جست‌وجوی استاد و پژوهشگر

Bari J Dzombaمشاهده پروفایل

دانشیار

• Health Informatics
• Public Health Informatics
• Health Data Analytics
• +۳ مورد دیگر

Bari J Dzomba serves as Associate Professor of Instruction and Graduate Program Director in the Department of Health Services Administration and Policy at Temple University's College of Public Health. With over 20 years of cross-sector experience spanning academic health systems, medical education, pharmaceuticals, and insurance, she joined Temple in 2018 after leadership roles at Penn State College of Medicine where she directed informatics initiatives for clinical research and medical school accreditation. Education: PhD in Leadership, Alvernia University MS in Information Science, The Pennsylvania State University BA in Advertising Design, The College of New Jersey Research Focus: Dr. Dzomba specializes in health informatics applications for public health, with emphasis on data analytics , machine learning , and health information systems . Her work integrates population health informatics with practical healthcare solutions, developing foundational courses in health data standards and text mining while leading initiatives like the research data warehouse for clinical translational science. Publication Trends: Recent research demonstrates her dual focus on public health surveillance (applying NLP to vaccine safety monitoring) and health informatics workforce development (analyzing educational programs against job market demands). This reflects her commitment to bridging technical health data capabilities with real-world healthcare system needs. Awards: No scientific awards were documented in the provided materials. Academic Leadership: As Graduate Program Director, she oversees curriculum development for health informatics degrees while teaching eight courses spanning healthcare database design, data analytics, and machine learning applications. Her prior role leading Penn State's medical school accreditation data initiatives demonstrates significant administrative impact. Technical Teams: Previously directed informatics and IT teams for Penn State's Clinical and Translational Science Institute, managing i2b2 implementation and research data warehouse operations that supported institutional research infrastructure.

Smaranda Muresanمشاهده پروفایل

دانشیار

• Artificial Intelligence (AI)
• Natural Language Processing (NLP)
• Computational Linguistics
• +۷ مورد دیگر

Smaranda Muresan is an Associate Professor in the Department of Computer Science at Barnard College, with affiliations at Columbia University. Her research focuses on artificial intelligence, natural language processing, and computational linguistics, emphasizing ethical AI applications, educational technology, and stylistic analysis. Academic Affiliation: Associate Professor at Barnard College Email: smuresan@barnard.edu Her work spans multiple subfields including fact-checking systems, argument mining, sentiment analysis, and figurative language processing. She explores abstract reasoning in LLMs through novel benchmarks like the New York Times Connections game and investigates AI's role in creativity, education, and social good initiatives. Recent publications highlight her contributions to global segmenting algorithms, explainable authorship attribution, and multimodal figurative language analysis. While no explicit student advisees are listed, her research intersects with interdisciplinary teams at Barnard and Columbia. She actively participates in academic workshops and conferences such as the ACL Annual Meeting and FigLang, shaping standards for computational analysis of nuanced language phenomena.

Eduardo Blancoمشاهده پروفایل

دانشیار

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

Eduardo Blanco is an Associate Professor in the Department of Computer Science at the University of Arizona. His research focuses on advancing Natural Language Processing (NLP) with an emphasis on understanding negation, hate speech dynamics, and computational linguistics applications. He explores how language models can better interpret ambiguous statements, predict conversation incivility, and counteract harmful online content through structured responses. Blanco’s work spans theoretical and applied NLP domains, including semantic role labeling, sentence embedding evaluation, and biomedical text analysis using protein language models. His research often integrates large language models with task-specific frameworks to address challenges in zero-shot learning and negation robustness. Key areas of investigation include: Negation processing and affirmative interpretation Social media discourse dynamics and hate speech detection NLP for biomedical applications (glycosylation site prediction) Semantic similarity alignment in sentence embeddings His recent articles highlight advancements in counterhate speech strategies, temporal QA systems, and LLM-based peer review tools. Notably, he secured the NSF CAREER Award for studying negation through positive interpretations (2022). Collaborative efforts focus on interdisciplinary teams addressing challenges in computational linguistics, with grants supporting projects like the ALIGN-SIM benchmarking framework and BEMEAE event argument extraction system.

