جست‌وجوی استاد و پژوهشگر

Alexander Waibelمشاهده پروفایل

استاد

• Speech Translation
• Multilingual Dialog Systems
• Machine Translation
• +۷ مورد دیگر

Dr. Alexander Waibel is a Professor of Computer Science at Carnegie Mellon University, Pittsburgh, and at the Karlsruhe Institute of Technology, Germany. He leads the International Center for Advanced Communication Technologies (interACT), a collaborative research center across eight global institutions focused on improving human-human and human-machine communication through multimodal and multilingual technologies. His research focuses on advancing speech and language technologies, particularly in multilingual dialog systems, automatic simultaneous interpretation, and cross-lingual human-robot interaction. His work bridges academic innovation with real-world deployment, especially in humanitarian and disaster relief contexts. The available articles and milestones reflect a strong trend in real-time, deployable speech translation systems, with emphasis on mobile, low-resource, and multimodal environments. His work spans disciplines including artificial intelligence, natural language processing, and human-computer interaction. Several awards for pioneering work on multilingual speech communication and translation technology Dr. Waibel has directed and coordinated numerous large-scale research programs, including EU-Bridge and Babel-Radical, and has founded successful ventures such as Jibbigo. He was a founding chairman of C-STAR, the Consortium for Speech Translation Research, and continues to lead international efforts in language portability and performance. He directs interACT, a multinational research hub that develops cutting-edge communication technologies, and his team has been instrumental in deploying speech translation systems in real-world scenarios.

Anand V. Bodapatiمشاهده پروفایل

دانشیار

• Marketing Analytics
• Consumer Psychology
• Decision Making
• +۱۱ مورد دیگر

Anand V. Bodapati is an Associate Professor of Marketing at the UCLA Anderson School of Management. His academic appointment places him within the Marketing Department, where he contributes to both research and teaching missions of the school. Dr. Bodapati received his Ph.D. in Business from Stanford University and an M.S. in Statistics from the same institution. His undergraduate education includes dual S.B. degrees from the Massachusetts Institute of Technology in Mathematics and Computer Science, and Management Science and Cognitive Psychology. Bodapati's research operates at the intersection of consumer psychology, decision making, statistics, marketing, and computer science. His scholarly work focuses on developing statistical models, methodologies, and decision support systems to address marketing challenges across value creation, value communication, customer acquisition, development, retention, and response assessment. His domain expertise spans advertising, retailing, direct marketing, digital marketing, and social marketing for health and public policy. His publication record demonstrates consistent impact across top marketing journals including the Journal of Marketing Research, Journal of Business and Economics Statistics, Marketing Letters, and Journal of Interactive Marketing. His work shows evolution from foundational statistical modeling approaches to increasingly sophisticated applications in digital marketing and social networks. American Marketing Association's Paul Green Award (twice) American Marketing Association's Lehmann Award Finalist for the O'Dell Award for work on recommendation systems Winner of the Paul Green 'Best Paper' Award for 'Determining influential users in internet social networks' (2011) Winner of the Paul Green 'Best Paper' Award for 'Recommendation systems with purchase data' (2009) Finalist for the Paul Green 'Best Paper' Award for 'A hybrid choice model that uses actual and ordered attribute value information' (2006) At UCLA Anderson, Bodapati teaches courses in Digital Marketing Analytics, Customer Analytics and Customer Insights, and Market Research. He has developed practical applications of his research through collaborations with marketing technology companies including Experian, Convertro, and Bliss Point Media. Bodapati serves on the editorial board of Marketing Science and was a founding member of the editorial board for the Journal of Interactive Marketing.

