جست‌وجوی استاد و پژوهشگر

Eva Ogiermannمشاهده پروفایل

دانشیار

• Cross-cultural pragmatics
• Politeness theory
• Bilingual communication
• +۳ مورد دیگر

Dr Eva Ogiermann is an Associate Professor in English Language and Applied Linguistics at King’s College London, serving as Programme Director for the MA in Language and Cultural Diversity. She holds a PhD from Carl von Ossietzky Universität Oldenburg (2007) and an MA from the University of Münster (2001). Her research focuses on cross-cultural pragmatics, politeness theory, and bilingual communication, with a particular interest in how language reflects cultural norms and identities. Current Position: Reader (Associate Professor) at King’s College London Previous Institutions: University of Oldenburg (Germany), University of Portsmouth, University of Surrey Editorial Roles: Former associate editor of the Journal of Pragmatics (2010–2018) Her work examines cultural specificity of language use, including studies on apology strategies, request negotiation, and family communication dynamics. Recent projects analyze pandemic-era signage’s role in disseminating risk communication and maintaining social cohesion during crises. She also explores epistemic authority in academic writing tutorials and the pragmatic functions of marketing language. Key research contributions include From Speech Acts to Lay Understandings of Politeness (2019) and numerous articles in top journals like Journal of Pragmatics and Pragmatics and Society . Her teaching spans BA and MA programs in applied linguistics and language education. Dr Ogiermann leads the Implementing Covid-19 measures project, studying how non-official signage adapts government health guidelines. She actively contributes to the Centre for Language, Discourse & Communication, fostering interdisciplinary research at the intersection of language, culture, and society.

Freda Shiمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing
• Multilingualism
• +۴ مورد دیگر

Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science, University of Waterloo, and a Faculty Member at the Vector Institute. She holds a Canada CIFAR AI Chair. Her research focuses on computational linguistics, natural language processing (NLP), and grounded language learning, with emphasis on multilingualism and spatial reasoning in vision-language systems. She earned her Ph.D. in Computer Science from the Toyota Technological Institute at Chicago (2024), advised by Karen Livescu and Kevin Gimpel, supported by a Google Ph.D. Fellowship. Her undergraduate degree is from Peking University (2018), with a minor in Sociology. Her academic career includes affiliations with the CompLING Lab at Waterloo and contributions to major conferences like ACL and NAACL. She has organized tutorials on NLP grounding and is actively involved in research on model robustness and cognitive insights. Awards include the Google Ph.D. Fellowship and Best Paper Nominations at ACL 2024 and EMNLP 2021, alongside her Thesis of Distinction. She teaches courses such as CS 784 (Computational Linguistics) and CS 486/686 (Artificial Intelligence), emphasizing both theoretical and applied aspects of NLP. Research trends in her articles highlight advancements in vision-language spatial reasoning, multilingualism, and model interpretability. Her work bridges cognitive science and computational methods, exploring how human language mechanisms inform the design of more trustworthy AI systems. Scientific Awards: Google Ph.D. Fellowship Best Paper Nominee (ACL 2024) Best Paper Nominee (EMNLP 2021) Thesis of Distinction (2024) Advising and Grants: As an advisor, she encourages prospective students to review her guidelines. Her grants include support from the Canada CIFAR AI Chair program and the Vector Institute. She collaborates in labs such as CompLING at Waterloo and co-organizes events at NAACL and ICLR. Labs/Teams: She leads the CompLING Lab at the University of Waterloo, affiliated with the Vector Institute. Her work integrates interdisciplinary teams focusing on grounded learning and multilingual NLP challenges.

