Éva Székely is an Assistant Professor and Researcher at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. Her work focuses on expressive speech synthesis, multimodal interaction, and the application of synthetic speech in human-robot and conversational systems. She teaches the course 'Human Perception for Information Technology (DM2350)'. Her research interests include adapting synthetic speech to situational context, analyzing prosody and pragmatic functions, and investigating ethical implications of synthetic voice design. Notable projects involve developing tools like ConnecTone for modular AAC systems and CreakVC for voice modulation. She has contributed to foundational studies on gender diversity in synthetic voices and the impact of disfluencies on speaker perception. Recent work explores spontaneous speech synthesis, controllable TTS with neural HMMs, and the integration of gesture and speech synthesis using flow matching techniques. Her research often bridges technical innovation with human-centered design principles.
Lorraine Li is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with the Interdisciplinary Science Program (ISP) and the School of Computing and Information. She holds a PhD from the University of Massachusetts Amherst (2022) and conducted postdoctoral research at AI2's Mosaic team. Her work focuses on NLP, machine learning, and socially responsible AI systems. Education: PhD in Computer Science (UMass Amherst, 2022) Research explores evaluation frameworks for commonsense knowledge, model interpretability, and ethical AI applications in domains like education and law. Key interests include probabilistic models, long-tail reasoning, and geographic robustness in LLMs. Recent publications address confirmation bias in reasoning chains (ACL 2025), geographically diverse prompting (CVPR 2024), and uncommon scenario reasoning (NAACL 2024). She co-organized the AAAI 2024 Make symposium and serves on committees for ACL, EMNLP, and NAACL. Grants: Pitt Cyber funding (2024) Lab: Pitt NLP Seminar group
Christian Meilicke is a Researcher at the Data and Web Science Group (DWS) within the School of Business Informatics and Mathematics at the University of Mannheim. His work focuses on artificial intelligence, ontology matching, and knowledge graph completion, with recent contributions to rule-based methods and their applications in business process modeling. He is heavily involved in teaching, coordinating courses such as 'Modeling Business Processes' and 'Artificial Intelligence.' His research interests include the integration of open and structured knowledge, probabilistic reasoning frameworks, and improving the efficiency of knowledge base systems. He has explored topics like inductive logic programming, automated debugging of ontologies, and the use of Markov Logic Networks for root cause analysis in IT systems. In terms of trends, his recent publications emphasize combining symbolic rule-based approaches with machine learning for knowledge graph tasks, such as activity recommendation and link prediction. He also investigates explainability in embeddings and temporal forecasting in knowledge graphs. His work often bridges theoretical advancements with practical applications in business informatics and data integration. No scientific awards have been explicitly mentioned. Christian has advised no formal students listed here but has contributed to teaching and mentoring through his courses and tutorials. His research and teaching are closely tied to the DWS Group, which focuses on data-centric AI and semantic technologies.
Georg Groh is an Adjunct Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology . His research focuses on modeling social context, social interaction mediated by IT systems, and ML-based natural language processing. He holds a doctorate (2005) and habilitation (2012) from TUM, with prior studies in physics and computer science. Key research areas include social signal processing, network analysis, and bias detection in AI systems. Notable awards include the 2019 Supervisory Award and 2016 Honorary Teaching Certificate. His work bridges computational methods with societal impacts, particularly in health informatics and ethical AI. Recent projects explore LLM hallucination detection, bias profiling, and cross-lingual text classification. Education: PhD in Computer Science (2005), TUM Habilitation in Computer Science (2012), TUM Studies in Physics (University of Kaiserslautern) and Computer Science (Universities of Hamburg, Kaiserslautern, TUM) Research Interests: Groh’s work spans social computing, NLP, and ethical AI . Current projects address bias in language models, hate speech detection, and data-driven health interventions. His methodologies emphasize contextual analysis of social interactions, leveraging ML and graph-based techniques. Awards: 2nd place Supervisory Award (2019) Best Paper Awards (2016, 2008) Advising & Grants: Advised on projects like Nutrilize (nutrition recommender system) and contributed to EU-funded initiatives on mHealth systems. Active in designing AI systems for dietary logging and stress management.
