacy-preserving methods, and applications across diverse fields from epidemiology to materials science. His work bridges theoretical statistics with practical applications, often developing novel methodologies to address challenges in complex data environments. Dr. Kolaczyk has received significant recognition for his contributions to the field: Elected Fellow of the American Association for the Advancement of Science (AAAS) Elected Fellow of the American Statistical Association (ASA) Elected Fellow of the Institute of Mathematical Statistics (IMS) Elected Senior Member of the IEEE Elected Member of the International Statistical Institute (ISI) Throughout his career, Dr. Kolaczyk has been actively involved in academic leadership and service. He has served as an associate editor for prestigious journals including the Journal of the American Statistical Association (JASA) and the Journal of the Royal Statistical Society, Series B (JRSSB). He previously served as co-chair of the U.S. National Academies of Sciences, Medicine, and Engineering Roundtable on Data Science Education and as president of the New England Statistical Society. His research has been supported by numerous grants that enable collaborations across disciplines. As the inaugural director of McGill's Computational and Data Systems Initiative (CDSI), Dr. Kolaczyk leads interdisciplinary efforts to advance computational and data-intensive research across the university. His leadership at CDSI fosters collaboration between statisticians, computer scientists, domain experts, and industry partners to tackle complex data challenges.

Shirin Jalaliمشاهده پروفایل

دانشیار

• Computational Imaging
• Machine Learning
• Information Theory
• +۴ مورد دیگر

Shirin Jalali is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. She holds a Ph.D. and M.Sc. in Electrical Engineering and Statistics from Stanford University, with earlier degrees from Sharif University of Technology. Prior to Rutgers, she was a Research Scientist at Nokia Bell Labs and held roles at Princeton University and NYU Tandon School of Engineering. Her research focuses on computational imaging, machine learning, and information theory, addressing challenges like speckle noise mitigation and snapshot compressive imaging. She received the NSF CAREER Award in 2023 and serves as an Associate Editor for IEEE Transactions on Information Theory. Education: Ph.D. in Electrical Engineering, Stanford University, 2010 M.Sc. in Statistics, Stanford University, 2010 M.Sc. in Electrical Engineering, Sharif University of Technology, 2004 B.Sc. in Electrical Engineering, Sharif University of Technology, 2002 Research Interests: Her work spans high-dimensional inference, computational imaging, and machine learning applications in imaging systems. She develops algorithms for coherent imaging in speckle noise environments and explores structure learning through information-theoretic methods. Recent trends in her publications emphasize unrolled neural networks, Bayesian despeckling, and theoretical frameworks for compressive imaging systems. Awards and Roles: 2023 NSF CAREER Award Associate Editor, IEEE Transactions on Information Theory (since 2021) Leadership in research groups addressing imaging and machine learning challenges Grants and Labs: Her NSF CAREER grant funds theoretical work on snapshot compressive imaging systems. She collaborates across academia and industry, leading interdisciplinary projects in signal processing and AI-driven imaging solutions.

Chad Dubéمشاهده پروفایل

دانشیار

• Cognitive Psychology
• Recognition Memory
• Signal Detection Theory
• +۷ مورد دیگر

Chad Dubé is an Associate Professor in the Department of Psychology at the University of South Florida, where he has been a faculty member since 2013, advancing from Assistant to Associate Professor in 2019. His research is centered on cognitive psychology, particularly in memory, perception, and decision-making. He completed his Ph.D. and M.S. in Cognitive Psychology at the University of Massachusetts, Amherst, under Caren Rotello, and earned his B.S. in Psychology from Eastern Michigan University. Ph.D., Cognitive Psychology, University of Massachusetts, Amherst (2011) M.S., Cognitive Psychology, University of Massachusetts, Amherst (2009) B.S., Psychology, Eastern Michigan University (2006) His research interests include recognition memory, signal detection theory, ROC analysis, visual short-term memory, alpha oscillations in attention and memory, and deductive reasoning. He employs quantitative and computational modeling approaches to investigate cognitive mechanisms underlying memory and perception. The recent publications reflect a strong focus on theoretical and empirical investigations in memory and perception, particularly using signal detection and computational modeling frameworks. Key themes include central tendency effects, serial dependency, ensemble coding, and the statistical structure of memory representations. His work bridges experimental psychology with formal modeling and information theory. Although no formal awards are listed in the provided text, his publication record in high-impact journals such as Quarterly Journal of Experimental Psychology and Journal of Memory and Language indicates scholarly recognition. Chad Dubé advises graduate and undergraduate students, as indicated by annotations (GA, UA) on his publications. He has collaborated with students such as K. Tong, J. Zepp, and M. Lowry. There is no mention of specific grants or funding sources in the provided text. He previously held a postdoctoral fellowship at Brandeis University’s Volen Center and an adjunct position at Babson College. He is affiliated with the Department of Psychology, which is part of the College of Arts and Sciences at the University of South Florida. His work contributes to the Cognitive and Neural Systems (CNS) specialty area within the department.

• Machine Learning
• Case-Based Reasoning
• Natural Language Processing
• +۳ مورد دیگر

Nirmalie Wiratunga is a Professor in Intelligent Systems at the School of Computing , Robert Gordon University, and serves as the Associate Dean for Research . She is also an Adjunct Professor at the Norwegian University of Science and Technology (IDUN program). Her academic excellence spans over two decades in Artificial Intelligence and Machine Learning , with a focus on Explainable AI (XAI) , Case-Based Reasoning (CBR) , and Natural Language Processing (NLP) . Her research explores innovative methodologies for knowledge-rich representations to automate decision-making through CBR for Retrieval-Augmented Q&A systems and human-centered AI platforms . She co-founded Attendr.app , a spinout for student and conference attendance tracking, and leads the Artificial Intelligence & Reasoning Research Group at RGU. Recent publications (2024–2025) highlight her work on LLM hallucination detection , counterfactual explanations in finance , cross-lingual biomedical review automation , and multi-query resolution in legal domains . Themes span AI explainability , NLP , CBR , and domain-specific knowledge integration across healthcare, law, and education. Her leadership extends to organizing international workshops on XAI , digital health , and Deep Learning , and co-chairing the ICCBR 2021 and 2022 conferences. She actively contributes to program committees for ECCBR , ECML/PKDD , and IJCAI .