Niki Kitturمشاهده پروفایل

استاد

• AI-Augmented Cognition
• Human-Machine Collaboration
• Sensemaking Systems
• +۴ مورد دیگر

Aniket 'Niki' Kittur is a Professor in the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science. His research focuses on AI-augmented cognition, exploring how human and machine intelligence can collaborate to enhance creativity, decision-making, and innovation. He leads projects like the Semantic Reader and Skeema browser extension, aiming to reduce cognitive overload through intelligent systems. Education: BA in Psychology & Computer Science from Princeton University; PhD in Cognitive Psychology from UCLA. His work bridges HCI, crowdsourcing, and cognitive science, with 100+ publications and 17 best paper awards. He advises industry partners including Google, Microsoft, and Toyota while maintaining a lab focused on real-world impact. Research interests center on accelerating knowledge acquisition via systems that scaffold sensemaking (e.g., Selenite for web exploration) and fostering analogical innovation through crowdsourced/AI hybrid approaches. Notable contributions include CrowdForge (human-machine workflows) and Kinetica (touch-based data visualization). Awards include NSF CAREER Award, Allen Newell Award, and CHI Academy membership. His lab's Skeema tool has achieved 79% 30-day retention in beta, reflecting impactful user-centered design principles. Current projects emphasize LLM integration for composite cognition, aiming to create systems where 'LLMs + Humans > Either Alone.' Funding来自NSF, NIH, ONR, and industry partners like Bosch and Wikimedia. Teaching includes PhD bootcamps and user-centered research courses. Over 100 students have contributed to his projects, many advancing to tech leadership roles.

Ke Yangمشاهده پروفایل

استادیار

• Algorithmic fairness
• Data ethics
• Data management
• +۷ مورد دیگر

Ke Yang serves as Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), College of Sciences. He founded and leads the Cohort for AI REsponsibility (CAREAI) initiative, while also holding core faculty positions in UTSA's School of Data Science and MATRIX (AI Consortium for Human Well-being). Education: Ph.D. from New York University (supervised by Prof. Julia Stoyanovich) Research Focus: Dr. Yang's work centers on AI trustworthiness and responsibility , with specialized expertise in algorithmic fairness, data ethics, and human-centered data science. His research addresses critical challenges including Large Language Model hallucinations, explainable AI frameworks, and algorithmic accountability mechanisms. He actively develops open-source tools like Ranking Facts and FairDAGs to implement these principles in practical systems. Publication Trends: Recent work (2020-2025) demonstrates evolving focus from foundational fairness in ranking systems toward generative AI safety and medical applications. His publications show strong theoretical grounding combined with real-world implementation, particularly in privacy policy analysis and medical question-answering systems using causal inference techniques. Scientific Recognition: Pearl Brownstein Doctoral Research Award (NYU Tandon School of Engineering) CDS Postdoctoral Fellowship (University of Massachusetts) Professional Development: Dr. Yang has secured significant research funding including the CDS Postdoctoral Fellowship at UMass. His graduate work at NYU and Drexel University was fully supported by research assistantships, demonstrating consistent funding acquisition throughout his career. He actively contributes to academic community building through conference tutorials and educational initiatives. Research Ecosystem: He directs CAREAI at UTSA while collaborating across institutional boundaries through MATRIX and the School of Data Science. Previously, he contributed to the Data systems Research for Exploration, Analytics, and Modeling (DREAM) lab and Center for Data Science at UMass Amherst, maintaining continuity in his responsible AI research trajectory.

• Human-Centered Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +۵ مورد دیگر

Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).

Bal Krishna Sharmaمشاهده پروفایل

دانشیار

• Applied Linguistics
• Sociolinguistics
• Intercultural Communication
• +۲ مورد دیگر

Bal Krishna Sharma is Associate Professor of Applied Linguistics at the University of Idaho's College of Letters, Arts and Social Sciences, with affiliate status at the Habib Institute for Asian Studies. His scholarship examines language globalization across education, tourism, and digital media contexts. Education credentials: Ph.D. in Second Language Studies, University of Hawaii at Manoa (2016) M.A. in Second Language Studies, University of Hawaii at Manoa (2011) M.Ed. in English Education, Tribhuvan University (2004) B.Ed. in English Education, Tribhuvan University (2001) Research explores: Neoliberal influences on language education Intercultural communication in tourism Translingual practices in digital spaces Multilingual policy in South Asia Recent publications analyze linguistic landscapes in pandemic communication, multilingual STEM education, and social media discourse in Nepal. His work integrates sociolinguistic theory with critical analysis of globalization. Teaches courses including Sociolinguistics, Intercultural Communication, and History of the English Language.