Kuan Fang is an Assistant Professor of Computer Science at Cornell University, specializing in robotics, machine learning, and computer vision. His research focuses on enabling robots to perform complex tasks in unstructured environments through deep learning-based perception and control systems. Previously, he was a postdoc at UC Berkeley under Sergey Levine and earned his Ph.D. and M.S. from Stanford University under Fei-Fei Li and Silvio Savarese, with a B.S. from Tsinghua University. He has also worked at RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia. Education: Ph.D. & M.S., Computer Science, Stanford University Bachelor's Degree, Tsinghua University Research Interests: Robot manipulation and control Reinforcement learning and policy optimization Robot perception and vision-language integration Generalization in robotics across tasks, environments, and robots Open-world robotic systems leveraging large-scale data Teaching: CS 6758: Deep Learning for Robotics (Fall 2024) CS 4756: Robot Learning (Spring 2025) Lab & Collaborations: His lab at Cornell develops scalable algorithms and systems for robotic perception and control, emphasizing data-driven methods. Notable work includes ReLIC for interlimb coordination, GLIDE for bimanual manipulation, and TRA for compositional task execution. He collaborates with institutions like Boston Dynamics AI Institute and UC Berkeley.
Slim Essid is a Full Professor at Télécom Paris and coordinator of the Audio Data Analysis and Signal Processing (ADASP) group. He holds a PhD and HDR from Université Pierre et Marie Curie (UPMC). His research focuses on machine learning, artificial intelligence, and signal processing applied to temporal data analysis, including multiview learning, representation learning, and structured prediction. Applications span music content analysis (MIR), multimodal perception (e.g., EEG data analysis), and human behavior analysis. He has advised 15 PhD students and collaborated on over 14 post-doctoral projects. Education: PhD in Signal Processing, Université Pierre et Marie Curie (2005) Habilitation (HDR), Université Pierre et Marie Curie (2015) M.Sc. in Digital Communication Systems, Télécom ParisTech (2002) Engineer Degree, École Nationale d’Ingénieurs de Tunis (2001) Research interests emphasize multimodal learning, self-supervised representation learning, and audio-visual fusion. Key projects include sound-prompted segmentation, zero-shot audio captioning, and EEG-based auditory attention decoding. Over 150 peer-reviewed publications exist across conferences like NeurIPS, ICML, and journals like IEEE Transactions. Active in reviewing for top-tier venues and advising French/EU research projects. Labs/Teams: Member of the Signal, Statistics and Learning (S2A) research team and the Information Processing and Communication Laboratory (LTCI).
