Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.
Paul D. Brooks is a Professor in the Department of Geology/Geophysics at the University of Utah, where he has been a faculty member since July 2014. His research focuses on understanding water, energy, and biogeochemical cycling in seasonally snow-covered catchments, with increasing emphasis on predicting how climate and land use changes impact snow accumulation, ablation, and snowmelt-derived surface and ground water resources. His educational background includes a BS in Biology and Chemistry from Florida State University, followed by an MS in Ecohydrology (1991) and PhD in Biogeochemistry (1995), both from the University of Colorado, Boulder. Prior to his position at the University of Utah, Dr. Brooks was a Professor in the Department of Hydrology and Water Resources at the University of Arizona from December 2000 to June 2014. Dr. Brooks' research spans multiple disciplines within earth sciences, focusing primarily on hydrology, ecohydrology, and biogeochemical cycling in mountainous, snow-dominated environments. His work examines how climate change affects snowmelt processes, groundwater-surface water interactions, and water resource availability in the western United States. He employs a combination of field measurements, isotope hydrology, and modeling approaches to understand complex hydrological processes across multiple spatial and temporal scales. His research increasingly involves collaboration with stakeholders to translate scientific findings into practical water resource management applications. Analysis of Dr. Brooks' recent publications reveals a strong focus on groundwater-surface water interactions in snowmelt-dominated systems, with particular attention to how climate change affects streamflow generation processes. His work bridges fundamental hydrological science with practical water resource concerns, examining topics such as runoff efficiency, groundwater storage dynamics, and the impacts of land cover changes on hydrological processes. A significant portion of his recent research investigates the Western United States water resources under changing climate conditions. AGU Fellow (American Geophysical Union) Dr. Brooks actively mentors graduate students through thesis research (both PhD and Master's level) as evidenced by his teaching activities. His lab conducts research supported by various grants focused on understanding water resources in mountainous regions, particularly examining how climate change affects snowmelt hydrology and water availability. He collaborates extensively with researchers across multiple institutions, as demonstrated by his numerous co-authored publications with scientists from various universities and research organizations. Dr. Brooks leads research efforts through his lab at the University of Utah and is involved with the Wasatch Environmental Observatory, a mountain-to-urban research network in the semi-arid Western US. His work integrates field measurements across complex terrain to understand how topography, vegetation, and climate interact to control water, energy, and biogeochemical cycling in seasonally snow-covered environments.
Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.
Carol Frost, PhD, is an Assistant Professor in the Department of Renewable Resources at the University of Alberta's Faculty of Agricultural, Life and Environmental Sciences. Her research focuses on arthropod community ecology, particularly biodiversity conservation and the impacts of human activities on ecological functions. She holds a PhD in Ecology from the University of Canterbury (2014), an MSc in Entomology from McGill University (2009), and a BSc in Animal Biology from the University of Alberta (2006). Her work spans three key areas: (1) identifying low-cost industrial/urban modifications for biodiversity conservation, (2) advancing ecological network approaches to predict community dynamics, and (3) documenting Alberta's arthropod biodiversity. Recent studies include analyzing pollinator networks, habitat edge effects, and agricultural landscape impacts on hoverflies and canola crops. Frost teaches courses such as Principles of Managing Natural Diversity (REN R 364/765), Exploratory Data Analysis (REN R 581), and Statistical Methods for Environmental Sciences (REN R 582). Her research outputs emphasize ecosystem resilience, keystone species roles, and multitrophic interactions, with a focus on applied conservation solutions.
