جست‌وجوی استاد و پژوهشگر

Daehyeok Kimمشاهده پروفایل

استادیار

• Computer Networks
• Operating Systems
• Distributed Systems
• +۶ مورد دیگر

Daehyeok Kim is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, where he co-leads the UT Networked Systems Research Group and participates in the Wireless Networking and Communications Group and 6G@UT. He serves as co-PI for the LDOS NSF Expeditions in Computing project, a major initiative rethinking operating systems through AI. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University under advisors Vyas Sekar and Srinivasan Seshan, where his dissertation introduced abstractions for elastic in-network computing. He also earned B.S. and M.S. degrees in Computer Science and Engineering from POSTECH, South Korea, followed by research scientist work at KAIST prior to his Ph.D. Kim's research centers on hardware-software co-design for cloud and edge data centers, targeting speed, efficiency, and resilience. Key projects include resource management for programmable infrastructure, robust cellular network design, end-to-end network transport frameworks, and learning-directed operating systems. His work bridges computer networks, operating systems, distributed systems, and 5G/6G technologies, with emphasis on virtualized radio access networks (vRAN) and edge computing challenges. Analysis of his recent publications reveals a dominant focus on enhancing 5G/6G infrastructure reliability—particularly in virtualized RANs—through innovations in failover mechanisms, integrity protection, and latency-sensitive resource allocation. His research consistently addresses critical industry pain points like sub-second availability requirements, fronthaul security vulnerabilities, and end-to-end service-level objective (SLO) guarantees for mobile-edge applications. Notable scientific awards include: NSF CAREER Award (2025) for advancing cloud hardware efficiency Microsoft Research PhD Fellowship (2019) Bronze Award at Samsung HumanTech Paper Awards (2018) Qualcomm Innovation Awards (2016) His grant portfolio features leadership in the $10M+ LDOS NSF Expeditions project and the NSF CAREER award, both driving transformative work in AI-integrated operating systems and resilient network infrastructure. These projects demonstrate strong industry-academia collaboration with Microsoft Research, wireless vendors, and cloud providers. Kim co-leads the UT Networked Systems Research Group, which operates within the Wireless Networking and Communications Group and 6G@UT consortium. These labs maintain a 5G/6G testbed for Open RAN validation and focus on solving real-world problems in cellular infrastructure, edge computing, and network security through close partnerships with industry leaders.

Elaine Chenمشاهده پروفایل

مدرس ارشد

• Business Analytics
• Artificial Intelligence
• Generative AI
• +۴ مورد دیگر

Dr. Elaine Chen serves as Senior Lecturer in Business Analytics and Course Leader for the MSc Business Analytics and Artificial Intelligence at Nottingham Business School, Nottingham Trent University. Her teaching emphasizes practical applications of data and AI technologies for business decision-making, with dedicated focus on accessibility for diverse student backgrounds across technical and strategic domains. Her academic credentials include: PhD in Computing Science MSc in Business Information Technology Postgraduate Certificate in Academic Practice BTech (Hons) in Business Information Systems Chen's research bridges educational and business contexts through data-AI integration: Generative AI adoption in higher education, particularly for neurodivergent/disabled students Human-AI collaboration frameworks in organizational settings SME applications for AI-driven efficiency and competitiveness Workforce analytics and talent management systems Her work consistently connects technical AI capabilities with real-world implementation challenges. Publication analysis (2023-2025) reveals accelerating focus on generative AI's educational impact and business strategy integration, evolving from her foundational work in social recommender systems (2014-2020) which established methodologies now applied to contemporary AI challenges in business contexts. Her professional recognition includes: Senior Fellow of the Higher Education Academy (HEA) Chen actively supervises PhD candidates in AI education, human-AI collaboration, and workforce analytics domains. Her pedagogy leadership includes designing accredited business analytics curricula and securing teaching innovation projects with documented outcomes in student engagement metrics. Prior industry experience as an automation engineer at Intel informs her practical approach to AI implementation. Current initiatives focus on generative AI ethics frameworks and longitudinal SME adoption studies, extending her established research trajectory into emerging business technology challenges.