• Electrical Energy and Power Systems
• Artificial Intelligence
• Electrical Facilities

Dr. Cenk Andiç is a Research Assistant at Istanbul Technical University's Department of Electrical Engineering. His work bridges Electrical Energy and Power Systems with Artificial Intelligence , focusing on optimization techniques for energy management and control systems. PhD in Electrical and Electronics Engineering (Düzce University) Integrated PhD program completion in 2024 Key research areas include: Automatic Voltage Regulation Load Frequency Control Dynamic Economic Load Dispatch Energy Optimization in Transportation Renewable Energy Integration Metaheuristic Optimization Algorithms Recent publications demonstrate a trend toward bio-inspired algorithms like Mountain Gazelle Optimizer , Puma Optimizer , and Sea Horse Optimizer for power system stability and renewable integration challenges. His work spans theoretical control systems to practical implementations in photovoltaic-thermal hybrid systems and data centres. Scientific recognition includes: 2023 Publication Award (Istanbul Technical University) 2024 Best Paper Award 2023 Cenk Andic Prize YÖK 100/2000 Scholarship He actively participates in international collaborations and serves as Principal Investigator for the 2025 project Improving Load Frequency Control in Interconnected Power Systems with Sled Dog Optimization Algorithm . Contact: andic18@itu.edu.tr

Bradley Gregerمشاهده پروفایل

دانشیار

• Neuroscience
• Bioengineering
• Neural Engineering
• +۷ مورد دیگر

Bradley Greger is an Associate Professor at Arizona State University's School of Biological and Health Systems Engineering. His research focuses on translating clinical needs into neural engineering solutions, particularly for sensory, motor, and cognitive disorders. He collaborates with clinicians at Barrow Neurological Institute and Phoenix Children’s Hospital. Key Research Areas: Neural prosthetics, deep brain stimulation, epilepsy, traumatic brain injury, sensory-motor integration. Clinical Collaborations: Barrow Neurological Institute, Phoenix Children’s Hospital. Technologies: Microelectrode arrays, neural signal processing, closed-loop stimulation systems. Research Trends: Recent publications emphasize electrophysiological signal analysis, non-oscillatory traveling wave detection, and optimizing neural stimulation for rehabilitation. His work bridges cortical mapping, neuromodulation, and biomedical device development. 2025 Projects: Automatic vagus nerve stimulation for stroke recovery, cortical information processing analysis. 2024 Studies: DBS sub-harmonic activity as a biomarker, seizure transition dynamics. Historical Work: Microelectrode array development (2016–2020), intrafascicular interfaces (2014). Affiliations: School of Biological and Health Systems Engineering, ASU; School of Medicine and Advanced Medical Engineering.

• Programming Languages
• Functional Programming
• Array Programming
• +۴ مورد دیگر

Troels Henriksen is an Assistant Professor on Tenure Track at the Department of Computer Science (DIKU) at the University of Copenhagen, where he is affiliated with the Programming Languages and Theory of Computation research section. His research focuses on programming languages, particularly functional array programming languages, compiler design, and parallel computing. He maintains an active research profile with numerous publications in top-tier programming language conferences. Dr. Henriksen's research interests center around programming language theory and implementation, with particular emphasis on functional array programming languages. His work bridges theoretical foundations with practical high-performance computing applications. His research spans type systems, compiler optimizations, parallelism, and memory management in the context of array programming languages, contributing to both academic knowledge and practical language implementations. His recent publications reveal a strong focus on array programming language design and implementation. There is a clear trend toward optimizing functional array languages for high-performance computing environments, with significant work on fusion optimizations, parallelism, and memory management. His research often intersects with practical applications in scientific computing and machine learning, particularly through work on automatic differentiation for array languages. Dr. Henriksen is actively involved in the programming languages research community, regularly publishing in prestigious venues such as the ACM SIGPLAN conferences. His collaborations span multiple institutions, indicating an active research network in the programming languages field.

