Maite Taboada is a Distinguished Professor in the Department of Linguistics at Simon Fraser University (SFU), directing the Discourse Processing Lab. She is also an Associate Member of the Cognitive Science Program and School of Computing Science, and affiliated with the Digital Democracies Institute and Visual and Interactive Computing Institute. Her research bridges discourse analysis and computational linguistics, focusing on mechanisms of coherence, discourse parsing, and sentiment analysis. Key projects include analyzing online news comments (SOCC corpus), detecting misinformation (MisInfoText), and monitoring gender representation in media (Gender Gap Tracker/Radar de Parité). Notable research interests span toxic language detection, constructive dialogue systems, and interdisciplinary work between social sciences and engineering. She has led projects funded by SFU and collaborated with organizations like Informed Opinion. Her awards include Fellowship in the Royal Society of Canada. Key contributions include developing the SFU Opinion and Comments Corpus, systems for comment moderation, and frameworks for gender bias analysis using topic modeling. Her work integrates theoretical discourse analysis with applied computational methods, addressing societal challenges like media bias and misinformation.
Dr. Fumiya Iida is a researcher affiliated with the University of Cambridge , contributing to interdisciplinary research through Cambridge Reproduction and the Department of Engineering . His work spans bio-inspired robotics , soft robotics , and embodied intelligence , with a focus on biomechanics and human-robot interaction. His research integrates evolutionary robotics , reservoir computing , and tactile sensing , aiming to bridge engineering, physiology, and synthetic biology. Recent publications highlight innovations in Soft robotic actuation Robust control systems Multimodal sensor integration Human-robot collaborative tasks Dr. Iida's 15 most recent 2025 articles emphasize reservoir computing , soft sensor design , and adaptive motor coordination , reflecting his commitment to advancing embodied intelligence in robotics. No formal awards or student advisement details were found in the provided texts.
Ellen Riloff serves as Department Head and Professor in the Department of Computer Science at the University of Arizona, where she leads research at the intersection of natural language processing (NLP) and artificial intelligence. Her work bridges theoretical advancements with real-world applications in social computing, planetary science, and crisis response systems. Education: Ph.D. in Computer Science, University of Massachusetts at Amherst (1994) Research Focus: Dr. Riloff specializes in affective computing and information extraction , developing techniques to recognize emotion, social cues, and embodied expressions in text. Her methodologies frequently employ bootstrapping, stacked learning, and semantic lexicon induction. Recent projects address crisis informatics (e.g., social cue recognition in emergencies) and interdisciplinary applications like the Mars Target Encyclopedia for planetary science data extraction. Publication Trends: Analysis of her 15 most recent publications (2021–2025) reveals three dominant trajectories: (1) affective event modeling in social contexts with applications to crisis response; (2) domain-specific NLP for planetary science and food systems; and (3) advanced language model techniques including retrieval-augmented generation and multi-view prompting. Her work increasingly integrates deep learning with traditional linguistic features. Grants and Leadership: Dr. Riloff has directed multiple NSF-funded projects, including RI: Small: Recognizing Implicit Personal States in Natural Language (2016) and RI: Small: Acquiring Domain Knowledge from Text through Cooperative Bootstrapping (2010). These initiatives pioneered bootstrapping frameworks for affective event recognition and information extraction. She also co-organized the Workshop on Pattern-based Approaches to NLP (2023), highlighting her leadership in advancing hybrid NLP methodologies. Collaborative Infrastructure: She co-developed the Mars Target Encyclopedia—a large-scale information extraction system that processes planetary science literature to create structured databases of Mars surface targets. This project demonstrates her commitment to building reusable scientific infrastructure through NLP.