Manling Liمشاهده پروفایل

استادیار

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Manling Li is an Assistant Professor at the Department of Computer Science, Northwestern University. She previously served as a postdoc at Stanford University's Vision and Learning Lab under Prof. Jiajun Wu and received her Ph.D. from the University of Illinois at Urbana-Champaign (advisor: Prof. Heng Ji). Her research spans Language + Vision + Robotics with applications in Embodied AI and AI for Science . Key Research Areas : Knowledgeable Foundation Models, Reasoning & Planning, Compositionality, Multimodal Knowledge Extraction, Factuality & Trustworthiness in AI Leadership Roles : Organizing Committee for ACL 2025, NAACL 2025, EMNLP 2024 Research Trends in her 15 most recent publications show: Advancing Embodied AI through structured reasoning and planning frameworks Developing Vision-Language Models for 3D layout optimization and video understanding Addressing LLM Hallucinations via knowledge shadowing and mechanistic interpretability Creating collaborative agent systems with out-of-sync recovery mechanisms Scientific Recognition : ACL 2024 Outstanding Paper SoCal NLP 2024 Best Paper Microsoft Research Fellowships (PhD & Postdoc) DARPA Riser & EE CS Rising Star Advising Impact : Mentored 19 students in developing the UIUC information extraction system. Currently advising 12 students across PhD, Master's, and undergraduate levels, with particular emphasis on supporting underrepresented groups. Led teams to rank 1st in DARPA AIDA evaluations.

Rajiv Shahمشاهده پروفایل

استاد مدعو

• Communication Technologies
• Law and Policy
• Societal Implications of Technology
• +۴ مورد دیگر

Rajiv Shah is an Adjunct Professor in the Department of Communication at the University of Illinois Chicago (UIC), part of the College of Liberal Arts and Sciences. He holds a PhD in Communications and a Juris Doctor from the University of Illinois Urbana-Champaign, with a visiting fellowship at Harvard University's Kennedy School. His research focuses on the legal and policy implications of communication technologies, societal impacts of AI, and privacy concerns. Shah is also Chief Evangelist at Contextual AI, with extensive industry experience at companies like Hugging Face, Snowflake, and DataRobot. He has authored over 20 research papers, holds 20 patents, and has been cited over 1,000 times. His academic work has been published in communications, computer science, and law journals. Shah is a sought-after speaker on AI topics, delivering talks at major events like Google Next, ODSC East, and the Generative AI World Summit. He actively engages with the public through social media, including TikTok and YouTube, where his channel @rajistics has garnered over 10 million views. His expertise spans generative AI, machine learning evaluation, and data-centric AI strategies.

• Biomedical Natural Language Processing
• Large Language Models in Healthcare
• Ophthalmology AI Applications
• +۳ مورد دیگر

Xuguang Ai, MS, is a researcher at Yale School of Medicine specializing in biomedical natural language processing and large language models for healthcare applications. Their work focuses on applying AI to medical domains, particularly ophthalmology, with numerous publications in 2024-2025. Research interests center on biomedical natural language processing , large language model evaluation in clinical contexts , and specialty-specific AI applications . Their work bridges the gap between artificial intelligence and practical healthcare needs, developing systems that can understand and process medical text with clinical accuracy. Their publication record shows a strong emphasis on benchmarking methodologies for medical AI systems, with multiple studies evaluating LLM performance across thousands of clinical questions. Recent work includes developing ophthalmology-specific language models and improving medical information extraction systems. Award recognition has not been specified in available materials, but their high-impact publications in venues like Nature Communications demonstrate significant scholarly contribution to the field. Research activities include leading projects on medical question answering systems, clinical decision support tools, and specialized language models for healthcare domains. The work integrates systems immunology, engineering, and AI to monitor, predict, and improve human health as part of Yale's broader research initiatives.