Jan G. Bjålieمشاهده پروفایل

استاد

• Neuroinformatics
• Brain Architecture
• Digital Brain Atlases
• +۷ مورد دیگر

Jan G. Bjålie is a Professor and Vice Dean for Research and Innovation at the Faculty of Medicine, University of Oslo. He has led neuroinformatics development since the 1990s and held leadership roles including Head of the Department of Basic Medical Sciences (2009-2016) and Head of Infrastructure Development for EBRAINS. His research bridges neuroanatomy, computational neuroscience, and digital brain atlases. Doctor of Medicine, University of Oslo (1990) Associate Professor (1991), Professor (1997) in Basic Medical Sciences 2016-: First Leader, International Neuroinformatics Coordinating Facility 2017-: Coordinator, Human Brain Project Neuroinformatics 2019-2021: Head, International Brain Initiative Research Interests span neuroinformatics, brain architecture, and digital brain atlases. His work focuses on: Developing standardized frameworks for brain atlas utilization 3D spatial mapping of neural systems Neuroanatomical connectivity in rodent models Integration of multi-modal neuroscience data International collaboration in brain research Computational modeling of brain structure-activity relationships Recent Publications highlight advancements in automated brain imaging registration, developmental brain mapping, cross-cultural neuroscience perspectives, and genetic influences on brain composition. His neuroinformatics tools (e.g., DeepSlice, Waxholm Space, AtOM) enable global standardization of brain data analysis. Leadership Roles include: Vice Dean for Research and Innovation (2023-2026) Infrastructure Development Lead for EBRAINS (2019-) International Brain Initiative Head (2019-2021) Coordinating EU-funded Human Brain Project neuroinformatics Collaborations extend across major brain projects in the USA, Japan, and Europe through initiatives like the International Brain Initiative and ESFRI roadmap projects.

Bethany A. Teachmanمشاهده پروفایل

استاد

• Clinical Psychology
• Anxiety Disorders
• Cognitive Bias Modification
• +۴ مورد دیگر

Bethany A. Teachman is a Professor of Psychology at the University of Virginia, where she serves as Director of Clinical Training and co-Director of Diversity, Equity and Inclusion for the Department of Psychology. She leads the Program for Anxiety, Cognition and Treatment (PACT) Lab, which focuses on cognitive processes underlying anxiety disorders and emotion dysregulation. Her work bridges clinical psychology with digital health innovation to increase access to evidence-based mental health interventions. Dr. Teachman's research centers on cognitive processes that contribute to anxiety disorders and emotion dysregulation, with particular emphasis on automatic thinking patterns outside conscious control. Her lab investigates how digital technologies like mobile apps and web-based cognitive bias modification programs can shift anxious thinking and overcome barriers to treatment access including cost, transportation, and stigma. Recent work explores dynamic monitoring approaches using mobile sensing to identify anxiety moments and deliver timely interventions. Her publication trends reveal a strong focus on digital mental health innovations for anxiety disorders, particularly cognitive bias modification techniques. She examines emotion regulation diversity, social anxiety mechanisms, and implementation challenges in real-world settings. Her work increasingly integrates cultural considerations, with recent studies on Spanish-language adaptations of interventions and culturally informed engagement strategies for Latinx populations. American Psychological Association Division 12 Society of Clinical Psychology Invited Speaker for the APF Spielberger EMPathy Symposium (2023) Featured Woman of the Month by the Committee on Women in Medicine and Science at UVA (2023) Inaugural Psychology Dept. Excellence in Diversity, Inclusion, and Equity Award (2020) Fellow, American Association for the Advancement of Science (2019) Public Voices Fellowship with the Op-Ed Project (2019) American Psychological Association Presidential Citation (2019) University of Western Australia Institute of Advanced Studies Visiting Fellow (2019) Society for a Science of Clinical Psychology Lawrence H. Cohen Outstanding Mentor Award (2018) Dr. Teachman actively mentors graduate students and has developed innovative approaches to support underrepresented students in psychology doctoral programs. Her grant-funded research focuses on developing and testing digital interventions for anxiety disorders, with recent projects examining culturally adapted interventions and implementation strategies for real-world settings. She serves as Director of the MindTrails public website for cognitive bias modification research. The PACT Lab maintains a strong commitment to anti-racism in psychological science, with explicit goals to recruit and support marginalized scholars, integrate sociocultural considerations into anxiety research, and increase access to digital mental health interventions across diverse communities. The lab actively works to create inclusive spaces for all members to contribute ideas and receive feedback about bias and privilege.

