جست‌وجوی استاد و پژوهشگر

Loreta Vilkienėمشاهده پروفایل

استاد

• Applied Linguistics
• Foreign Language Teaching and Acquisition
• Language Testing
• +۹ مورد دیگر

Loreta Vilkienė is a Professor at the Department of Lithuanian Studies, Vilnius University, since October 2020, and served as its Head from 2018–2023. She has been affiliated with Vilnius University since 2007, initially as an Associate Professor, and has lectured since 2001. Her academic background includes a PhD in Humanities (2000) and a qualification as a philologist and teacher of Lithuanian (1986), both from Vilnius University. Education: PhD in Humanities, Vilnius University (2000) Philologist and Teacher of Lithuanian, Vilnius University (1986) Research Interests: Applied linguistics (foreign language teaching/assessment, language attrition, partial acquisition) and sociolinguistics (linguistic attitudes, diaspora language dynamics, language-identity links). Her work focuses on multilingualism, heritage language maintenance, and sociolinguistic transitions in post-Soviet contexts. Article Trends: Recent publications analyze language attitudes in Lithuania, lexical proficiency in native speakers, diaspora language policies in Kazakhstan and Russia, and sociolinguistic impacts on identity. Her studies integrate quantitative methods (e.g., vocabulary size estimation) and qualitative discourse analysis. Scientific Awards: Recipient of the 2006 Lietuvos Science Award (applied sciences) for collaborative work on Lithuanian language pedagogy. Grants & Projects: Managed EU-funded projects like ARQUS (2020) and international diaspora language studies (2020–2022). Active in UNESCO-related language preservation initiatives and digital outreach platforms. Outreach: Delivered public lectures on diaspora identity, contributed to podcasts, and authored 15+ peer-reviewed articles. Co-developed teaching tools like the Po truputį Lithuanian language series.

Olle Järvمشاهده پروفایل

پژوهشگر

• Human mobility
• Big data analysis
• Spatial segregation
• +۷ مورد دیگر

Dr. Olle Järv is an Academy Research Fellow at the University of Helsinki's Department of Geosciences and Geography. As Docent in Human Geography and holder of a joint PhD from University of Tartu and Ghent University, his research focuses on leveraging big data sources like mobile phone records and social media for analyzing human mobility patterns across European cross-border regions. Key research areas: Spatial segregation, urban diversity, transnationalism, and pandemic-induced mobility changes Current projects: BORDERSPACE (2020-2025) – Mapping transnational spaces through mobility data MOBI-TWIN (2023-2026) – Regional mobility patterns in twin transitions WinWin4WorkLife (2024-2027) – Sustainable cross-border remote work His methodological innovations include developing tools for analyzing social media data and mobile phone records to study urban dynamics. Recent recognitions include the 2024 Research Council of Finland Award and 2021 Best Paper Award from Journal of Location Based Services.

Lyle Ungarمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۱۵ مورد دیگر

Lyle Ungar is a Professor at the Department of Computer and Information Science at the University of Pennsylvania . He is affiliated with multiple graduate groups, including Genomics and Computational Biology in the School of Medicine , Operations, Information and Decisions in the Wharton School , and Psychology in the School of Arts and Sciences . His research focuses on explainable machine learning , deep learning , and natural language processing for psychology and medical research , analyzing social media and sensor data to understand well-being, empathy, and stress. His work spans bioinformatics , applied economics , and group decision-making . Recent publications examine LLM-based tutoring , cross-cultural translation , and AI in palliative care , showing trends in reinforcement learning , mobile health , and health data analytics . He has contributed to Google Scholar , PubMed , and DBLP with over 15 papers since 2023. Scientific Awards : 2019 Alan I. Leshner Leadership Institute Public Engagement Fellow His students include Vitoria Aquino Guardieiro , Yihao Li , and co-advised researchers like Shreya Havaldar with Eric Wong. He leads projects at interdisciplinary centers such as the Annenberg Public Policy Center , Center for Cognitive Neuroscience , and Institute for Translational Medicine .