Univ-Prof. Dr. med. Malek Bajbouj serves as Director of the Institute for Affective Neuroscience and Emotion Modulation at Charité – University Medicine Berlin's Campus Benjamin Franklin (CBF), operating within the Department of Neurology, Neurosurgery and Psychiatry (CC 15). His position integrates clinical leadership with translational neuroscience research focused on severe mental illnesses. Dr. Bajbouj's research program centers on affective neuroscience and emotion dysregulation mechanisms in psychiatric disorders, particularly schizophrenia spectrum conditions and depression. He pioneers multimodal intervention approaches combining neuromodulation (tDCS), oxytocin augmentation, mindfulness therapies, and digital health tools. His work emphasizes translational biomarker development using neuroimaging, machine learning, and physiological stress parameter analysis to personalize treatment for treatment-resistant populations. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) novel treatment combinations for negative symptoms in schizophrenia (oxytocin + mindfulness, yoga therapy); (2) real-world implementation of neuromodulation (at-home tDCS protocols, technical efficacy monitoring); and (3) global mental health responses to crises (pandemic impacts on vulnerable groups, culturally adapted refugee interventions). His methodology consistently employs rigorous randomized controlled trials with embedded biomarker studies. As director of his eponymous institute, Dr. Bajbouj leads a multidisciplinary team conducting neuroimaging studies, clinical trials, and international collaborations focused on emotion modulation pathways. The institute coordinates research across CC 15's clinical infrastructure at CBF Building V, with particular emphasis on bridging laboratory neuroscience with clinical psychiatry through the DepressionDC and OXYMIND trial frameworks.
Karen Prager is a Professor of Psychology and Gender Studies at the University of Texas at Dallas within the School of Interdisciplinary Studies, where she has taught for over 30 years. A Board-certified couple and family psychologist, she specializes in romantic relationship dynamics with expertise in intimacy, conflict management, and emotional recovery. Dr. Prager earned her PhD in Counseling Psychology and an M.A. in Measurement and Evaluation from The University of Texas at Austin. Her academic foundation supports her dual focus on clinical practice and scholarly research. Her research investigates how couples reconcile after conflicts and restore intimate bonds, emphasizing emotional recovery processes, differentiation of self, and attachment security. The Couples Daily Lives Lab conducts real-time studies on post-conflict behavior in day-to-day relationships, revealing how reconciliation efforts impact long-term relationship health. Analysis of her publication record shows sustained focus on intimacy restoration mechanisms across 30 years, utilizing daily diary methods and experimental designs to map conflict-recovery pathways. Her work bridges theoretical frameworks with practical therapeutic applications for couples navigating relationship challenges. She holds Board Certification in Couple and Family Psychology from the American Board of Professional Psychology (ABPP), reflecting clinical excellence in the field. Dr. Prager mentors undergraduate students through the Couples Daily Lives Lab, providing hands-on research experience in relationship science. Her studies are supported by research grants though specific funding sources aren't detailed in available materials. The Couples Daily Lives Lab currently executes two major projects: one examining partner behavior during and after conflict to understand emotional recovery patterns, and another developing a classification system for reconciliation efforts to determine their effectiveness in restoring intimacy.
Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California (USC), with appointments spanning Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head & Neck Surgery. He serves as Research Director of the Information Sciences Institute and Director of the Ming Hsieh Institute. PhD in Electrical Engineering (UCLA, 1995) Engineer and MS in Electrical Engineering (UCLA, 1992 and 1990) BE in Electrical Engineering (Anna University, India, 1988) His interdisciplinary research focuses on human-centered signal processing and machine intelligence , addressing societal challenges in health, education, defense, and media arts. Key areas include: Behavioral signal processing Affective computing Multimodal signal processing Computational speech science Biomedical applications Scientific Awards : IEEE James L. Flanagan Speech and Audio Processing Award (2025) Edward J. McCluskey Technical Achievement Award (2024) ISCA Medal for Scientific Achievement (2023) Claude Shannon-Harry Nyquist Technical Achievement Award (2023) ACM ICMI Sustained Accomplishment Award (2020) USC Distinguished Faculty Service Award With over 1,000 publications and 19 patents , his work has been commercialized through startups like Behavioral Signals Technologies and Lyssn . He leads transformative university initiatives and has served in editorial roles for top journals including Computer Speech and Language and IEEE Transactions on Affective Computing .
