Mihaela Vela is a Senior Lecturer at the Department of Language Science and Technology at Saarland University. Her research focuses on machine translation evaluation, post-editing strategies, and translation technologies. Prior to her academic role, she worked as a researcher at the Language Technology Lab of DFKI (2007–2011), contributing to projects like ontology schema extraction from financial news. She holds a PhD in Computational Linguistics (2011) from Saarland University, supervised by Hans Uszkoreit and Thierry Declerck, and a Licentiate degree in Linguistics from West University of Timisoara. Her teaching portfolio includes courses such as Translation and Content Management , Applied Language Technologies , and Machine Translation , reflecting her expertise in integrating computational methods with translation practice. She has developed tools like TeLeMaCo (a collaborative teaching repository) and Catalog (a post-editing interface). Her work emphasizes improving translation workflows through better CAT tool design, metadata preservation, and cognitive load analysis in post-editing tasks. Key contributions include the SubCo corpus of learner translations and studies on post-editing effort in low-resource languages. Her research bridges theoretical linguistics with practical applications, addressing challenges in legal text classification, parliamentary discourse analysis, and neural post-editing systems.
Almas Baimagambetov is a Senior Lecturer and Subject Co-Lead for Computing and Mathematics at the University of Brighton's School of Architecture, Technology and Engineering. He leads the Robotics AI lab and focuses on applied research in robotics, artificial intelligence, and human-robot interaction. BSc Computer Science (Games), University of Brighton (2015) PhD in Automated Visualization of Grouped Networks Using Euler Diagrams and Graphs, University of Brighton (2020) His research explores data visualization , robotics AI , and human-robot interaction , with recent work on energy-efficient trajectory planning, multilingual NLP integration, and LLM code generation for robotics. He emphasizes practical implementation in teaching and outreach through game jams, hackathons, and open-source projects like FXGL. 2025 : 3 publications on robotic manipulation, trajectory planning, and NLP integration 2024 : 2 studies on face recognition factors and LLM code generation 2021 : FXGL game engine development Almas supervises projects at the intersection of artificial intelligence , data visualization , and robotics , with a focus on practical implementation. He maintains an active open-source presence and collaborates internationally on robotics applications.
Dr. Rohini K. Srihari is a full Professor and Associate Chair in the Department of Computer Science and Engineering at the University at Buffalo, The State University of New York , and also serves as an Adjunct Professor in the Department of Linguistics within the School of Engineering and Applied Sciences . Her expertise lies at the intersection of natural language processing , artificial intelligence , information retrieval , and AI for social good . Education: PhD in Computer Science, University at Buffalo, 1992 B.Math, University of Waterloo, Canada Research Interests: Dr. Srihari’s research spans a wide range of topics including conversational AI systems (especially empathetic and socially-aware chatbots), disinformation detection and attribution , multilingual text mining for low-resource languages, and predictive analytics for social unrest using heterogeneous data sources. She also explores multimedia information retrieval and AI-driven early warning systems for social and economic disruption. Recent Research Trends: Her recent publications reflect a strong focus on conversational AI and AI for social good , with applications in mental health support, misinformation mitigation, and empathetic dialogue systems. She has led interdisciplinary teams in competitions like the Alexa Prize Socialbot Grand Challenge , where her team won the bronze medal in 2021. Scientific Awards & Recognition: Bronze Prize, Alexa Prize Socialbot Grand Challenge 4 (2021) NSF Grant: Purposeful Conversational Agents based on Hierarchical Knowledge Graphs Holds two US patents in multilingual text mining and face recognition Advising & Funding: Dr. Srihari has supervised numerous PhD and Master's students across departments and has led research funded by NSF , DARPA , IARPA , and the US Government . She has also served as Chief Data Scientist at PeaceTech Lab, leading AI initiatives for conflict prevention. Research Labs & Teams: She directs the Conversational AI for Social Good research group, which focuses on building trustworthy, empathetic, and socially responsible AI systems. The group integrates deep learning, knowledge graphs, and symbolic AI to address real-world societal challenges.