Emrah Demir is a Professor of Operational Research in the Department of Logistics and Operations Management at Cardiff Business School, Cardiff University . Prior to this, he was an Assistant Professor at Eindhoven University of Technology, Netherlands. He holds a PhD in Management Science from the University of Southampton and degrees in Industrial Engineering from Baskent University, Turkey. Education: PhD in Management Science, University of Southampton, UK (2012) MSc in Industrial Engineering, Baskent University, Turkey (2008) BSc in Industrial Engineering, Baskent University, Turkey (2005) His research centers on green logistics, artificial intelligence, human/AI collaborative decision-making, and operational research , with a focus on optimizing freight transportation and supply chains for sustainability. He applies quantitative and analytical methods—including exact and heuristic optimization techniques—to address real-world logistics challenges. His recent work explores AI-driven routing, drone and robot delivery, maritime emissions, and behavioral aspects of algorithm adoption. The analysis of his recent publications reveals a strong integration of AI, sustainability, and logistics optimization . Key themes include last-mile delivery with autonomous systems, green vehicle routing, maritime decarbonization, and human-AI collaboration in decision-making. His work spans both theoretical modeling and practical applications in industry contexts, particularly with Ocado and in international sustainability initiatives. Scientific Contributions and Editorial Roles: Area Editor, Journal of Heuristics (Logistics and Supply Chain Management) Associate Editor, IMA Journal of Management Mathematics Associate Editor, Frontiers in Future Transportation – Freight Transport and Logistics Associate Editor, OR Spectrum Associate Editor (Engineering), Arabian Journal for Science and Engineering (from 2025) He has secured multiple external grants, including from the Colombia-UK PACT Programme (GIRO-ZERO) and Ocado Group plc , focusing on decarbonizing freight and optimizing supply chains. He supervises several PhD students on topics such as driver behavior, AI collaboration, and greenhouse gas emissions in shipping. He has held academic leadership roles including Programme Director for the MSc in Logistics and Operations Management and Deputy Head of Section for Learning & Teaching. Research Labs and Teams: He is affiliated with the Logistics and Systems Dynamics Group (LSDG) , Co-Director of the PARC Institute of Manufacturing, Logistics and Inventory , and Director of the Business & Economics Artificial Intelligence Research (BEAR) Network , reflecting his interdisciplinary engagement in AI, logistics, and sustainable operations.
Jing Jiang is a prominent researcher at Singapore Management University, specializing in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence. His work spans diverse areas including Vision-Language Models, Machine Translation, Knowledge Graph Reasoning, Sentiment Analysis, and Social Media Discourse Modeling. Key contributions include frameworks for consistent client simulation in mental health counseling and counterfactual contrastive prefix-tuning for many-class classification. He has pioneered methods in zero-shot VQA with interpretable reasoning graphs , cross-lingual understanding with universal syntax , and modularized zero-shot architectures . His research often combines theoretical insights with practical implementations, as seen in works on stereotypical bias in vision-language models (VLStereoSet), tensorized self-attention for dependency modeling, and collaborative relation-augmented attention for knowledge graph completion. Jing Jiang's collaborations span global experts in NLP and AI, with co-authors from institutions like SMU, Waseda University, and Microsoft Research.
Hanh Thi Nguyen is a Professor of Applied Linguistics in the Department of English and Applied Linguistics at Hawaii Pacific University, College of Liberal Arts. She earned her Ph.D. in English Language and Linguistics from the University of Wisconsin-Madison and holds a B.A. from the University of Hue, Vietnam. Her research centers on conversation analysis, interactional competence, second language acquisition, pragmatics, classroom and workplace discourse, learner identity, and Vietnamese linguistics . She explores how language learners develop communicative abilities across contexts, from classrooms to professional settings, using detailed interactional data. Her work bridges theory and pedagogy, informing language teaching and assessment practices. Dr. Nguyen has published extensively, including books such as Developing Interactional Competence at the Workplace (2024) and Developing Interactional Competence: A Conversation Analytic Study of Patient Consultations in Pharmacy (2012), and co-edited volumes on conversation analysis and Vietnamese pragmatics. Her recent articles examine epistemic stance, language ideologies in family talk, and computer-mediated language learning, showing a growing interest in longitudinal development and multimodal interaction. She has received numerous awards and grants, including the Golden Apple Award for Excellence in Scholarship (2019), multiple Faculty Development Grants, and a 2024 U.S. Department of State grant as Principal Investigator. She has served on editorial boards for journals such as RELC Journal and TESOL International Journal . Dr. Nguyen mentors students and collaborates widely, with frequent co-authorship with scholars like Taiane Malabarba and Minh Thi Thuy Nguyen. She teaches courses in sociolinguistics, discourse analysis, corpus linguistics, and language assessment. She also leads research labs and teams focused on conversation analysis and language socialization, and continues to present at major international conferences through 2025.
