جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Health Informatics
• Human Computer Interaction
• +۳ مورد دیگر

Prithwi Raj Chakraborty is a Lecturer in the Department of Information Technology at Southern Cross University's Faculty of Science and Engineering. He holds a PhD in affective computing, emotion analysis, and machine learning from Queensland University of Technology (QUT). His academic career began as a Research Fellow at QUT before transitioning to SCU. Active collaborations include partnerships with Sydney University, Griffith University, QLD Health, and QCH. His research focuses on machine learning applications in health informatics, human-computer interaction, and affective computing. Education: PhD in Computer Science from Queensland University of Technology (QUT). Key research areas include cognitive load modeling, wearable sensor analytics, and mHealth solutions like the myPainPal chronic pain management app. He has published impactful work on fall risk detection for the elderly and biometric-based video analysis. Current projects involve optimizing high-performance office environments and generic viewer interest modeling. Professional affiliations include Southern Cross University's Faculty of Science and Engineering, with past experience as a Casual Academic in the School of Business and Tourism. His work bridges computational methods with real-world human behavior analysis in healthcare and workplace settings.

Bo Xiaoمشاهده پروفایل

استادیار

• Construction Informatics
• Computer Vision
• Construction Robotics
• +۳ مورد دیگر

Bo Xiao is an Assistant Professor in Construction Management at Michigan Technological University, with joint appointments in Civil Engineering and the College of Business. His research develops AI and robotics solutions for construction challenges. His work focuses on: Computer vision for construction site monitoring and productivity analysis Natural language processing for automated documentation Blockchain applications in contract management Cybersecurity for construction data Robotics and automation in building processes His publications demonstrate innovations in vision-based monitoring, including nighttime equipment tracking, safety hazard detection, and automated report generation. Recent articles integrate deep learning with construction management applications. He received the 2022 Outstanding Reviewer Award from ASCE and third place in ASCE's Best Data Competition. He leads NSF-funded projects on cyber-training platforms and geodynamic modeling frameworks. He directs the Computing and Robotic Construction (CaRC) Lab, advancing automation in construction through computer vision, robotics, and AI technologies.

Hadi Ali Akbarpourمشاهده پروفایل

استادیار

• Artificial Intelligence
• Computer Vision
• Autonomous Systems
• +۷ مورد دیگر

Hadi Ali Akbarpour is an Assistant Professor in the Department of Computer Science at Saint Louis University's School of Science and Engineering . He earned a Ph.D. in Electrical and Computer Engineering from the University of Coimbra, Portugal (2012). Research focuses on Artificial Intelligence and Computer Vision at the intersection of Autonomous Systems , Remote Sensing , and Robotics Key topics: Deep Learning , 3D Reconstruction , Homography Modeling , and Sensor Fusion Recent publications highlight advancements in: Structure-from-Motion for aerial imagery Deep Learning for novel view generation Georegistration accuracy in wide-area contexts 3D Point Cloud integration Agricultural Monitoring via aerial mosaicking Professional accolades include: Best Paper , Presentation , and Challenge Awards Co-chaired the 51st IEEE Applied Imagery Pattern Recognition Workshop Guest Editor for Sensors Journal special edition (2021) His funded projects include roles as Principal Investigator ( $1.1M ), co-PI ( $8M ), and task leader in $6M SBIR projects .

