جستجوی استاد راهنمای دانشگاهی

Heng Ji is a Professor at the Siebel School of Computing and Data Science , affiliated with the Department of Computer Science , Electrical and Computer Engineering Department , and multiple research labs including the Coordinated Science Laboratory and Carl R. Woese Institute for Genomic Biology at the University of Illinois Urbana-Champaign. She serves as an Amazon Scholar and Founding Director of the Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE) and CapitalOne-Illinois Center on AI Safety and Knowledge Systems (ASKS) . B.A. and M.A. in Computational Linguistics from Tsinghua University M.S. and Ph.D. in Computer Science from New York University Her research bridges Natural Language Processing with Vision-Language Models , Knowledge-Enhanced LLMs , and AI for Science (e.g., chemical language modeling). She leads major multi-institutional projects such as DARPA ECOLE MIRACLE , KAIROS RESIN , and DEFT Tinker Bell , while advising governments (U.S. Air Force Data Analytics Expert Panel) and industry (Amazon, Google, IBM). Her work on multimodal reasoning, agent-based systems, and chemical language models (e.g., mCLM ) has been supported by NSF, DARPA, and corporate partners. Recent publications (2025) focus on LLM agents , vision-language integration , and scientific knowledge acquisition . Awards include NSF CAREER , IEEE Intelligent Systems' AI's 10 to Watch , and multiple Outstanding Paper Awards at ACL/NAACL. She advises students like Chi Han (ACL/NAACL awardee) and post-docs Xiusi Chen and Yuji Zhang , and leads the BLENDER Lab , which develops frameworks like WiNELL (Wikipedia updating) and ProteinZero (protein generation). She has also served as NAACL Secretary and Program Co-Chair for ACL-IJCNLP2022. Outstanding Paper Award at ACL2024 Two Outstanding Paper Awards at NAACL2024 Young Scientist by World Laureates Association (2023-2024) AI's 10 to Watch by IEEE (2013) NSF CAREER (2009) Google/IBM/Bosch Research Awards

• Natural Language Processing
• Data Mining
• Social Science
• +۲ مورد دیگر

Karen Panetta is a Professor at Tufts University School of Engineering with appointments in Electrical and Computer Engineering, Computer Science, Mechanical Engineering, and Academic Services. She currently serves as Dean of Graduate Education for the School of Engineering and holds the title of Distinguished Professor. Ph.D. in Electrical Engineering, Northeastern University M.S. in Electrical Engineering, Northeastern University B.S. in Computer Engineering, Boston University Dr. Panetta's research focuses on developing efficient algorithms for simulation, modeling, and signal and image processing for security and biomedical applications. Her work brings together artificial intelligence, machine learning, and visual sensing systems to create solutions for robot vision and biomedical imaging. She develops algorithms inspired by the human visual system to enable machines to 'see' like humans, with applications in homeland security, biomedicine, facial recognition, and search and rescue operations. Her research has significant humanitarian applications, addressing global challenges facing women and children. Dr. Panetta has received numerous prestigious awards including induction into the National Academy of Engineering (2023), the Presidential Award for Science and Engineering Education and Mentoring (2011), and the IEEE Award for Distinguished Ethical Practices (2013). She is a fellow of multiple prestigious academies including the National Academy of Inventors, European Academy of Sciences and the Arts, and IEEE. Member, National Academy of Engineering (2023) Presidential Award for Science and Engineering Education and Mentoring (2011) IEEE Award for Distinguished Ethical Practices (2013) Fellow, National Academy of Inventors Fellow, European Academy of Sciences and the Arts Fellow, Asia-Pacific Artificial Intelligence Association As an educator and mentor, Dr. Panetta founded the nationally acclaimed Nerd Girls program to promote engineering to young students, particularly women. She previously served as worldwide director for IEEE Women in Engineering and editor-in-chief of the IEEE Women in Engineering magazine. Her approach to graduate education emphasizes the importance of building strong collaborative relationships between faculty and students, with a focus on proactive communication and documentation of research progress. Dr. Panetta's humanitarian research applies engineering solutions to global challenges, including developing technology to help doctors find cancerous tumors, security screeners find concealed weapons, and law enforcement agencies find criminals and missing children. Her work demonstrates a commitment to 'Doing The Right Thing' by addressing issues affecting populations with limited resources or 'voice' in society.

• Signal Processing
• Image Processing
• Simulation Modeling
• +۶ مورد دیگر

Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.