Selma Tekirمشاهده پروفایل

دانشیار

• Natural Language Processing
• Text Mining
• Machine Learning
• +۵ مورد دیگر

Selma Tekir is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology. She completed her undergraduate studies in Computer Engineering at Ege University in 2001, followed by a master's degree from Izmir Institute of Technology in 2004, and earned her PhD from Ege University in 2010. In 2009, she worked as a visiting researcher at the University of Konstanz, Germany. Her educational background includes: B.Sc.: Computer Engineering, Ege University (2001) M.Sc.: Computer Engineering, Izmir Institute of Technology (2004) Ph.D.: Computer Engineering, Ege University (2010) Dr. Tekir's research spans multiple areas of natural language processing and computational linguistics, with a particular focus on Turkish language processing. Her work explores text mining, news analysis, information warfare, and the application of deep learning techniques to linguistic problems. She has made significant contributions to counterfactual detection in Turkish, multi-modal language models, and the integration of symbolic reasoning with neural approaches. Her research often bridges theoretical advances with practical applications, particularly in the Turkish language context which presents unique challenges as a morphologically rich agglutinative language. Analysis of her recent publications reveals a strong trend toward advancing natural language understanding systems for Turkish, developing methods for counterfactual detection, enhancing question answering systems with knowledge graphs, and applying graph neural networks to biological sequence analysis. Her work demonstrates growing interest in combining symbolic reasoning with neural approaches and exploring self-reflection capabilities in language agents. Dr. Tekir has received research funding for projects including: Consistency and Reliability Assessment in News Chains (TÜBİTAK ARDEB 3501) Application of Data Analysis and Visualization Techniques on Historical Sources (BAP) She teaches courses including CENG 381 - Stochastic Processes, CENG 613 - Scientific Research Methods in Computer Science, and SEDS 501 - Introduction to Data Science.

Jacopo Staianoمشاهده پروفایل

استادیار

• Natural Language Processing
• Artificial Intelligence
• Social Network Analysis
• +۷ مورد دیگر

Jacopo Staiano is a Senior Assistant Professor (RTDb) at the Department of Economics & Management, University of Trento (Italy). His academic journey includes previous positions as Head of Research at reciTAL.ai, research affiliate at Data-Pop Alliance, senior data scientist at Fortia Financial Solutions, and post-doctoral researcher at LIP6, UPMC - Sorbonne Universités and Fondazione Bruno Kessler. Staiano received his BSc in Computer Engineering from the University of Pisa (2003), an MA in Sonic Arts from Queen's University of Belfast (2005), and an MSc in Human Language Technologies and Interfaces from the University of Trento (2010). He completed his PhD under Prof. Nicu Sebe at the Department of Information Engineering and Computer Science, University of Trento. His academic visits include the Intelligent Systems Lab at University of Amsterdam, Ambient Intelligence Research Lab at Stanford University, Human Dynamics Lab at MIT Media Lab, and Telefonica I+D. Staiano's research spans multiple domains with a strong focus on Natural Language Processing and Human-Computer Interaction. His work ranges from modeling human behavior when interacting with technology to analyzing social network structures and virality dynamics. His recent publications demonstrate a significant shift toward large language models, with applications in sustainability reporting, medical diagnostics, financial analysis, and bias detection. His work shows sophisticated integration of technical AI capabilities with social science perspectives. Staiano has received several prestigious awards throughout his career, including an Honorable Mention at ACM DIS 2012, Best Paper Award at ACM UbiComp 2014, the UMUAI James Chen Award 2016, and most recently the Ten Year Technical Impact Award from ACM ICMI 2024. These awards reflect the sustained impact and quality of his research across multiple domains. His research has been supported by collaborations with major institutions including MIT Media Lab, Stanford University, and Telefonica I+D, as well as industry partnerships. His work on projects like DepecheMood for emotion analysis and SALSA for multimodal group behavior analysis has established him as a significant contributor to affective computing and social signal processing. Staiano maintains active connections with multiple research communities, particularly in the areas of computational social science, natural language processing, and affective computing. His work bridges technical AI development with real-world applications in finance, healthcare, and social good initiatives.

• Human-AI Collaboration
• Fairness in AI
• Software Engineering
• +۳ مورد دیگر

Zhe Yu is an Assistant Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT), leading the hil-se (Human-in-the-Loop Software Engineering) lab. He holds a Ph.D. in Computer Science from North Carolina State University (NC State), where he worked in the RAISE lab under Dr. Tim Menzies. Prior to his Ph.D., he earned his M.S. and B.S. in Control Science and Engineering and Automation from Shanghai Jiao Tong University in China. His research focuses on enhancing human-AI collaboration to create systems where humans and AI act as complementary partners. Key areas include fairness in AI, ethical software engineering, and leveraging machine learning for code analysis and vulnerability detection. He emphasizes AI's role in augmenting human capabilities rather than replacing them. Teaching responsibilities include courses like Foundations of Data Science (DSCI-633), Research Methods for Artificial Intelligence (IDAI-720), and Software Testing (SWEN-352). He supervises capstone projects (SWEN-780/781), guiding students through research design and conference paper submission processes. Research projects explore topics such as bias mitigation in ML models (FairBalance, Fairway), code search optimization, and human-centered AI ethics. He actively publishes in top-tier software engineering and AI venues, with a focus on practical applications of AI in development workflows and fairness-aware systems.

