جست‌وجوی استاد و پژوهشگر

Hao Chenمشاهده پروفایل

دانشیار

• Anomaly/signal detection for streaming data
• Graph-based methods
• Two-sample test for high-dimensional and non-Euclidean data
• +۷ مورد دیگر

Hao Chen, Ph.D. is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional and non-Euclidean data, including anomaly detection, graph-based methods, and change-point analysis. He also explores AI security, multimodal models, and geospatial applications. His work bridges statistical theory and practical machine learning challenges. Education: Ph.D., Graduate Group in Biostatistics, Stanford University Research Interests: Dr. Chen’s expertise spans statistical methods for streaming data, categorical data analysis, and allele-specific copy number variation. He has pioneered work in detecting signals in complex datasets and developing robust AI systems. His recent focus includes mitigating modality interference in LLMs, enhancing model safety via guardrails, and advancing geospatial AI through projects like GeoLM. Publications: His recent work addresses cutting-edge topics such as multimodal model vulnerabilities, unlearning algorithms, and clinical radiology applications. Key themes include improving model robustness, ethical AI design, and interdisciplinary data integration. Labs/Teams: Engages in collaborative projects at UC Davis, though specific lab names are not listed in the provided information.

Michael Danieleمشاهده پروفایل

دانشیار

• Bioelectronics Engineering
• Wearable Biosensors
• Microphysiological Systems
• +۳ مورد دیگر

Michael Daniele is an Associate Professor at North Carolina State University, jointly appointed in the Department of Electrical & Computer Engineering and the Joint Department of Biomedical Engineering . His research focuses on bioelectronics engineering, particularly in developing microsystems for monitoring, mimicking, and augmenting biological functions. He leads the @BiointerfaceLab , exploring wearable/implantable biosensors, microphysiological systems, and process analytical technologies for biomanufacturing. Education : Ph.D. in Materials Science & Engineering (Clemson University, 2012) Bachelor's in Materials Science & Engineering (Rutgers University, 2009) Research Highlights : Developing "injury-on-a-chip" models for coagulation studies Pioneering hydrogel microneedles for diagnostic devices Advancing light-controlled peptide ligands for protein purification Collaborating with Novartis on viral vector manufacturing Award Recognition : 2024 William F. Lane Outstanding Teaching Award 2019 NSF CAREER Award 2022 University Faculty Scholar Grants & Initiatives : Co-leader of the NC-Viral Vector Initiative (2023–present) NSF-funded projects in biosensor integration and biomanufacturing His work bridges engineering and medicine, with applications in gene therapy, wearable diagnostics, and precision agriculture.

• Marketing
• Sustainability
• Technology in Business
• +۳ مورد دیگر

Carolina Luis Bassa is a Research Professor at the Universitat Pompeu Fabra Barcelona School of Management (UPF-BSM), serving as Academic Director and leading the Department of Business and Strategic Management. She directs multiple master’s programs and heads the Mercadona UPF-BSM Chair of Circular Economy. Her expertise spans Marketing, Sustainability, and Technology applied to business strategy, with a focus on digital transformation and circular economy practices in industries like agri-food distribution. Her academic journey includes roles as Vice-Dean of Teaching Staff and Academic Director, reflecting her commitment to educational innovation. Research interests emphasize sustainable consumer behavior, data-driven business models, and the integration of technology in CRM and customer experience management. Over 35 years, she has advised students and professionals on strategic applications of marketing and technology. Research Trends: Recent publications highlight circular economy adoption in agriculture, transparency in sharing economies, and data-driven strategies in customer-centric models. She bridges academic insights with industry challenges, particularly in leveraging technology for sustainable business practices. Awards & Grants: No specific awards are listed, but her leadership roles and chair position indicate significant institutional recognition. Grants and partnerships likely stem from her involvement with initiatives like the Mercadona Chair. Labs & Teams: Leads the Mercadona UPF-BSM Chair, focusing on circular economy research and collaboration with industry partners.