Liam Cross is a Lecturer in Psychology at the School of Psychology, University of Bath. His research focuses on social psychology, particularly how synchronized movement (e.g., dancing, drumming) fosters prosocial behavior, affiliation, and group cohesion. He also investigates autism, theory of mind, deception, media effects on prejudice, and the therapeutic potential of games. His research interests span Social Psychology, Prosocial Behavior, Synchronization, Theory of Mind, Autism, Nonverbal Communication, Media Effects, and Gamification . He explores how rhythmic coordination influences social bonding and identity, and how digital and tabletop games can support individuals with autism. His work integrates experimental, virtual reality, and qualitative methods to understand complex social dynamics. The recent articles highlight a strong trend in using technology—especially virtual reality and games—to study and enhance empathy, reduce prejudice, and improve social interaction. Key themes include the Proteus Effect, synchronized movement in VR, video games for refugee empathy, and board games for understanding autistic experiences . These works reflect interdisciplinary approaches bridging psychology, human-computer interaction, and social intervention. Liam Cross has no explicitly listed scientific awards in the provided text. While no direct mention of student advising or grants is made, his collaborative publications suggest active mentorship and research leadership. He is affiliated with the NeuroPlay Lab , indicating involvement in a dedicated research team focused on play, technology, and social cognition. His work contributes to UN Sustainable Development Goals related to well-being and reduced inequalities. He is actively involved in research and teaching in Social Psychology, Research Methods, and Statistics , and leads innovative projects using immersive technologies and games to address social challenges. The NeuroPlay Lab serves as a hub for experimental and applied research in prosocial technology and autism interventions.
Prof. Dr. Katja Rösler is a Professor of Automotive Engineering at the Institute of Mechanical Engineering, Ruhr West University of Applied Sciences since March 2012. Her career spans academic research and industrial development with key positions at TU Braunschweig, Volkswagen AG, and Fraunhofer Institute. Education: Industrial Mathematics degree completed under standard period Doctorate: Engineering (Driver Modeling) from TU Braunschweig, 2008 Her research focuses on automotive engineering with special emphasis on modeling/simulation, vehicle dynamics, driver assistance systems, accident research, alternative drives, and mobility concepts. She actively combines simulation with experimental verification and has significant involvement in Formula Student projects. Recent publications highlight her work in intelligent mobility systems (2018-2020), with particular attention to electromobility, accessibility solutions for elderly/disabled populations, and micromobility analysis. Earlier works established her expertise in driver modeling, vehicle measurement technology, and simulation-experiment correlation. Labs: Automotive Engineering Lab Teaching: Mechanics (Statics, Strength of Materials, Dynamics), Vehicle Dynamics, Driver Assistance Systems
Professor Meredith Crowley is a Professor of Economics and Deputy Chair of the Faculty at the University of Cambridge, where she also serves as a Research Fellow at CEPR (London). She is a Fellow of St. John's College and serves as a Research Coordinator for the Janeway Institute's Transmission Mechanisms and Economic Policy Research Theme. Professor Crowley has established herself as a leading expert in international trade and trade policy, with extensive engagement with international institutions including Bruegel, CEPII, and the Kiel Institute. Professor Crowley's educational background includes: PhD (2001) and MS (1999) in Economics from the University of Wisconsin-Madison MPP (1996) in International Trade and Finance from Harvard University AB (1990) in Asian Studies and Chemistry from Bowdoin College Professor Crowley's research focuses on international trade and trade policy, with particular expertise in trade agreements, trade remedies, currency invoicing, and the economic impacts of Brexit. Her work examines how trade policy uncertainty affects firm behavior, how trade agreements shape market competition, and how exchange rate movements influence trade flows. She has made significant contributions to understanding pricing-to-market dynamics, the role of dominant currencies in trade, and the challenges facing the global trading system, frequently using detailed firm-level data in her empirical analyses. Analysis of Professor Crowley's recent publications reveals a consistent focus on the intersection of trade policy and firm behavior. Her work demonstrates how trade policy uncertainty influences market entry decisions, how trade agreements create competitive effects across markets, and how currency invoicing patterns respond to major economic events like Brexit. Her research combines rigorous empirical methods with deep policy relevance, often using detailed firm-level data to examine how international trade operates in practice, with particular attention to the impacts of trade disputes, tariff threats, and exchange rate fluctuations. Professor Crowley has received significant recognition for her work, including being elected as a Research Fellow at the Centre for Economic Policy Research (CEPR) in 2016. She has secured multiple research grants, including a UK in a Changing Europe Fellowship from the Economic and Social Research Council (2019-2022) and a Data Impact Fellowship from JISC (2017). She also received an ESRC grant for research on 'The impact of trade policy and exchange rate shocks on trade' (2017-2018). Professor Crowley has supervised several PhD students, including Chuan-Han Cheng (International Macroeconomics, Trade, and Monetary Policy) and Yi (Amanda) Wang (International Trade, Innovation, Productivity), and advised others such as Deniz Atalar (Inter-Sectoral Trade Linkages in Aggregate Fluctuation) and Ana Lleo-Bono (Behavioral, Experimental, and Organizational Economics). She serves as co-investigator for the publicly-funded Centre for Inclusive Trade Policy and has been actively involved in policy discussions, serving on the Trade and Economy Panel of the UK Department for International Trade and providing scientific advice to international institutions. Professor Crowley is a key member of the Janeway Institute for Economics at Cambridge and actively contributes to the Centre for Inclusive Trade Policy. Her research group focuses on empirical microeconomics, particularly examining international trade phenomena through detailed firm-level data analysis. She has established herself as a leading voice in trade policy discussions, with over 100 media appearances including BBC, The New York Times, Financial Times, and The Economist, where she explains complex trade issues to the public and policymakers.
