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Enrica Dall'AraView profile

Associate Professor

• Practice-based research
• Public space design
• Urban renewal
• +3 more

Dr. Enrica Dall'Ara serves as the Associate Dean of Planning and Landscape Architecture and an Associate Professor at the University of Calgary's School of Architecture, Planning and Landscape. Her work bridges academic research and professional practice, exemplified through her firm PARC, which specializes in public space design, urban renewal, and the rehabilitation of industrial sites. She emphasizes interdisciplinary collaboration, integrating fields like environmental engineering and biology into landscape architecture. Education: Laurea in Architettura (Master Degree equivalent in Architecture), University of Ferrara, Italy Master in Landscape Architecture, ETSAB, Polytechnic University of Catalonia, Spain PhD in Landscape Design, University of Florence, Italy Research Interests: Dr. Dall'Ara focuses on practice-based research, public space design, service landscapes, and the integration of cultural advocacy into landscape architecture. Her work explores site-specific design solutions, the role of agriculture in urban-rural dynamics, and the preservation of traditional rural landscapes. She advocates for designs that serve life through poetic, functional, and culturally sensitive approaches. Contributions: Through her teaching, Dall'Ara encourages students to view design as research, fostering critical thinking and interdisciplinary dialogue. Her firm’s projects, such as the roundabout gardens in Imola, Italy, demonstrate her commitment to merging ecological, functional, and aesthetic considerations in landscape interventions. Professional Affiliations: Canadian Society of Landscape Architects (CSLA) Alberta Association of Landscape Architects (AALA) Associazione Italiana di Architettura del Paesaggio (AIAPP) Ordine degli Architetti, Pianificatori, Paesaggisti e Conservatori (Province of Forlì-Cesena, Italy)

Daniel M. KaneView profile

Professor

• Number Theory
• Combinatorics
• Complexity Theory
• +3 more

Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.

Berrak SismanView profile

Assistant Professor

• Artificial Intelligence
• Speech Processing
• Machine Learning
• +3 more

Berrak Sisman is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). She leads the Speech & Machine Learning Lab (SmILe Lab), focusing on AI-driven speech technologies. She received her PhD from the National University of Singapore in 2020 and was previously a tenure-track faculty member at the University of Texas at Dallas (2022–2024). Research Interests: Her work spans artificial intelligence, speech synthesis, voice conversion, emotion analysis in speech, medical speech applications, and secure speech technology. She develops neural models for expressive and adaptive speech processing. Publications: Her recent articles (2024–2025) emphasize speech emotion recognition, zero-shot prosody control, accent conversion, and disentangled representations in TTS, reflecting a focus on cross-modal learning, robustness, and real-world applications. Awards & Grants: NSF CAREER Award (2024) Amazon Faculty Research Award (2022) Singapore Ministry of Education Award (2021) A*STAR Singapore International Graduate Award (2016–2020) Leadership: She directs the SmILe Lab, recruiting PhD/Master’s students for projects in neural speech modeling. Her grants include NSF and Amazon funding for voice conversion and emotion synthesis research.

Susan RobertsonView profile

Professor

• Sociology of Education
• Globalization and Education
• Education Governance
• +4 more

Susan Robertson holds the Chair in Sociology of Education at the Faculty of Education, University of Cambridge, where she serves as Director of the Doctoral Programme and Editor-in-Chief of Globalisation, Societies and Education . She joined Cambridge in 2016 after academic appointments at the University of Auckland and University of Bristol, bringing over three decades of international expertise in critical education studies. Her educational foundation includes doctoral research in Sociology/Policy at the University of Calgary (1990), preceded by professional experience in the Research Division of Western Australia's Ministry of Education. This trajectory established her cross-sectoral perspective on education systems and policy implementation. Robertson's research critically examines state-education relationships through governance frameworks that determine educational equity and outcomes. She investigates how global and regional actors mediate the social contract between states and citizens, with particular focus on geopolitical shifts, OECD influence, and neoliberal transformations in higher education. Her work bridges sociology, political economy, and decolonial perspectives to address social justice imperatives in education. Analysis of her 2024-2026 publications reveals sustained engagement with higher education geopolitics, digital transformation challenges, and global governance mechanisms. Key thematic clusters include critiques of OECD-driven policy frameworks, analyses of ordinal citizenship in education systems, and examinations of how digitalization reshapes university missions while exacerbating existing inequalities. As Director of the Doctoral Programme, Robertson oversees comprehensive PhD training while convening the Culture, Politics and Global Justice research cluster that fosters interdisciplinary collaboration on transnational education challenges. Her service on the ESRC funding council positions her at the nexus of research strategy and resource allocation in UK social sciences. The Culture, Politics and Global Justice cluster under her leadership functions as a dynamic hub for critical scholarship, hosting regular seminars and collaborative projects that connect Cambridge researchers with global partners to address emergent issues in education governance and social transformation.