Chen Wei Wayneمشاهده پروفایل

استادیار

• Generative Design AI
• Machine Learning
• Uncertainty Quantification
• +۳ مورد دیگر

Chen Wei Wayne is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. His research focuses on generative design AI, machine learning, uncertainty quantification, and advanced manufacturing. He leads the DIGIT Lab, which develops AI methods for design innovation, automation, and manufacturing integration. Education: Ph.D., Mechanical Engineering, University of Maryland, College Park (2019) M.S., Mechanical Engineering, Chongqing University, China (2015) B.S., Mechanical Engineering, Chongqing University, China (2012) Research Interests: Generative adversarial networks (GANs) for design synthesis Data-driven metamaterials and multiscale systems Uncertainty quantification in engineering design AI-driven design automation Awards & Honors: ASME Journal of Mechanical Design Reviewer of the Year Award (2023) ASME DAC Best Paper Award (2022) Journal of Mechanical Design Editors’ Choice Honorable Mention (2021) Lab Activities: Recent lab milestones include successful completion of TAMUQ Summer Research Programs (2024) Hosts undergraduate researchers like Wisam Gadam and Eddie Guerrero

Valen Johnsonمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Medical Imaging
• +۷ مورد دیگر

Valen E. Johnson is a University Distinguished Professor and Dean Emeritus of the College of Science at Texas A&M University, where he has been a faculty member since 2012. He previously held professorships at the University of Texas M. D. Anderson Cancer Center (2004–2012), the University of Michigan (2002–2004), and Duke University (1989–2001). He also served as a Technical Staff Member at Los Alamos National Laboratory (2001–2002). Johnson earned his Ph.D. in Statistics from the University of Chicago (1989), M.A. in Applied Mathematics from the University of Texas at Austin (1985), and B.S. in Mathematics from Rensselaer Polytechnic Institute (1981). His research focuses on Bayesian methodology, including hypothesis testing, variable selection in high-dimensional spaces, latent variable models, and applications in medical imaging, clinical trials, and educational assessment. He has contributed to the development of non-local prior densities and Bayesian diagnostics for MCMC convergence. His work bridges Bayesian and classical statistical approaches, emphasizing reproducibility and rigorous evidence assessment in scientific research. Johnson has held editorial roles, including Co-Editor of Bayesian Analysis (2010–2014) and Associate Editor of the Journal of the American Statistical Association (2011–present). He is a Fellow of the American Statistical Association and the Royal Statistical Society and has served on the Board of Directors of the International Society for Bayesian Analysis. His advocacy for revised statistical significance standards has sparked major debates in scientific methodology. Johnson has supervised numerous doctoral students, including those whose theses won prestigious awards like the Savage Award. He has collaborated on grants addressing medical imaging, system reliability, and cancer symptom management, reflecting his interdisciplinary impact in biostatistics and public health.

Giovanna Tinettiمشاهده پروفایل

استاد

• Extrasolar Planets
• Space Science
• Molecular Spectroscopy
• +۳ مورد دیگر

Giovanna Tinetti is a Professor of Astrophysics and Vice Dean (Research) at King's College London's Faculty of Natural, Mathematical & Engineering Sciences. She leads the European Space Agency's Ariel mission, a space telescope surveying exoplanet atmospheres, set to launch in 2029. As co-founder of the London Centre for Space Exochemistry Data and Blue Skies Space Ltd, she pioneers satellite technology for scientific data collection. She holds a PhD in Theoretical Physics from the University of Turin, with prior affiliations at Caltech/JPL, the Institute of Astrophysics in Paris, and University College London (UCL), where she was a Royal Society University Research Fellow. Her research focuses on exoplanetary atmospheres, molecular spectroscopy, and advanced data science techniques. With over 300 publications, her 2019 paper on water vapor in K2-18b's atmosphere achieved the highest altmetric score in Physical Sciences that year. She has delivered over 350 international talks and lectures. Education: PhD in Theoretical Physics (University of Turin) Affiliations: King's College London, UCL (past), ESA's Ariel Mission, Blue Skies Space Ltd Research Interests: Exoplanet atmospheres, molecular spectroscopy, space science, data-driven analysis methodologies, and atmospheric modeling. Her work bridges observational astronomy with computational chemistry to interpret exoplanet compositions and climates. Awards: Royal Society University Research Fellow Highest Altmetric Score (2019 Physical Sciences) Grants & Projects: Principal Investigator for ESA's Ariel mission Co-leader of the Ariel Data Challenge 2025 Labs/Teams: London Centre for Space Exochemistry Data, Blue Skies Space Ltd technical team, and the international Ariel collaboration network.