Christopher Piechمشاهده پروفایل

استادیار

• Computer Science Education
• Educational Data Science
• Artificial Intelligence in Education
• +۳ مورد دیگر

Christopher Piech is an Assistant Professor (Teaching) in the Department of Computer Science at Stanford University, with a courtesy appointment in the Graduate School of Education. He serves as a Faculty Affiliate at the Institute for Human-Centered Artificial Intelligence (HAI) and is affiliated with the Symbolic Systems Program. Current courses: AI for Social Good (CS 21SI), Introduction to Probability for Computer Scientists (CS 109), Researching Presenting and Publishing Work in AI & Education (CS 220/EDUC 481) Advises 11 Master's students and co-advises 3 Doctoral students His research focuses on computational education, leveraging artificial intelligence to enhance learning analytics, student collaboration detection, and knowledge tracing in programming education. Publications span ACM Technical Symposium on Computer Science Education (SIGCSE) and NeurIPS conferences. Key article trends include: (1) AI-driven educational tools for code analysis, (2) collaboration monitoring in large classes, and (3) probabilistic models for student learning trajectories.

Aravind Rajeswaranمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Representation Learning
• World Models
• +۵ مورد دیگر

Aravind Rajeswaran is a Research Scientist at Meta AI (FAIR) and Visiting PostDoc/Collaborator at Berkeley AI Research Lab (BAIR) at UC Berkeley's College of Engineering, Department of Electrical Engineering and Computer Sciences. He completed his PhD in Computer Science at the University of Washington under Profs. Sham Kakade and Emo Todorov, with additional collaborations with Sergey Levine and Chelsea Finn, and previously earned his bachelor's degree with the best undergraduate thesis award from IIT Madras working with Balaraman Ravindran. His research focuses on building generalist AI agents that operate in open worlds, combining reinforcement learning, representation learning, and world models. Key projects include Locate 3D for real-world object localization, OpenEQA for embodied question answering with foundation models, VC-1 as an artificial visual cortex for embodied intelligence, and R3M as a universal visual representation for robot manipulation. His work demonstrates how pre-trained visual representations can significantly enhance robotic capabilities with minimal supervision. Rajeswaran's publication record shows consistent high-impact contributions across premier AI conferences including NeurIPS, ICML, CVPR, and RSS from 2018 through 2025, with research spanning reinforcement learning, representation learning, robotics, and computer vision. His work on Decision Transformer demonstrated how sequence modeling frameworks can effectively train reinforcement learning policies. Best Paper Award, Scaling Robot Learning Workshop at ICRA 2022 best undergraduate thesis award from IIT Madras As an educator and mentor, Rajeswaran has guided numerous PhD students who have gone on to positions at Stanford, MIT, CMU, Berkeley, and top AI companies including Meta, DeepMind, and Anthropic. He designed and co-taught the Deep Reinforcement Learning course (CSE599G) at UW in 2018, with materials adopted by courses at MIT and CMU, and served as lead TA for Machine Learning for Big Data (CSE547). His research has been supported through his role as Principal Investigator for the Cortex Team at FAIR.