• Natural Language Processing
• Machine Learning
• Artificial Intelligence Ethics
• +۳ مورد دیگر

Anders Søgaard is a Professor at the University of Copenhagen , affiliated with both the Department of Computer Science and the Department of Communication. His research bridges Natural Language Processing and Machine Learning with a focus on AI ethics , explainability , and human-AI interaction . Primary Affiliation: Department of Computer Science, University of Copenhagen Secondary Affiliation: Department of Communication, University of Copenhagen Email: soegaard@di.ku.dk, soegaard@hum.ku.dk Research Interests His work spans Natural Language Processing , Machine Learning , and AI ethics , with recent studies addressing: Trustworthiness in AI systems Explainable AI (XAI) frameworks Multilingual model fairness and alignment Human-AI collaboration in reasoning tasks Ethical implications of social robots Mental health analytics using ML Recent Publications His 2025 output highlights trends in: AI ethics (e.g., fairness metrics, trustworthy systems) Multilingual model analysis (knowledge retention, cross-lingual transfer) Human-centric AI (gaze data, cultural considerations) Applications in healthcare and social good

• German Literature
• Linguistics
• Multilingualism
• +۴ مورد دیگر

Michaela Bürger-Koftis serves as Associate Professor in the Department of Modern Languages and Cultures at the University of Genoa's School of Humanities. Her teaching portfolio includes advanced courses such as German Literature and Culture III , German Linguistics (LM) , and Cultures of German-speaking Countries across undergraduate and master's programs focused on teaching, publishing, and intercultural communication. Her research investigates polyphonic structures in multilingual literature, with emphasis on German-Russian linguistic intersections and autobiographical narratives. Key projects explore lyrical diary forms and creative writing through the lens of polyphony, examining how multilingual expression shapes narrative identity and literary innovation in contemporary works. Recent publications demonstrate consistent focus on the Polyphony research initiative, revealing evolving methodologies in multilingual creativity and writing. This work establishes frameworks for analyzing cross-linguistic literary production while documenting the project's development from 2018-2023. No scientific awards are documented in available sources. While specific student advisees remain unlisted, Bürger-Koftis contributes to academic governance as a member of the School of Humanities Library Board. Her collaborative research with institutions like Catania University demonstrates institutional partnership development within multilingual literary studies. The Polyphony project functions as an interdisciplinary research hub with dedicated web infrastructure, facilitating scholarly exchange on multilingual writing practices and serving as a model for digital humanities collaboration in literary studies.

Allison Koeneckeمشاهده پروفایل

استادیار

• Algorithmic Fairness
• Computational Social Science
• Public Health
• +۴ مورد دیگر

Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech and a field faculty member in Computer Science at Cornell University. Previously, she was a postdoctoral researcher at Microsoft Research New England and completed her PhD at Stanford University’s Institute for Computational & Mathematical Engineering. Her research focuses on algorithmic fairness, computational social science, and causal inference in public health, addressing disparities in automated systems like speech recognition and policy decision-making. Education : PhD, Stanford Institute for Computational & Mathematical Engineering MA/MS, Stanford University BA/BS, Massachusetts Institute of Technology Research Interests : Dr. Koenecke’s work bridges economics and computer science, emphasizing equity in AI systems. Key areas include: Algorithmic bias in speech recognition (e.g., racial disparities in voice assistants) Fairness in policy tools like environmental justice data systems Causal analysis in public health interventions Ethical implications of large language models in education Media & Impact : Her research has been featured in outlets like New York Times , Science , and Scientific American . Notable studies include exposing racial gaps in speech-to-text systems and advocating for inclusive dataset development. She also explores societal impacts of AI in education and governance. Awards : Sloan Research Fellow in Computer Science Forbes 30 Under 30 in Science NSF Awards Cornell CIS Teaching Excellence Award (2024) Teaching & Outreach : She teaches courses like Designing Fair Algorithms and Data Science for Global Development , emphasizing interdisciplinary collaboration. Her PhD Professionalization course addresses hidden curricula in academia. She advises on AI ethics for nonprofits, tech companies, and government agencies.

Freda Shiمشاهده پروفایل

استادیار

• computational linguistics
• natural language processing
• computational multilingualism
• +۵ مورد دیگر

Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science at the University of Waterloo and a Faculty Member at the Vector Institute, where she holds a Canada CIFAR AI Chair. She joined the University of Waterloo in July 2024 after completing her Ph.D. at the Toyota Technological Institute at Chicago. Educational Background: Ph.D. in Computer Science, Toyota Technological Institute at Chicago (2024), advised by Professors Karen Livescu and Kevin Gimpel Bachelor's degree in Intelligence Science and Technology (Computer Science Track) with a minor in Sociology, Peking University (2018) Dr. Shi's research focuses on computational linguistics and natural language processing, particularly on deeper understandings of natural language and the human language processing mechanism. She is especially interested in learning language through grounding, computational multilingualism, and related machine learning aspects. Her work aims to inform the design of more efficient, effective, safe, and trustworthy NLP systems. She leads the CompLING Lab at the University of Waterloo, which investigates how language models process spatial relationships and acquire linguistic structures through grounded experiences. Her publication record shows a consistent trajectory of high-impact research, with recent work focusing on spatial reasoning in vision-language models, multilingual chain-of-thought capabilities, and grounded language acquisition. She has published in top-tier conferences including ACL, EMNLP, ICLR, and NAACL, with several papers receiving notable recognition including Best Paper Nominee status at multiple venues. Her research bridges theoretical linguistics with practical NLP applications, demonstrating how linguistic insights can improve AI systems. Scientific Recognition: Canada CIFAR AI Chair (2024) Google Ph.D. Fellowship Thesis of Distinction for her doctoral work Multiple Best Paper Nominee awards at major NLP conferences Dr. Shi teaches CS 784: Computational Linguistics and CS 486/686: Introduction to Artificial Intelligence at the University of Waterloo. She actively contributes to the NLP research community through conference participation, program committee service, and collaborative projects. Her research has significant implications for creating more robust, human-like language understanding systems and advancing the field of grounded language learning in artificial intelligence.

• Natural Language Processing
• Machine Learning
• Multimodal Learning
• +۵ مورد دیگر

Ekaterina Shutova is an Associate Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam. She concurrently holds a Visiting Associate Professor position in the Computer Science Department at Stanford University. She leads the Amsterdam Natural Language Understanding Lab and heads the NLP & Digital Humanities research unit at ILLC. An ELLIS Scholar, she earned her PhD from the University of Cambridge Computer Laboratory and Pembroke College. Her research has been funded by ERC, Innovate UK, British Academy, Leverhulme Trust, Google, Meta, and Deloitte. Her research spans natural language processing and machine learning, with core interests in: Few-shot learning for NLP Multilingual and cross-lingual systems Joint modeling of language and vision Cognitive processing and semantic representation Figurative language interpretation Computational social science applications Her recent publications (2024-2025) predominantly focus on multimodal learning, cultural alignment in AI, metaphor processing, and evaluation methodologies for language models. These works reflect a trend toward integrating cognitive science with multilingual systems and ethical considerations. Awards & Fellowships: ERC Consolidator Grant (2025) ELLIS Scholar Outstanding Paper Award at ACL 2023 Finalist for Outstanding Certification by TMLR Runner-up Best Paper Award at NAACL-HLT 2016 Research Leadership: She directs the Amsterdam Natural Language Understanding Lab, supervising 8 PhD students, 1 MSc student, and 34 alumni. Her projects include an ERC-funded initiative on globally accessible language technology and an AI Democratization grant for hate speech detection.

Enamul Hoque Princeمشاهده پروفایل

دانشیار

• Information Visualization and Visual Analytics
• Natural Language Processing
• Human-Computer Interaction

Enamul Hoque Prince is an Associate Professor and Director of the School of Information Technology at York University. He leads the Intelligent Visualization Lab, funded by the Canada Foundation for Innovation (CFI) and Ontario Research Funds (ORF). He holds a PhD in Computer Science from the University of British Columbia and completed postdoctoral work at Stanford University. His research integrates information visualization, human-computer interaction (HCI), and natural language processing (NLP) to address information overload challenges. Dr. Prince's educational background includes a PhD from UBC, an MSc from Memorial University of Newfoundland, and a BSc from Chittagong University of Engineering & Technology. He has conducted research at institutions like Tableau Software and the Qatar Computing Research Institute and serves on committees for top conferences like ACL and IEEE Vis. His work is supported by grants from NSERC, CFI, and others. Research interests focus on NLP-driven visual analytics, user-adaptive visualization, and accessible interfaces. Notable projects include Evizeon (natural language interfaces for visual analytics), ConVisIT (topic modeling for online conversations), and CIDER (concept-based image search). Publications highlight trends in multimodal systems, chart comprehension, and accessibility. Key awards include the NSERC Discovery Grant (2019) and a Best Paper Honorable Mention at DIS 2021. He supervises graduate and undergraduate students in areas like visualization, NLP, and HCI. Labs and collaborations emphasize interdisciplinary approaches, with the Intelligent Visualization Lab advancing tools for data exploration and user-centered design. Teaching includes courses on design principles and information visualization.