Spencer Caplanمشاهده پروفایل

استادیار

• Computational Linguistics
• Psycholinguistics
• Language Acquisition
• +۱۳ مورد دیگر

Spencer Caplan is an Assistant Professor in the Department of Linguistics at the CUNY Graduate Center. He directs the Psycholinguistics Lab, contributes to the Cognitive Science affiliation, and is the interim director of the master's program in computational linguistics through Fall 2025. Research Interests: His work integrates linguistics, computation, and cognition, focusing on language acquisition and processing. He develops computational and algorithmic models of language learning and mental representation, while also employing experimental methods such as eye-tracking, parsing, categorization, and perceptual learning. His research applies quantitative, corpus, and statistical techniques to address questions in theoretical linguistics. Secondary interests include Chinese languages, cognitive psychology (attention, memory, learning), formal language theory, NLP, phonology, and software engineering. Publication Trends: His recent publications appear in high-impact journals like PLOS One , Psychological Science , Cognition , PNAS , and Glossa . The work spans computational modeling of language learning, experimental investigations of linguistic computation, and corpus-based syntactic analysis, reflecting a strong interdisciplinary approach combining theoretical insight with empirical and quantitative rigor. Scientific Affiliations and Mentors: Ph.D. Advisor Team: Charles Yang, John Trueswell, Mitch Marcus Undergraduate Mentor: Eugene Charniak Collaborators: Deniz Beser, Kajsa Djärv, Neal Fox, Doug Guilbeault, Alon Hafri, Jordan Kodner, Eric Mandelbaum, Griffin Pion, Jake Quilty-Dunn, Katie Schuler, Elliot Schwartz, Hongzhi Xu, Chen Yu Advising and Grants: While specific advising and grant records are not detailed in the text, his leadership roles (lab director, interim program director) and active publication record suggest significant involvement in mentoring students and securing research support. His GitHub repositories indicate ongoing, computationally intensive research projects. Labs and Teams: He leads the Psycholinguistics Lab at CUNY Graduate Center and has been affiliated with the Language Learning Lab at the University of Pennsylvania and the Developmental Intelligence Lab at the University of Texas at Austin.

Snigdha Chaturvediمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۸ مورد دیگر

Snigdha Chaturvedi is an Associate Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. She previously held faculty positions at the University of California, Santa Cruz, and has conducted postdoctoral research at the University of Pennsylvania and University of Illinois, Urbana-Champaign. PhD in Computer Science from University of Maryland, College Park Bachelor's degree in Computer Science and Engineering from Indian Institute of Technology (IIT) Kanpur Her research spans Natural Language Processing with emphasis on Narrative Understanding , Text Summarization , and Socially Aware Language Generation . She advances Fairness in AI through ethical NLP applications in Mental Health and Educational Technology . Recent work focuses on 2025 publications in ACL and NAACL journals, alongside 2024 contributions to EMNLP Findings and ICLR . Earlier projects include the NarraSum dataset (2022) and MOOC forum analysis (2020). Scientific recognitions include: ACM Student Research Competition First Place (2014) IBM PhD Fellowship (2014-2015, renewed in 2015) Kulkarni Summer Research Fellowship (2015) WPI STEM Faculty Launch Program Participant (2015) Her team has advised 13 PhD and Master's students with notable placements at Bloomberg, AI2, and University of Southern California. Research integrates Accessibility challenges through collaborations with Google and IBM labs.

• Explainable AI
• Human-AI Interaction
• Machine Learning
• +۷ مورد دیگر

Andreas Holzinger is a Professor at Graz University of Technology, with additional affiliations at Medical University Graz and University of Natural Resources and Life Sciences Vienna in Austria. He is recognized as an IFIP Fellow (2021) for his significant contributions to information processing and computer science. His work spans multiple institutions across Europe, with notable collaborations extending to the University of Alberta in Canada. Professor Holzinger's research focuses on Human-Centered AI, Explainable AI (XAI), and their practical applications across diverse domains. His work bridges theoretical AI advancements with real-world implementations in healthcare, forestry, and human-robot interaction. He has pioneered approaches in counterfactual explanations, graph neural networks, and human-in-the-loop systems that emphasize transparency and trustworthiness in AI decision-making processes. His recent publications demonstrate a strong trend toward integrating large language models with traditional AI systems while maintaining explainability. Holzinger's work consistently emphasizes the human element in AI systems, ensuring that technological advancements serve human needs rather than obscuring decision processes. His research in medical AI, smart forestry, and agricultural applications shows a commitment to solving practical problems with human-centered technological solutions. Scientific Awards: IFIP Fellow (2021) Professor Holzinger has been instrumental in establishing design guidelines for explainable AI systems, particularly through his work on post-hoc versus ante-hoc explanations. His research on Kandinsky Patterns has provided valuable experimental frameworks for pattern analysis and machine intelligence. He has secured significant research funding for projects bridging AI with practical applications in healthcare and environmental monitoring. His leadership extends to the organization of major conferences and workshops, including the CD-MAKE conference series, where he has fostered interdisciplinary collaboration between AI researchers and domain experts. His work on the CLARUS platform demonstrates practical implementations of interactive explainable AI for medical applications.

