جست‌وجوی استاد و پژوهشگر

Lotte Schüsslerمشاهده پروفایل

پژوهشگر ارشد

• Media Studies
• Theatre Studies
• History of the Humanities
• +۴ مورد دیگر

Lotte Schüssler is a media and theatre scholar currently working as a postdoctoral fellow at the Max Planck Institute for the History of Science (MPIWG) in Berlin. Her research focuses on the use and reuse of paper in the history of the humanities, as well as the intersection of exhibitions, media, and academic practices around 1900. She obtained her PhD in media studies from Humboldt-Universität zu Berlin in 2020, with a thesis on large-scale theatre exhibitions. Prior to her current position, she served as a research associate at Humboldt-Universität’s Department of Musicology and Media Studies and Freie Universität Berlin’s Institute of Theatre Studies. She also held a junior fellowship at the Vossius Center for History of Humanities and Sciences at the University of Amsterdam. Schüssler’s research integrates media history, theatre studies, and epistemology, examining how material practices like paper usage shaped academic disciplines. Her work with the MPIWG’s Research Group Data, Media, Mind explores these themes in depth. Her publications highlight interdisciplinary approaches to understanding historical knowledge production. Themes include exhibition media, archival practices, and the material conditions of scholarship. Scientific Awards: Förderpreis der Gesellschaft für Theatergeschichte Schüssler has no formal advising responsibilities listed, but her collaborative projects and fellowships indicate active participation in academic networks. She has been involved with the International Max Planck Research School (IMPRS) and other research teams.

Ivan Beschastnikhمشاهده پروفایل

دانشیار

• Distributed Systems
• Software Engineering
• Cloud Computing
• +۵ مورد دیگر

Ivan Beschastnikh is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. He leads research in distributed systems, software engineering, and formal methods, with a focus on privacy-preserving technologies, blockchain systems, and machine learning. His work bridges theoretical foundations and practical system implementations, emphasizing empirical validation and real-world deployment. Research Interests: His primary areas include distributed systems (e.g., consensus algorithms, transaction processing), formal methods for system correctness (e.g., PGo compiler for verified implementations), and privacy in federated learning and blockchain. He also explores interdisciplinary topics like AI ethics, human-computer interaction for developers, and system security. Key Projects: Includes PGo (formal specification to Go compiler), Biscotti (decentralized federated learning), Erlay (Bitcoin transaction relay optimization), and Teleoscope (text corpus analysis tool). His work has practical impact in blockchain scalability, secure multi-party ML, and distributed system validation. Awards: Recognized with the ASPLOS Distinguished Artifact Award, UBC Faculty Teaching Award, and ESEM Best Paper Award. His contributions span academic excellence, pedagogy, and collaborative research. Lab Affiliations: Active in the Systopia Lab and Software Practices Lab (SPL) , and collaborates with UBC’s Data Science Institute and Blockchain@UBC. He emphasizes interdisciplinary research, mentoring students in both theoretical and applied systems work.

Alec Christieمشاهده پروفایل

پژوهشگر ارشد

• Biodiversity Conservation
• Evidence-Based Decision-Making
• Artificial Intelligence in Conservation
• +۳ مورد دیگر

Alec Christie is an Imperial College Research Fellow at the Centre for Environmental Policy, Imperial College London (2024–2028), and holds concurrent roles as Visiting Researcher at the University of Cambridge's Department of Zoology and Associate Lecturer at the Open University. He is also a Bye Fellow at Downing College, Cambridge. His research focuses on evidence-based conservation, AI applications in conservation decision-making, and addressing biases in conservation evidence. Christie holds a PhD in Zoology from the University of Cambridge (2021), an MSc in Conservation Science and Practice (co-convenor role), and a BSc in Marine Biology from the University of St Andrews (2017). Research Interests: Evaluation of evidence-based practices in conservation Cost-benefit analysis of conservation interventions AI-driven tools for accelerating evidence synthesis Quantifying biases in conservation study designs Global conservation priorities and policy alignment Teaching: Co-teaches the MSc Conservation Science and Practice program at Imperial College, specializing in evidence-based decision-making modules. Previously, he held a Henslow Fellowship (2021–2024) and contributed to the Open University's S397 Ecosystems module. Awards: Vice Chancellor’s Award for Research Impact (2023) and Fellow of the Higher Education Academy (2022). Collaborations: Leads projects with the Conservation Evidence team and develops decision-support tools for practitioners. Active in fieldwork across Antarctica, the Great Barrier Reef, and Türkiye.

