Dr. Kalliopi Zervanou is an Assistant Professor at the Faculty of Science, Utrecht University, specializing in Natural Language Processing and Text Mining within the Data Intensive Systems research group. Her work bridges Artificial Intelligence, Semantic Web technologies, and healthcare informatics. Research Focus: Information extraction from unstructured texts, interpretable AI methods, and data integration for real-world applications in mental health, logistics, and digital humanities. Education: PhD in Computer Science (University of Manchester, UK), MSc in Machine Translation (UMIST, UK), and BA in French Literature & Linguistics (Aristotle University, Greece). Experience: Former positions at Leiden University, TU/e, Radboud University, Tilburg University, and Technical University of Crete. Visiting researcher at NaCTeM (UK) and USC Viterbi School (USA). Awards: ICAART 2020 Best Industrial Paper award for baggage mishandling prediction research. Her methodological expertise spans rule-based systems, unsupervised learning, and large language models, with a focus on historical texts, OCR error correction, and multilingual challenges. She contributes to healthcare analytics through EHR classification and prognosis modeling.
Erik Derner is an ELLIS Postdoctoral Researcher at the ELLIS Unit Alicante, focusing on human-centric AI and ethical LLMs. He collaborates with Prof. Robert Babuška's Machine Learning team at the Czech Institute of Informatics, Robotics, and Cybernetics (CIIRC) and contributes to the VIVES project under the PERTE of New Language Economy. His work addresses biases in LLMs, AI safety, and security, while also exploring symbolic regression for robotics and reinforcement learning. Education: Ph.D. in Robotics and Machine Learning (2022) from Czech Technical University in Prague, with B.Sc. and M.Sc. in Open Informatics. Erik's research spans human-centric AI , large language models , robotics , computer vision , and reinforcement learning . He emphasizes ethical and secure AI development, particularly for underrepresented languages. His work integrates symbolic regression with neural networks to create interpretable and efficient models for robotics. Recent publications highlight his focus on LLM security , gender bias analysis , and symbolic regression for dynamic systems. Notable trends include ethical AI, multimodal red-teaming, and physics-informed modeling. Scientific Awards: Werner von Siemens Award (2023) for his Ph.D. thesis. CTU FEE Dean's Award (2023) for a prestigious dissertation. Erik supervises student projects in robotics, AI ethics, and LLM security, including internships and theses on topics like mental health risks, toxicity evaluation, and real-time assistance for the visually impaired.
Ibrahim Said Ahmad is an Assistant Professor in the Department of Computing and New Media Technologies at the University of Wisconsin–Stevens Point, joining in 2025. He previously served as a Lecturer at Bayero University Kano (2014–2023) and worked as a Postdoctoral Research Fellow at Northeastern University’s Institute for Experiential AI. Education: Ph.D. in Computer Science from Universiti Kebangsaan Malaysia M.S. in Information Technology from The University of Nottingham B.S. in Computer Science from Bayero University Kano His research focuses on Natural Language Processing (NLP) with cultural inclusivity, including ethical AI, bias mitigation in multilingual systems, and democratization of data science. He explores cross-cultural collaborative research and predictive modeling applications. Notable Article Trends: Recent publications emphasize cultural nuances in emotion perception across African languages, multilingual benchmarking (ArtELingo-28), and explainable machine learning approaches for analyzing social media impacts on movie performance. Scientific Awards: Best Resource Paper Award, Association for Computational Linguistics (2025) SemEval Best Task Award co-located with ACL2025 He is affiliated with the Internet Society (ISOC) and teaches courses like Foundations of Artificial Intelligence and Introduction to Computing.