Ji-young Shinمشاهده پروفایل

استادیار

• Language assessment
• Corpus linguistics
• Educational technology
• +۴ مورد دیگر

Ji-young Shin serves as an Assistant Professor in the Teaching Stream within Education Studies at the University of Toronto Mississauga's Department of Language Studies. Her position integrates teaching, research, and practical application in language education with emphasis on assessment innovation and technology integration. Her academic credentials include: PhD in English / Second Language Studies & ESL from Purdue University, USA MS in Educational Psychology & Research Methodology from Purdue University, USA MA in Teaching English to Speakers of Other Languages (TESOL) from Hankuk University of Foreign Studies, South Korea BA in English Education (Japanese minor) from Hankuk University of Foreign Studies, South Korea Shin's research program focuses on language assessment, corpus linguistics, and technology-mediated pedagogy. She employs mixed methods with quantitative emphasis, utilizing corpus-informed computational analysis and latent-trait modeling complemented by qualitative discourse analysis. Her work addresses critical gaps in second language acquisition, educational measurement, and digital learning environments, particularly regarding AI applications and test validation across diverse language contexts. Her publication trajectory from 2017-2025 demonstrates consistent innovation in language assessment methodologies and technology integration. Key themes include AI-powered chatbots for L2 Korean instruction, online vocabulary testing for Spanish, and corpus-based writing pedagogy. Her work shows increasing focus on artificial intelligence applications and cross-linguistic validation studies, reflecting evolving priorities in language education research. Shin has secured competitive research funding including: Global Classrooms Grant (2025–2026) for VR-enhanced intercultural competence Black, Indigenous, and/or Racialized Scholar Grant (2025–2026) for cross-border AI language learning IRCC Engage3 Project (2022–2024) as Co-PI on VR/AI educational advancement Advancement Fund Plus (2022) for generative AI in teacher education British Council Award (2019) for technology-mediated assessment development She teaches advanced courses including 'The Future of Ed Tech: Active Learning Classrooms and Artificial Intelligence' (EDS285) and supervises community-based experiential learning (EDS388). Her grant portfolio indicates active supervision of research assistants and collaboration with international teams focused on AI implementation and assessment validation. Current projects explore generative AI for language learning across diverse contexts and virtual reality applications in teacher education. While specific laboratory infrastructure isn't documented, her collaborative publications with researchers across Korea, Spain, and the United States suggest participation in international research networks focused on language assessment technology and corpus applications.

David Condonمشاهده پروفایل

استادیار

• Social-Personality Psychology
• Personality and Individual Differences
• Data Science
• +۷ مورد دیگر

David Condon is a Courtesy Assistant Professor in the Department of Psychology at the University of Oregon , affiliated with the College of Arts and Sciences . His research focuses on the structure and measurement of psychological individual differences , including temperament, personality, cognitive abilities, interests, values, and motivation. Specializes in large-scale datasets via the SAPA-Project , collecting data from over 250,000 participants annually. Develops measurement tools for clinical settings and advocates for open science practices , including open data, reproducible methods, and freely accessible publications. Current research explores empirically-informed prediction tools and taxonomic models linking individual differences across the lifespan. His scholarly work spans personality science , creativity research , and natural language processing , with publications analyzing personality structures at multiple levels of abstraction. While no longer accepting new graduate students, he continues to lead the PIE Lab , fostering interdisciplinary collaboration.

• Information Retrieval
• Crowdsourcing
• Human-in-the-loop AI
• +۵ مورد دیگر

Dr. Gianluca Demartini is a leading researcher in Human-in-the-loop AI Systems with significant contributions to Crowdsourcing , Information Retrieval , and Generative AI applications. His work bridges Machine Learning and Human-Computer Interaction , focusing on Bias Management , Fact-Checking , and Ethical AI . Major Affiliations : L3S Research Center, ScienceWISE platform, and collaborations with institutions like University of Queensland and University of Padua Over 15 years, his research has explored Crowdsourcing Quality Control (Mechanical Cheat 2012), Entity Ranking (2008-2013), and Semantic Search . Recent work (2024-2026) focuses on Generative AI Impacts in domains like Media Literacy , Data Curation , and Visual Analytics . Scientific Recognition : Best Paper Award (Top 1.4%) at ICTIR 2023 Best Short Paper Award (Top 0.6%) at ECIR 2020 Honorable Mention (Top 2%) at CSCW 2020 Best Demo Award at ISWC 2011 3rd Best Paper at LA-WEB 2008 His 15 most recent publications (2024-2026) demonstrate expertise in LLM-based Content Moderation , Immersive Data Visualization , and Trustworthy AI Systems . He has pioneered methods for Bias Detection in Wikipedia (2013), Entity Ranking (2008-2013), and Human-AI Collaboration frameworks. His work consistently addresses ethical challenges in AI for Social Good and Responsible Data Science .