Steven Andrew CulpepperView profile

Professor

• Quantitative Methods in the Social Sciences
• Psychometrics
• Latent Class Models
• +6 more

Steven Andrew Culpepper is a Professor of Statistics at the University of Illinois at Urbana-Champaign, holding additional appointments as Professor in the Beckman Institute for Advanced Science and Technology, Psychology, and Educational Psychology. He specializes in quantitative methods for social sciences, focusing on psychometric models, latent class analysis, and statistical computing. Education: PhD, Educational Psychology, University of Minnesota, 2006 BS, Economics, Bowling Green State University, 2001 Research interests include advanced statistical methodologies such as latent class models, high-stakes testing analysis, and applications of Bayesian computing in education and organizational research. His work emphasizes improving large-scale assessment systems through innovative modeling approaches. His publications consistently address latent structure modeling, cognitive diagnosis frameworks, and methodological advancements in educational and behavioral statistics. While no scientific awards are explicitly listed, his contributions to psychometric theory and statistical software development are notable. Steven has grants and consulting projects related to statistical methodologies but specific grant details are not provided in the texts. He has no listed advisees/PhD students in the provided information. He collaborates across disciplines through affiliations with the Beckman Institute and maintains active software development projects, including R packages like 'rrum' and 'pathmodelfit'.

• Statistical Modeling
• Bayesian Analysis
• Educational Data Analysis
• +4 more

Dr. Haiyan Liu is an Associate Professor of Quantitative Methods, Measurement, and Statistics in the Department of Psychological Sciences at the University of California, Merced, within the School of Social Sciences, Humanities, and Arts. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2018). Her research focuses on advanced statistical modeling of psychological and educational data, including high-dimensional, longitudinal, and social network data. She develops Bayesian methodologies and machine learning techniques to enhance understanding of human behavior, with recent emphasis on structural equation modeling, network dynamics, and nonparametric growth curves. Her work addresses challenges in survey methodology and behavioral data analysis. Dr. Liu’s educational background includes a Ph.D. in Quantitative Psychology from the University of Notre Dame (2018), complementing her current academic role. Her lab, accessible at https://sites.google.com/view/ucmhaiyanliu , supports her research activities. Her research interests span Bayesian SEM, social network analysis, and applications of machine learning to behavioral data, aiming to bridge methodological innovation with practical psychological inquiry. Her recent articles highlight advancements in Bayesian model selection, longitudinal sentiment analysis, and social network mediation. She emphasizes prior specification rigor in Bayesian frameworks and explores nonlinear relationships in social dynamics. Though no awards are explicitly listed, her contributions to statistical methodologies in psychological research reflect significant scholarly impact. Dr. Liu advises students in quantitative methods and has developed software tools like logistic4p for misclassification correction in logistic regression. Her work integrates computational methods with theoretical advancements, positioning her as a key contributor to modern quantitative psychology.

Laurie WilcoxView profile

Professor

• Vision Science
• Neuroscience
• Binocular Vision
• +4 more

Laurie Wilcox is a Full Professor in the Department of Biology at York University, affiliated with the Faculty of Science. Her research focuses on stereopsis, binocular vision, and depth perception, particularly exploring how the visual system processes binocular disparity signals. She leads a laboratory investigating cortical systems for fine and coarse disparities, with studies on amblyopia and applied collaborations with companies like Christie Digital and IMAX. Her work bridges basic neuroscience and applied research, addressing depth perception in 2D/3D displays and VR environments. Key interests include stereoscopic volume representation, perceptual grouping, and the impact of monovision on depth judgments. Recent studies examine lightness constancy in virtual reality, depth magnitude errors in 3D displays, and neural activation patterns in object-selective visual cortex. Wilcox has published extensively on binocular vision mechanisms, including coarse stereopsis in strabismus patients and the role of motion parallax in depth perception. Her applied projects evaluate visual fidelity in stereoscopic content, compression algorithms, and ergonomic considerations for XR devices. She also investigates how environmental context (e.g., familiar size, natural scenes) modulates depth perception accuracy across real and virtual environments. Her research emphasizes translational applications, aiming to optimize display technologies through insights from human visual processing. Ongoing work explores perceptual integration of binocular and monocular cues, attention modulation by depth, and the neurophysiological underpinnings of stereoscopic vision.