• Educational Technology
• Teaching Psychology
• Learning Psychology
• +۴ مورد دیگر

Pauline Frick is a Research Associate at the Tübingen Center for Digital Education (TüCeDE) since January 2025, specializing in multimedia learning and cognitive processes during educational technology use. She serves as deputy coordinator of the Innovative Educational Technologies research group, focusing on text-picture interaction mechanisms. Doctoral thesis: Reading Illustrated Texts: How Pictures Influence Activation, Integration, and Validation Processes (2025) Master's in Psychology (University of Trier, 2020) Bachelor of Psychology (University of Koblenz-Landau, 2017) Her research examines cross-modal validation processes in illustrated texts, eye-tracking methodologies, and knowledge revision mechanisms. Key contributions include extending the RI-Val model to multimedia contexts and investigating automatic validation in text-picture combinations. Current work explores passive cross-modal reactivation phenomena. Articles demonstrate interdisciplinary focus on cognitive psychology, educational technology, and text comprehension, with recurring themes in eye-tracking studies, media effects, and learning analytics. Collaborates with PD Dr. Anne Schüler and Prof. Dr. Stephan Schwan. Primary affiliation: Tübingen Center for Digital Education Previous affiliation: Leibniz Institute for Knowledge Media (2021-2025) Location: Wilhelmstr. 31, Room 204, Tübingen Contact: +49 7071 29-73617 | pauline.frick@uni-tuebingen.de

Farnaz Adib Yaghmaieمشاهده پروفایل

استادیار

• Control Theory
• Reinforcement Learning
• Machine Learning for Control Systems
• +۵ مورد دیگر

Farnaz Adib Yaghmaie serves as an Assistant Professor and Docent at Linköping University's Department of Electrical Engineering (ISY), specializing in the Automatic Control division. Her research bridges control theory and machine learning, with significant contributions to reinforcement learning applications in complex systems. Education: Ph.D. in Electronic and Electrical Engineering, Nanyang Technological University (NTU), Singapore (2017, Best Thesis Award) Master's Degree in Electrical Engineering (Control), K. N. Toosi University of Technology, Tehran, Iran (2011) Bachelor's Degree in Electrical Engineering (Control), K. N. Toosi University of Technology, Tehran, Iran (2009) Her research focuses on redefining machine learning paradigms for control problems , particularly through reinforcement learning applications. Current projects include Foundation Models and RL for General-Purpose Control (2025-present), Online Control with Adversarial Noise (2022-present), and RL for Partially Observable Systems (2021-present). She explores how foundation models can enable control systems to generalize across diverse tasks and adapt to unseen scenarios, with applications in robotics and autonomous systems. Her work integrates large language models and generative AI into control frameworks, addressing fundamental challenges in sequential decision-making under uncertainty. Scientific Recognition: Best Thesis Award at Nanyang Technological University (2017) CENIIT research grant (2020) Co-founded SEDDIT competence centre (2024) As an educator, she teaches Ph.D.-level courses including Reinforcement Learning (2021-2024) and Advanced Robotics (2025), with her WASP course ranking as the second-best Ph.D. course in the program (3.9/5 evaluation). She actively mentors Ph.D. students including Abbas Pasdar, who is working on Foundation Models for General-Purpose Control. Her research is supported by grants including CENIIT and collaborations through the SEDDIT competence centre. She contributes to the academic community through workshops like the LINK-SIC Core Competence Workshops on Reinforcement Learning and by chairing sessions at major conferences including ECC 2025.

Reza Jafariمشاهده پروفایل

دانشیار

• Data mining and data analytics
• Statistical modeling & forecast
• Machine learning and deep learning for prediction & classification
• +۲ مورد دیگر

Reza Jafari is a Collegiate Associate Professor of Computer Science at the Virginia Tech Innovation Campus. He previously served as faculty and program director of the ECE program at East Coast Polytechnic University and as a professional lecturer in the data science program at George Washington University. His research focuses on data mining, statistical modeling, machine learning/deep learning for prediction/classification, recurrent neural networks, and stability analysis of neural-based systems. Education: Ph.D., Computer and Electrical Engineering, Oklahoma State University (2012) M.S., Applied Mathematics, Oklahoma State University (2011) M.S., Mechatronics, American University of Sharjah (2005) B.S., Engineering, University of Tehran (2001) Research Interests: Jafari's work spans data analytics, statistical forecasting, machine learning, and neural networks. He emphasizes practical applications, such as temporal forecasting using deep learning and stability analysis of control systems. His contributions include advancements in recurrent neural networks and their hardware implementations. Publications: His recent work includes comparative studies of deep learning architectures for temporal forecasting (2025), precision robotics control (2024), and stability analysis of neural network-based controllers (2023). These studies highlight a trend toward integrating traditional methods with modern machine learning techniques. Awards: Innovator of the Year Award (2014) Innovator of the Year Award (2016) Professional Roles: Jafari has served as a peer reviewer for IEEE Transactions , IJCNN , and Automatic Control . His research bridges theoretical foundations and applied systems, with a focus on interdisciplinary solutions in data science and engineering.