Prof. Dr. Andreas Bulling is Full Professor of Computer Science at the University of Stuttgart , leading the Collaborative Artificial Intelligence research group at the Institute for Visualization and Interactive Systems. He is also a founding director of the Stuttgart ELLIS Unit and serves on multiple prestigious boards including IEEE Transactions on Visualization and Computer Graphics . Education: MSc in Computer Science (KIT), PhD in Information Technology (ETH Zurich) Research Interests: Human-Computer Interaction, Eye Tracking, Wearable Computing, Computer Vision, and Privacy-Preserving AI Scientific Leadership: UbiComp Steering Committee member, ACM ETRA General Chair (2020), and extensive editorial/guest editor roles Key Awards: ERC Starting Grant (2018), Henriette Herz Scout (2024), and multiple best paper awards at CHI, ETRA, and UIST Technical Contributions: Developed datasets (LPW, VisRecall++), created novel methods for gaze estimation, mental face reconstruction, and saliency prediction in visualizations His work bridges AI and Human-Computer Interaction with applications in immersive systems and healthcare technologies.
Andreas Fender is a researcher at the Visualization Institute of the University of Stuttgart (VISUS), affiliated with the Schmalstieg Working Group. He has held postdoctoral positions at ETH Zurich (Switzerland) and the University of Sussex (England), following his PhD at Aarhus University (Denmark) with an internship at Microsoft Research (USA). His research focuses on Human-Computer Interaction, particularly in Augmented and Virtual Reality (AR/VR) and camera networks. He develops novel input methods for productivity and artistic expression in Mixed Reality environments, creating hybrid physical-digital systems like OptiBasePen, PressurePick, InfinitePaint, and DeltaPen. Recent work includes contributions to Mixed Reality input methods (UIST 2024, CHI 2022) and collaborative systems (Asynchronous Reality). Projects like GuitarPie (UIST 2025) and OptiBasePen (UIST 2024) demonstrate his focus on mobile interaction and ergonomic design. Scientific accolades include the Best paper award at CHI 2022 Best application paper award at ISS 2019 . At VISUS, he collaborates with Dieter Schmalstieg and leads hiring for PhD students and PostDocs exploring physical-digital workflows, artistic tools, and critical AI engagement. His work integrates machine learning, hardware prototyping, and spatial user interfaces to redefine future workplaces.
Guillem Alenyà Ribas is a Researcher and Director of the Perception and Manipulation group at the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of the Spanish National Research Council (CSIC) and the Polytechnic University of Catalonia (UPC), Barcelona. His research focuses on integrating robots into human environments, particularly in assistive robotics and the manipulation of deformable objects such as garments. His research interests span Human-Robot Interaction (HRI) , assistive robotics , explainable AI , robot personalization , deformable object manipulation , and benchmarking . He aims to make robots more transparent, adaptive, and safe in real-world applications. His work combines AI planning, vision, learning from demonstration, and user-centered design to develop systems that can assist in healthcare, domestic, and industrial settings. Recent publications reveal a strong trend in explainability and personalization in HRI, with a focus on frailty assessment in elderly care , real-time explanations , and counterfactual reasoning . His team also advances benchmarking in cloth manipulation , 3D reconstruction of clothed humans , and ontology-based reasoning for robot plans . This reflects a multidisciplinary approach combining robotics, AI, and social sciences. Coordinator of ROB-IN, CLOE-GRAPH, and BURG projects Principal Investigator in SeCuRoPS and DEMETER 5.0 Former coordinator of SIMBIOTS, HuMoUR, and SOCRATES He has supervised numerous PhD students, many of whom have received prestigious awards such as the Georges Giralt PhD Award and the AIHUB.CSIC Prize. His leadership in technology transfer and European projects highlights his role in bridging academic research with real-world applications. He leads the Perception and Manipulation group at IRI, fostering collaboration across disciplines and mentoring a large team of researchers, PhD students, and technical staff. The group actively contributes to open science through standardized datasets and reproducible methodologies.