Chang S. (CS) Nam is a Professor of Industrial and Systems Engineering at North Carolina State University, with affiliations in the UNC/NCSU Joint Department of Biomedical Engineering and the Department of Psychology. He holds editorial roles as Editor-in-Chief of Brain-Computer Interfaces journal and is a Fellow of the Human Factors and Ergonomics Society. His research focuses on advancing human-centered technologies through neuroergonomics, brain-computer interfaces (BCIs), and rehabilitation engineering, supported by grants from NSF, AFRL, AFOSR, and the Laboratory for Analytic Sciences. Education: Ph.D. in Industrial Engineering, Virginia Tech (2003) MSIE in Industrial Engineering, State University of New York at Buffalo (2000) Research Interests: His work spans neurorehabilitation, haptic interaction design, and BCI applications, aiming to bridge human cognitive and neural processes with technological systems. Key areas include adaptive human-computer interaction, EEG-based emotion recognition, and spatial augmented reality for skill training. Grants & Awards: Recipient of NSF CAREER Award (2010), Air Force Summer Faculty Fellowship (2018), and the Leland S. Kollmorgen Spirit of Innovation Award (2019) Editor of Brain-Computer Interfaces Handbook: Technological and Theoretical Advances (CRC Press) Advising & Labs: Advises students like Lingchao Mao and Priscille Koutouan Leads the Brain-Computer Interface and Neuroergonomics Lab, developing BCI systems for communication in vegetative states and neurorehabilitation
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Professor Diana Inkpen is a faculty member at the School of Electrical Engineering and Computer Science, University of Ottawa. She holds a Ph.D. from the University of Toronto's Department of Computer Science and degrees from the Technical University of Cluj-Napoca, Romania (M.Sc. and B.Eng.). Her research focuses on computational linguistics, natural language processing (NLP), and artificial intelligence, with specialties in natural language understanding/generation, lexical semantics, and semantic web agents. Education: Ph.D., Computer Science, University of Toronto M.Sc., Computer Science and Engineering, Technical University of Cluj-Napoca B.Eng., Computer Science and Engineering, Technical University of Cluj-Napoca Affiliations: Director, NLP Lab Editor-in-Chief, Computational Intelligence journal Associate Editor, Natural Language Engineering journal Her research has led to roles such as program co-chair for AI 2012 and leadership in organizing international workshops. She has received funding from NSERC, SSHRC, and OCE, and was honored with a Visiting Professor title at the University of Wolverhampton, UK. Her teaching spans courses like Information Retrieval, Natural Language Processing, and Prolog programming. Professor Inkpen's work bridges NLP innovations with societal challenges, including mental health surveillance via social media analysis and bias mitigation in AI systems. She actively contributes to interdisciplinary projects, such as detecting hate speech and legal text entailment, while maintaining a global research network through collaborations and international conference involvement.
Univ.-Prof. Torsten Möller, PhD is a Professor at the University of Vienna and serves as Head of the Research Group Visualization and Data Analysis and Head of the Research Network Data Science. His work spans data visualization, visual analytics, and human-computer interaction, with a focus on biomedical, environmental, and societal data applications. Academic rank: Professor Research group: Visualization and Data Analysis Network: Data Science Email: torsten.moeller@univie.ac.at Research interests include: Visual data analysis for complex systems Interdisciplinary applications in climate science and medicine Human-computer interaction in data exploration Image processing and computer graphics Recent publication trends show expertise in: Visualizing climate change and pandemic data Multi-volumetric and network analysis Algorithmic transparency and user-centered design Interdisciplinary collaborations (e.g., astrophysics, medical imaging) Statistical and uncertainty visualization Design frameworks for visualization recommendation Teaching includes courses in: Computer graphics and visualization Image processing and analysis Human-computer interaction Data analysis projects Doctoral research seminars
Berrak Sisman is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). She leads the Speech & Machine Learning Lab (SmILe Lab), focusing on AI-driven speech technologies. She received her PhD from the National University of Singapore in 2020 and was previously a tenure-track faculty member at the University of Texas at Dallas (2022–2024). Research Interests: Her work spans artificial intelligence, speech synthesis, voice conversion, emotion analysis in speech, medical speech applications, and secure speech technology. She develops neural models for expressive and adaptive speech processing. Publications: Her recent articles (2024–2025) emphasize speech emotion recognition, zero-shot prosody control, accent conversion, and disentangled representations in TTS, reflecting a focus on cross-modal learning, robustness, and real-world applications. Awards & Grants: NSF CAREER Award (2024) Amazon Faculty Research Award (2022) Singapore Ministry of Education Award (2021) A*STAR Singapore International Graduate Award (2016–2020) Leadership: She directs the SmILe Lab, recruiting PhD/Master’s students for projects in neural speech modeling. Her grants include NSF and Amazon funding for voice conversion and emotion synthesis research.