Azeez Aileruمشاهده پروفایل

استاد

• Hypertension
• Neuropharmacology
• Metabolic Syndromes
• +۳ مورد دیگر

Dr. Azeez Aileru is a Professor of Neuroscience and Division Director of Basic Sciences at East Carolina University's School of Dental Medicine (SoDM), where he also serves as Interim Chair of the Department of Foundational Sciences. He holds a PhD in Physiology & Biophysics from Howard University and completed postdoctoral training in neuropharmacology at the University of Maryland School of Medicine. His research focuses on neural control of hypertension, metabolic syndromes, and the interplay between the brain and kidney in cardiovascular regulation. He also maintains an adjunct professorship at Wake Forest University School of Medicine. Dr. Aileru's research employs multi-disciplinary approaches, including electrophysiological and molecular techniques to study angiotensin II actions, baroreceptor reflex control, and renal-cardiovascular interactions. His work has been supported by NIH grants (NIMHD, NIGMS, NHLBI) and has led to over 100 publications and conference presentations. Key honors include Fellow of the American Heart Association (2015), Top 20 NC Science & Technology Professors (2013), and multiple teaching/research awards from Winston-Salem State University. He directs NIH training grants, coordinates basic science education at SoDM, and leads initiatives in biomedical research infrastructure. His professional memberships include the American Physiological Society, Society for Neuroscience, and American Heart Association's Hypertension Council. Beyond academia, he enjoys soccer, running, and travel, reflecting a balance between professional rigor and personal passion.

Konstantinos Kyriakopoulosمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Dr. Konstantinos Kyriakopoulos is a Senior Lecturer in Cyber Intelligence, specializing in cybersecurity challenges and AI-driven solutions for converged Information and Operational Technology infrastructures. His work integrates machine learning with cyber-physical systems to enhance decision-making under uncertainty, with a focus on resilient AI algorithms and autonomous defense mechanisms. He has contributed to projects funded by Dstl, Innovate UK, and industry partners, addressing Industry 4.0 security, smart vehicles, electromagnetic attacks, and smart buildings. His research interests include adversarial machine learning, evidence theory for uncertainty modeling in ML, and autonomous cyber defense systems. He has developed novel approaches using reinforcement learning, hidden Markov models, and Bayesian optimization. His work emphasizes creating ML algorithms resistant to adversarial attacks while improving cybersecurity resilience in dynamic environments. Dr. Kyriakopoulos has also explored applications of AI in educational technology, such as automated assessment of spoken English proficiency. His recent publications focus on federated learning security, causal reinforcement learning, and anomaly detection techniques. He has experience in translating research into practical solutions, including business model pitches to investors in Canary Wharf for threat modeling tools. His contributions span over 40 peer-reviewed articles, with notable work in network intrusion detection, attack prediction via HMMs, and wavelet-based compression for network measurements. His expertise bridges academia and industry, addressing both theoretical advancements and real-world implementation challenges.