Igor Rudanمشاهده پروفایل

استاد

• Genetics
• Genomics
• Genome-Wide Association Study
• +۷ مورد دیگر

Igor Rudan is Professor of International Health and Molecular Medicine at the University of Edinburgh, serving as Co-Head of the Centre for Global Health within the Usher Institute, part of the College of Medicine and Veterinary Medicine. His academic position is described as a 'Personal Chair of International Health & Molecular Medicine' at the Deanery of Molecular, Genetic and Population Health Sciences. His research interests include: Genome-Wide Association Studies Genetics and Genomics Genetic Divergence Meta-Analysis Single Nucleotide Polymorphism research Systematic Reviews in global health Epidemiology of respiratory diseases Health inequalities and social determinants of health Professor Rudan's recent publications reveal a strong focus on the intersection of molecular medicine and population health. His work spans genetic epidemiology, pandemic response research, and health equity studies, with particular emphasis on understanding how genetic, social, and environmental factors interact to influence health outcomes across diverse populations. His research frequently employs large-scale genomic analyses combined with population health approaches to address global health challenges. His scientific recognition includes: NIHR Impact Prize (2025) for respiratory disease research RSE Mary Somerville Medal (2023) Professor Rudan leads and collaborates on numerous international research projects including EQUI-RESP-AFRICA (improving respiratory health outcomes in Africa), the NIHR Global Health Research Unit on Respiratory Health (RESPIRE-2), and documentation of the EQUIST tool's global impact. His work often involves large-scale data analysis and international collaborations across multiple continents, positioning him at the forefront of global health research that bridges molecular medicine with population-level health interventions.

Matthew Holdenمشاهده پروفایل

دانشیار

• Surgical Data Science
• Machine Learning
• Computer-Assisted Surgery
• +۳ مورد دیگر

Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.

Kathrin Seleمشاهده پروفایل

مدرس ارشد

• Organizational Routines
• Routine Dynamics
• Innovation Management
• +۳ مورد دیگر

Kathrin Sele is a Senior Lecturer at the Department of Management Studies, Aalto University. Her research focuses on organizational routines, routine dynamics, and innovation management. She explores how organizations adapt to complex challenges through sociomaterial practices and embodied work. Key contributions include frameworks for analyzing routine ecologies, M&A synergy realization, and digital transformation in HR. Her work bridges practice theory, actor-network theory, and strategic management. Sele’s research spans extreme contexts (e.g., crisis management), gender equality, and ethical dilemmas in emerging markets. She collaborates on interdisciplinary projects like the ‘Routine Dynamics’ framework, emphasizing organizational adaptation in flux. Her publications appear in top journals such as Journal of Management Studies and Organization Studies . Her advising and grant activities are not explicitly detailed in the provided texts, though her prolific publication record suggests active research leadership. She contributes to academic communities through conference proceedings and edited volumes, particularly on grand challenges and innovation networks.

• AI Ethics
• Epistemology
• Algorithmic Fairness
• +۳ مورد دیگر

Will Fleisher is Assistant Professor in Georgetown University's Department of Philosophy and Research Assistant Professor at the Center for Digital Ethics. His research examines ethical, political, and epistemic implications of AI systems, with specialization in algorithmic fairness, explainable AI, and epistemology of inquiry. His work bridges technical AI development and philosophical frameworks, addressing fairness metrics in machine learning, epistemic norms in AI systems, and ethics of human-AI interaction. Recent publications explore disagreement in AI alignment, fragmentation of evidence, and intellectual virtues in inquiry. Professor Fleisher's research has appeared in Noûs, Philosophical Studies, Philosophy of Science, and AAAI/ACM conference proceedings. He previously held postdoctoral positions at Northeastern University and Washington University in St. Louis.

Nicola Breugstمشاهده پروفایل

استاد

• Entrepreneurship
• Psychology of Entrepreneurship
• Entrepreneurial Teams
• +۳ مورد دیگر