Matthias Krauledatمشاهده پروفایل

استاد

• Machine Learning
• Brain-Computer Interface
• Signal Processing
• +۳ مورد دیگر

Prof. Dr. Matthias Krauledat is a faculty member at Hochschule Rhein-Waal , specifically within the Faculty of Technology and Bionics . His academic career spans both theoretical research and industrial application, with a focus on Machine Learning and Brain-Computer Interfaces . After completing his PhD in Electrical Engineering/Computer Science at Technische Universität Berlin , he has contributed significantly to the advancement of EEG-based communication systems and neural signal processing methodologies. Born in Essen, Germany Studied Mathematics with a minor in Computer Science at University of Münster/Oxford Doctoral research at TU Berlin on Brain-Computer Interfaces Industrial experience at Henkel AG & DMT GmbH Research Interests focus on Machine Learning applications in Neuroscience and Biomedical Engineering , specifically Brain-Computer Interfaces , EEG Signal Processing , and Adaptive Classification Systems . His work explores how algorithms can be developed to enable self-learning computers to solve complex tasks involving neural data interpretation and prediction for previously unseen data in clinical and technological contexts. Publications demonstrate a consistent contribution to Neuroscience and Machine Learning fields, with particular emphasis on Brain-Computer Interface systems from 2004 through 2009. His research has focused on reducing training requirements, improving signal processing accuracy, and developing novel interaction paradigms like the Hex-o-Spell mental typewriter while addressing statistical challenges like covariate shift in neural data analysis. Professional Experience includes academic research at TU Berlin's Intelligent Data Analysis group, industrial software development roles at Henkel AG's Scientific Computing department, and TÜV Nord Group's Optical Metrology and Machine Diagnostics divisions. He maintains active research connections through collaborative publications with leading experts in the field.

Kishlay Jhaمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Kishlay Jha is an Assistant Professor at the University of Iowa's College of Engineering in the Department of Electrical and Computer Engineering. He is also a researcher at the Center for Bioinformatics and Computational Biology and the Iowa Initiative for Artificial Intelligence. PhD in Computer Science from University of Virginia (2022) Email: kishlay-jha@uiowa.edu Office: 3320 Seamans Center, Iowa City, IA 52242 Phone: (319) 467-0096 His research focuses on data science and artificial intelligence with emphasis on data mining, machine learning, and their applications in biomedical domains. He develops methodologies for transforming heterogeneous clinical, genomic, and bibliographic data into actionable knowledge for scientific advancement. Recent work includes: Semantic knowledge integration in biomedical language models Dynamic representation learning for evolving systems Hypergraph-based contrastive learning for healthcare applications Continual learning frameworks for time-sensitive domains Knowledge-guided representation learning Biomedical hypothesis generation He leads the Data Mining and Machine Learning Laboratory, where his team develops innovative tools for both biomedical discovery and general AI applications.

• Transportation planning
• Dynamic traffic assignment
• Traffic estimation and prediction
• +۵ مورد دیگر

Xuesong Zhou is a Professor of Transportation Systems at the School of Sustainable Engineering and the Built Environment , Arizona State University (ASU). He leads the ASU Transportation+AI Lab and develops open-source tools like DTALite, NEXTA, and OSM2GMNS with over 100,000 downloads. His research focuses on multimodal transportation planning , dynamic traffic assignment , and rail scheduling with methodological contributions to traffic flow theory and operations research . Dr. Zhou's research bridges transportation system operations , computer applications for ITS , and logistics optimization . His work on differentiable programming reformulations and state-space-time network modeling has advanced real-time traffic prediction and multi-echelon facility scheduling . Scientific awards include: 2022 Elsevier Multimodal Transportation Best Article Award 2018 Transportation Research Part C Best Associate Editor Award 2012 INFORMS Railway Applications Section Best Paper Award He has advised 9 PhD students and 6 postdoctoral researchers to completion, with mentees now at institutions like Georgia Institute of Technology and Michigan State University. Current projects include NSF CONNECT and DOE Argonne collaborations on multi-scale traffic simulation and smart campus cyberinfrastructure .