Christopher McCombمشاهده پروفایل

دانشیار

• Sociotechnical Systems
• Machine Learning for Engineering Design
• Human-AI Collaboration
• +۱۷ مورد دیگر

Christopher McComb is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads research in sociotechnical systems, machine learning for engineering design, and human-AI collaboration. He is affiliated with the Block Center for Technology and Society, Manufacturing Futures Institute, NextManufacturing Center, and Wilton E. Scott Institute for Energy Innovation. Previously, he was an assistant professor at Penn State, where he directed the Center for Research in Design and Innovation and led the Technology and Human Research in Engineering Design Group. Ph.D., Mechanical Engineering, Carnegie Mellon University M.S., Mechanical Engineering, Carnegie Mellon University B.S., Civil Engineering and Mechanical Engineering, California State University-Fresno His research centers on human-AI teaming , sociotechnical systems , and computational design , with applications in additive manufacturing, STEM education, and energy systems. He explores how machine learning can enhance engineering design processes, particularly through human-centered AI, generative design, and agent-based modeling. His work emphasizes the integration of human cognition and behavior into AI systems to improve collaboration and innovation. The 15 most recent publications (2025) demonstrate a strong trend in AI-driven design automation , neural surrogate modeling , human-AI interaction , and data generation for engineering simulations . Topics span from using large language models for material selection and design concept generation to developing datasets and benchmarks for advanced manufacturing and CAD systems. There is a clear emphasis on real-world applications in aerospace, finance, and global manufacturing, particularly in Africa. National Science Foundation Graduate Research Fellow McComb has received research funding from NSF, DARPA, and private corporations, and has collaborated with Boeing through their Visiting Professorship Program. He advises students in mechanical engineering and design, and leads the Human+AI Design Initiative and the Design Research Collective. His research has been applied in partnerships with NASA and in addressing manufacturing challenges in Africa. He leads or contributes to interdisciplinary research teams focused on AI in design, additive manufacturing, and energy systems. His labs and initiatives include the Human+AI Design Initiative and the Design Research Collective, which foster collaboration between human-centered design and artificial intelligence.

Liwei Wangمشاهده پروفایل

استادیار

• Machine Learning
• Computational Engineering
• Data-Driven Design
• +۱۹ مورد دیگر

Liwei Wang is an Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, where he leads the Computational and Physical Intelligence Laboratory (CPhI Lab). His research integrates computational modeling, machine learning, and mechanics to design advanced materials and smart systems. Education: Ph.D., Mechanical Engineering, Shanghai Jiao Tong University (2022, Honors) B.S., Mechanical Engineering, Shanghai Jiao Tong University (2017, Honors) Liwei Wang's research interests lie at the intersection of machine learning, computational engineering, and advanced materials . He develops data-driven and physics-informed frameworks for topology optimization, metamaterials, 3D/4D printing, soft robotics, and programmable materials systems . His work emphasizes physical intelligence —designing materials that can sense, adapt, and respond to their environments. Applications span mechanical protective cloaks, flexible electronics, minimally invasive surgery, and mechanical computing . His recent publications reveal a strong trend toward multi-scale, data-efficient design of metamaterials using advanced machine learning techniques such as Gaussian processes, latent variable models, and neural networks. He focuses on scalable, differentiable, and task-aware optimization methods that enable rapid discovery and deployment of functional material systems. Scientific Awards: ASME Design Automation Dissertation Award ASME Design Automation Conference Best Paper Award Institution-level Outstanding Ph.D. Dissertation Award Dr. Wang advises a growing research group at CMU and leads the CPhI Lab, where his team develops next-generation computational tools for materials innovation. While specific grants are not listed, his research is likely supported by federal and private funding given the scope and impact of his work. His lab emphasizes interdisciplinary collaboration and translation of computational designs into physical prototypes via additive manufacturing. Laboratory & Team: The Computational and Physical Intelligence Laboratory (CPhI Lab) focuses on co-designing materials and structures with embedded intelligence, combining simulation, data science, and experimental validation to push the boundaries of what engineered materials can achieve.