Curdin Derungs is a Lecturer in Data Science at the Lucerne School of Computer Science (HSLU), Switzerland. He specializes in applying statistical and machine learning techniques to energy systems, spatial analysis, and natural language processing. His professional competencies include data-driven automation, deep learning, time series analysis, and energy systems optimization. Derungs holds a PhD in Natural Sciences (University of Zurich, 2013) with a focus on NLP and spatial analysis, and a DAS in Applied Statistics (ETH Zurich, 2019). Earlier degrees include a Master's in Geography (2008) and Atmospheric Physics (ETH Zurich, 2008). His research explores intersections between data science and environmental sustainability, including energy efficiency optimization, spatial language dynamics, and landscape modeling. Recent work focuses on occupant behavior in energy systems, geotagged text analysis, and soil formation modeling. Key projects include SCCER FEEB&D Work Package 2 on renewable energy systems, the Romande Energy Demonstrator, and urban greening studies. He actively contributes to interdisciplinary initiatives like the IGE Innovationswettbewerb 2019 and Innovationspark Zentralschweiz.
Jordi Alonso is an Instructor in the Department of English at Louisiana State University (LSU), affiliated with the College of Humanities & Social Sciences. He holds a PhD in English (2021) from the University of Missouri, an MA in Classical Studies from Columbia University (2023), an MFA in Poetry from Stony Brook University (2016), and a BA in Creative Writing from Kenyon College (2014). His research focuses on classical reception in 19th-century poetry, 18th/19th-century Latin composition, Victorian women’s education, and ancient Mediterranean cult practices. Current projects include editing 1842 Latin translations of Sophocles’ Theban Cycle and translating works by 18th-century Latin poet Ubertino Carrara. He is revising his doctoral dissertation into a book manuscript titled An Island of Nymphs: the Fear and Fantasy of Educated Women in Victorian England . Alonso’s publications span poetry analysis, translation theory, and creative writing pedagogy. His work appears in venues like Kenyon Review Online and Siblíní Journal . He maintains a literary recovery project for Regency poet Letitia Elizabeth Landon (LEL) through his personal website. His academic contributions bridge classical antiquity, Victorian studies, and contemporary creative writing, emphasizing interdisciplinary approaches to textual history and cultural reception.
Dr. Valentin Danchev is an Assistant Professor in Business Analytics at Queen Mary University of London's School of Business and Management. He holds a DPhil in Development Studies from the University of Oxford and has held postdoctoral positions at Stanford University School of Medicine and the University of Chicago. His research focuses on computational social science, network analysis, reproducible research practices, and data governance in AI/health domains. He is affiliated with the Centre for Globalisation Research (CGR). Education: DPhil in Development Studies (University of Oxford), postdoctoral training at Stanford University School of Medicine and the University of Chicago. Research Interests: Reproducible data science workflows, transparency in AI/health research, migration network analysis, and meta-research on scientific ecosystems. Publications span topics like data governance frameworks, clinical trial transparency, and migration patterns. He authored the open textbook Reproducible Data Science with Python (2022) and is a Fellow of the Software Sustainability Institute. Teaching includes courses on machine learning, network analysis, and responsible data practices. He advises PhD students on topics like causal inference, digital health interventions, and science policy.
Dr. Alexis Palmer is an Associate Professor in the Department of Linguistics at the University of Colorado. She specializes in computational discourse and semantics, computational linguistics for low-resource languages, and automated detection of offensive language in social media. Previously, she served as an Assistant Professor at the University of North Texas and held postdoctoral positions in Germany. She leads the NSF-funded FOLTA project (From One Language to Another), focusing on cross-linguistic methods for low-resource language processing tools. Her work emphasizes language documentation accessibility and pedagogical material development for endangered languages. Education: PhD in Linguistics from the University of Texas at Austin (2009). Research Interests: Computational methods for endangered languages Low-resource NLP systems Social media discourse analysis Universal Meaning Representation (UMR) frameworks Linguistic documentation infrastructure Notable Achievements: Recipient of NSF CAREER Grant Pioneered the OLEA tool for offensive language analysis Developed the BELT initiative for endangered language technology Grants & Projects: FOLTA Project (NSF-funded) National Decade of Indigenous Languages initiatives Collaborative research on UMR infrastructure Labs/Teams: Directs the Computational Linguistics Lab at CU, collaborating with global partners on language documentation and revitalization efforts.