Jomol Puthuparampil Mathew serves as an Adjunct Assistant Professor in the Population and Quantitative Health Sciences Department at UMass Chan Medical School's T.H. Chan School of Medicine . His academic appointments also include affiliation with the Health Informatics And Implementation Science Division . Previously associated with UW-Madison School of Medicine and Public Health, his work bridges clinical research and computational health sciences. BS Science - Kerala Agricultural University, Thrissur, KL, India MS Science - Kerala Agricultural University, Thrissur, KL, India PhD Plant and Soil Sciences - University of Massachusetts Amherst, Amherst, MA, United States Dr. Mathew's research centers on health informatics and population health , with emphasis on electronic health record analytics , health disparities measurement , and machine learning applications in clinical settings. His work frequently examines social determinants of health outcomes, particularly in cancer surgery and pandemic response. Recent publications demonstrate growing focus on generative AI analysis of medical records and privacy-preserving data commons for substance misuse research. Publication trends reveal significant contributions to COVID-19 research (15+ papers 2020-2025), with work appearing in Lancet Digital Health , JAMA Internal Medicine , and Annals of Surgical Oncology . His collaborative network spans 19+ co-authors across institutions, with frequent work on national consortia like the National COVID Cohort Collaborative . Developed privacy-preserving patient record linkage systems for substance misuse research Created real-time point-of-care patient recruitment platforms (JITA) Identified neighborhood disadvantage impacts on pancreatic cancer outcomes Validated GPT-4 for multilingual medical note analysis Dr. Mathew actively contributes to health equity research through statewide EHR networks, examining Medicaid enrollment impacts on surgical care and developing novel metrics for health disparity measurement. His work integrates computational biology with clinical implementation science, maintaining strong connections to translational cancer research through earlier genomic EHR integration studies.
Claudio Fantinuoli is an Associate Professor at Mainz University specializing in Interpreting Studies and Language Technology. He is a renowned expert in digital transformation, speech technologies, and AI-driven multilingual communication, with a dual focus on academic research and industry innovation. As founder of InterpretBank , a leading computer-assisted interpreting tool, and former Chief Technology Officer (CTO) and Head of Innovation, he bridges theoretical and practical advancements in his field. Research Interests : Fantinuoli explores AI systems for automatic speech translation, real-time interpreting, and the intersection of computational linguistics with cognitive science. His work emphasizes reducing latency, enhancing expressivity in machine interpreting, and ensuring ethical, privacy-centric technological solutions. Scientific Recognition : Acknowledged as one of the 16 thought leaders in translation for 2025 by Multilingual magazine, he advises institutions like the European Parliament and Commission, alongside private companies. His publications address systemic challenges in AI interpreting and the evolving role of technology in professional practice. Engagement : A sought-after public speaker, he has upcoming talks at the Association for Machine Translation in the Americas and TRANSFORUM , with a keynote at the Lucentino Conference in 2026. His work spans academic research, industry innovation, and thought leadership in multilingual communication.
Sydney Levine is a cognitive scientist working at the intersection of human moral judgment and AI safety. Currently a Visiting Research Scientist at Google Deepmind, she will join New York University as an Assistant Professor in the Psychology Department in Spring 2026. Her research uses cognitive science methods to engineer AI systems that align with pluralistic human moral values while remaining interpretable and steerable. Dr. Levine's academic journey includes: PhD in Psychology from Rutgers University (2016), supervised by Alan Leslie Postdoctoral work at Harvard Psychology Department (supervised by Fiery Cushman) and MIT Brain and Cognitive Sciences Department (supervised by Joshua Tenenbaum) Research Scientist position at the Allen Institute for AI (2022-present) Research Affiliate status with both Harvard and MIT Her research focuses on understanding the cognitive mechanisms underlying human moral judgment and applying these insights to AI safety. She develops computational models of moral cognition, particularly examining how people make judgments about rule-breaking, universalization, and moral compromise. Her work bridges cognitive science, moral philosophy, and artificial intelligence, seeking to create frameworks for value-aligned AI systems that can navigate pluralistic human values. Dr. Levine's publication record demonstrates a strong trajectory in both cognitive science and AI ethics, with articles appearing in top journals like Nature: Machine Intelligence, Behavioral and Brain Sciences, Cognition, and PNAS. Her research shows increasing integration of AI and moral psychology, with recent work focusing on language model alignment with human moral values across cultural contexts. Her scientific achievements include: Best Paper Award at NeurIPS Workshop for 'Language Model Alignment in Multilingual Trolley Problems' (2024) Conference-wide Disciplinary Diversity and Integration Award at Cognitive Science Society (2022) Multiple conference-wide poster awards at Society for Philosophy and Psychology Dr. Levine has secured significant research funding, including a $1.5M Templeton World Charity Foundation grant and a $690K NSF grant, demonstrating strong support for her interdisciplinary research program. She has advised numerous students at various levels and maintains active collaborations with leading researchers in cognitive science and AI. Her upcoming lab at NYU will continue this important work at the intersection of cognitive science and AI safety, with a focus on creating interpretable and steerable AI systems that align with pluralistic human moral values.