Jerry Taltonمشاهده پروفایل

استادیار

• Computer Graphics
• Machine Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Jerry Talton is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign (UIUC). He holds a PhD in Computer Science from Stanford University (2011), and BS and MS degrees from UIUC (2003 and 2006, respectively). His research focuses on computer graphics, machine learning, and human-computer interaction, with notable work on emoji-first communication, rating systems analysis, and data-driven design tools. Key contributions include the Opico study on emoji communication in social apps and the Bricolage algorithm for web design transfer. He received the Siebel School Young Alumni Achievement Award (2022). His work spans academic collaborations with institutions like Stanford and industry applications in social media and design. Research interests emphasize user interaction, algorithmic ranking, and leveraging big data for design automation. His publications address challenges in social networks, fluid simulation, and educational outreach through initiatives like Scavenger Hunt, enhancing student retention in computer science.

Dominic Erikssonمشاهده پروفایل

پژوهشگر

• Marine Biology
• Metagenomics
• Species Distribution Modelling
• +۳ مورد دیگر

Dominic Eriksson is a junior scientist and PhD candidate at ETH Zurich. Employed since April 2020 under Dr. Meike Vogt, he is advancing species distribution modelling expertise. His upcoming PhD (starting April 2021) is a collaborative program between Prof. Nikolas Gruber (Department of Environmental Systems Sciences) and Prof. Shinichi Sunagawa (Department of Biology), focusing on Tara Oceans metagenomic data analysis. Education: B.Sc. in Biology (Ecology focus) from University of Vienna; M.Sc. in Marine Biology from University of Algarve (2020). His master’s thesis revealed prokaryotic communities in gorgonian corals Eunicella labiata and developed cultivation protocols for their associated bacteria. Research interests span marine biology, metagenomics, species distribution models, diazotroph ecology, and nitrogen cycling. His work bridges microbial communities with large-scale oceanographic patterns, emphasizing biogeographical and biogeochemical processes. Publications focus on diazotroph diversity-nitrogen fixation relationships, global ocean biogeography, and statistical model validation. Collaborates with initiatives like Tara Oceans and AtlantECO, contributing to marine ecosystem understanding through data-driven approaches. No formal academic awards or grants explicitly listed in the provided texts.

• Clinical Natural Language Processing (NLP)
• Medical Informatics
• Machine Learning
• +۳ مورد دیگر

Jens-Michalis Papaioannou is a prominent Researcher in clinical natural language processing (NLP) and medical informatics, with extensive publications in top-tier venues like ACL, LREC, and EMNLP. His work focuses on improving clinical decision support systems through advanced machine learning techniques. 2024 : Revisiting clinical outcome prediction for MIMIC-IV with biomedical transformers 2023 : Developing MEDBERT.de for German medical NLP and MedAlpaca conversational AI 2022 : Introducing ProtoPatient for interpretable diagnosis prediction 2021 : Creating self-supervised knowledge integration frameworks for admission note analysis His research spans seven major themes : Clinical outcome prediction from admission notes Cross-lingual knowledge transfer in medical NLP Prototypical network applications Data drift analysis in longitudinal datasets Knowledge integration techniques Model optimization for healthcare LLM interpretability frameworks He has collaborated with Wolfgang Nejdl, Alexander Löser, and Betty van Aken on 13+ publications , with over 445 citations. Notable contributions include: Novel patient similarity modeling approaches ICD code hierarchy integration methods Multilingual clinical model strategies Adversarial robustness analysis Medical conversational AI frameworks

