Mani Golparvar Fard is a Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Siebel School of Computing and Data Science and the Department of Civil and Environmental Engineering. He also contributes to the Technology Entrepreneur Center. His research focuses on integrating artificial intelligence, computer vision, and data analytics to advance construction management, infrastructure monitoring, and automation. Key areas include BIM integration, reality capture systems, and deep learning-based progress tracking. His work emphasizes automated construction progress monitoring through semantic segmentation, vision-language models, and UAV-based data collection. He has pioneered methods like Scan2BIM-NET for converting point clouds into BIM models and developed frameworks for worker safety analysis using machine learning. Awards: Walter L. Huber Civil Engineering Research Prize (2018) Daniel W. Halpin Award for Scholarship (2016) Advising & Grants: While no specific grant details are provided, his research is supported by collaborations with industry and government initiatives, such as the Japanese national bridge inspection project. He advises a team focused on AI-driven construction solutions and maintains active partnerships with engineering firms. Labs & Teams: Leads research groups in vision-based construction analytics, automated scheduling systems, and BIM integration. His work is disseminated through platforms like the VisualSiteDiary system and the InstaDam open-source platform for structural damage analysis.
Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He previously served as an Assistant Professor at Georgia Tech (2013-2017) and holds the Halicioğlu Chair in Computer Architecture . As founder/director of the Alternative Computing Technologies (ACT) Laboratory and associate director of UCSD's Center for Machine Integrated Computing and Security (MICS) , his research drives cross-stack solutions for next-generation computer systems. Early tenure recipient at UCSD Coined the term "dark silicon" in computer architecture Developed Tabla/DnnWeaver open-source frameworks Research Interests span: Approximate Computing Neural Acceleration FPGA/ASIC Hardware Design Machine Learning Systems Dark Silicon Challenges Security/Privacy in Accelerated Systems Scientific Recognition : 4 CACM Research Highlights 4 IEEE Micro Top Picks Distinguished Paper Award (HPCA 2016) Inducted to ISCA Hall of Fame (2018) Teaching : Developed courses on accelerator design (CSE 240D) and alternative computing (CS 8803 ACT) Advocates for hands-on FPGA-based learning in Processor Design with FPGAs courses
Darren Dahl is a Professor at the University of British Columbia (UBC) Sauder School of Business , holding the RBC Financial Group Professor of Entrepreneurship chair. His research focuses on new product development , creativity , consumer emotions , social influence , and social marketing . Education: BCom from the University of Alberta, PhD from UBC Leadership: Dean of the Sauder School of Business Research Trends: Recent work emphasizes innovation strategies , consumer identity dynamics , ethical marketing , and social impact . He explores how organizational structures influence perceptions, the role of identity in consumption, and the psychological drivers of prosocial behavior. Contact: Office HA 769, Tel: 604-822-8556, Email: darren.dahl@sauder.ubc.ca
Ira Lit is a Teaching Professor at the Graduate School of Education at Stanford University. They serve as Faculty Director of the Stanford Teacher Education Program (STEP) and are affiliated with the Stanford Woods Institute for the Environment . Their work bridges teacher education, elementary education, and educational equity. Key research areas include: Equity in education systems Design of schooling models Experiences of marginalized students Family-school partnerships Recent publications analyze teacher preparation programs, voluntary desegregation, and multilingual education. Lit has supervised doctoral students like Elena Darling Hammond and Nallely Aceves , with a focus on race, identity, and policy in California schools. They hold a Ph.D. from Stanford in Curriculum Studies and Teacher Education (2003) and have taught at Stanford since 1986.