Massimo Piccardiمشاهده پروفایل

استاد

• Natural Language Processing
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.

Yang Zhouمشاهده پروفایل

دانشیار

• Big Data Algorithms
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Yang Zhou is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, part of the Samuel Ginn College of Engineering. His research focuses on big data algorithms, machine learning, data mining, and distributed computing. He has contributed to advancements in federated learning frameworks, graph mining tools, and spatial machine learning for environmental applications like flood mapping. Education includes a Ph.D. in Computer Science from Georgia Tech (2021), M.E. in Computer Application Technology from Chongqing University (2016), and B.E. in Engineering from Jiangnan University (2014). His work emphasizes scalable algorithms for large-scale systems, with tools like DirDense for dense subgraph mining and FedASMU for federated learning optimization. Recent publications explore adversarial robustness, blockchain strategies in IoT, and curriculum-based learning for large language models. He advises on interdisciplinary projects at the intersection of AI and environmental science.

Alexandra Birchمشاهده پروفایل

استاد

• Multilingual Natural Language Processing
• Multimodal Natural Language Processing
• Machine Translation
• +۵ مورد دیگر

Professor Alexandra Birch serves as Chair of Multilingual Natural Language Processing at the Institute for Language, Cognition and Computation (ILCC) within the School of Informatics at the University of Edinburgh. She is a leading member of the StatMT group and Edinburgh Natural Language Processing group, while also co-founding and serving as Chief Scientist of Aveni.ai, a fintech company delivering GenAI solutions to the UK finance sector. Her research centers on advancing multilingual natural language processing with applications that improve people's lives. Professor Birch has made significant contributions to the paradigm shift toward neural networks and large language models in NLP, with expertise spanning multilingual and multimodal processing, ethical considerations, model explainability, and computational efficiency. Her work addresses critical challenges in making language technologies accessible across diverse linguistic contexts. Professor Birch's publication record includes over 100 peer-reviewed papers that demonstrate strong trends in multilingual machine translation, low-resource language processing, and ethical AI frameworks. Her research bridges theoretical advances with practical implementations, particularly evident in her current leadership of the FinLLM project for the financial sector. As a research leader, she has secured and directed major EU-funded projects including EuroLLM (developing a large language model for European languages), UTTER (creating a multilingual meeting assistant), and GoURMET (focusing on low-resource machine translation for media content). Her industry collaboration through Aveni.ai demonstrates successful translation of academic research into commercial applications, particularly in the financial services domain. Professor Birch maintains active leadership in both academic and industry communities, with her current work on domain-specific language models representing an important direction in adapting general-purpose LLMs to specialized professional contexts while addressing multilingual challenges.

Sanjay Subrahmanyamمشاهده پروفایل

استاد

• Early Modern History
• South Asian History
• Indian Ocean History
• +۷ مورد دیگر

Sanjay Subrahmanyam is Distinguished Professor of History and Irving & Jean Stone Endowed Chair in Social Sciences at UCLA's Department of History. A globally recognized historian of the early modern period (15th-18th centuries), he previously held prestigious positions at the Collège de France (2013-2021), University of Oxford (2002-2004), and Ecole des Hautes Etudes en Sciences Sociales in Paris (1995-2002). He founded UCLA's Center for India and South Asia (2005-2011) and maintains significant academic engagement in France through media appearances and lectures. His educational background includes: M.A. in Economics (First Rank with Gold Medal), Delhi School of Economics (1982) Ph.D. in Economic History, Delhi School of Economics (1987) Honorary D.Litt., University of Calcutta (2015) Doctorat honoris causa, Université catholique de Louvain (2017) Subrahmanyam's research revolutionized global historical methodology through his pioneering concept of 'connected histories,' which challenges Eurocentric periodization and emphasizes horizontal connections across regions. His work spans South Asian political culture, Indian Ocean trade networks, Iberian imperial systems, and Mughal court dynamics, consistently integrating multilingual archives and visual sources. He demonstrates exceptional mastery in synthesizing Portuguese, Spanish, Persian, Tamil, and Sanskrit materials to reconstruct non-Western perspectives on early modern globalization. His recent publications reveal intensified focus on transcultural encounters, maritime archaeology, and the material dimensions of empire. Works like Across the Green Sea (2024) and Mirrors of Empire (2026) showcase his shift toward analyzing ego-documents and visual culture to explore identity formation in frontier zones. The consistent thread remains his critique of civilizational boundaries and emphasis on fluid cultural exchanges across the Indian Ocean world. His scientific recognition includes: Infosys Humanities Prize (2012) Dan David Prize for History (2019) Prix International de l'Histoire (2020) Fellowship in American Academy of Arts and Sciences (2009) Corresponding Fellowship in British Academy (2016) Guggenheim Fellowship (2011-12) Subrahmanyam has mentored nine doctoral students to completion, maintaining active collaborations with Muzaffar Alam (University of Chicago), Velcheru Narayana Rao (University of Wisconsin), and David Shulman (Hebrew University). His editorial leadership includes co-editing The Cambridge World History (2015) and serving over a decade as Joint Managing Editor of the Indian Economic and Social History Review . He currently directs major international projects on Indo-Persian travel writing and Mughal intellectual networks. Though not leading a traditional laboratory, he coordinates the 'Connected Histories' research initiative across UCLA, Collège de France, and University of Chicago, fostering interdisciplinary teams examining early modern knowledge circulation. His public engagement includes regular contributions to French media (France 24, France Info) and curated exhibitions like the Codex Casanatense project.

