جست‌وجوی استاد و پژوهشگر

Frances Yungمشاهده پروفایل

پژوهشگر ارشد

• Discourse Relations
• Computational Linguistics
• Natural Language Processing
• +۴ مورد دیگر

Dr. Frances Yung is a Postdoctoral Researcher at Saarland University's Department of Language Science and Technology within the Department of Computer Science. She has been affiliated with Prof. Vera Demberg's research group since April 2017 and is currently working on the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding," specifically on project B2 "Cognitive modelling of information density for discourse relations." She is pursuing her habilitation, indicating career progression toward a higher academic position in the German university system. Dr. Yung's research focuses on discourse relations at the intersection of NLP, corpus linguistics, and experimental psycholinguistics. Her work explores how information density affects discourse relation marking through cognitive modeling approaches. She has developed expertise in discourse parsing, resource construction, annotation aggregation, and experimental pragmatics, with particular attention to multilingual aspects of discourse phenomena. Her research combines computational modeling with experimental methods to understand how speakers produce and comprehend discourse relations. Analysis of Dr. Yung's recent publications reveals a strong focus on discourse relation resources, particularly multilingual corpora like DiscoGeM 2.0 covering English, German, French, and Czech. Her work increasingly incorporates crowdsourcing methodologies and examines how large language models can be leveraged for discourse annotation tasks. She has made significant contributions to understanding the challenges of implicit discourse relation annotation and the biases introduced by different task designs in crowdsourcing environments. Active reviewer for major computational linguistics conferences (ACL, EMNLP, NAACL, EACL, COLING, IJCNLP) and workshops since 2016 Served as area chair for Sigdial 2024 Regular service on program committees for discourse-related workshops Dr. Yung has supervised multiple Master's theses on topics related to discourse relations, implicit relation identification, and domain adaptation. Her teaching portfolio includes courses on crowdsourcing linguistic annotations, discourse relations from cognitive and NLP perspectives, and recent advances in discourse processing. She has also served as a teaching assistant for data science and AI courses, demonstrating her commitment to interdisciplinary education at the intersection of computer science and linguistics.

Rob van der Gootمشاهده پروفایل

دانشیار

• Natural Language Processing
• Robustness in NLP
• Low-Resource Languages
• +۷ مورد دیگر

Rob van der Goot is an Associate Professor at the IT University of Copenhagen , specializing in robustness in natural language processing (NLP). His work focuses on non-standard language varieties, low-resource languages, and scenarios with limited training data, particularly in syntactic tasks. He is involved in projects like MaChAmp and MoNoise , which address multi-task learning and lexical normalization. Supervises PhD student Arzu Burcu Güven and mentors postdoc Elisa Bassignana Member of the HPC committee at ITU and the AI Pioneer Center Active in research collaborations including the COST Action Multi3Generation His research has earned accolades, including a Best Paper Award at WNUT 2022, an Outstanding Paper Award at EACL2021, and the Most Reviews Award at NAACL 2019. He frequently presents at international venues, such as LMU, DeiC Konference, and the University of Helsinki. His recent publications explore cross-domain dialogue act classification, code-switched lexical normalization, and multi-task learning frameworks. His work has been featured in media outlets like Omrop Fryslan and Linear Digressions. 2022 : Best Paper Award for Increasing Robustness for Cross-domain Dialogue Act Classification on Social Media Data (WNUT) 2021 : Outstanding Paper Award for MaChAmp (EACL) 2019 : Most Reviews Award (NAACL) As part of the HPC committee and DeIC Science Forum, he contributes to computational infrastructure and data management initiatives. His supervision history includes advising Anders Giovanni Møller (shared first place in WNUT 2021 shared task) and collaborations with researchers like Barbara Plank and Malvina Nissim.