Georgia Zellou is an Associate Professor in the Department of Linguistics at the University of California, Davis, where she co-directs the Phonetics Lab and conducts award-winning research at the intersection of phonetics, speech perception, and human-AI interaction. Her work investigates how phonetic detail is cognitively represented through variations in speech production, with significant contributions to understanding speech alignment with voice assistants, face-masked speech intelligibility, and cross-linguistic perception of synthetic voices. Her academic credentials include a Ph.D. in Linguistics from the University of Colorado at Boulder (2012), an M.A. in Linguistics from Stony Brook University (2007), and a B.A. in Linguistics & Anthropology from the University of Florida (2005, Cum Laude, Phi Beta Kappa). Ph.D., Linguistics, University of Colorado at Boulder (2012) M.A., Linguistics, Stony Brook University (2007) B.A., Linguistics & Anthropology, University of Florida (2005) Professor Zellou's research program centers on laboratory phonology approaches to real-world communication challenges, examining how acoustic-phonetic details influence speech perception across contexts. Her studies span speech alignment with voice-AI systems (e.g., Amazon Alexa), sociophonetic variation in bilingual speech, and the cognitive mechanisms underlying perceptual compensation for coarticulation. She employs experimental methods including eye-tracking, acoustic analysis, and perceptual testing to uncover how phonetic variation functions pragmatically in human communication and human-machine interaction. Analysis of her 15 most recent publications (2023-2025) reveals three dominant research trajectories: (1) human-AI voice interaction dynamics, including prosodic alignment and social evaluation of TTS voices; (2) intelligibility optimization in challenging contexts (face masks, clear speech for diverse listeners); and (3) cross-linguistic phonetic variation in vowelless words and consonant clusters. These works consistently bridge theoretical phonology with applied speech technology, demonstrating how fine-grained phonetic detail influences communication effectiveness in both human-human and human-machine contexts. Her scientific recognition includes: Fulbright Scholar (2022) for research in France Chancellor’s Award for Excellence in Undergraduate Mentoring (2019) Fellow of the Linguistic Society of America (2020) Amazon Faculty Research Award (2019) for Alexa-related speech studies Dean’s Fellow designation at UC Davis (2020-2023) Professor Zellou maintains an active mentoring practice recognized with the Chancellor’s Award, supervising undergraduate researchers in the Phonetics Lab while teaching core linguistics courses from introductory to advanced graduate levels. Her research program is supported by competitive grants including NSF funding, Amazon Research Awards, and UC Davis internal grants (Hellman Foundation, ISS Junior Faculty Grant), reflecting the translational value of her work for speech technology development. She has co-directed major initiatives including the 2019 LSA Linguistic Institute. The Phonetics Lab she co-leads serves as a hub for experimental phonetics research, focusing on speech production-perception relationships through projects investigating vocal accommodation to voice assistants, nasal coarticulation dynamics, and cross-linguistic prosody. Current collaborations with industry partners aim to implement human speech adaptation principles into voice assistant design to enhance naturalness and engagement.
Eugenia Rho is an Assistant Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), part of the College of Engineering. Her research focuses on data analytics, machine learning, natural language processing, and human-computer interaction, with particular emphasis on social media discourse, online identity dynamics, and ethical AI applications. Education includes a Ph.D. in Information and Computer Sciences from the University of California, Irvine (2020), and a B.A. in Political Science from Columbia University (2011). Her interdisciplinary background bridges computer science and social sciences. Research interests span AI-assisted communication tools, counterspeech strategies for online hate mitigation, and neurodivergent perspectives in technology design. Her work often integrates computational methods with social science theories to address real-world challenges such as bias detection, mental health support, and ethical AI deployment. Recent publications highlight themes like AI collaboration in writing, identity-driven online interactions, and the efficacy of counterspeech. Her projects frequently involve designing human-centered technologies that prioritize accessibility and ethical considerations. No scientific awards are explicitly mentioned in the provided text. She maintains an active Google Scholar profile and a personal homepage (URLs not provided in the text).
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 .
Timo Kaartinen serves as Professor of Social and Cultural Anthropology and Vice Dean for Research and Doctoral Education at the University of Helsinki 's Faculty of Political Science . Since 1992, he has conducted extensive research in Southeast Asia with a focus on Indonesia, exploring communicative practices, narratives, and cultural responses to uncertainty. His teaching spans social change in bachelor's, master's, and doctoral programs, covering environmental anthropology, political anthropology, cosmologies, religions, and linguistic anthropology. PhD in Anthropology, University of Chicago (2001) Docent (Adjunct Professor), University of Helsinki (2011) Research interests center on ethical discourse in Indonesian Islamic universities, resource politics in oil palm estates, and linguistic anthropology with emphasis on storytelling, folk poetry, and minority language revitalization. His work combines ethnographic focus on Southeast Asia with theoretical contributions to frontier studies and postcolonial anthropology. Recent publications examine frontier temporalities (2020-2025), Islamic transformations (2019), and oral narrative structures (2017-2013). Key projects include ARCTIC Research Centre (2025-2029), Diversity in Society and Life (DIVSOL) (2025-2030), and Religious Movements in Ethical Life (2019-2023). Grants from the Academy of Finland and University Profiling Funding support these initiatives. Academic activities include keynote speeches at international conferences (e.g., Empowering Local Wisdom 2014, Labor Migration in Asia-Pacific 2013) and editorial roles. He has conducted fieldwork in Indonesia since 1992, focusing on Bandanese cultural heritage and Maluku religious dynamics . His methodological approach combines ethnographic fieldwork with historical document analysis .