Ernests Lavrinovicsمشاهده پروفایل

پژوهشگر

• Machine Translation
• Transfer Learning
• Large Language Models
• +۴ مورد دیگر

Ernests Lavrinovics is a PhD fellow at the Department of Computer Science , Aalborg University , affiliated with the Technical Faculty of IT and Design and Data, Knowledge and Web Engineering research group. His work focuses on Natural Language Processing (NLP) , Large Language Models (LLMs) , and addressing challenges in machine translation and knowledge graph integration for resource-poor languages. His research explores critical NLP themes including hallucination detection in LLMs , adapter-based regularization techniques for multilingual transfer, and multitask benchmark development for creole languages. He contributes to datasets like MultiHal and CreoleVal , emphasizing semantic grounding and cross-lingual evaluation. Recent publications analyze adapter architectures as regularizers in low-resource settings (ACL 2025) He co-developed MultiHal (2025), a multilingual dataset for LLM hallucination evaluation His work spans knowledge graph applications , transfer learning , and multilingual representation challenges As an active academic, he has organized events like the Copenhagen NLP Symposium (2025) and AAU NLP Symposium (2024). He participates in technical programs at venues such as ACL and MRL Workshops , with citations in Scopus and Mendeley readership.

William Harbaughمشاهده پروفایل

استاد

• Neuroeconomics
• Experimental Economics
• Behavioral Economics
• +۷ مورد دیگر

William Harbaugh is a Professor in the Economics Department at the University of Oregon’s College of Arts and Sciences. He is the founder of the SAIL (Summer Academy to Inspire Learning) program and conducts pioneering research in neuroeconomics, experimental economics, and the economic behavior of children. His work integrates neuroscience, psychology, and economics to understand decision-making processes. Education: Ph.D., University of Wisconsin, Madison, 1995 His research interests focus on neuroeconomics , where he investigates the neural underpinnings of economic decisions, particularly altruism and charitable giving, using fMRI. His seminal 2007 Science paper showed that both personal monetary gain and charitable donations activate similar brain reward regions. In experimental economics , he explores risk attitudes, public goods, bargaining, and the endowment effect. A major theme is the economic behavior of children , where his studies demonstrate that children exhibit rational choice behavior and that preferences for fairness develop with age. His work often challenges and refines standard economic models using experimental data. The recent articles highlight a consistent research trajectory in behavioral and experimental economics. They demonstrate a deep focus on testing the robustness of economic theories —such as expected utility, revealed preference, and the environmental Kuznets curve—using controlled experiments with both adults and children. A significant trend is the methodological scrutiny of how results depend on elicitation procedures, as seen in the 2010 paper on the fourfold pattern of risk attitudes. There is a strong interdisciplinary thread, particularly in neuroeconomics , merging brain imaging with economic decision tasks. The work on children’s behavior forms a unique and sustained subfield, exploring the developmental origins of economic preferences. Scientific Awards: Best in Show, 2009 Mayor's Art Show, Eugene Oregon (for 'Warm Glow' fabric brain art) Harbaugh advises undergraduate honors students in economics, several of whom have co-authored academic papers and pursued careers in top graduate programs, the Federal Reserve, and law. He has not mentioned specific grants in the provided text, but his research involving fMRI and large-scale experiments suggests external funding. He is actively involved in teaching, offering courses like Behavioral and Experimental Economics (Econ 428/528) and graduate seminars. He is the curator of the Museum of Scientifically Accurate Fabric Brain Art , an interdisciplinary project that translates neuroscience data into textile art. He also co-founded the SAIL program , an experiential learning initiative for low-SES youth, involving over 25 faculty and staff, which teaches economics, neuroscience, and physics. His research team includes long-term collaborators such as Kate Krause, Lise Vesterlund, Ulrich Mayr, and Jim Andreoni.