Lisa Beinborn is a Professor for Human-Centered Data Science at the University of Göttingen, leading the Human-Centered Data Science group. Her research bridges natural language processing with cognitive science, focusing on multilingual models and interpretability. PhD in Computer Science (2016), Technische Universität Darmstadt MSc in Computational Linguistics (2010), Saarland University & Bolzano, Italy BSc in Computational Linguistics (2008), Saarland University & Barcelona, Spain Her research explores cognitive plausibility in NLP, analyzing how language models process language differently from humans. Key areas include multilingual model interpretability, semantic drift, eye-tracking, and readability prediction. Recent work examines input representation stability in neural models, cross-lingual transfer of complexity, and aligning language models with human cognitive patterns. Her team has presented findings at EMNLP, CoNLL, ACL, and CoLING. VENI Grant for "Interpretability of Transfer in Multilingual Models" Early Career Partnership by Royal Dutch Academy of Science "Most Interesting Paper" Award at BabyLM Challenge "Best Project Award" by Network Institute She has taught courses like Language as Data and Advanced NLP at University of Göttingen, VU Amsterdam, and TU Darmstadt. Her group collaborates with institutions like Gemeente Amsterdam and NT2 on multilingual text simplification and learner correction.
Dr. Otto Lappi is a Research Fellow at the University of Helsinki's Department of Digital Humanities, Faculty of Arts. His work bridges cognitive science and human performance in complex dynamic tasks, particularly focusing on predictive processing in visuomotor control. PhD in Cognitive Science Title of Docent at University of Helsinki Academy Research Fellow (2020-2025) His research explores: Cognitive mechanisms in high-speed sports and driving Predictive gaze strategies and steering control Flow states in skill acquisition VR and eye-tracking applications in archaeomusicology Neural substrates of domain expertise Recent publications analyze: Flow-state dynamics in visuomotor learning Predictive gaze behavior in automated driving Computational models of attention in distracted driving Chunking mechanisms in high-speed sports performance Scientific contributions include: Academy Research Fellow (Research Council of Finland) Editorial work on high-performance cognition Organizing cognitive science workshops As doctoral supervisor, he has guided: Samuel Tuhkanen (Predictive Gaze and Steering) Jesse Kuokkanen (Mechanistic Computationalism) Ida Maasalo (Driver Behavior) Markus Mattsson (Psychometrics)
Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.
Dr. Jenna Yentes is an Associate Professor in the Department of Kinesiology and Sport Management at Texas A&M University, affiliated with the College of Education and Human Development. Her research focuses on functional resiliency in aging populations, biomechanics of chronic obstructive pulmonary disease (COPD), and nonlinear analysis of human movement. She leads studies on gait stability, respiratory-gait coupling, and exoskeleton-assisted walking. Notable contributions include quantifying locomotor reserve and exploring firefighter performance in protective gear. Education: Ph.D. in Biomechanics (University of Nebraska, 2013), M.S. in Kinesiology (California State University Fullerton, 2006), B.A. in Kinesiology (University of Northern Colorado, 2000). Research Interests: Reserve capacity in older adults' mobility and cognition Biomechanical adaptations in COPD patients Methodological rigor in nonlinear data analysis (e.g., entropy metrics) Firefighter physical performance under protective gear Recent Articles Trends: Focus on entropy-based gait analysis, COPD biomechanics, dual-task interference in aging, and exoskeleton effects on interlimb coordination. Methodological rigor in parameter selection for nonlinear algorithms is a recurring theme. Awards: 2023 Faculty Climate Award (Texas A&M), 2019 Promising Scientist Award (International Society of Posture and Gait Research), and 2019 Chancellor's Commission on the Status of Women Award (University of Nebraska). Advising & Grants: Mentors graduate students (noted in publications) and collaborates with TEEX Fire Academy and multiple Texas A&M research centers including the Huffines Institute for Sports Medicine and the Center for Population Health and Aging. Research supported by institutional and federal grants. Labs/Teams: Active in Texas A&M's Human Movement and Aging Lab, collaborating with interdisciplinary teams in sports medicine, rehabilitation engineering, and pulmonary research.