Mrigank Rochanمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Dr. Mrigank Rochan is an Assistant Professor in the Department of Computer Science at the University of Saskatchewan. Previously, he served as a Senior Researcher in Huawei Noah's Ark Lab's Autonomous Driving Perception team. He holds a Ph.D. and M.Sc. from the University of Manitoba (2020 and 2016) and a B.Tech. from Amrita University, India (2011). His research focuses on Deep Learning applications in Computer Vision, including video highlight detection, domain adaptation, and autonomous systems perception. Notable awards include the 2020 CIPPRS John Barron Doctoral Dissertation Award and the 2024 Google PaliGemma Academic Program Award. Education Ph.D. in Computer Science, University of Manitoba, Canada (2020) M.Sc. in Computer Science, University of Manitoba, Canada (2016) B.Tech. in Computer Science and Engineering, Amrita Vishwa Vidyapeetham University, India (2011) Research Interests His work emphasizes unsupervised learning techniques, domain adaptation for autonomous systems, and multimodal learning (e.g., audio-visual fusion). Recent projects include improving LiDAR-based 3D object detection and developing adaptive crowd counting models. He explores video summarization methods and cross-modal networks for referring segmentation. Grants & Awards NSERC Discovery Grant (2024) Google PaliGemma Academic Program Award (2024) CIPPRS John Barron Doctoral Dissertation Award (2020) Advisees & Teaching He teaches CMPT 489/828: Deep Learning and CMPT 318: Data Analytics . While no named students are listed, his publications suggest active collaboration with graduate researchers like Zahidul Islam, Sujoy Paul, and Linwei Ye. Labs & Collaborations Prior work at Huawei's Autonomous Driving Perception team informs his current focus on real-world perception challenges. His lab at USask likely addresses interdisciplinary AI applications in vision systems.

Alexandros Potamianosمشاهده پروفایل

دانشیار

• Speech Processing
• Natural Language Processing
• Multimodal Dialogue Systems
• +۵ مورد دیگر

Alexandros Potamianos is an Associate Professor in the Division of Signals, Control and Robotics at the School of Electrical and Computer Engineering, National Technical University of Athens. His research focuses on Speech Processing, Natural Language Processing, and Multimodal Systems, with contributions to areas such as sentiment analysis, multimodal fusion, and robotics. Education: Diploma in Electrical and Computer Engineering, NTUA (1990) M.Sc. in Electrical Engineering, Harvard University (1991) Ph.D. in Electrical Engineering, Harvard University (1995) M.Sc. in Business Administration, New York University (2003) Research Interests: Speech and Audio Processing Machine Learning and Multimodal Fusion Robotics and Human-Computer Interaction Natural Language Processing His recent publications (2023-2025) emphasize advancements in domain adaptation for speech recognition, multimodal sentiment analysis, and regularization techniques for deep neural networks. He leads the ICCS laboratory, focusing on cognitive multimodal processing and human-robot interaction. Active in academic service, he coordinates the School's research initiatives and collaborates with international projects like BabyRobot, addressing child-robot interaction for autism spectrum disorder support.

Faisal Qureshiمشاهده پروفایل

استاد

• Computer Vision
• Deep Learning
• Camera Networks
• +۵ مورد دیگر

Dr. Faisal Qureshi is a Professor of Computer Science in the Faculty of Science at Ontario Tech University, where he leads the Visual Computing Lab. He holds a concurrent role as Guest Professor at Mid Sweden University. His research focuses on visual sensor networks, computer vision, and deep learning, with applications in smart camera networks, video surveillance, and hyperspectral imaging. He earned his PhD in Computer Science from the University of Toronto (2007), following degrees from Punjab University (BSc 1993) and Quaid-e-Azam University (MSc Electronics 1995). Key research areas include multicamera tracking, graphics for vision, and neural networks for computer vision. He has pioneered work on self-organizing visual sensor networks and developed computational models for autonomous visual perception. His teaching includes courses on computer graphics, algorithms, and advanced computer vision. He has authored over 100 peer-reviewed publications, including a Best Paper Award at CRV 2017 for optical flow estimation research. Dr. Qureshi’s lab develops innovative solutions for video summarization, optical flow estimation, and activity analysis. His work bridges theoretical computer science with real-world applications in robotics, aerospace, and environmental monitoring. Recent projects explore adversarial machine learning for satellite communications security and hyperspectral image compression using neural representations.