• Neural Machine Translation
• Computational Linguistics
• Natural Language Processing
• +۳ مورد دیگر

Yang Liu is a researcher at the Institute of Computing Technology (ICT), Tsinghua University , and affiliated with the Beijing Academy of Artificial Intelligence . His work spans computational linguistics, machine translation, and dialogue systems, with additional collaborations in medical NLP and cross-lingual knowledge representation. Doctorate from Purdue University Collaborations with Microsoft, Amazon, and Facebook researchers Research focuses on Neural Machine Translation (NMT) advancements through: Self-supervised alignment and regularization Document-level context integration Robust translation frameworks for low-resource scenarios Adapter-based pluggable models Recent works extend to conversational AI, including: Subjective knowledge integration in task-oriented dialogue Context representation analysis for open-domain dialogue Synthetic conversation dataset generation Key contributions include: THUMT: Open-source NMT toolkit Earth Mover's Distance for lexicon induction Context Gates for translation fluency Publications demonstrate continuous engagement with top-tier conferences including ACL, EMNLP, and COLING, collaborating with prominent figures like Maosong Sun and Qun Liu.

Meriem Beloucifمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Translation
• Lexical Semantics
• +۳ مورد دیگر

Meriem Beloucif serves as an Assistant Professor in Computational Linguistics at Uppsala University's Department of Linguistics and Philology, focusing on neural machine translation and lexical semantics for low-resource languages. Her work bridges theoretical linguistics and practical NLP applications, particularly for underrepresented language communities. Education: PhD in Machine Translation from HKUST (Hong Kong) Prior research appointments at Hamburg University (Comparative QA) and Copenhagen University (Semantic Parsing) Research Interests: Core expertise in low-resource language processing for African and Indigenous languages Specialization in sentiment analysis benchmarks (AfriSenti) and semantic role labeling Investigation of pre-trained language model capabilities through probing techniques Development of preprocessing strategies for challenging languages like Inuktitut Her publication trajectory (2021-2023) reveals concentrated efforts in creating evaluation frameworks for African languages, advancing cross-lingual transfer methods, and analyzing semantic compositionality in transformers. Key contributions include standardized datasets for semantic relatedness and novel approaches to zero-shot sentiment classification in Nigerian Pidgin. Collaborative work with researchers like Shamsuddeen Muhammad and Micaella Bruton demonstrates active mentorship in computational linguistics projects. While specific grant details aren't documented, her leadership in SemEval shared tasks indicates significant community engagement in NLP evaluation standards.

Pooja Raniمشاهده پروفایل

پژوهشگر ارشد

• Empirical Software Engineering
• Green Software Engineering
• Natural Language Processing
• +۴ مورد دیگر

Pooja Rani is a Senior Researcher at the Software Evolution and Architecture Lab (SEAL), Department of Informatics, University of Zurich. She holds a PhD from the University of Bern (2022) and an M.E. in Software Systems from BITS Pilani (2017). With industry experience at Samsung, VMware, and People Interactive Ltd., she bridges theoretical and practical software engineering. Her research focuses on: Empirical Software Engineering, particularly developer practices in code comprehension and maintenance. Green Software Engineering, including energy anti-patterns and sustainability tooling. NLP and machine learning applications in software evolution, documentation quality, and polyglot environments. AI-driven approaches for debugging, testing, and optimization. Her publications emphasize energy efficiency in software systems, AI-assisted development tools, code comment analysis, and metamorphic testing. Recent work explores LLMs for sustainable coding, object-centric debugging, and autonomous system validation. She actively advises students, with ongoing supervision of 15+ BSc/MSc theses on topics like comment quality, energy anti-patterns, and polyglot development. She collaborates internationally with institutions including TU Delft, Inria, and the University of Victoria.