Liying Cheng is a Professor in the Faculty of Education at Queen's University. She specializes in language assessment, second language testing, and the impact of large-scale testing on education systems. Her work emphasizes washback effects and the academic integration of international students. She holds a PhD in Language Assessment from the University of Hong Kong and an MA in Teaching English as a Foreign Language from the University of Reading. Education History: PhD (1997): Language Assessment, University of Hong Kong MA (1993): Teaching English as a Foreign Language, University of Reading Research Interests: Global impact of standardized testing Educational equity and assessment fairness Language acculturation of immigrant professionals Classroom assessment practices in multilingual contexts Her landmark studies on washback theory have reshaped understanding of how testing influences teaching and learning. Notable contributions include analyzing the TOEFL iBT, CELPIP, and Gaokao systems. Scientific Awards: TOEFL Award for Outstanding Dissertation (1998) TESOL Leadership Mentoring Award (2002) Advising & Grants: Over 1.8M CAD in research funding since 2000, with over 230 conference presentations and 150+ publications. Current roles include Faculty Director of the Assessment and Evaluation Group at Queen's. Labs/Teams: Lead researcher in language assessment at Queen's, collaborating with institutions globally on testing policy and implementation.
Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.
Professor Daniel M. Berry is a faculty member in the Cheriton School of Computer Science at the University of Waterloo, specializing in Software Engineering, Requirements Engineering, and Electronic Publishing. He holds a Ph.D. from Brown University (1974) and a B.S. from Rensselaer Polytechnic Institute (1969). His research focuses on ambiguity in requirements specifications, multilingual publishing systems, and formal methods. He participates in the IFIP Working Group 2.9 on Requirements Engineering and is on the editorial boards of the Requirements Engineering Journal and Empirical Software Engineering Journal . Berry teaches courses in software abstraction, requirements engineering, and social implications of computing. He has advised numerous students and contributed to projects like the WD-PIC system and the Universal History Translation Project. His work extends to interdisciplinary areas such as biblical commentary and public lectures on avoiding jet lag and finishing PhDs.
Nunne Englund is an Associate Professor in Psychology at the Department of Psychology, Norwegian University of Science and Technology (NTNU), affiliated with the Faculty of Humanities and Social Sciences. She holds a PhD in Developmental Psychology from NTNU. Her research focuses on phonological development, infant directed speech, and general language development in early childhood. Englund is a member of two research groups: the Speech, Cognition and Language Research Group (SCaLa) and the Learning and Skill Development (Mind, Brain and Education) group. Her work explores how infants perceive and process speech, particularly in natural caregiver interactions, and examines the impact of environmental factors like media use on child development. Her research has been published in journals such as Scandinavian Journal of Psychology , Applied Psycholinguistics , and Behavior Research Methods . Key themes include audiovisual speech perception, hypoarticulation in infant-directed speech, and the role of physical fitness in reading skills development. Englund teaches courses such as PSY2111 (Specialization in Developmental Psychology) and introductory cognitive psychology modules. She has engaged in extensive public outreach, including media appearances on NRK, interviews in Aftenposten , and workshops on early language stimulation for parents and educators. Her contributions span interdisciplinary research areas, integrating developmental psychology, linguistics, and educational practices to understand early childhood language acquisition and its societal implications.
Lily Popova Zhuhadar is a Professor of Analytics & Information Systems at Western Kentucky University's Gordon Ford College of Business, where she also serves as Director of the WKU Center for Applied Data Analytics and Coordinator of the Undergraduate Business Data Analytics Program. Her academic journey began with a Master of Sciences in Computer Science from Western Kentucky University in 2004, followed by a Doctor of Philosophy in Computer Engineering and Computer Science from the University of Louisville in 2009. Dr. Zhuhadar's educational background includes: Doctor of Philosophy, Computer Engineering and Computer Science, University of Louisville (2009) Master of Sciences, Computer Science, Western Kentucky University (2004) Her research interests span Machine Learning, Artificial Intelligence, Data Mining, Pattern Recognition, Data Science, Analytics, Semantic Web, Smart Cities, and Computers in Human Behavior. Dr. Zhuhadar's work bridges theoretical computer science with practical applications in healthcare, business analytics, and educational technology. She has made significant contributions to semantically enriched e-learning resources, intelligent tutoring systems, and data analytics for crisis management, with particular expertise in applying analytical techniques to solve complex real-world problems across multiple domains. Dr. Zhuhadar's publication record demonstrates a consistent focus on applying advanced data analytics techniques to solve real-world problems. Her recent work has increasingly focused on healthcare applications, including stroke prediction, diabetes diagnosis, and ecosystem management. She has also maintained a strong interest in educational data mining, particularly in understanding student success pathways in STEM disciplines. Her research methodology often combines semantic web technologies with traditional data mining approaches to create innovative solutions. Her scientific achievements have been recognized through numerous awards: College Faculty Award for Research & Creativity (2023-2024) College Faculty Award for Service (2023-2024) College Faculty Award for Research & Creativity (2022-2023) College Faculty Award for Teaching (2021-2022) University Award for the Most Prolific Grant Proposer (2014-2015) Dr. Zhuhadar has secured over $600,000 in external research funding and more than $100,000 in internal grants. She has served as Principal Investigator on diverse projects ranging from AI applications in healthcare to financial fraud detection. Her mentorship of undergraduate students through the WKU Faculty-Undergraduate Student Engagement program has resulted in numerous collaborative research projects and conference presentations, with over 15 students co-authoring publications and presentations with her in the past two years alone. As Director of the WKU Center for Applied Data Analytics, Dr. Zhuhadar has built a thriving research infrastructure that supports faculty and student research across multiple disciplines. Under her leadership, the Center secured over $101,000 in research grants in 2022-2023, with 33% of funds allocated to support student educational pursuits. She has organized multiple webinar series and research enrichment programs to build data analytics capacity across the university community.