Sodiq Adewoleمشاهده پروفایل

مدرس

• Machine Learning
• Medical Image Analysis
• Healthcare
• +۲ مورد دیگر

Sodiq Adewole is a Lecturer at the School of Data Science, University of Virginia. His research focuses on healthcare AI applications, particularly developing machine learning solutions for medical image analysis and video capsule endoscopy. Dr. Adewole's work bridges artificial intelligence and clinical medicine, specializing in few-shot lesion recognition, abnormality localization in endoscopic videos, and synthetic healthcare data generation. Recent publications demonstrate innovative approaches using graph convolutional networks, metric learning, and Bayesian methods to address challenges in gastrointestinal diagnostics. His research output shows consistent focus on medical video analysis, particularly capsule endoscopy interpretation. Key technical contributions include weakly supervised localization techniques, temporal segmentation methods, and few-shot learning frameworks adapted for medical imaging. No specific advising relationships, awards, or laboratory affiliations are detailed in available materials.

Haytham Fayekمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. Haytham Fayek is a Senior Lecturer in the School of Computing Technologies at RMIT University, leading the Machine Learning and Intelligence Group. He holds roles in the Evolutionary Computing and Machine Learning (ECML) Group, AI Innovation Lab, and the Centre for Industrial AI Research & Innovation (CIARI). His academic journey includes a PhD from RMIT (2019), postdoctoral work at Meta (2018-2020), and industry experience as an electrical engineer. He teaches courses like Foundations of Artificial Intelligence and Programming Fundamentals for Scientists. Research focuses on AI systems that generalize from limited data, emphasizing continual learning, multimodal learning, and applications in satellite systems, structural engineering, and gaming. Key projects include SAR interference mitigation, graph neural networks for automotive design, and cognitive load measurement in video games. He has supervised over 15 PhD/MS students and authored 60+ peer-reviewed publications. Notable achievements include a 2025 Spotlight presentation at ICLR and 2023 Best Paper nominations. Awards include EACL 2024 publication and IEEE Transactions contributions. His work bridges theory (e.g., SWAP-NAS NAS framework) with applications like autonomous satellite systems and aircraft defect tracking through NLP.

• Strategic Communication
• Narrative Analysis
• Public Health Communication
• +۳ مورد دیگر

Steven Corman is a Professor at the Hugh Downs School of Human Communication at Arizona State University (ASU). His research focuses on strategic communication, narrative analysis, and the intersection of social media with public health and political discourse. He has conducted extensive studies on topics such as anti-vaccine subgroups, climate change framing, and the impact of social media deplatforming on political communities. His work integrates computational methods with social science theory, addressing challenges in conflict resolution, organizational behavior, and public policy. Dr. Corman’s research emphasizes real-world applications, such as developing tools like the LookingGlass contextual mapping system for tracking population movements. His expertise spans interdisciplinary domains, including artificial intelligence, narrative detection, and behavioral economics. He has contributed to understanding how narratives shape extremist ideologies and how strategic communication can counter harmful messaging. Key areas of focus include: Public health communication during crises (e.g., pandemic vaccination strategies) Social media analytics and network ripple effects Organizational influences on athlete safety perceptions Computational models for detecting extremist narratives His recent work highlights trends in AI-driven question-answering systems (e.g., UnSeenTimeQA) and innovative approaches to event argument extraction. Dr. Corman’s contributions bridge academic research with practical solutions for global challenges in communication and conflict resolution.

Chun-Kit Nganمشاهده پروفایل

عضو هیئت علمی

• Data Science
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Chun-Kit Ngan is an Associate Teaching Professor in the Data Science department at Worcester Polytechnic Institute (WPI). He holds a Ph.D. in Information Technology from George Mason University, an MBA in Management Information Systems from California State University, Chico, and a BEng in Electronic Engineering from the Hong Kong University of Science and Technology. His research focuses on Decision Guidance and Support Systems (DGSS), encompassing Decision Optimization Models, Machine/Deep Learning, Data Analytics, Natural Language Processing, and Computer Vision. He develops computational algorithms to enhance decision-making in healthcare, environmental engineering, and robotics. Teaching interests include Database Management, Data Mining, AI, and Software Programming. Recent research highlights include applying LLMs for medical data parsing, optimizing satellite imagery analysis, and predicting cancer patient outcomes. His work bridges domain knowledge with data-driven approaches, emphasizing actionable recommendations for stakeholders. Ngan advises projects in AI advancements, including a notable Summer Program highlighted in WPI news. His lab explores interdisciplinary applications of data science across health, engineering, and robotics domains.