Cengiz ZopluogluView profile

Associate Professor

• Quantitative Methods in Education
• Item Response Theory
• Computational Psychometrics
• +4 more

Cengiz Zopluoglu is an Associate Professor in the Department of Special Education and Clinical Sciences at the University of Oregon's College of Education. His research focuses on quantitative methods in education, item response theory, computational psychometrics, and educational data science. He teaches advanced courses on psychometrics, statistical methodology, and data analysis using R. Education: PhD, 2013: University of Minnesota (Educational Psychology, Quantitative Methods) MA, 2009: University of Minnesota (Educational Psychology, Quantitative Methods) BA, 2005: Abant Izzet Baysal University (Mathematics Education, K-8) Research Interests: Zopluoglu's work emphasizes integrating machine learning and statistical models into educational measurement. He develops methods to detect test misconduct (e.g., item preknowledge) using response time and accuracy data, and explores automated scoring of open-ended responses using AI (e.g., transformers). His contributions include advancements in continuous response models, multidimensional IRT, and DETECT analysis for dimensionality assessment. Awards: 2023 Runner-up Prize in NAEP Math Automated Scoring Challenge (NCES) 2021 3rd Place in NIJ Recidivism Forecasting Challenge 2013 Graduate Student Research Award (University of Minnesota) Advising & Grants: Zopluoglu has advised on projects related to test security, automated scoring, and machine learning applications. His work often involves open-source tools like R and Stan, with a focus on reproducible research. Labs & Collaborations: He collaborates on initiatives like the Deterministic Gated Models for Test Security and the WrightRightNow automated scoring platform. His research leverages interdisciplinary approaches, blending psychometrics with computer science and data science.

Pietro BortolottiView profile

Researcher

• Multidisciplinary design, analysis and optimization (MDAO) of wind energy systems
• Wind turbine rotor design
• Aeroservoelasticity of wind turbines
• +3 more

Pietro Bortolotti is a senior researcher at the National Wind Technology Center, focusing on wind energy systems. He leads U.S. Department of Energy-funded projects like Big Adaptive Rotor and Holistic Systems Engineering, advancing land-based wind turbine technology through multidisciplinary design and multi-fidelity optimization approaches. Education: Bachelor in Energy Engineering, Politecnico di Milano Master in Sustainable Energy Technology, Technical University of Delft PhD in Mechanical Engineering, Technical University of Munich His research spans MDAO of wind energy systems , rotor design , and aeroservoelasticity , with applications in turbine aerodynamics, noise reduction, and AI-driven environmental monitoring. Recent work includes comparing downwind/upwind turbine configurations, bat tracking systems, and large-scale turbine optimizations. Key trends in his 2024–2025 publications reflect advancements in rotor dynamics , multi-fidelity modeling , and AI integration for wildlife monitoring. Collaborations include institutions like DTU, TU Delft, and NREL, with tools like WISDEM, WEIS, and OpenFAST underpinning his research. His professional journey includes postdoctoral work at NREL (2018–2019), research roles at Technical University of Munich (2013–2018), and a research assistantship at Denmark Technical University (2012–2013). He contributes to projects improving numerical models for wind turbine analysis and optimization.

Brooke MagnusView profile

Associate Professor

• Psychometrics
• Item Response Theory (IRT)
• Categorical Data Analysis
• +3 more

Brooke Magnus is an Associate Professor in the Department of Psychology and Neuroscience at Boston College. She earned her PhD in Psychology (with a minor in Biostatistics) from the University of North Carolina at Chapel Hill's L.L. Thurstone Psychometric Laboratory. Her research focuses on psychometric model development for clinical and health outcomes, including item response theory (IRT) applications to survey data. She teaches statistics courses and mentors graduate students applying psychometric methods to substantive research areas. Her work emphasizes improving measurement practices in clinical settings, particularly through zero-inflated models for symptom data and IRT-based instrument validation. Key research areas include traumatic brain injury outcomes, neurodivergent youth bullying assessment, and pediatric health measurement. She collaborates across psychology, medicine, and public health disciplines. Recent work highlights advancements in TBI severity characterization, concussion assessment tool comparisons, and autism spectrum disorder psychometric analyses. Her methods bridge quantitative psychology and biostatistics to address gaps in clinical measurement precision.