Gergely Csibraمشاهده پروفایل

استاد

• Developmental Psychology
• Cognitive Development
• Social Cognition
• +۵ مورد دیگر

Gergely Csibra is a Professor and Principal Investigator at the Central European University. He leads the Cognitive Development Center, focusing on cognitive development in human infants, particularly how infants understand actions, social interactions, and communication. His research employs behavioral and neuroimaging methods to explore topics like social cognition, episodic memory, and symbol use. His academic background spans developmental psychology and cognitive science. Current and former PhD students include Mariem Diané, Denis Tatone, and others. Csibra’s work emphasizes methodological diversity, integrating experimental and theoretical approaches. Research interests include infants' understanding of social relationships, mental state attribution, and symbolic representation. His studies investigate how infants interpret communication, learn from others, and form social evaluations. Key themes involve the cognitive foundations of cooperation and communication in early development. Despite prolific publication output (over 100 articles listed), no explicit scientific awards are cited in the provided text. His lab, the Cognitive Development Center, serves as a hub for collaborative research projects and methodological innovation in developmental science.

Narada Dilp Warakagodaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Autonomous Systems
• Computer Vision
• +۳ مورد دیگر

Narada Dilp Warakagoda is an Associate Professor at the University of Oslo, affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on Artificial Intelligence, Autonomous Systems, Computer Vision, Robotics, Machine Learning, and Deep Learning, with notable contributions to underwater acoustics, renewable energy forecasting, and unmanned vehicle control systems. He collaborates extensively with researchers across disciplines to advance applications in environmental monitoring, energy systems, and autonomous technologies. His work integrates advanced machine learning techniques with domain-specific challenges, such as synthetic aperture sonar analysis, spatio-temporal wind forecasting, and reinforcement learning for autonomous navigation. He has published in leading journals and conferences, addressing topics like probabilistic solar irradiance prediction, graph-based wind energy modeling, and deep learning for seabed munitions detection. Warakagoda’s research bridges theoretical advancements with practical implementations, emphasizing applications in robotics, environmental science, and defense technologies. His projects often involve interdisciplinary teams, reflecting his commitment to solving real-world problems through innovative AI-driven solutions.

Lieve Mackenمشاهده پروفایل

دانشیار

• Machine Translation
• Natural Language Processing
• Translation Technology
• +۵ مورد دیگر

Lieve Macken is Associate Professor at Ghent University's Department of Translation, Interpreting and Communication within the Faculty of Arts and Philosophy. With 28 years of experience in language technology, she leads research in machine translation, natural language processing, and translation technology applications. Her work bridges academic research and practical implementation through the Language and Translation Technology Team (LT3). Dr. Macken's research focuses on machine translation evaluation, human-machine interaction in translation workflows, literary translation with MT, and educational applications of translation technology. Her work spans multiple dimensions including quality assessment metrics, eye-tracking studies of translation processes, syntactic difficulty prediction, and specialized applications for asylum seekers and language learners. She has pioneered research on translationese in machine translation output and developed methodologies for assessing translation quality through both process and product metrics. Her recent publications reveal a strong trend toward examining the intersection of large language models with traditional machine translation systems, particularly in literary contexts. She investigates how different translation workflows impact textual characteristics, reading effort, and translator productivity. A significant portion of her recent work addresses social applications of translation technology, particularly through the MaTIAS project serving asylum seekers, and educational applications through projects like WiLMa and IVESS. Guest editor of Special Issue 'Advances in Computer-Aided Translation Technology' in Informatics journal (2019) Extensive experience as reviewer/referee for journals, conferences, and research projects External examiner for seven PhD candidates Dr. Macken has supervised numerous PhD projects including Joke Daems' work on translation robots, Arda Tezcan's research on MT quality estimation, and Bram Vanroy's study of syntactic difficulties in translation. She currently supervises the Horizon Europe Marie Sklodovska Curie Fellowship of Paola Ruffo and multiple PhD projects including Margot Fonteyne's WiLMa project and Jasper Degraeuwe's IVESS project. Her research is funded through diverse channels including Horizon Europe, BOF UGent, FWO, and AMIF grants. She coordinates the postgraduate program Computer-Assisted Language Mediation and co-coordinates the European Master's in Technology for Translation and Interpreting (EM-TTI), teaching courses in Computer-Assisted Translation, Machine Translation, and Dissertation. As coordinator of the Language and Translation Technology Team (LT3), Dr. Macken leads a vibrant research group that regularly presents at major conferences including MT Summit, EAMT, and ICTIC. The team's work spans theoretical research in translation technology and practical implementations addressing real-world challenges in scholarly communication, language education, and social integration.