David Krakauerمشاهده پروفایل

استاد

• Complex Systems
• Evolution of Intelligence
• Information Theory
• +۱۲ مورد دیگر

David Krakauer is President and William H. Miller Professor of Complex Systems at the Santa Fe Institute (SFI), a leading center for interdisciplinary research in complex adaptive systems. He leads SFI’s scientific vision and contributes actively to research on the evolution of intelligence, information processing, and problem-solving matter across biological and cultural domains. Research Interests: David’s work explores how life and intelligence emerge and evolve, focusing on the mechanisms of memory, communication, and computation in genetic, neural, social, and cultural systems. His research integrates mathematical modeling, computational frameworks, and empirical data to understand collective intelligence, niche construction, and the thermodynamic and evolutionary foundations of cognition. He investigates deep questions about the relationship between physical laws and information processing in living systems. Recent Research Trends: His recent publications analyze optimal learning rates in ecological niches, the outsourcing of memory through environmental modification, and theoretical visions for the future of complexity science. These works collectively emphasize the role of information efficiency, metabolic constraints, and collective computation in adaptive systems. Scientific Awards: Wired Magazine Smart List 2012 - One of 50 people who will change the world Entrepreneur Magazine Visionary Leader 2016 - Advancing global research and business Advising and Grants: While specific students are not listed, Krakauer has mentored numerous researchers through SFI programs, workshops, and collaborative projects. He has led major interdisciplinary initiatives, including those on the origins of life, AI and cognition, and complexity in law and regulation. His leadership in founding the Wisconsin Institute for Discovery and directing collective computation research indicates substantial grant acquisition and team leadership. Labs and Teams: He co-directed the Center for Complexity and Collective Computation at the University of Wisconsin–Madison and founded the Wisconsin Institute for Discovery. At SFI, he leads the Science Steering Committee and Science Board, shaping the institute’s research agenda through collaborative working groups and long-term projects in complexity science.

• Computational Intelligence
• Cloud Computing
• Big Data Applications
• +۶ مورد دیگر

Jun Shen is a Professor at the School of Computing and Information Technology, University of Wollongong. He specializes in computational intelligence, cloud computing, and big data applications, with a focus on AI-driven solutions for real-world challenges in transport systems, healthcare, education, and environmental management. He has secured over 40 research grants totaling AU$4.5 million and supervised 26 completed PhD projects. His work spans interdisciplinary areas including bioinformatics, smart manufacturing, and digital health. Research interests include bio-inspired algorithmic optimization, AI in arts/media, and edge computing for IoT systems. He has pioneered research centers in applied computing since 2014 and holds editorial roles in top journals like IEEE Transactions. As an IEEE Distinguished Lecturer, he actively promotes AI ethics and interdisciplinary collaboration. Recent publications emphasize adversarial machine learning defenses, UAV systems, and multimodal data fusion. His supervision includes projects in intelligent transport systems, cloud computing, and e-learning. Grants include projects on resilient energy systems and UAV geolocation verification. Leadership roles include leading over 20 researchers and chairing conferences. He advocates for digital transformation in public services and has conducted fieldwork at MIT, UCI, and Georgia Tech.

Eric Kimمشاهده پروفایل

استادیار

• Health Psychology
• Psychological Well-being
• Aging
• +۴ مورد دیگر

Dr. Eric S. Kim is an Assistant Professor of Psychology at the University of British Columbia (UBC) and a Visiting Scientist with the Lee Kum Sheung Center for Health and Happiness (Harvard T.H. Chan School of Public Health) and the Human Flourishing Program (Harvard University’s Institute for Quantitative Social Science). With a Ph.D. from the University of Michigan (2015) and postdoctoral training at Harvard (2017), he focuses on psychological well-being and social connection’s role in reducing age-related chronic conditions. His work spans health psychology, social epidemiology, gerontology, and translational science. Education: B.A. (2009), Ph.D. (2015) from University of Michigan; Postdoctoral Fellowship at Harvard (2017) Dr. Kim’s research examines biobehavioral pathways linking psychological well-being to physical health through health behaviors , biological mechanisms , and stress-buffering . He leverages population-based cohort studies , causal inference methods , and machine learning to assess psychosocial factors via social media footprints. His work addresses social adversities like racial disparities , neighborhood cohesion , and economic shocks . His 15 most recent publications reveal trends in using longitudinal designs to study adolescent well-being’s long-term health impacts , epigenetic aging , and social cohesion interventions . Key journals include PNAS , JAMA Psychiatry , and Health Psychology , with media coverage in New York Times , BBC , and Washington Post . Scientific Awards MSFHR Scholar (2020) APA Division 20 Early Career Award (2020) AJE Article of the Year (2020) NIH K99/R00 (2018) Forbes 30 Under 30 in Healthcare (2016) IPPA Early Career Researcher Award Dr. Kim provides pro bono statistical consulting to nonprofits and collaborates with AARP , UnitedHealth Group , and Chan Zuckerberg Initiative . His lab at UBC, the Psychosocial Flourishing and Health Lab , trains graduate and undergraduate researchers in healthy aging and population-level interventions .