Lei Li is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where they serve as Co-Director of the UCSB NLP Group. Their research focuses on developing algorithms and systems for machine learning, natural language processing, machine translation, reasoning, and AI-powered drug discovery. Dr. Li received their PhD from Carnegie Mellon University and completed their undergraduate studies at Shanghai Jiao Tong University. Dr. Li's research spans multiple critical areas in artificial intelligence and machine learning. Their work in natural language processing encompasses machine translation, speech translation, multilingual NLP, large language models, text generation, program synthesis, reasoning, privacy, and watermarking. Additionally, they have made significant contributions to AI applications in drug design and efficient machine learning techniques. Their research bridges theoretical foundations with practical applications, particularly in the emerging field of AI for biological discovery. Analysis of Dr. Li's recent publications reveals a strong focus on advancing natural language processing capabilities while addressing critical challenges in model efficiency, security, and evaluation. Their work spans machine translation systems that handle hundreds of languages, techniques for improving large language model capabilities in zero-shot settings, methods for evaluating text generation quality, and novel approaches for protecting intellectual property in language models. Notably, they've also made significant contributions to applying AI to biological problems, particularly in antimicrobial peptide discovery and protein sequence design. Dr. Li has received notable recognition for their research, including: Best Paper Award at ACL 2021 for "Vocabulary Learning via Optimal Transport for Neural Machine Translation" Dr. Li actively advises PhD and Master's students in computer science at UCSB, with recent advisees working on topics including antimicrobial peptide discovery, protein sequence design, diffusion models, speech translation, and language model watermarking. Their research group, the UCSB NLP Group, appears to be well-funded and productive, with consistent publications in top-tier conferences including ACL, EMNLP, ICML, KDD, and NeurIPS. The group has developed several influential frameworks and benchmarks, including MTG (Multilingual Text Generation benchmark) and SEScore2 (text generation evaluation metric). The UCSB NLP Group, co-directed by Dr. Li, maintains an active research agenda with multiple ongoing projects spanning natural language processing, machine learning, and their applications to scientific discovery. The group collaborates with researchers across disciplines, particularly in the biological sciences for drug discovery applications.
Lusine Aghajanova is a Clinical Associate Professor at Stanford University School of Medicine, specializing in Reproductive Endocrinology & Infertility . She serves as Director of Third Party Reproduction at Stanford Fertility and Reproductive Health since 2019 and assumed the role of Fellowship Director in 2025. Affiliated with the Maternal & Child Health Research Institute (MCHRI) , her clinical focus spans endometrial receptivity, implantation, endometriosis , and fertility preservation. MD (1996) and PhD (2006) from Yerevan State Medical University Residency in Obstetrics & Gynecology at Baylor College of Medicine (2012) and UCSF (2014) Fellowship in Reproductive Endocrinology & Infertility at UCSF (2017) Her research explores endometrial biomarkers (e.g., BCL6/SIRT1 levels) and their correlation with fertility outcomes, while recent studies examine AI-driven clinical decision support systems and gestational surrogacy legal frameworks . She has authored over 50 peer-reviewed publications and provides peer-review services for 12 journals. Current clinical interests include optimizing non-surgical ectopic pregnancy treatments using methotrexate protocols and investigating endometrial regeneration post-miscarriage. As a multilingual clinician (Russian, Armenian), she integrates cross-cultural perspectives into reproductive healthcare delivery.