Goran Nenadicمشاهده پروفایل

استاد

• Natural Language Processing
• Text Mining
• Biomedical Information Extraction
• +۳ مورد دیگر

Goran Nenadic is a Professor in the Department of Computer Science at the University of Manchester and a Turing Fellow at the Alan Turing Institute. His research focuses on natural language processing (NLP), text mining, and semi-automated knowledge curation from unstructured data, particularly in biomedical and healthcare domains. He leads the UK Healthcare Text Analytics Network (Healtex) and has extensive experience in clinical text mining, including projects with healthcare providers and industrial partners. Education: PhD in Computer Science (2003, School of Sciences, University of Salford) MSc in Computer Science (1997, Faculty of Mathematics, University of Belgrade) BSc in Mathematics and Computer Science (1993, Faculty of Mathematics, University of Belgrade) Research Interests: Nenadic's work emphasizes large-scale extraction of clinical/epidemiological findings from EHRs, healthcare social media, and biomedical literature. His team addresses challenges like medication prescription extraction, patient-generated data analysis, and clinical decision support systems. Projects include semi-automated anonymization of clinical narratives and mental health symptom identification in social media. Key Projects: Integrating hospital outpatient letters into the healthcare data space (2021–2025) Centre for Digital Trust and Society (2021–present) SC-SVI: Social Care Strengths and Vulnerability Index (2022–2024) Assembling the Data Jigsaw for MSK Disease Research (2020–2024) Labs/Teams: Leads the Clinical Knowledge Management group and collaborates with the Health eResearch Centre (HeRC). His group's website is gnteam.cs.manchester.ac.uk . His research contributes to UN Sustainable Development Goals related to health and well-being through innovations in clinical text analytics and healthcare data utilization.

• Information Retrieval
• Natural Language Processing
• Social Media Analysis
• +۳ مورد دیگر

Marco Viviani is a researcher affiliated with University of Milano-Bicocca (Italy) and University of Torino (Candiolo, Italy), focusing on information retrieval , health informatics , and social media credibility . His work spans multi-dimensional relevance assessment generative AI applications misinformation detection privacy-aware systems Research highlights include ROMCIR workshops on reducing online misinformation, Web2Vec for structural content analysis, and LLM reasoning capabilities evaluation. Key collaborations with Gabriella Pasi, Rishabh Upadhyay, and Marinella Petrocchi appear across 103 publications from 2004-2025. His work integrates machine learning and knowledge graphs for healthcare applications, with recent focus on blockchain social media dynamics and privacy-utility trade-offs . He contributes to ECIR WI/IAT CLEF eHealth MDAI conferences and journals like IEEE Access and Frontiers in Artificial Intelligence .

• Artificial Intelligence
• Human Language Technologies
• Speech and Language Processing
• +۷ مورد دیگر

Ricardo Daniel Santos Faro Marques Ribeiro is an Associate Professor at Iscte - Instituto Universitário de Lisboa, where he is affiliated with the Department of Information Science and Technology (ISTA) within the School of Technologies and Architecture. He is also a researcher at INESC-ID Lisboa, focusing on Human Language Technologies and speech and language processing. He has held leadership roles including Director of the Master's and Bachelor's in Computer Engineering and Coordinator of the Artificial Intelligence scientific area. PhD in Informatics and Computer Engineering, Instituto Superior Técnico (2011) Master's in Electrical and Computer Engineering, Instituto Superior Técnico (2003) Bachelor's in Mathematics/Computer Science, University of Beira Interior (1996) His research centers on artificial intelligence, with a strong emphasis on natural language processing, text mining, computational linguistics, and machine learning. He explores applications in sentiment analysis, hate speech detection, document summarization, information extraction, and generative AI, particularly in domains such as legal texts, social media, and customer feedback. The recent publications reflect a consistent trend in leveraging advanced NLP techniques, especially pretrained language models and deep learning, to solve real-world problems in text classification, information extraction, and domain-specific understanding (e.g., legal, medical, financial). The work spans both Portuguese and multilingual contexts, emphasizing practical deployment and automation. He has successfully co-supervised three doctoral students and over 70 master’s students, with several currently in progress. He has led significant research projects, including European-funded initiatives like RAGE and national projects with the Ministry of National Defense and the Supreme Court of Justice, demonstrating strong grant acquisition and management capabilities. Ricardo Ribeiro leads the Artificial Intelligence scientific area at Iscte and has directed multiple academic programs. He also contributes to the broader scientific community through participation in major conferences (IJCAI, ECAI, LREC) and editorial work in computational processing of Portuguese.