Prof. Nicola Breugst is a Professor of Entrepreneurial Behavior at the TUM School of Management, Technical University of Munich. Her research focuses on the psychological dimensions of entrepreneurship, particularly the interplay between founders' thought processes, emotions, and their social/environmental contexts. She explores how entrepreneurial teams influence decision-making, employee motivation, and venture success. Her work challenges the 'lone entrepreneur' myth, emphasizing systemic interactions between founders and their ecosystems. Education: B.A. in Psychology from Ludwig Maximilian University of Munich; Ph.D. in Entrepreneurship from Friedrich Schiller University Jena. Professional Experience: Held academic roles at the University of Duisburg-Essen, Max Planck Institute for Economics, Indiana University Kelley School of Business, and National University of Singapore. Research Interests: Entrepreneurial cognition, team dynamics, equity distribution, founder relationships, and the socio-economic factors shaping new ventures. Notable contributions include studies on trust in teams, envy's role in performance, and pivoting strategies in uncertain markets. Awards: 2022 Teaching Excellence Award (TUM School of Management), 2020 Best Paper in Entrepreneurial Cognition (Academy of Management), and multiple recognitions for mentorship and scholarly contributions. Grants/Advising: Extensive supervision of doctoral students (2018 award), with research supported by international collaborations. Active in entrepreneurial education and cross-cultural studies.

Véronique Hosteمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Véronique Hoste is Senior Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy, where she serves as Department Head of Translation, Interpreting and Communication and Director of the LT3 language technology research team. She also holds the position of Research Director for the Faculty of Arts and Philosophy. Her educational background includes a PhD in Computational Linguistics from the University of Antwerp (2005) focused on optimization in machine learning for coreference resolution. Key research areas encompass machine learning for natural language processing, semantic and discourse modeling, including specialized work in event detection, entity/event coreference resolution, irony detection, and emotion analysis. Hoste's publication trends reveal strong interdisciplinary focus, with recent work bridging NLP with crisis communication, digital humanities, and ethical AI. Her team develops high-quality datasets (e.g., EmoTwiCS for emotion trajectories) and collaborates extensively with commercial partners on projects like SentEMO for aspect-based sentiment analysis. Current research emphasizes multimodal emotion analysis, fuzzy rough set methods for sentiment detection, and cross-document event coreference. Elected member of the Royal Flemish Academy of Belgium for Science and the Arts (KVAB) Francqui Chair appointment by Université Libre de Bruxelles (2023-2024) Co-founded LT3 spin-off AlfaSent (2024) for customer feedback analysis Authored first Dutch-language book on NLP: "Taaltechnologie ontrafeld" She actively supervises multiple PhD students on projects including Common-sense knowledge in irony detection (Common-sense), cross-document event coreference (Encore), and empathy modeling in conversational agents (FlandersAI). Her team secures funding through interdisciplinary collaborations like NewsDNA for news recommendation and METRICS for emotion trajectory analysis. Hoste also engages in public outreach through the "AI at school" initiative and advises on language technology integration in high school curricula. The LT3 laboratory under her leadership maintains strong industry partnerships and develops practical NLP tools including EmotioNL and Automatic Term Extraction systems, while advancing core research through projects like CLARIAH-VL for data curation.

Sarath Chandar ANBIL PARTHIPANمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Sarath Chandar is an Associate Professor at Polytechnique Montréal and Core Faculty Member at Mila, the Quebec AI Institute. He holds a Canada CIFAR AI Chair and Canada Research Chair in Lifelong Machine Learning. His research focuses on developing interactive learning algorithms for continual and lifelong learning, with expertise in deep learning, reinforcement learning, and natural language processing. Education: Ph.D. in Computer Science, University of Montreal (advisor: Yoshua Bengio) M.S. in Computer Science, Indian Institute of Technology Madras (advisor: Balaraman Ravindran) Research Themes: Continual Learning and Lifelong Learning Deep Reinforcement Learning Optimization for Deep Networks Natural Language Processing AI for Scientific Discovery Notable Contributions: Founder of the Conference on Lifelong Learning Agents (CoLLAs) Developed Chandar Research Lab (CRL), focusing on adaptive learning algorithms Contributions to model-based reinforcement learning and bias mitigation in AI systems Awards & Grants: Canada CIFAR AI Chair Canada Research Chair Tier 2 MITACS-funded projects on reinforcement learning applications Lab & Collaboration: CRL collaborates with academic/industrial partners (e.g., IBM, Samsung) Hosts annual symposium showcasing research in AI, optimization, and multi-agent systems