• Human-Robot Interaction
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Lizi Liao is an Assistant Professor at the School of Computing and Information Systems , Singapore Management University (SMU) , specializing in Artificial Intelligence and Conversational AI . Her research bridges Machine Learning , Natural Language Processing , and Multimodal Systems , focusing on proactive dialogue systems, multimodal conversational search, and task-oriented interactions. Education : PhD in Computer Science (2019) from the National University of Singapore (NUS) , advised by Professor Tat-Seng Chua . Research Interests center on principles of human conversational understanding and machine implementation, particularly in proactive conversational agents , multimodal dialogue systems , and target-driven conversation planning . Key applications include emotional support systems , intelligent shopping assistants , and learning companions . Recent Publications (2024-2025) highlight her work on LLM-based proactive dialogue , multimodal emotion recognition , and dynamic graph modeling , often integrating NLP , Multimedia , and Knowledge Graphs . Collaborative projects with her CoAgent Lab team emphasize human-AI interaction and ethical response generation . Scientific Awards : Google South Asia & Southeast Asia Research Award 2023 Lee Kong Chian Fellow Teaching includes Visual Analytics for Business Intelligence (undergraduate) and Text Analytics and Application (graduate). She also serves as Associate Editor for TOIS and TOMM , and organizes tutorials at ACL , SIGIR , and WSDM .

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

Zhen Ming (Jack) Jiang is an Associate Professor and York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems at York University's Department of Electrical Engineering and Computer Science. His research bridges software engineering, artificial intelligence, and computer systems with significant industrial impact. Dr. Jiang earned his Ph.D. from Queen's University's School of Computing and MMath/BMath degrees from the University of Waterloo's David R. Cheriton School of Computer Science. During his doctoral studies, he collaborated with BlackBerry's Performance Engineering team, developing tools now used daily to monitor commercial software systems. His research focuses on engineering rigor for AI-powered applications , software engineering evolution in the Generative AI era , and performance optimization of large-scale systems . Key areas include software performance engineering, mining software repositories, debugging distributed systems, source code analysis, and software visualization. His work combines empirical studies with practical tool development. Recent publications reveal strong trends in applying AI to software engineering challenges, particularly in machine learning systems reliability, blockchain efficiency, and AIOps solutions. His research consistently emphasizes empirical validation using real-world systems and industrial case studies. Scientific recognition includes: York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems NSERC Discovery Accelerator Supplements (DAS), 2020 Best Paper Award at ICST 2016 IEEE Software Best SEIP Paper at ICSE 2015 Ph.D. Research Achievement Award at Queen's University Multiple best paper awards at WCRE, MSR, and ICSE Dr. Jiang actively supervises graduate students and has secured competitive research funding including NSERC grants. His service includes program committee roles for top conferences (ICSE, ASE, ICSME) and editorial work for leading journals (TSE, TOSEM, EMSE). He leads research initiatives focused on foundation model-powered systems, collaborating with industry partners on performance monitoring and debugging solutions for large-scale distributed environments.

• Software Engineering
• Artificial Intelligence
• Computer Systems
• +۶ مورد دیگر

Nikos Aletras is a Professor of Natural Language Processing at the University of Sheffield's School of Computer Science, where he serves as Head of the Natural Language Processing research group and is co-affiliated with the Machine Learning group. His academic journey began with a Bachelor's degree in Computer Science from the University of Crete, followed by a PhD in Natural Language Processing at the University of Sheffield. Prior to his current position, he worked as a research scientist at Amazon (Core ML and Alexa) and as a research associate at UCL's Department of Computer Science. Aletras' research spans multiple domains within AI, with particular emphasis on Natural Language Processing applications across social science, legal contexts, and data science. His work demonstrates a consistent focus on practical implementations of NLP techniques to solve real-world problems, especially in computational social science and legal technology. He has developed innovative text analysis methods that bridge traditional disciplinary boundaries, creating tools applicable across multiple scientific domains. His recent publications reveal a strong trend toward efficient and responsible AI, with significant work on model compression, hallucination mitigation in language models, and ethical considerations in computational social science research. The publications also show deep engagement with multilingual NLP challenges, explainable AI, and applications of NLP to social media analysis and legal contexts. Area Chair Award: Society and NLP (2023) Aletras has secured substantial research funding as both Principal Investigator and Co-Principal Investigator, including grants from EPSRC, ESRC, Leverhulme, EC Horizon 2020, and industrial partners like Amazon. His current projects focus on efficient deployment of large language models, addressing socio-technical limitations of LLMs for medical and social computing, and developing speech and language technologies. He actively supervises PhD students and collaborates with researchers across multiple disciplines. He leads the Natural Language Processing research group at Sheffield, which focuses on advancing NLP methodologies while applying them to diverse domains including computational social science, legal informatics, and healthcare technologies. The group maintains strong industry connections, particularly with technology companies working on language technologies, and collaborates with legal scholars and social scientists on interdisciplinary projects.