Keenan Craneمشاهده پروفایل

دانشیار

• Computer Science
• Mathematics
• Scientific Computing
• +۷ مورد دیگر

Keenan Crane is the Michael B. Donohue Associate Professor of Computer Science and Robotics at Carnegie Mellon University , with membership in the Center for Nonlinear Analysis and mentorship in the Geometry Collective . His research bridges differential geometry and computer science to develop fundamental algorithms for geometric data processing. Education : BS from University of Illinois at Urbana-Champaign, PhD from Caltech Fellowships : Google PhD Fellow, NSF Mathematical Sciences Postdoctoral Fellow Research focuses on Discrete Differential Geometry , addressing PDE solutions, mesh processing, and geometric modeling through methods like: Walk on Spheres for PDEs Intrinsic Triangulations for robust geometry Repulsive Energy formulations for collision avoidance Recent publications span 2025–2021 , emphasizing grid-free algorithms , anisotropic mesh generation , and differentiable systems . Scientific accolades include Packard Fellowship and NSF CAREER Award . Students include Nicole Feng , Olga Gutan , and Zoë Marschner . During his 2024 sabbatical at Roblox , he does not accept new researchers. Key software contributions include Penrose (math diagram generation) and I♥Mesh (domain-specific language for mesh algorithms).

Mitchell L. Neilsenمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Distributed Systems
• Scientific Computing
• +۷ مورد دیگر

Mitchell L. Neilsen is a Professor in the Department of Computer Science at Kansas State University's College of Engineering, where he also serves as the graduate program director. He holds the Warren and Gisela Kennedy - Carl and Mary Ice Keystone Research Scholar position and maintains an active research program with multiple ongoing projects. His educational background includes a Ph.D. in Computer Science (1992), M.S. in Computer Science (1989), and M.S. in Mathematics (1987), all from Kansas State University, plus a B.S. in Mathematics Education from the University of Nebraska-Kearney (1982). After beginning his career as an assistant professor at Oklahoma State University, he returned to K-State in 1996. Research Interests: Cyber-Physical Systems: Design, Analysis, Verification of systems integrating computing, networking, and physical processes Distributed Systems: Algorithms, design, and analysis of distributed computing systems Scientific Computing: Computational Fluid Dynamics, Finite Element Analysis, High Performance Computing, and Simulation Application Areas: Agriculture technology, Dam safety analysis, Mobile applications, Natural resources management, and Real-time Embedded Systems His research program shows clear evolution toward agricultural technology applications, particularly high-throughput phenotyping, while maintaining strong foundations in cyber-physical systems and scientific computing. Recent publications indicate increasing integration of machine learning and computer vision techniques into traditional research areas. Research Funding: National Science Foundation U.S. Department of Agriculture Sandia National Laboratories Department of Homeland Security Private industry partners Dr. Neilsen has mentored numerous graduate students through their M.S. and Ph.D. programs, with recent advisees focusing on applications in agricultural technology, dam safety, and embedded systems. His advising approach emphasizes practical applications of theoretical computer science concepts. Current Teaching (Fall 2024): CIS 450 - Computer Architecture and Operations CIS 625 - Concurrent Software Systems CIS 720 - Advanced Operating Systems

Marco Donatoمشاهده پروفایل

استادیار

• Hardware for Machine Learning
• Non-Volatile Memories
• SoC Design
• +۵ مورد دیگر