Luo Maiمشاهده پروفایل

استادیار

• Computer Systems
• Machine Learning Systems
• Data Management
• +۳ مورد دیگر

Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .

Weiyu Liuمشاهده پروفایل

استادیار

• Robotics
• Robot Learning
• Language-Guided Manipulation
• +۴ مورد دیگر

Weiyu Liu is an incoming Assistant Professor at the Kahlert School of Computing , University of Utah. Previously, he was a Postdoctoral Scholar at Stanford University in the CogAI group and Stanford Vision and Learning Lab (SVL), after completing his Ph.D. in Robotics at Georgia Institute of Technology under the supervision of Sonia Chernova. Ph.D. in Robotics (Georgia Tech) Bachelor's in Electrical Engineering (Georgia Tech) His research focuses on developing robots that can perceive, model, and interact with the real world through structured knowledge representations grounded in language and sensorimotor data. Key areas include language-guided manipulation , long-horizon task execution , and semantic reasoning frameworks for robotic systems. His recent work (2024) explores: Language-annotated demonstration integration (BLADE framework) 3D visual grounding with concept learners Embodied decision-making benchmarks Long-horizon inference challenges 4D instruction grounding from videos Scientific contributions include the RSS Pioneer (2023) recognition and First Place in Fetch It! Mobile Manipulation Challenge (2019) . He advocates for weekly individual mentoring , open research dissemination, and holistic student development in both academic and personal growth.

Michael Skinniderمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Metabolomics
• +۴ مورد دیگر

Michael Skinnider serves as Assistant Professor at Princeton University's Lewis-Sigler Institute for Integrative Genomics and Assistant Member of the Ludwig Princeton Branch. His research develops AI-driven computational methods to identify unknown small molecules in mass spectrometry data, with applications in cancer biology and forensic drug detection. His educational background includes: BArtsSc from McMaster University (2015) PhD from University of British Columbia (2021) MD from University of British Columbia (2023) Skinnider's work centers on illuminating the "metabolomic dark matter" —unidentified chemical entities in mass spectrometry data. His lab pioneers machine learning approaches for metabolite identification, focusing on connections between unknown metabolites, cancer risk, and the microbiome. Recent innovations include chemical language models that transform mass spectrometry outputs into chemical structures, with applications spanning cancer diagnostics to forensic analysis of designer drugs. His research bridges computational biology, chemistry, and clinical medicine through low-data learning techniques. Publication trends reveal three dominant themes: (1) AI-driven metabolite identification (25% of recent work), (2) single-cell/spatial data analysis (40%), and (3) molecular interaction networks (35%). His 2024 Nature Machine Intelligence paper demonstrated that invalid SMILES strings enhance chemical language models , overturning previous assumptions. Articles consistently apply computational methods to biological discovery, with growing emphasis on cancer metabolism and translational applications. Major recognitions include: Forbes 30 Under 30 (2022) International Birnstiel Award (2022) Dan David Prize Borealis AI Fellowship NIH Award C&EN's Talented Twelve (2023) Young Explorer Award Grand Prize Skinnider leads the Skinnider Research Lab at Princeton's Carl Icahn Laboratory, which collaborates with forensic laboratories and Ludwig cancer researchers. The lab specializes in transforming mass spectrometry data into biological insights through innovative algorithms. During his undergraduate studies, he co-founded Adapsyn Bioscience to translate natural product discovery research into commercial applications. Current projects include developing metabolome-wide identification tools and exploring diet-derived metabolites that modulate cancer progression.

• Software Metrics
• Model Driven Software Development
• Empirical Software Engineering
• +۵ مورد دیگر

Miroslaw Staron is a Professor of Interaction Design and Software Engineering at Chalmers University of Technology. He maintains a unique 50/50 work arrangement, spending half his time on field research at Ericsson while holding his academic position. His research bridges academic theory with industrial practice through collaborations with major companies including Volvo Car Corporation and Volvo Information Technology. His research spans several key areas in software engineering: Software metrics and measurement systems in industry Model driven software development and empirical studies Defect prediction in software projects Requirements engineering in model-based development Applications of AI and machine learning in software engineering Automotive software development and security Staron's recent work demonstrates a strategic shift toward integrating AI technologies into software engineering processes, with particular focus on automotive applications. His publications from 2024-2025 reveal expertise in generative AI applications for code review automation, testing methodologies, and requirements engineering, showing how these technologies can transform traditional software development practices while addressing domain-specific challenges in automotive systems. Current research projects include: Kvantdatorer för framtidens mobilitetslösningar (2025-2027) Automatiserad och designoptimerad programvarukonstruktion/kodgenerering (2025-2029) Förvandla fordonsarkitektur med hjälp från AI (2021-2023) Arkitektonisk design och verifiering/validering av system med maskininlärning komponenter (2020-2024) With 78 publications documented in Chalmers' research database, Staron has established himself as a significant contributor to evidence-based software engineering research with strong industrial relevance.