Daniel Hlubinkaمشاهده پروفایل

دانشیار

• Statistics
• Theoretical Foundations
• Multivariate Functional Processes
• +۴ مورد دیگر

Daniel Hlubinka is an Associate Professor at the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University. He has been an academic staff member since 1999 and was promoted to associate professor in 2007. His teaching includes courses like Statistics for Financial Mathematicians 2 (NMFM332) and Proseminar in Probability and Mathematical Statistics (NMSA262). He supervises bachelor's, diploma, and doctoral theses, with over 30 bachelor's, 20 diploma, and 5 doctoral theses supervised to date, including former students now working as associate professors. Education: Mathematical Physics (1989-1994) at Charles University; Erasmus stay at Limburgs Universitaire Center (1994-1995); Doctorate (1995-1999) under Professor Josef Štěpán. Research Interests: Statistics, theoretical foundations, multivariate functional processes, nonparametric asymptotics, data depth, optimal transport, and mathematics of chance. Academic Affiliations: Member of the Union of Czech Mathematicians and Physicists, Czech Mathematical Society, Czech Statistical Society, European Mathematical Society, Bernoulli Society, and Institute of Mathematical Statistics. Hlubinka's recent research focuses on functional data analysis, multivariate quantiles, and nonparametric testing. His work applies optimal transport theory, empirical characteristic functionals, and permutation tests to functional statistical problems. He contributes to methodological advancements in depth-based classification, time reversibility testing, and regression models.

Kavita Balaمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Computer Graphics
• +۶ مورد دیگر

Kavita Bala is the 17th Provost of Cornell University and a Professor of Computer Science. She previously served as the inaugural Dean of the Cornell Ann S. Bowers College of Computing and Information Science, leading its transition to a degree-granting college by 2025, and as Chair of Cornell’s Department of Computer Science. Her academic leadership includes expanding faculty, establishing research programs like the Bowers CIS Undergraduate Research Experience (BURE), and securing a new research facility for computing and information science. Education: B.Tech (IIT Bombay), M.S. and Ph.D. (MIT, Computer Science) Bala’s research focuses on computer vision, artificial intelligence, and computer graphics , with groundbreaking work in material and style recognition using deep learning. Her innovations in crowdsourced training data and differentiable rendering have advanced visual search technologies and translucent material modeling, powering her startup GrokStyle. She pioneered AI techniques applied to environmental monitoring through projects like MONITRS and AllClear , addressing Earth observation challenges. Her scientific awards include: American Academy of Arts and Sciences (2025) SIGGRAPH Computer Graphics Achievement Award (2020) IIT Bombay Distinguished Alumnus Award (2021) ACM Fellow (2019) SIGGRAPH Academy Fellow (2020) As Provost, Bala drives strategic initiatives like the Cornell AI Initiative , creating interdisciplinary minors in AI and AI in Society, and establishing the Schmidt AI in Science postdoctoral program. She co-chaired a task force for generative AI guidelines in education.

• Digital Poverty
• Digital Inequalities
• Emerging Technologies
• +۳ مورد دیگر

Professor Efpraxia D. Zamani is a Full Professor of Information Systems at Durham University Business School, previously serving as Senior Lecturer at the University of Sheffield's Information School. Her research examines the organizational and social dimensions of Information Systems, with particular emphasis on digital poverty, emerging technologies, and workplace implications. Her research focuses on three interconnected domains: Digital poverty and inequalities (examining access disparities and policy impacts), Making sense of emerging technologies (analyzing adoption and resistance patterns), and Information Systems in the workplace (investigating hybrid work dynamics and gendered experiences). Zamani employs critical and qualitative methodologies to uncover how technological transformations affect marginalized groups and organizational practices. Analysis of her 15 most recent publications (2023-2025) reveals consistent engagement with societal challenges through an Information Systems lens. Her work spans digital governance, healthcare technology, crowdwork platforms, and AI ethics, predominantly published in top-tier journals like Information Systems Journal and Journal of Information Technology . A distinctive pattern is her focus on vulnerable populations—including informal caregivers, women in developing economies, and older adults—within technology adoption contexts. Key recognitions include: AIS Sandra Slaughter Service Award (2024) AIS Distinguished Member - Cum Laude (2025) Best Paper Award at ICIS 2013 Runner Up for SIG GTM Best Paper Award (2021) She supervises PhD candidates including Myrto Dimitriou and Sohee Kim while leading significant research initiatives. Current projects feature the Marie Skłodowska-Curie Actions Doctoral Network AGORA (funded by UKRI EPSRC) for Metaverse research training. Completed work includes British Academy-funded studies on digital inclusion in Derbyshire and Research England projects addressing digital poverty in South Yorkshire. Her grant portfolio demonstrates strong alignment with societal impact through technology. Zamani actively contributes to academic communities as co-chair of the AIS Women's Network College and Vice President of the UK Academy for Information Systems, while maintaining editorial roles at Information Systems Journal and Information Technology & People .