Marco Donato is an Assistant Professor in both the Department of Electrical and Computer Engineering and the Department of Computer Science at Tufts University. He leads the TECS Lab (Testchip, Embedded Computing Systems) focused on hardware design for emerging applications. Prior to joining Tufts, he was a postdoctoral fellow at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Dr. Donato received his academic training from prestigious institutions: Ph.D. in Electrical Sciences and Computer Engineering from Brown University (2016) M.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2010) B.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2008) Dr. Donato's research primarily focuses on designing reliable and energy-efficient hardware systems leveraging emerging technologies. His work centers on co-design methodologies for building specialized architectures for machine learning applications that utilize dense, fault-prone embedded non-volatile memories. He investigates noise modeling and reliability aspects of next-generation memory technologies, with particular emphasis on how these can be effectively integrated into system-on-chip (SoC) designs for edge computing and IoT applications. His research bridges the gap between circuit-level design and system-level architecture to create holistic solutions for hardware acceleration of machine learning workloads. Analysis of Dr. Donato's publication record reveals a strong focus on hardware acceleration for machine learning, particularly through innovative memory system designs. His work spans multiple domains including non-volatile memory technologies, energy-efficient circuit design, and flexible SoC architectures. A notable trend is his exploration of how emerging memory technologies can be leveraged to create more efficient implementations of deep neural networks, with particular attention to the trade-offs between reliability, density, and energy consumption. His research often involves full-stack approaches that consider everything from device physics to system architecture. Dr. Donato is actively involved in mentoring and has indicated he is "looking for Ph.D. students." His work has been supported by significant research grants that have enabled the fabrication of multiple test chips, as evidenced by his extensive publication record in top-tier venues including IEEE Journal of Solid-State Circuits, ISSCC, and MICRO. He leads the TECS Lab at Tufts University, which focuses on testchip development, embedded computing systems, and hardware acceleration. The lab appears to maintain connections with researchers at Harvard University and other institutions, reflecting Dr. Donato's collaborative approach to research. The lab's work emphasizes practical, real-world implementations of novel hardware concepts through actual silicon fabrication, which is relatively rare in academic settings.

Teng Wuمشاهده پروفایل

استاد

• Wind Engineering
• Hurricane Engineering
• Structural Engineering
• +۸ مورد دیگر

Teng Wu is a Professor and Director of Graduate Studies in the Department of Civil, Structural and Environmental Engineering at the School of Engineering and Applied Sciences, University at Buffalo . His research focuses on wind engineering, hurricane risk assessment, and climate change adaptation in infrastructure systems. PhD, Civil Engineering, University of Notre Dame (2013) MS, Civil Engineering, University of Notre Dame (2012) MS, Bridge Engineering, Tongji University (2010) BS, Civil Engineering, Tongji University (2007) Minor, Financial Engineering, Fudan University (2006) Teng Wu's research spans multiple domains including Wind Engineering , Hurricane Engineering , Structural Engineering , and Climate Change Adaptation . He specializes in nonlinear aerodynamics, performance-based wind design, and computational fluid dynamics applications to infrastructure resilience. His recent publications focus on machine learning applications for storm surge prediction, wind-induced structural response analysis, and climate change-informed infrastructure recovery frameworks. Key methodologies include hierarchical deep neural networks, knowledge-enhanced models, and reinforcement learning-based control systems. Contact: tengwu@buffalo.edu | Office: 226 Ketter Hall, Buffalo, NY 14260

Md Arifuzzamanمشاهده پروفایل

استادیار

• High-Performance Networking
• Quantum Networking
• Distributed Systems
• +۳ مورد دیگر

Dr. Md Arifuzzaman is an Assistant Professor in the Department of Computer Science at Missouri University of Science and Technology (Missouri S&T). He specializes in High-Performance Systems, Quantum Networking, and Distributed Systems, focusing on optimizing large-scale system performance and scalability. His work addresses challenges in next-generation networks and storage systems, with publications in top venues like IEEE TPDS and ACM Supercomputing. Education: Ph.D. in Computer Science and Engineering, University of Nevada, Reno (2023) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2016) Research Interests: Dr. Arifuzzaman's work spans cutting-edge topics including quantum entanglement routing, reinforcement learning for network optimization, and high-speed file transfer protocols. His research emphasizes practical solutions for emerging technologies like terabit networks and quantum communication systems. Publications: Recent work focuses on quantum network protocols, machine learning-driven network probing, and storage reliability. His articles highlight advancements in both theoretical frameworks and real-world system implementations. Awards: No specific awards mentioned in the provided information. Advising/Grants: Details regarding student advising and grant activities are not explicitly stated in the text.