Michael Mark Dowling is a Professor at DCU Business School, Dublin City University, Ireland. With an extensive research portfolio spanning finance, economics, and emerging technologies, he has published 58 documents with 2,347 citations and maintains an h-index of 20. His work bridges traditional financial systems with innovative blockchain applications, positioning him at the forefront of digital finance research. Dr. Dowling's research interests span several critical areas in contemporary finance and economics. His primary focus includes Decentralized Finance (DeFi) , Cryptocurrency markets , and Blockchain technology applications . He has made significant contributions to understanding NFT markets, Bitcoin volatility, and the intersection of financial systems with virtual worlds. His work on economic policy uncertainty and its impact on cryptocurrency markets has been particularly influential. Additionally, he explores Environmental Economics , examining the relationship between economic growth and carbon emissions in emerging economic blocs. His recent work on AI applications in finance, particularly with large language models like ChatGPT, demonstrates his ability to engage with cutting-edge technological developments. Analysis of Dr. Dowling's recent publications (2022-2024) reveals several key trends in his research. There's a clear progression from traditional financial analysis toward emerging digital asset markets, with a particular emphasis on non-fungible tokens (NFTs) and their market dynamics. His work demonstrates a sophisticated methodological approach, frequently employing advanced statistical techniques, machine learning, and big data analysis. The interdisciplinary nature of his research is evident in publications spanning finance, environmental economics, sports risk management, and virtual reality economics. His recent focus on AI applications in finance represents a timely exploration of how emerging technologies are transforming financial analysis and research methodologies. Dr. Dowling has made significant contributions to academic discourse through his extensive publication record. His research on the relationship between economic policy uncertainty and Bitcoin markets has provided valuable insights for investors and policymakers. The development of FinSentGPT represents an innovative approach to financial sentiment analysis across multiple languages. His work on NFT market dynamics has helped establish foundational understanding of these emerging digital markets. Through his bibliometric analyses, he has also contributed to mapping research landscapes in areas like advertising expenditure and stock performance, as well as Islamic economics and finance.
Pierrette Bouillon is affiliated with the University of Geneva as a researcher, focusing on machine translation, accessible communication, and language technology applications in healthcare. Her work bridges computational linguistics, speech processing, and inclusive design. Institution: University of Geneva Research Focus: Medical translation, sign language processing, text/pictograph translation Research Interests : Bouillon specializes in machine translation and computational linguistics , with particular emphasis on: Accessible communication for patients with language barriers Sign language and pictograph translation systems (e.g., BabelDr) Speech-to-speech translation in emergency settings Text simplification for intellectual disabilities Historical French normalization for NLP Multilingual speech recognition and post-editing Article Trends : Her recent publications highlight the integration of large language models with accessibility tools in healthcare, focusing on Swiss French sign language corpora , pictograph sequences , and spontaneous speech simplification . Work spans both NLP and human-computer interaction in medical contexts. Education and Teaching : While specific educational details are omitted, Bouillon contributes to translation pedagogy through tools like COPECO and MT3 , integrating speech technologies into post-editing workflows.