• Dataspaces
• Semantic Web Technologies
• Ontologies
• +۶ مورد دیگر

Johannes Theissen-Lipp is a senior scientific researcher at the Fraunhofer FIT Office Aachen with significant academic affiliations at RWTH Aachen University. He operates within the Department of Computer Science (Informatik 5) under the Faculty of Mathematics, Computer Science and Natural Sciences, working from the Databases and Information Systems (DBIS) group led by Professor Stefan Decker. His research focuses on semantic foundations of dataspaces with practical applications in manufacturing and mobility sectors. He earned his Computer Science degree from RWTH Aachen University, where he later conducted research at the Institute for Information Management in Mechanical Engineering (IMA) for nearly two years before joining the "Knowledge Pipelines" research group in December 2019. He successfully defended his PhD thesis titled "Semantic Foundations of Dataspaces" on September 25, 2024, completing his doctoral studies at RWTH Aachen. Theissen-Lipp's research spans semantic web technologies, ontologies, and data interoperability with particular expertise in RDF validation (SHACL), semantic mappings (RML), and FAIR data implementation within dataspaces. His work bridges theoretical foundations with industrial applications, especially in manufacturing contexts where data integration across organizational boundaries presents significant challenges. He has pioneered approaches for enhancing dataspaces through large language models and developing scalable validation techniques for distributed data environments. His publication record demonstrates consistent contributions to major semantic web venues including ESWC, SEMANTiCS, and dedicated workshops on dataspaces. As both author and editor (notably for the Second International Workshop on Semantics in Dataspaces proceedings), he has helped shape research directions in this emerging field. His recent work increasingly explores the intersection of traditional semantic technologies with contemporary AI approaches. Theissen-Lipp actively supervises multiple thesis projects including "FAIRification of Data Models in Manufacturing," "Leveraging Solid Pods for Sovereign Data Sharing in the Cultural Sector," and "Identification Techniques for Data-driven Feedback Loops in Manufacturing." He has taught the "Dataspaces Proseminar" in the summer semester of 2025, demonstrating his commitment to academic education alongside his research activities. As part of the Cluster of Excellence Internet of Production, he contributes to advancing data interoperability in industrial contexts. His professional presence extends to GitHub (JohannesLipp), LinkedIn, and ResearchGate, with a personal website providing additional details about his research portfolio and professional activities.

Heather J. Kulikمشاهده پروفایل

استاد

• Computational Chemistry
• Materials Science
• Machine Learning in Chemistry
• +۳ مورد دیگر

Heather J. Kulik is the Lammot du Pont (1901) Professor of Chemical Engineering and Professor of Chemistry at MIT. She leads the Kulik Group, focusing on computational chemistry, materials science, and machine learning for catalysis and materials discovery. Her affiliations include MIT's Department of Chemical Engineering, the Chemistry Department, and interdisciplinary centers like the Center for Enhanced Nanofluidic Transport (CENT) EFRC and the NSF Center for the Chemistry of Molecularly Optimized Networks (MONET). Education: B.E. in Chemical Engineering, Cooper Union (2004) Ph.D. in Materials Science and Engineering, MIT (2009) Postdoctoral research at Lawrence Livermore National Laboratory (2010) and Stanford University (2010–2013) Research Interests: Her work spans multi-scale modeling, electronic structure calculations, and machine learning for designing new materials (e.g., metal-organic frameworks, enzymes, organometallics). Key areas include computational catalysis, enzyme mechanism elucidation, and autonomous method selection for simulations. She emphasizes predictive accuracy in open-shell transition metal chemistry. Articles Trends: Recent work highlights machine learning-driven discovery of catalysts (e.g., methane-to-methanol), stability analysis of metal-organic frameworks, and quantum mechanical insights into enzymatic reactions. Publications emphasize applications in energy storage, environmental remediation, and biochemical processes. Awards: Burroughs Wellcome Fund Career Award (2012–2017) Sloan Fellowship (2021) AAAS Marion Milligan Mason Award (2019–2020) Advising & Grants: Advises students on computational and experimental projects, leveraging funding from agencies like NSF, DARPA, and the Department of Energy. Recent grants support machine learning integration in catalysis and materials informatics. Labs & Teams: Collaborates with experimental groups in biochemistry, materials science, and engineering. Her lab develops tools like the molSimplify toolkit and the CoRE MOF DB for data-driven discovery.