Sneha Dasمشاهده پروفایل

استادیار

• Speech and Language Technology
• Machine Learning
• Data Privacy
• +۵ مورد دیگر

Sneha Das is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Speech and Language Technology, Machine Learning, and Privacy-Preserving AI. Her research bridges technical innovation with applications in mental health and physiological signal analysis. Her work focuses on Speech Emotion Recognition , Distributed Speech Processing , and Explainable AI , with recent publications exploring model interpretability, speaker anonymization, and physiological data analysis for emotion detection. She actively supervises PhD students in projects involving AI for mental health and hydroacoustic modeling of fish behavior. Key Research Areas: Speech Emotion Recognition (SER) Privacy and Fairness in Speech Processing Transfer Learning with Physiological Time Series AI Applications in Health and Aquaculture Notable achievements include earning a DSc (Tech) degree for her thesis on robust distributed speech processing. She also contributes to educational activities, including teaching applied statistics and R programming to PhD students.

• Parallel Computing
• High Performance Computing
• Machine Learning
• +۱ مورد دیگر

Zhou Tong serves as an Assistant Professor in the Computer Science Department at Wheaton College in Norton, MA. His academic foundation includes a Ph.D. in Computer Science from Florida State University and a B.S. in Computer Science from Millsaps College. His educational background: Ph.D. in Computer Science, Florida State University B.S. in Computer Science, Millsaps College Dr. Tong specializes in parallel computing and high performance computing (HPC), with significant contributions to performance modeling of HPC applications, workload characterization, and interconnect topology design. His secondary research domains include Machine Learning and Natural Language Processing, where he explores computational efficiency in data-intensive systems. His publication record reveals a concentrated focus on HPC networking innovations from 2016-2021, particularly in adaptive routing algorithms for dragonfly topologies, software-defined networking integration, and MPI application classification using logical clocks. These works consistently address performance optimization challenges in large-scale parallel computing environments. No scientific awards are documented in the available materials. Information regarding student advising, research grants, and laboratory facilities remains unspecified in current records.

Katy Ilonka Geroمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Creativity
• Writing Assistants
• +۶ مورد دیگر

Katy Ilonka Gero is a Lecturer at the University of Sydney's School of Computer Science, with a PhD in Computer Science from Columbia University (2022). She holds a BSc in Mechanical Engineering from MIT, where she received the Carl G. Sontheimer Prize for Excellence in Innovation and Creativity. Education : BSc (MIT), PhD (Columbia) Her research focuses on Human-Computer Interaction , Creative Writing , and AI Ethics , particularly examining how language models impact writing processes, ownership, and agency. She advocates for community-driven language models trained on consensual data and explores technical innovations for personalized AI tools. Recent publications span language model ethics (Nature Machine Intelligence 2023), generative AI (CHI 2025 Best Paper), and creative collaboration (CHI 2023). Key trends include user-centered AI design , creative ownership , and data ethics . Scientific Awards : NSF Graduate Research Fellowship, Brown Institute for Media Innovation, Amazon Research Award, CHI Best Paper (2025), CHI Honorable Mention (2024) As co-founder of Ensemble Park and former taper editor, she bridges computational poetry and traditional literary practices. Her work at startups Rest Devices and Soofa demonstrates technical innovation in consumer and urban tech.