Rainald Loehner is a Distinguished Professor of Fluid Dynamics at George Mason University's Center for Computational Fluid Dynamics. Since 2003, he has led the Center for Computational Fluid Dynamics at George Mason University. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) for 2023, hosted by Professors Kai-Uwe Bletzinger and Roland Wüchner in the 'Adjoint-Based System Identification of Large-Scale Structures' Focus Group. Loehner received his Diplom Ingenieur (Maschinenbau) degree from the Technical University of Braunschweig, and his PhD and a DSc in civil engineering from the University College of Swansea, Wales. After teaching at Swansea for a year, he worked at the Naval Research Laboratory in Washington, DC, followed by a research professorship at George Washington University. He joined George Mason University as an associate professor and was promoted to full professor in 1995 and distinguished professor in 2004. With over 35 years of experience, Professor Loehner's research spans the complete pipeline of numerical solvers and simulation tools. His expertise includes pre-processing, grid generation, numerical methods, field solvers, parallel computing, adaptive mesh refinement, fluid-structure interaction, shape optimization, system identification, and computational crowd dynamics. His current work focuses on developing advanced field solvers for compressible and incompressible flows, acoustics, electromagnetic wave propagation, heat and mass transfer, structural mechanics, and fluid-structure interaction. Key application areas include blast mitigation, ship hydrodynamics, blood flow, contaminant transport, and pedestrian safety. Loehner's recent research output (2020-2024) shows a strong trend toward digital twin technology and adjoint-based methods for structural analysis and optimization. His publications focus on high-fidelity digital twins for detecting structural weaknesses, risk assessment in engineering systems, and optimization of sensor placement. His work bridges computational mechanics with machine learning approaches, particularly in system identification and inverse problems, demonstrating how computational methods can solve complex real-world engineering challenges. 2020: Ranked #15119 in the Stanford List of Most Influential Scientists of the World; #8 in Aerospace and Aeronautics 2010: Distinguished International Career Award, Argentine Association of Computational Mechanics 2008: Fellow, International Association for Computational Mechanics 2006: Associate Fellow, AIAA 2005: Honorary Professor, University of Wales Swansea 2005: Advisory Professor, Shanghai Jiao Tong University 2004: Distinguished Professor of Fluid Dynamics, George Mason University 1999: Computational Mechanics Achievements Award, Japan Society of Mechanical Engineering 1993: Doctor of Science in Civil Engineering, University College of Swansea 1979-1983: Studienstiftung des Deutschen Volkes (Top 1% of German Students) Professor Loehner has mentored numerous students through his work at George Mason University and has supervised research in computational fluid dynamics, structural mechanics, and related fields. His research has been supported by various grants from government agencies and industry partners, enabling the development of advanced simulation tools applied in aerodynamics, hydrodynamics, shock-structure interaction, and medical applications. His codes and methods have been widely adopted in industry and academia for applications ranging from aircraft and ship design to medical simulations and urban pathogen transmission modeling. Loehner leads the Center for Computational Fluid Dynamics at George Mason University, which focuses on developing cutting-edge computational methods for fluid dynamics and related multiphysics problems. The center works on strategic application areas including blast mitigation, ship hydrodynamics, blood flow simulation, and pedestrian movement modeling. As a TUM-IAS Fellow, he collaborates with the Chair of Computational Modeling and Simulation at TUM on adjoint-based system identification of large-scale structures, bringing together expertise in computational mechanics and digital twin technology to address complex engineering challenges.