Prof. Dr. Pia Knoeferle is a leading academic at Humboldt-Universität zu Berlin , where she serves as a Professor in the Department of German Language and Linguistics . She is a principal investigator in the Collaborative Research Center (CRC 1412) focused on register phenomena and leads the Reaction Time, Eye-tracking, and EEG Laboratories . Her research spans psycholinguistics , cognitive neuroscience , and computational modeling of language , with a central interest in how real-time language comprehension interacts with social context , formality-register congruence , and morphosyntactic processing . Appointments: Professor at Humboldt-Universität (2024), CRC 1412 member Methodologies: Eye-tracking, EEG, Visual World Paradigm, ERP Her work investigates lifespan language processing (children, adults, older adults) through contextual cue integration , including emotional , spatial , and social context such as gender cues , eye gaze , and facial expressions . Key questions include: How do pragmatic and contextual factors modulate lexical and grammatical processing ? What representations underlie register sensitivity in spoken and written comprehension ? Recent articles (2022-2024) focus on register congruence effects in German sentence processing, age-related differences in formality-register anticipation , and interactions between register and morphosyntactic knowledge . Using eye-tracking and visual world paradigms , her team examines incremental integration of socially-situated context with verb-argument relations and grammatical constraints . Findings suggest subtle late-stage register effects and interference between pragmatic and syntactic processing . Her lab collaborates with researchers like Katja Maquate , Valentina Nicole Pescuma , and Camilo Ronderos , contributing to the Frame text of the Second Phase Proposal for CRC 1412 (2020) and subsequent reviews in Frontiers in Psychology (2023). The work emphasizes complementary methods to model register variability across languages , modalities , and cultural contexts .
Dr. Jon Gruda is an Assistant Professor and Lecturer in Organisational Behaviour at Maynooth University's School of Business. He holds a PhD in Management from emlyon Business School (France) and a joint Dr. rer. nat. in Psychology from Goethe University Frankfurt. His research focuses on relational leadership, dark leadership traits, anxiety in the workplace, and personality psychology, with a strong emphasis on integrating machine learning and AI methodologies. Gruda has been recognized with prestigious awards, including selection for the Lindau Nobel Laureates Meeting in Economic Sciences (2020). His interdisciplinary work includes predicting anxiety and personality traits via social media data analysis. He serves as an Associate Editor for journals like Personality and Individual Differences and Frontiers in Psychology . Education PhD in Management, emlyon Business School (2012–2017) Dr. rer. nat. in Psychology, Goethe University Frankfurt (2012–2017) MSc in Affective Neuroscience, Maastricht University (2018) MSc in Management Research, emlyon Business School (2012–2014) Triple MSc in Management, City University London/ESCP Europe (2010–2012) BSc in International Business & Management, University of Groningen (2007–2010) Research Interests Gruda’s work bridges leadership studies, personality psychology, and data science. Key areas include: Dark leadership traits (e.g., narcissism, Machiavellianism) and their organizational impacts Machine learning applications for detecting anxiety and personality traits via social media Cross-cultural studies on leadership perceptions and attachment orientations Impact of physiological/psychosocial factors on leadership effectiveness Publications & Projects Recent projects include predicting state-level health outcomes linked to narcissism and developing algorithms to track anxiety using Twitter data. Over 20 peer-reviewed articles since 2017 highlight his contributions to organizational behavior and computational social science. Awards 7th Lindau Nobel Laureates Meeting on Economic Sciences (2020) Benedictine University Award (Academy of Management, 2020) Wharton Global Faculty Development Program (2020) Grants & Collaborations Gruda leads projects on pro-environmental behavior and collaborates with the National Care Experience Programme on healthcare feedback analysis. Seed funding includes €301,930 for computational text analytics in healthcare. Labs & Teams Interdisciplinary research collaborations span psychology, data science, and public health, with a focus on applying machine learning to organizational challenges.
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.