Rolf Fagerbergمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Computational Biology
• +۷ مورد دیگر

Rolf Fagerberg is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU). His research centers on algorithms, data structures, and their applications in computational biology and cheminformatics. His research interests include: Algorithms and data structures, particularly dynamic and geometric data structures Graph theory with emphasis on subgraphs, Yao graphs, and hypergraphs Algorithmic cheminformatics and modeling of chemical reaction networks Computational biology, including metabolic pathway analysis Theoretical computer science and discrete mathematics Recent publications highlight a strong trend toward interdisciplinary research combining computer science with chemistry and biology, particularly in modeling chemical reaction networks using hypergraphs and mixed-integer linear programming. His work also includes algorithmic solutions for palindromic subsequence problems and efficient extraction of reaction rules from large databases, reflecting a blend of theoretical and applied algorithm design. Rolf Fagerberg has served as senior coordinator on multiple research projects, including 'Algorithmic Cheminformatics' and 'Fundamental Data Structures', funded by the Danish Ministry of Higher Education and Research. He has also contributed to peer review and editorial work for major conferences such as the ACM Symposium on Parallelism in Algorithms and Architectures and the International Symposium on Experimental Algorithms. He has supervised PhD students and is actively involved in academic service, including membership in assessment committees at Aarhus University and IT University of Copenhagen. His research has received media attention, including coverage of a 'mathematical breakthrough' and applications in understanding intestinal systems in obesity.

Ramsey M. Wehbeمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Ramsey M. Wehbe, MD, MSAI is a dual-appointed Assistant Professor at the Medical University of South Carolina (MUSC) , holding positions in both the Division of Cardiology and the Biomedical Informatics Center . As a physician scientist , he merges clinical cardiology with artificial intelligence to advance cardiovascular care. Education: Duke University (undergraduate), University of North Carolina at Chapel Hill (MD), Northwestern University (internal medicine residency, cardiovascular disease fellowship, and advanced heart failure fellowship) Specialized Training: Master of Science in Artificial Intelligence from Northwestern McCormick School of Engineering Dr. Wehbe's research interests focus on applying deep learning techniques to multi-modal unstructured clinical data including cardiac imaging, electronic health records, and sensor data. His work aims to uncover pathophysiologic insights , enhance diagnostic accuracy , and improve patient outcomes in heart failure management. The Heart AI Lab (HEAL) under his leadership develops innovative approaches to bridge AI and clinical cardiology . His recent publications demonstrate a strong emphasis on large language models for clinical applications, AI-driven cardiac imaging analysis , and predictive modeling for heart failure patients. The research spans multimodal data fusion , electronic health record mining , and clinical decision support systems . While the text doesn't explicitly list scientific awards , it does mention he has received multiple research grants in the field of clinical artificial intelligence applications. As part of MUSC's advanced heart failure program , he works closely with LVAD patients and explores AI-enhanced phenotyping of heart failure syndromes.

Nuwan Indikaمشاهده پروفایل

استادیار

• Econometrics
• Big Data Analytics
• Health Data Science
• +۳ مورد دیگر

Nuwan Indika is an Assistant Professor of Business Analytics at the Department of Business Analytics, College of Business, Loyola University New Orleans. He holds a Ph.D. in Economics from Kansas State University (2019), an MA in Economics from Western Illinois University (2013), and a BA in Economics from the University of Colombo, Sri Lanka (2009). A Fulbright Scholar and econometrician, his research spans econometrics, big data, health data science, and biomedical text processing. Education: Ph.D. (Economics, Kansas State University, 2019); MA (Economics, Western Illinois University, 2013); BA (Economics, University of Colombo, 2009) Research Interests: Focus on econometric modeling of complex health data (human and veterinary medicine), biomedical text processing, and soda industry vertical merger analysis. Engages in large-scale data mining and computational epidemiology. Collaborations: Research associate with 1DATA Consortium (Kansas State University) and sports analytics collaborations with Prof. Sanders at Syracuse University. Scientific Awards: Recognized as a Fulbright Scholar. Collaborative work spans oncology, pharmacokinetics, and behavioral economics. Consulting: Analytics consultant for Plan International, World Bank, and GTZ projects.