Brendan O'Connorمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Natural Language Processing
• Computational Social Science
• +۵ مورد دیگر

Brendan O'Connor is an Associate Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His research focuses on computational social science and natural language processing (NLP), particularly exploring how social factors influence language technologies and using text analysis to understand societal trends. His work includes studies on racial bias in NLP, political event analysis, and social media linguistics. He holds a PhD in Machine Learning from Carnegie Mellon University (2014) and dual MS/BS in Symbolic Systems from Stanford University (2006). Education: PhD in Machine Learning, Carnegie Mellon University (2014) MS in Symbolic Systems, Stanford University (2006) BS in Symbolic Systems, Stanford University (2006) Research interests span AI ethics, social media analysis, and computational methods for studying language and society. Notably, he investigates racial disparities in NLP systems, linguistic variation in African American English, and event detection in news and social media. His work has been recognized with NSF CAREER and Google Faculty awards, and his research has been cited thousands of times. His lab, the Statistical Social Language Analysis Lab, develops tools for analyzing large-scale text data. He is affiliated with the Center for Data Science, Center for Intelligent Information Retrieval, and Computational Social Science Institute. Recent projects include analyzing global news coverage of critical events and developing frameworks for zero-shot argument explication. Awards and Honors: NSF CAREER Award Google Faculty Research Award Best Paper Award Advising and Grants: O'Connor has advised projects on social media polling representativeness and demographic analysis. His grants include collaborative research on sociopolitical event extraction and bias mitigation in AI systems. He has also contributed to platforms like Rookie for news archive exploration and ezCoref for coreference resolution. Labs/Teams: Leads the Statistical Social Language Analysis Lab and collaborates with the UMass NLP Group and Harvard Institute for Quantitative Social Science. His work bridges NLP with social science methodologies, emphasizing transparency in algorithms and causal inference using text data.

Muhammad Abdul-Mageedمشاهده پروفایل

دانشیار

• Natural Language Processing
• Deep Learning
• Socio-Pragmatics
• +۵ مورد دیگر

Dr. Muhammad Abdul-Mageed is an Associate Professor in the School of Information at The University of British Columbia, with joint appointments in Linguistics and an associate membership in Computer Science. He holds the Canada Research Chair in Natural Language Processing and Machine Learning. His research focuses on deep learning, socio-pragmatics, and speech/language technologies, particularly for Arabic and African languages. He leads the UBC Deep Learning & NLP Group and co-directs SSHRC-funded grants like I Trust AI and Ensuring Full Literacy. He is a founding member of the Center for Artificial Intelligence Decision making and Action and a member of the Institute for Computing, Information, and Cognitive Systems. His work spans automatic speech recognition, machine translation, computational socio-pragmatics, and low-resource language technologies. Notable projects include developing Arabic speech recognition systems, multidialectal Arabic benchmarks, and tools for African language processing. He has authored over 100 peer-reviewed papers and leads initiatives like the NADI Arabic Dialect Identification shared task and the NileChat project for culturally-aware LLMs. His research aims to create equitable, socially-aware AI systems for health, social media, and information management.