Dr. Ahmed F. Abdelghany is the Associate Dean for Research and Professor of Operations Management at the David O'Maley College of Business, Embry-Riddle Aeronautical University, since January 2006. He specializes in commercial airlines, airports, big data cloud computing, business analytics, and operations research models. Prior to his academic career, Dr. Abdelghany worked in enterprise optimization at United Airlines, Chicago. Education: Ph.D. in Civil Engineering (Transportation Systems) from the University of Texas at Austin (2001) Dr. Abdelghany’s research focuses on airline network planning, flight scheduling, simulation of complex transportation systems, and NextGen air traffic management. He has authored two influential books: Modeling Applications in the Airline Industry (Routledge 2010) and Airline Network Planning and Scheduling (Wiley 2018). His publications analyze airline operations, competitive dynamics, and crowd management in transportation facilities. He teaches courses like Airline Management (BA 315) and Airline Operations & Mgmnt (BA 609), and participates in industry short courses. Dr. Abdelghany contributes to research projects such as NextGen air traffic implementation, integrated airport initiatives, and benefit-cost analysis of arrival management systems. His work bridges academic theory with real-world airline and transportation challenges.
Anthony Hornof is a Professor in the Department of Computer Science at the University of Oregon, part of the College of Arts and Sciences. He has been a faculty member since 1999 and was granted tenure in 2005. His research is centered on human-computer interaction, with strong emphases on cognitive modeling, eye tracking, and assistive technology. He leads an active research laboratory and has secured substantial funding from the National Science Foundation and the Office of Naval Research. University: University of Oregon School: College of Arts and Sciences Department: Department of Computer Science Position: Professor Email: hornof@uoregon.edu, hornof@cs.uoregon.edu Office: 356 Deschutes Hall Phone: (541) 346-1372 Education: B.A. in Computer Science, Columbia University, 1988 M.S. in Computer Science and Engineering, University of Michigan, 1996 Ph.D. in Computer Science and Engineering, University of Michigan, 1999 Research Interests: Dr. Hornof's research lies at the intersection of human cognition and computing. He is particularly interested in understanding and modeling the perceptual, cognitive, and motor processes involved in human-computer interaction. His work uses eye tracking both as an evaluation tool for cognitive models and as a real-time input method for creative expression and accessibility. A major focus is assistive technology, especially developing tools like EyeDraw that enable children with severe motor impairments to create art using only eye movements. He also explores eye-controlled musical compositions, bridging technology and artistic expression. His research is grounded in participatory design, involving end-users directly in the development process. Publication Trends: His recent publications demonstrate a consistent focus on modeling human behavior in complex interactive tasks. Key themes include visual search strategies, dual-task performance, cognitive modeling using eye-tracking data, and accessibility. His work spans top venues in HCI (CHI, TOCHI), cognitive science (CogSci, ICCM), and specialized conferences like ETRA and NIME. There is a strong methodological thread involving data calibration, model validation, and the development of predictive tools for interface design. Scientific Awards: Best Paper Award (Top 1%) at CHI 2014 (two papers) Honorable Mention Paper (Top 5%) at CHI 2010 Siegel-Wolf Award for Best Applied Paper at ICCM 2010 Advising and Grants: Dr. Hornof actively seeks to mentor exceptional undergraduate students, graduate students, and postdoctoral researchers in his lab. He emphasizes rigorous and creative scientific research. He has been awarded over $2.9 million in single-investigator research grants from prestigious agencies including the National Science Foundation (NSF) and the Office of Naval Research (ONR). Notably, he served as an NSF Program Director from 2012 to 2014, contributing to funding decisions for approximately $65 million in research. Labs and Teams: He leads the Human-Computer Interaction Laboratory at the University of Oregon, where interdisciplinary research is conducted on cognitive modeling, eye tracking, and assistive technologies. His team has developed software such as VizFix for visualizing eye-tracking data and has ported the Eyegaze system to Macintosh. The lab fosters collaborations with new media artists and musicians, and engages in participatory design with children who have disabilities.