• Radiative heat transfer
• Computational thermal sciences
• Turbulence-radiation interaction
• +4 more

Pedro Jorge Martins Coelho is a Professor in the Mechanical Engineering Department at Instituto Superior Técnico, University of Lisbon, Portugal. His academic career spans several decades with a focus on advanced thermal sciences and computational methods. His research has significantly contributed to the understanding of radiative heat transfer phenomena in complex systems. Dr. Coelho's educational background includes a Ph.D. in Mechanical Engineering, which has provided the foundation for his extensive research in thermal sciences. His work demonstrates a strong theoretical foundation combined with practical applications across various engineering domains. His primary research interests encompass radiative heat transfer, turbulence-radiation interaction, combustion modeling, and numerical methods for thermal systems. Recent work has expanded into biomedical applications of thermal radiation, particularly in laser-tissue interactions for cancer detection and treatment. His publications reveal a consistent focus on developing and refining computational methods for solving complex heat transfer problems, with particular emphasis on the radiative transfer equation in various media and geometries. Analysis of his recent publications shows a clear evolution toward more complex and interdisciplinary applications, including biomedical thermal applications, advanced turbulence modeling, and thermal management of electrical systems. His work consistently bridges fundamental theoretical developments with practical engineering applications, particularly in combustion systems, energy recovery, and thermal management. Dr. Coelho has served on editorial boards for prestigious journals including Heat Transfer Research, Computational Thermal Sciences, and International Journal of Energy for a Clean Environment, demonstrating his standing in the thermal sciences community. His research collaborations span numerous institutions and researchers worldwide, as evidenced by his extensive publication record with various co-authors across different countries. He has also been involved in conference organization, serving as Associate Editor for major international heat transfer conferences.

Andrew HoView profile

Professor

• Educational Assessment
• Diversity and Inclusion in Education
• Educational Inequality
• +3 more

Andrew Ho is the Charles William Eliot Professor of Education at Harvard University's Graduate School of Education (HGSE). He holds a Ph.D. in Educational Psychology and an M.S. in Statistics from Stanford University. His research focuses on improving educational assessment design, particularly in measuring educational progress and inequality. He developed the Stanford Education Data Archive (SEDA), a national repository of student achievement data, and advocates for low-stakes assessment use in policy. Ho has held leadership roles including Immediate Past President of the National Council on Measurement in Education and trustee of the Carnegie Foundation. He advises seven U.S. states' testing programs and teaches graduate courses in statistics and psychometrics at HGSE. His work emphasizes the importance of accurate assessment during crises like the pandemic, advocating for standardized testing as part of a multi-measure 'census' approach to identify learning disparities and allocate resources effectively. Education: Ph.D. in Educational Psychology (Stanford, 2005), M.S. in Statistics (Stanford) Affiliations: Harvard Graduate School of Education, Technical Advisory Committees for 7 states Key Projects: SEDA, Assessment Literacy initiatives, pandemic-era testing advocacy His research bridges psychometrics with policy, emphasizing equitable assessment practices. Notable contributions include frameworks for interpreting test scores in multi-measure systems and critiques of 'learning loss' terminology favoring actionable 'learning lag' perspectives. Ho's recent work addresses pandemic-era education challenges through rigorous data analysis and policy recommendations.