• Multimedia Data Analysis
• Video Summarization
• Action/Activity Recognition
• +۴ مورد دیگر

Tanveer Hussain is a Lecturer in Computer Science at Sejong University, Seoul, South Korea. He holds a PhD in Software Convergence from Sejong University (2018–2022) and a BSc in Computer Science from Islamia College Peshawar (2013–2017). His research focuses on multimedia data analysis, video summarization, action/activity recognition, and scene understanding for autonomous driving. He has contributed to resource-constrained programming and fire/smoke detection systems, with over 58 publications in top venues like CVPRW, IEEE Transactions, and Pattern Recognition. Research Interests: Multimodal video analysis Deep learning applications in surveillance Edge computing for IoT Computer vision for autonomous systems Publications Trends: Recent work emphasizes hybrid neural architectures (Transformers/CNNs), anomaly detection in surveillance systems, and robust vision models for adverse environments. His 2024–2025 papers explore medical imaging fusion, weather-agnostic autonomous driving, and multimodal video retrieval frameworks. Professional Activities: IEEE member, editorial board member of Journal of Artificial Intelligence and Systems , and review editor for Frontiers in Artificial Intelligence . Active in open-source contributions via GitHub (https://github.com/tanveer-hussain). Labs/Teams: Associated with the Centre for Intelligent Visual Computing Research at Sejong University.

• Visualization
• Virtual Reality
• Augmented Reality
• +۵ مورد دیگر

Steffen Koch is a Senior Lecturer at the Institute for Visualization and Interactive Systems (VIS), University of Stuttgart. His work bridges visualization, virtual reality, and digital humanities, focusing on tools for analyzing text, spatiotemporal data, and cultural datasets. He collaborates extensively with institutions like the Visualisierungsinstitut der Universität Stuttgart and IEEE. Research Interests : Visualization techniques, virtual/augmented reality applications, digital humanities, text analytics, and human-in-the-loop systems. Key Publications : 2024 ChoreoVis (dance formation analysis), 2024 ViSCitR (hotel review comparison), 2023 Animated Transitions (small-scale visualization design), 2021 Sparse Spherical K-Means (document clustering), and 2016 VA 2 (evaluation methodology). Techniques Developed : Triangulation of eye-tracking and interaction data, pyramid tag maps for document exploration, multifocus navigation in text analysis, and real-time social media monitoring tools. His recent work trends emphasize human-centric visualization , combining machine learning with interactive interfaces for complex tasks in cultural analysis, social media monitoring, and temporal data exploration. While no explicit awards are listed, his prolific output in top venues like IEEE Transactions and Computer Graphics Forum indicates recognition in visualization and digital humanities communities. He frequently collaborates with Thomas Ertl, Johannes Knittel, and Markus John, spanning applications from religious history to cinematic storytelling.