Romila Pradhan is an Assistant Professor in the Department of Computer & Information Technology at Purdue University. Her research focuses on responsible data science, machine learning, and trustworthy decision-making systems, emphasizing explainability, fairness, and accountability. Education: Ph.D. in Computer Science, Purdue University (2018) M.S. and B.S. in Mathematics and Computing, Indian Institute of Technology Kharagpur (2008) Research Interests: Data management frameworks for ethical AI Algorithmic fairness in machine learning systems Explainable AI (XAI) methodologies Bias mitigation in data-driven decision-making Grants & Awards: NSF Grant: Data Preparation for Fair and Trusted Machine Learning (2024) NSF Grant: Data Preparation for Trusted and Fair Data Science (2023) Bias in AI award for fair machine learning models (2023) Professional Experience: Postdoctoral Researcher, Halıcıoğlu Data Science Institute, UC San Diego Visiting Assistant Professor, Purdue University Department of Computer Science Labs & Teams: Actively involved in Purdue's Purdue Polytechnic Institute and the newly founded Applied AI Research Center.
Danielle O. Pyun is an Associate Professor with tenure in the Department of East Asian Languages and Literatures at The Ohio State University, part of the College of Arts and Sciences. She specializes in Korean language pedagogy and has held visiting roles at Yonsei University and Kyung Hee University. Her expertise spans second language acquisition, assessment strategies, and instructional material development. Education: Ph.D. in Foreign and Second Language Education from The Ohio State University, M.A. in Korean Studies from Ewha University, and B.A. in English Linguistics and Pedagogy from Ewha University. Research Interests: Classroom-based L2 acquisition mechanisms L2 assessment methodologies (TOPIK, ACTFL OPI) Impact of learner variables (motivation, anxiety) on language proficiency Development of KFL (Korean as a Foreign Language) curricula and materials Recent Work Trends: Her publications focus on pragmatic aspects of Korean language use, heritage learner education, and technology-enhanced language learning. Recent studies examine the role of Hallyu (Korean Wave) in motivating learners and the discourse functions of Korean politeness markers. Awards/Grants: Recipient of Korea Foundation and NEAC grants for material development projects, including the Vocabulary Builder for Advanced Learners of Korean . Holds certifications as an ACTFL OPI Tester and WPT rater for Korean. Teaching & Service: Teaches courses ranging from introductory Korean language to advanced linguistic theory. Served on editorial boards for The Korean Language in America and reviewed for journals like Foreign Language Annals . Directed the Critical Language Scholarship (CLS) program in Korea (2013-2014).
Hadeel Alnegheimish is a dual-affiliated academic serving as an Ibn Khaldun Postdoctoral Research Fellow at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and an Assistant Professor in the Department of Computer Science at King Saud University. Her research focuses on advancing machine learning and natural language processing, particularly in neuro-symbolic reasoning, model interpretability, and robust numerical reasoning. She holds a PhD from Imperial College London, advised by Alessandra Russo and Pranava Madhyastha, and completed an internship at DeepMind's Cognition team. Education: PhD in Computer Science, Imperial College London (2023) M.Sc. in Artificial Intelligence, Imperial College London B.Sc. in Computer and Information Sciences, King Saud University Research Interests: Compositional reasoning, model evaluation, and neuro-symbolic integration. She emphasizes transparent and reliable systems that demonstrate how answers are derived, alongside advancing evaluation methodologies. Current projects explore symbolic rule learning for LLMs and preserving word order sensitivity in neural models. Grants & Advising: Currently recruiting MIT UROPs for summer 2025. Collaborates actively with peers in neuro-symbolic NLP and evaluates model behavior through initiatives like Forced Invalidation. Labs & Teams: Participates in CSAIL's machine learning initiatives and leads pedagogical efforts at King Saud University, fostering interdisciplinary research in computational linguistics and AI.