Kevin VinsenView profile

Research Fellow

• Machine Learning
• High-performance Computing
• Peta-scale systems
• +7 more

Kevin Vinsen is a Senior Research Fellow at the University of Western Australia, working in the Data Intensive Astronomy (DIA) Program of the International Centre for Radio Astronomy Research (ICRAR) since 2009. He is also affiliated with the UWA Defence and Security Institute and holds an ORCID ID of 0000-0001-5332-3784. His work focuses on translating ICRAR software capabilities into practical industry applications across diverse domains. His research interests include: Peta-scale systems High-performance Computing Machine Learning applications in multiple fields Wave and weather forecasting Digital Assistive Technologies Agricultural applications of ML Large language models Vinsen heads the Translation and Impact work of the DIA team and leads the development of Machine Learning systems. His current projects include ML for wave forecasting on the NW shelf, wind and temperature forecasting, honey traceability and provenance, and digital assistive technology for people with disabilities. His work contributes to UN Sustainable Development Goals related to industry, oceans, agriculture, food, disability, and defense. His research output demonstrates a strong trend toward applying machine learning techniques to solve real-world problems across astronomy, environmental science, agriculture, and disability support. This interdisciplinary approach showcases the versatility of his computational expertise across scientific and social domains. Vinsen has an h-index of 11 with 621 citations across 33 research outputs. As the ICRAR/UWA Summer Studentship Co-ordinator, he mentors emerging researchers and contributes to building research capacity. His collaborative network spans multiple institutions and research areas, reflecting his ability to bridge academic research with practical applications.

• Marketing
• Consumer Economics
• Business Administration
• +4 more

Professor Tatsuya Kimura serves at Waseda University's Graduate School of Business and Finance, holding a Ph.D. from Waseda University and an MBA from Lancaster University (UK). His academic appointments include Professor at the Faculty of Commerce and Graduate School of Business and Finance, with additional affiliations at the Global Education Center. His research spans Consumer Economics and Society , Marketing , Internal Marketing , and Service Management , with particular focus on Japanese market adaptations of customer satisfaction metrics. Kimura pioneered the Promoter Score Japan (PSJ) as a culturally adapted alternative to Net Promoter Score (NPS), demonstrating through empirical studies that Japanese consumer response patterns differ significantly from Western models. His publication trends reveal evolving specialization from foundational marketing theory (1990s-2000s) toward culturally-specific metric development (2010s-2020s). Recent work analyzes care worker motivation through internal marketing frameworks and examines service industry dynamics through Japanese cultural lenses. His research consistently bridges academic theory with practical business applications across manufacturing and service sectors. Nikkei Advertising Award Category Award Winner (since 1990) Fujisankei Advertising Award (since 1986) Kimura leads multiple Japan Society for the Promotion of Science research projects including Research on the promotion requirements for internal marketing (2018-2023) and Internal Marketing (2011-2013), with findings published in Routledge's Internal Marketing: Another Approach to Marketing for the Growth (2016). His international collaborations include research at Columbia University (2012-2013) on global marketing strategy. He teaches core graduate courses including Marketing & Brand Management , Service Marketing & Management , and Marketing Management , with particular emphasis on case-based learning methodologies for Japanese business professionals.

• Discrete-event systems
• Control theory
• Petri net modeling
• +6 more

Ramavarapu S Sreenivas is a Professor in the Industrial and Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign , with research appointments at the Coordinated Science Laboratory (CSL) and the Information Trust Institute (ITI ). He holds a joint affiliation with the Electrical and Computer Engineering department and serves as the Arthur Davis Faculty Scholar since 2016. Ph.D. , Electrical and Computer Engineering, Carnegie Mellon University (1990) M.S.E.E. , Carnegie Mellon University (1987) B.Tech , Electrical Engineering, Indian Institute of Technology Madras (1985) His research focuses on Discrete-Event/Discrete-State (DEDS) systems , applying Coding Theory, Machine Learning, and Information Theory to develop near-optimal supervisory policies for applications in wireless networks, automated manufacturing, and healthcare systems . He leads the Center for Autonomous Construction and Manufacturing at Scale (CACMS) , established in 2023. Recent publications highlight advancements in liveness enforcement in Petri nets , fault-tolerant control , and IoT-based load scheduling . His work bridges theoretical rigor with practical implementations in Distributed Control, Network Coding , and Reinforcement Learning . UIUC Campus Award for Excellence in Graduate and Professional Teaching (2023) Arthur Davis Faculty Scholar (2016) Senior Member, IEEE (2002) James Franklin Sharp Outstanding Teaching Award in Industrial Engineering (2017, 2012) Sreenivas has taught graduate and undergraduate courses in Control Systems, Integer Programming, and Financial Computing since 1992. He co-instructed courses in Health Technology and contributed to the Master of Science in Financial Engineering (MSFE) program, which ranks 4th nationally.