• Mother-tongue education
• Persuasive writing
• Digital literacy
• +۳ مورد دیگر

Sümeyye KONUK is a faculty member and researcher at Marmara University, Institute of Educational Sciences, where she has been serving as a Research Assistant with a Ph.D. since 2017. Previously, she held a similar position at Trakya University Education Faculty from 2012 to 2017. She completed her doctoral studies in 2017 with a thesis titled "An action research on the development of persuasive writing skill," and her master’s thesis focused on "The examination of emphasises and tones in natural speeches and emotion detection." Education: Doctoral Degree – Marmara University, Institute of Educational Sciences (2017) Master’s Degree – Marmara University (2011) Bachelor’s Degree – Marmara University (2009) High School – Graduated 2005 Research Interests: Her research spans a wide range of topics within educational sciences, with a primary focus on mother-tongue education , persuasive writing development , and digital literacy . She has conducted extensive studies on writing strategies , text analysis , and teacher training programs . Her work also delves into virtual classroom engagement , email literacy in academic settings, and student-produced educational content such as short videos in teacher education. Another key area of her research is the comparative analysis of language education , including the structural comparison of Turkish and English in foreign language teaching contexts. She has also explored literacy habits among youth , creative writing in Turkish education , and the use of folklore in language teaching materials . Publication Trends: Her recent publications reflect a strong emphasis on digital pedagogy , writing skill development , and intercultural perspectives on Turkish language education . Articles such as "Effectiveness of student-made short videos in the education of pre-service literacy teachers" and "E-mail Literacy in Higher Education Academic Settings" highlight her commitment to integrating technology into language education. Her earlier work includes foundational studies on persuasive writing rubrics and text summarization strategies for students. Projects and Contributions: She led a project titled "Process-focused book summary writing strategy and evaluation rubric development" between 2015 and 2016. She has also contributed to several book chapters and edited volumes, including "Education Sciences International Research V" and "Action Research in Education" . Conferences and Scholarly Engagement: She has actively participated in numerous international and national conferences, presenting on topics such as visual literacy in Turkish curricula , humor in social media discourse , and comparative text analysis between PISA and Turkish curricula . These engagements underscore her role in shaping contemporary educational discourse in Turkey and beyond.

Amy Pavelمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Accessibility
• Artificial Intelligence
• +۴ مورد دیگر

Amy Pavel is an Assistant Professor in the Department of Computer Science at the University of Texas at Austin. Prior to this role, she was a Postdoctoral Fellow at Carnegie Mellon University and a Research Scientist at Apple. Her research bridges Human-Computer Interaction and Accessibility, focusing on AI-driven systems for efficient and inclusive communication. Education: PhD in Computer Science from UC Berkeley (2019), advised by Björn Hartmann and Maneesh Agrawala. Teaching: Regularly teaches Human-Computer Interaction courses at UT Austin and UC Berkeley. Her work addresses accessibility challenges through systems like Rescribe (audio descriptions), CrossA11y (video accessibility), and GenAssist (image generation). Recent projects explore AI applications for low-vision learners, photosensitivity warnings in VR, and collaborative video editing. Award highlights include 2023 UIST Best Paper 2022 UIST Best Paper 2020 CHI Honorable Mention (twice) 2018 UC Berkeley EECS Outstanding Graduate Student Instructor She advises PhD students like Mina Huh and Yi-Hao Peng, as well as undergraduates and masters students. Her lab collaborates with institutions including Google, Carnegie Mellon, and UC Berkeley.

• Natural Language Processing
• Artificial Intelligence
• Information Extraction
• +۴ مورد دیگر

Assoc. Prof. Pavel Pecina, Ph.D., is a faculty member at the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. His primary academic role as an Associate Professor focuses on Natural Language Processing , Artificial Intelligence , and related areas. Research Interests: Information extraction, information retrieval, machine translation, multimodal data interpretation, optical music recognition Teaching: Courses since 2012-2022 including Natural Language Processing , Information Retrieval , and Statistical Methods in NLP Key Research Projects include GI-Insight (AI for healthcare), RES-Q+ (global stroke care registry), and MEMORISE (heritage digitization). His recent publications (2023-2025) demonstrate expertise across clinical NLP , historical document analysis , and music technology domains. Advising: Supervised 10+ graduate students in areas ranging from semantic search to optical music recognition. Collaborates with international institutions on multilingual translation and medical informatics challenges through initiatives like IWSLT and CLEF eHealth.