Tingyu Lin is a predoctoral researcher at the Computer Vision Lab, Institute of Visual Computing & Human-Centered Technology, Faculty of Informatics, Technische Universität Wien, Austria. She is affiliated with the FWF doctoral program "Visual Analytics and Computer Vision Meet Cultural Heritage (VaCoViCu)" supervised by Prof. Robert Sablatnig, focusing on preserving cultural heritage through automated analysis and visualization of historical photographs and amateur films. Her research interests include Computer Vision , Deep Learning , and Image Processing , with a strong emphasis on applications in Cultural Heritage Preservation . Recent work spans Historical Image Retrieval , Spectral Reconstruction , and Code Understanding benchmarks. The 2024 article CodeScope introduces a multidimensional benchmark for LLMs in code analysis, while her 2023 paper HSGAN explores hyperspectral reconstruction using GANs. Earlier works focus on legacy photo editing and spectral analysis from RGB images. Her research integrates techniques like Generative Adversarial Networks , Multitask Learning , and Feature Composition , contributing to cross-domain applications in cultural heritage and computational linguistics.
Jose Camacho Collados is a Professor at Cardiff University's School of Computer Science and Informatics, leading the Cardiff NLP group. He is a UKRI Future Leaders Fellow since 2021 and has held roles including General Chair of *SEM 2024. His research focuses on NLP, particularly lexical semantics, distributional semantics, and social media analysis. Key contributions include the RelBERT model, the TweetNLP platform, and benchmarks like WiC and SuperTweetEval. He holds a PhD from Sapienza University of Rome and has worked at ATILF-CNRS. Notable awards include the AIJ 2023 Prominent Paper Award and the Welsh Chess Champion title. His work spans multilingual NLP, temporal language models (TimeLMs), and ethics in AI. Education: Erasmus Mundus Master in Natural Language Processing, BSc in Mathematics (5-year), and a Google Doctoral Fellowship. Research emphasizes integrating knowledge resources (e.g., BabelNet) with NLP applications. Active in organizing challenges like SemEval and promoting open datasets. Supervises PhD students and advocates for reproducible research through platforms like TweetEval. Awards highlight his scientific contributions, including the 2023 AIJ award and fellowship recognitions. His work on social media analysis addresses mental health, misinformation, and public health monitoring. A passionate advocate for NLP education, he teaches and publishes extensively on embeddings and their applications.
Brent Hecht is an Associate Professor of Computer Science and Communication Studies at Northwestern University and Director of Applied Science at Microsoft. His research focuses on human-centered AI, spatial computing, and algorithmic fairness. He holds a PhD in Computer Science from Northwestern and has dual BS degrees in Computer Science and Geography. His work bridges HCI, social computing, and geography, addressing algorithmic bias and ethical AI. He has led studies on remote work impacts, data labor, and AI ethics, publishing at top venues like CHI, CSCW, and SIGKDD. His lab, People, Space, and Algorithms Research Group, explores sustainable AI ecosystems. He received the NSF CAREER Award and multiple best paper awards. He advises students in computer science and technology/social behavior programs. Hecht collaborates with Microsoft Research, Xerox PARC, and Google Research. His work has been featured in major media outlets. His current roles include co-leading Microsoft's future-of-work initiatives and promoting equitable data practices in AI.