Maarten Sap is an Assistant Professor at Carnegie Mellon University's Language Technologies Institute , with a courtesy appointment in the Human-Computer Interaction Institute . He also serves as a part-time Visiting Research Scientist & AI Safety Lead at the Allen Institute for AI (2024–present). PhD in Computer Science & Engineering (2015–2022) from University of Washington, advised by Noah Smith and Yejin Choi BSc in Communications and Information Systems (2010–2014) from École Polytechnique Fédérale de Lausanne His research focuses on socially intelligent AI systems through three core areas: (1) measuring and improving AI's social/interactional intelligence, (2) assessing and mitigating socio-cultural biases in language models, and (3) building narrative technologies for prosocial outcomes. Key themes include AI ethics, toxicity detection, cultural adaptability, and human-AI collaboration . Recent publications highlight his work on safety frameworks like HAICOSYSTEM, clinical reasoning alignment (ALFA), multilingual moderation (PolyGuard), and social intelligence evaluation (SOTOPIA). He received the 2025 Okawa Research Grant and NAACL 2025 Best Paper Runner-Up award. His lab advises 10 PhD/MLT students across CMU and MIT, with co-advising relationships at UW and EPFL. Scientific Awards : 2025 Okawa Research Grant (7 US recipients) NAACL 2025 Best Paper Runner-Up EMNLP 2023 Outstanding Paper FAccT 2023 Best Paper Advising : Primary advisor to Joel Mire , Mingqian Zheng , Jimin Mun , and Akhila Yerukola Co-advisor to Dan Chechelnitsky , Karina Halevy , Jocelyn Shen , and Xuhui Zhou Leadership : Co-organizer of Agent Workshop @ CMU (2025) Advisory board member for Socially Responsible Language Modeling (NeurIPS 2024) Senior Program Committee - ACL (2020–present)
Joseph Attieh is a Doctoral Researcher at the University of Helsinki , affiliated with the Faculty of Arts and the Department of Digital Humanities . He is part of the Doctoral Programme in Language Studies , focusing on interdisciplinary research at the intersection of Languages and Computer and Information Sciences . PhD candidate in Digital Humanities ORCID: 0000-0001-6841-9877 Email: joseph.attieh@helsinki.fi His research spans Natural Language Processing (NLP) , Machine Translation , and Artificial Intelligence , with a particular emphasis on multilingual systems , sustainable language technology , and privacy-preserving methods . Key themes include: Modular translation architectures and their generalization Federated learning for privacy in face recognition Embedding isotropy optimization in text classification Low-resource language processing and synthetic data generation His recent work, including collaborations with teams like NordicsAlps and GreenNLP, highlights contributions to open-source frameworks and ethical AI. While no specific scientific awards are mentioned, his projects are supported by major grants from the Research Council of Finland and European Research Council .
Dr. Toral Ruiz is an Assistant Professor at the University of Groningen 's Faculty of Arts . Her research focuses on Machine Translation , Computational Linguistics , and Natural Language Processing , with particular emphasis on translation quality assessment, literary adaptation, and ethical automation frameworks. Expertise: Machine Translation, Computational Linguistics, NLP, Translation Quality, Literary Adaptation, Ethical Automation Contact: a.toral.ruiz@rug.nl Her recent work explores speech-text discrimination , tokenization strategies , and lexical diversity in literature . She contributes to the European Association for Machine Translation and collaborates on projects like LT-LiDER for digital literacy in translation. Press engagement includes discussions on machine translation limitations in creativity and cross-lingual literary reception . Notable collaborations include studies on Catalan/Dutch translation reception and sustainability frameworks for translation automation.
Dr. Luciana C. de Oliveira is Associate Dean for Academic Affairs and Graduate Studies and Professor in the Department of Teaching and Learning at Virginia Commonwealth University's School of Education. She holds a PhD in Education from UC Davis and specializes in K-12 multilingual education, language-based content instruction, second language writing, and teacher preparation. Her research develops practical frameworks like the Language-Based Approach to Content Instruction (LACI). With over 30 years of teaching experience across K-12 and higher education contexts, she has authored/edited 30+ books including Supporting Multilingual Learners' Academic Language Development (2023) and Scaffolding for Multilingual Learners (2022). Her work emphasizes functional approaches to language development and genre-based pedagogy. Honors include the TESOL International Association presidency (2018-2019), Mid-Career Award from AERA (2017), and serving on iCivics' Expert Advisory Council. She collaborates extensively with K-12 schools to implement language-based instructional approaches.
Amy J. Ko is a Professor in the Information School at the University of Washington. Her work focuses on computing education, human-computer interaction (HCI), and accessibility. She has contributed significantly to critical pedagogy in CS education, inclusive design, and LGBTQ+ inclusion in STEM. Her research emphasizes equity, ethics, and the social impact of technology. Affiliations: University of Washington, Information School. Awards: John Henry Award (2019). Her research interests include programming education, HCI for marginalized communities, and the intersection of technology and social justice. She has led projects on inclusive design techniques, queer community-building in computing, and AI tools for youth education. Articles focus on equity in CS education, disability accommodations, and innovative teaching methods. Her work bridges theory and practice, advocating for systemic changes in computing curriculum and policy.