David J. Crandall is the Luddy Professor of Computer Science at Indiana University's Luddy School of Informatics, Computing, and Engineering. He serves as Director of the Luddy Artificial Intelligence Center and leads the IU Computer Vision Lab. With joint appointments in Informatics, Cognitive Science, Data Science, and Statistics, his work spans computer vision, machine learning, and AI. He holds a Ph.D. from Cornell University and previously worked at Eastman Kodak Research Labs. His research focuses on developing statistical and machine learning methods to analyze visual information, including object recognition, human activity analysis in video, 3D reconstruction, social media mining, and computational studies of visual attention. Key applications include egocentric vision systems, social robotics for healthcare, and cross-disciplinary collaborations with developmental psychology. Recent publications demonstrate strong emphasis on egocentric video analysis (Ego4D), human-robot interaction (CHI/HRI), and explainable AI (IJCAI). Medical imaging, nanoscale security systems, and computational social science represent emerging interdisciplinary directions. His work consistently integrates deep learning with real-world applications in health, environmental monitoring, and cultural analytics. Tracy M. Sonneborn Award (2024) Distinguished Member of the ACM (2023) Luddy Professorship (2021) NSF CAREER Grant (2013) Trustees Teaching Award (2017) He has advised over 20 Ph.D. graduates, with current students working on computer vision, robotics, and AI ethics. Major grants include $20M for the NSF AI Institute on Engaged Learning, $4.4M for trusted AI research, and funding from NIH, Google, ONR, and NASA. He directs the Computer Vision Lab and collaborates with Selma Sabanović's robotics group on social agents for older adults.
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.
Yuuko Uchikoshi Tonkovich is a Professor at the School of Education, University of California, Davis, where she has served since 2004, advancing from Assistant to Associate and then to full Professor. Her work centers on language and literacy development in young dual language learners, with a focus on bilingualism, early literacy, and the role of family and media in language acquisition. Her research interests include: Early Childhood Development Multilingual Learners and Bilingual Education Language Acquisition and Literacy Parent-Child Interaction and Home Language Environment Quantitative Analysis of Educational Outcomes Educational Television and Multimedia Her recent publications span topics such as cognitive distancing in parent-child reading, morphological awareness in bilingual readers, and the impact of teacher language use in multilingual classrooms. The research consistently emphasizes longitudinal, cross-cultural, and interdisciplinary approaches, particularly focusing on Chinese American and Mexican American immigrant families. Her scientific awards include: AERA Vocabulary SIG Notable Vocabulary Researcher Award (2025) Foundation for Child Development Young Scholars Program Grant International Reading Association Dissertation Fellowship and Finalist Award Harvard Graduate School of Education Fellowships She has advised numerous research projects and secured significant funding from NIH/NICHD, the Foundation for Child Development, and other organizations. She teaches courses such as Language Development, Early Literacy, and Research on Text Comprehension. She is actively involved in national initiatives, serving on the Technical Advisory Group for the First 5 California Dual Language Learner Study and as Associate Editor of Applied Psycholinguistics . She leads the Language & Literacy Development Lab at UC Davis, which investigates the cognitive, linguistic, and socio-emotional development of dual language learners.
Shinji Watanabe is an Associate Professor at Carnegie Mellon University's Language Technologies Institute and a Courtesy Professor in the Electrical and Computer Engineering department. He holds a Ph.D. (Dr. Eng.) from Waseda University, Japan, and has held research roles at NTT Communication Science Laboratories, Mitsubishi Electric Research Laboratories (MERL), and Johns Hopkins University. His research focuses on automatic speech recognition, speech enhancement, and machine learning for speech processing. Watanabe has published over 300 peer-reviewed papers and received the Best Paper Award at IEEE ASRU 2019. His work emphasizes robust speech processing in challenging environments, multilingual models, and neural audio codecs. He leads the ESPnet toolkit development for end-to-end speech processing systems and contributes to technical committees like IEEE SLTC and APSIPA SLA. Recent research trends include streaming speech systems, universal speech enhancement (URGENT challenges), and fusion of discrete speech units with self-supervised representations. He explores scalable speech foundation models through benchmarks like ML-SUPERB 2.0 and investigates cross-modal audio-visual processing in challenges like MISP 2025. Education : B.S., M.S., Ph.D. (Waseda University) Affiliations : CMU Language Technologies Institute, CMU ECE, Former roles at MERL and Johns Hopkins Key Projects : ESPnet, OpenWhisper-Style Models, URGENT Challenge Frameworks