Seth Polsleyمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Intelligent Systems
• Educational Technology
• +۴ مورد دیگر

Seth Polsley is an Assistant Professor in the Jeffrey S. Raikes School of Computer Science and Management at the University of Nebraska-Lincoln. His academic home resides in the School of Computing, where he bridges intelligent systems design with human-computer interaction to enhance educational and universal computing experiences. BS in Computer Engineering (2014) - University of Kansas MS (2017) & PhD (2023) in Computer Engineering - Texas A&M University His research explores: Intelligent tutoring systems Brain-computer interfaces Accessible educational technologies Machine learning for child development assessment Sketch recognition in STEM learning Recent publications demonstrate expertise in tactile learning interfaces, sketch-based developmental assessment, and equitable AI systems. Key disciplines span Human-Computer Interaction, Machine Learning, and Educational Technology. Scientific recognition includes: James Blackiston Memorial Graduate Fellowship Sigma Xi Research Award With professional experience at Lexmark International and MIT Lincoln Lab, Polsley combines practical engineering with educational innovation. His work on sketch-based tools and wearable systems addresses both technical and societal challenges in computing.

• Educational Equity
• K-12 Education
• Race and Education
• +۴ مورد دیگر

Ain A. Grooms serves as an Assistant Professor in the Educational Leadership and Policy Analysis Department at the University of Wisconsin-Madison School of Education. Her research centers on K-12 educational equity, specifically examining access and outcomes for students of color and traditionally disenfranchised communities through the critical lens of race and place. She investigates how educational policies impact student achievement and leadership practices across school, district, and state levels. Her primary research domains include: Educational equity frameworks and implementation Race and place intersections in policy analysis Retention strategies for educators of color Equity-focused principal pipeline development Computer science education policy implementation Chronic absenteeism in marginalized communities Analysis of Dr. Grooms' recent publications reveals a consistent focus on dismantling systemic barriers through leadership development and policy innovation. Her work examines how educational leaders navigate racialized contexts, with particular attention to computer science equity, pandemic response adaptations, and the sustainability of equity-focused initiatives. Publications span high-impact journals including Educational Policy and Urban Education, demonstrating rigorous mixed-methods approaches to educational justice. Her scholarship connects theoretical frameworks with practical leadership applications, emphasizing community-engaged solutions to persistent educational inequities. Current projects investigate institutional responses to demographic shifts and the development of counterspaces for marginalized students and educators.

Katherine Mickley Steinmetzمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Emotional Memory
• Attention Processes
• +۴ مورد دیگر