Dr. İsmail ÖZTEL serves as an Assistant Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences. He has been a faculty member since 2019, following his tenure as a Research Assistant from 2012-2019 at the same institution. His educational background includes: Doctorate in Computer and Information Engineering (2014-2018) from Sakarya University with thesis on "Facial expression detection on partial and full face images using machine learning methods" Master's Degree in Computer and Information Engineering (2012-2014) from Sakarya University with thesis on "Driver simulator for educational purposes" Bachelor's Degree in Computer Engineering (2007-2011) from Sakarya University Dr. ÖZTEL's research focuses on artificial intelligence, deep learning, and computer vision with significant applications in healthcare, mobile technology, and public safety. His work has evolved from foundational facial expression recognition to sophisticated medical applications including skin disease classification using smartphones, monkeypox detection from skin lesions, and pandemic response systems for face mask detection. His research demonstrates strong interdisciplinary connections between computer science and healthcare. An analysis of his publication trends reveals a clear progression toward increasingly complex deep learning architectures applied to real-world problems, with recent work emphasizing medical applications using mobile technology and public health safety systems. His 2023-2025 publications show particular focus on skin disease classification, intelligent vehicle systems, and hybrid feature extraction methods for pandemic response. Dr. ÖZTEL has served as a reviewer for numerous prestigious journals including Expert Systems With Applications (multiple years), World Wide Web, Multimedia Tools and Applications, and Journal of King Saud University - Computer and Information Sciences, demonstrating his recognition in the academic community across multiple domains. His research projects include work on facial expression detection in open scientific databases (2020), performance evaluation of transfer learning approaches (2019), and current projects on brain tumor classification systems and earthquake safety education for individuals with developmental disabilities. His international research experience includes collaboration with Filiz Bunyak in 2017, indicating global engagement in his field.
Stella Yu is a Professor specializing in computer vision, machine learning, and AI applications across medical imaging, robotics, and wildlife recognition. She emphasizes adaptive advising tailored to individual student strengths, with regular one-on-one and group meetings to discuss research progress and paper presentations. Yu's research group focuses on unsupervised learning, deep learning workflows, and interdisciplinary applications such as MRI reconstruction, meibography analysis, and aerial wildlife monitoring. Her work bridges theoretical advancements with practical tools like DeepInPy for inverse problems. She strongly advocates for teaching experience through GSI roles and ensures students have conference funding to present findings at top venues. Education Expectations: Regular literature review, lab presence for junior students, and clear authorship protocols. Key Research Themes: Feature learning, medical AI, computational optics, and wildlife population surveys. Her lab promotes a collaborative environment with structured feedback loops, emphasizing both technical rigor and creative problem-solving. Current projects include BatVision for 3D spatial navigation using audio signals, and AI-driven solutions for dry eye diagnosis and building information modeling.
Aleksandar Kostic is a Lecturer in the Department of Architecture and Built Environment at South East Technological University (SETU), where he also serves as Lead Researcher. He holds a PhD from University College Dublin, awarded in 2022, and is actively contributing to the academic and professional architecture community. PhD in Design Theory, University College Dublin (2022) His research lies at the intersection of philosophy and architectural design, with a strong focus on design theory, epistemology, ethics, and aesthetics. He explores fundamental questions such as aporia (intellectual impasses) in design, the influence of Platonic philosophy, and the ethical responsibilities of architects. His work emphasizes critical thinking and philosophical inquiry in architectural practice and education. The trends in his publications reveal a consistent engagement with philosophical concepts applied to architectural problems. From examining 'aporetic' design challenges to analyzing Plato’s views on expertise, his research bridges abstract thought and practical design. Recent works continue this trajectory, addressing ethical values, non-material aspects of design, and the role of contradiction in innovation. Nomination for MIES EU Award for Architecture (2009) Aleksandar Kostic advises PhD students and is actively involved in research dissemination through publications, peer-review, and conference organization. He serves on the steering committee of the All Ireland Architecture Research Group and is a member of the Centre for Platonic Studies at Trinity College Dublin and the Royal Institute of the Architects of Ireland. His peer-review activities include journals such as She JI and Building Material . He is a key member of the Architecture Research Group at SETU and collaborates across disciplines, particularly with philosophy. His upcoming activity includes organizing the 'Ethics in Architecture' conference in 2025, highlighting his leadership in integrating ethical discourse into architectural research.
Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.