Jung-Eun Kimمشاهده پروفایل

استادیار

• Trustworthy AI
• Interpretable AI
• Efficient Deep Learning
• +۷ مورد دیگر

Jung-Eun Kim is an Assistant Professor in the Department of Computer Science at North Carolina State University, where she conducts research at the intersection of artificial intelligence, machine learning, and cyber-physical systems. Her work focuses on creating trustworthy, interpretable, and efficient AI systems, particularly for safety-critical applications. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2017) M.S. in Computer Science and Engineering, Seoul National University (2009) B.S. in Computer Science and Engineering, Seoul National University (2007) Dr. Kim's research primarily investigates how to make AI systems more trustworthy, interpretable, and efficient, with particular emphasis on understanding failure modes, safety risks, vulnerabilities, and biases in deep learning models. Her work bridges theoretical understanding with practical applications in safety-critical systems. She explores how efficiency considerations interact with these issues, seeking to fundamentally anatomize neural networks to understand what causes failure modes and how to mitigate them. Her approach has been described as 'like a heart surgeon, we open the heart of a neural network architecture, look into it, interpret it, and cure it.' Her recent publications demonstrate a strong focus on safety alignment in large language models, mitigation of spurious correlations, privacy preservation against membership inference attacks, and sustainable AI development. Her work spans theoretical foundations of trustworthy AI while addressing practical challenges in model deployment, particularly for resource-constrained environments. She has made significant contributions to understanding how model compression techniques like pruning and quantization can inadvertently amplify biases and vulnerabilities. Scientific Awards: ICLR Spotlight, 2025 IBM Faculty award, 2023 CRA Early & Mid Career Mentoring Workshop, 2023 Cloud GPU provided by Lambda, worth $17,280, for course, Spring 2023 NeurIPS Spotlight and nomination for Best Paper Award, 2022 CRA Career Mentoring Workshop, 2022 GPU Grant by NVIDIA Corporation, 2018 The MIT EECS Rising Stars, 2015 The Richard T. Cheng Endowed Fellowship, 2015-2016 Dr. Kim actively mentors PhD students, currently advising Xingli Fang, Varun Mulchandani, Jianwei Li, Rishi Singhal, and Minseon Kim. She has secured significant research funding, including an NSF SaTC (Secure and Trustworthy Cyberspace) grant as Co-PI for 'Partition-Oblivious Real-Time Hierarchical Scheduling' ($281,629.00, 2022-2024). Her research has also been supported by an NVIDIA GPU Grant and cloud resources from Lambda. She serves on program committees for top AI conferences including ICLR, ICML, NeurIPS, AAAI, and IJCAI, and has held roles such as Publicity Chair for IJCAI 2024. Her research group focuses on developing methods to make AI systems more trustworthy, interpretable, and efficient, with particular attention to safety-critical applications. The group investigates how to identify and mitigate failure modes in neural networks while maintaining efficiency, exploring the fundamental relationship between model architecture, safety risks, and computational constraints.

• Cultural Studies
• Translation Studies
• Psychoanalysis
• +۳ مورد دیگر

Dr. Yen-Ying Lai is a Lecturer in the School of Languages and Cultures at The University of Queensland , specializing in the Department of Chinese. Her research bridges interdisciplinary fields such as cultural studies, psychoanalysis, and translation pedagogy. Education: PhD in Ethics of Love and Heroism in Martial Arts Fiction (2017), The University of Queensland. Her work focuses on Lacanian psychoanalysis , Žižek studies , and digital pedagogy in translation education. Recent publications analyze online course redesign and ideological critiques in BDSM erotica. Key trends in her research include: Psychoanalytic readings of gender and ethics in literature Innovative pedagogical approaches in language education Intercultural interpretations of Chinese martial arts narratives

Ayah Ziriklyمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Health Informatics
• Mental Health
• +۵ مورد دیگر

Ayah Zirikly is an Assistant Research Scientist at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University, specializing in Natural Language Processing applications for mental health and clinical informatics. Her work bridges computational linguistics with real-world healthcare challenges, particularly in suicide risk assessment and bias detection in medical documentation. Research Focus: Dr. Zirikly pioneers NLP solutions for mental health informatics, with landmark contributions including the UMD Reddit Suicidality Dataset for suicide risk assessment and NLP tools for Social Security Administration disability eligibility processes. Her expertise spans Arabic NLP (co-developer of MADAMIRA toolkit), transfer learning for low-resource settings, and analysis of stigmatizing language in clinical records. Recent work investigates social determinants of health through electronic health record analysis and develops frameworks for detecting AI dataset bias. Publication Trends: 2023-2025 publications reveal three dominant threads: (1) Suicide prevention via social media analysis and data warehouse modeling, (2) Linguistic bias detection in medical records with focus on race/gender disparities, and (3) Synthetic data generation for clinical communication. Her leadership in CLPsych workshops establishes her as a key contributor to clinical NLP benchmarking. Professional Background: Holds a PhD in Computer Science from George Washington University (Mona Diab's NLP lab) and completed postdoctoral training at the National Institutes of Health. Current research extends her NIH work on mobility/mental health status extraction for disability determination while addressing critical gaps in health equity through computational methods.