Dr. Katherine Mickley Steinmetz is a Professor in the Department of Psychology at Wofford College, where she directs the Neuroscience of Cognition and Emotion Lab. Her office is located in the R. Milliken Science Center 231D, and she can be reached at (864) 597-4643 or steinmetzkr@wofford.edu. She has established herself as a prominent researcher in emotional memory and cognition through her extensive publication record and laboratory work. Dr. Steinmetz earned her undergraduate degree in Psychology and Neuroscience from Allegheny College, where she participated in the Neuroscience in the Humanities program and completed a thesis on electrophysiological changes caused by learning words in emotional contexts. She then pursued graduate studies at Boston College under Dr. Elizabeth Kensinger, using neuroimaging (fMRI), eye-tracking, electrophysiological, and behavioral techniques to investigate neural mechanisms influencing memory for emotional events. Following this, she completed a post-doctoral fellowship focusing on stress and sleep effects on memory at Boston College and Notre Dame. Her research program focuses on understanding the neuroscience behind how emotional processing influences attention and memory. Dr. Steinmetz combines behavioral testing, event-related potential (ERP) recordings, and salivary hormonal assays to examine both cognitive and neural processes guiding attention toward and memory for emotional information. She is particularly interested in how factors like stress and anxiety modulate these processes. Her work spans multiple domains including emotional memory enhancement, cross-cultural differences in memory, physiological correlates of emotional processing, and the interaction between sleep, stress hormones, and memory consolidation. Analysis of Dr. Steinmetz's publication history reveals consistent focus on emotional memory mechanisms across various contexts. Her research demonstrates how emotional content differentially affects item versus background memory, how stress hormones like cortisol modulate memory processes, and how attentional mechanisms interact with emotional processing. Her methodological approach integrates multiple techniques including ERP, eye-tracking, physiological monitoring, and behavioral paradigms to provide comprehensive insights into emotion-cognition interactions. Dr. Steinmetz has authored numerous peer-reviewed publications in prominent journals including Journal of American College Health, Frontiers in Physiology, Cognition and Emotion, Memory, and Affective Science. She has also contributed book chapters and co-authored the book "How Does Emotion Affect Attention & Memory?" (2010) with Elizabeth A. Kensinger, which reviews behavioral and neuroimaging evidence on emotion's effects on memory and attention in individuals with and without PTSD. As an educator, Dr. Steinmetz has focused on developing effective undergraduate research experiences in cognitive neuroscience. Her work in this area addresses the challenges of preparing students for careers in this interdisciplinary field while maintaining sustainable faculty development. She appears to be actively involved in mentoring undergraduate researchers through her laboratory work at Wofford College. Dr. Steinmetz's Neuroscience of Cognition and Emotion Lab serves as the primary research hub for her investigations. The lab employs multiple methodological approaches to study emotion-cognition interactions, providing students with hands-on research experience in cognitive neuroscience techniques. Her research program demonstrates continuity from her graduate and postdoctoral training while developing its own distinct focus on the interplay between emotional processing, attention, and memory.

• Scientific Computing
• GPU Computing
• Signal Processing
• +۵ مورد دیگر

Professor Wes Armour is a Professor of Scientific Computing at the University of Oxford and serves as the Associate Head of Department for Research in the Department of Engineering Science. He previously directed the Oxford e-Research Centre, an interdisciplinary research center within the Engineering Science Department. With over £31 million secured as PI or Co-I, his work spans supercomputing, signal processing, machine learning, computational fluid dynamics, and protein crystallography. Professor Armour's research focuses on extracting science from data through fundamental challenges in modeling, simulation, and data processing. His work draws from numerical analysis, signal processing, and machine learning to develop technologies enabling future scientific discoveries, particularly for the Square Kilometre Array (SKA) telescope. Key interests include GPU computing, high performance computing, and machine learning applications across diverse domains from radio astronomy to finance. As Director and Principal Investigator of JADE and JADE2, a 700-GPU machine, he established the UK's first national High Performance Computer facility dedicated to advancing Artificial Intelligence and Machine Learning. His research group has pioneered GPU applications across multiple fields, including Square Kilometre Array data processing, protein crystallography, and graphene simulations. Current projects span energy-efficient machine learning, stock price prediction in finance, prime number prediction in cryptography, and multi-modal CCTV data analysis. Professor Armour has been instrumental in developing real-time signal processing techniques for astronomical observations, including the ARTEMIS system for millisecond radio transient detection. His publications demonstrate consistent innovation in GPU-accelerated computing dating back to early work in 2008 on accelerating conjugate gradient routines for electron transport in graphene.

• Entrepreneurial Education
• Experiential Learning
• Digital Business Models
• +۳ مورد دیگر

Apurva Ganoo is a Lecturer at the Department of Management Studies, Aalto University. Their research focuses on experiential learning methodologies in entrepreneurial education, digital business models, and future-ready skill development. They explore topics such as anti-rivalry dynamics in digital goods, transdisciplinary collaboration, and pedagogical frameworks for innovation. Research interests emphasize bridging theoretical concepts with practical application through experiential approaches. Their work analyzes student perceptions in multidisciplinary team settings and the design of educational systems that foster entrepreneurship and adaptability. No academic awards or grants are explicitly mentioned in the provided materials. Their professional contributions center on advancing management education through innovative teaching methods and curriculum development.