• Natural Language Processing
• Machine Learning
• Computational Social Science
• +۲ مورد دیگر

Dr. Andy Nguyen is a Senior Lecturer in Structural Engineering at the University of Southern Queensland, within the School of Engineering. He is an active researcher and educator, specializing in the Structural Health Monitoring (SHM) of critical civil infrastructure such as bridges, buildings, and transport tunnels. Bachelor of Engineering (BEng), NUCE, 1999 Master of Engineering (MEng), NUCE, 2003 Doctor of Philosophy (PhD), Queensland University of Technology (QUT), 2014 Dr. Nguyen's research is at the forefront of integrating advanced technologies into civil engineering. His primary focus is on developing and deploying sophisticated SHM systems that utilize sensors, data analytics, and machine learning to provide real-time insights into the structural integrity of ageing infrastructure. His work aims to enable proactive maintenance, extend the lifespan of structures, and enhance public safety. He has successfully implemented monitoring systems on major bridges and high-rise buildings in Queensland and New South Wales, with systems capable of even detecting distant earthquake events. His research interests span Structural Health Monitoring, Machine Learning for Engineering, Damage Detection, Finite Element Model Updating, Sustainable Building Materials like bamboo, and the application of AI for automated condition assessment of transport infrastructure. The analysis of his recent publications reveals a strong and consistent research trajectory centered on the application of data-driven and AI methods to solve practical problems in civil infrastructure. His work frequently combines signal processing techniques (like Stockwell Transform) with deep learning models for tasks such as crack detection in concrete and pavement. He also conducts significant research on model updating for complex structures like cable-stayed and arch bridges, using vibration data and optimization algorithms. The integration of machine learning for overload classification and the development of cost-effective, automated monitoring systems are key trends in his recent output. Advanced Queensland Fellow (2024-2027) Dr. Nguyen is actively involved in research supervision and collaboration. He is currently supervising several postgraduate students on projects related to AI-powered condition assessment, bamboo as a sustainable building material, and railway track design. He receives research funding from the Queensland Government through his Advanced Queensland Fellowship. His research has direct practical applications, as evidenced by his public engagement, such as writing for The Conversation on safeguarding ageing bridges, and his work with the Australian Network of Structural Health Monitoring. Dr. Nguyen's work embodies the development of a next-generation 'Living' Laboratory for engineering education, where research, teaching, and real-world infrastructure monitoring are integrated. His current projects involve creating smart, automated fault detection systems and advancing 'digital twin'-based monitoring platforms for infrastructure.

• Structural Health Monitoring
• Civil Infrastructure
• Machine Learning for Engineering
• +۷ مورد دیگر

Prof. Dr. Harald Ritz serves as Professor of Practical Computer Science, especially Business Informatics, at the Technical University of Central Hesse (THM) within the Department of Mathematics, Natural Sciences and Computer Science since 2003. He holds leadership roles as Chair of Examination Committees for B.Sc. and M.Sc. Business Information Systems and Spokesperson for the MNI department in the Business Informatics Working Group (AKWI). His educational background includes a Diplom in Business Informatics (Dipl.-Wirtsch.-Inform.) and doctorate (Dr. rer. pol.) from the Technical University of Darmstadt, following professional experience at SAP SI AG and a professorship at Heilbronn University of Applied Sciences. Ritz's research centers on AI-driven digital transformation for data-driven enterprises, with focus on the “Data to Decision” value chain encompassing Framing, Allocation, Analytics, and Preparation phases. His work integrates business intelligence, data warehousing, machine learning, and SAP ecosystems to address challenges in SME digitalization, operational IT management, and educational technology. Current projects emphasize AI applications in higher education, including intelligent tutoring systems and automated feedback mechanisms. Analysis of his 15 most recent publications reveals a consistent trajectory toward applied AI solutions in business contexts, particularly in intelligent chatbots for educational support, financial trading algorithms, and cloud-based data infrastructure. The research demonstrates increasing integration of no-code platforms, real-time analytics, and domain-specific AI applications across logistics, banking, and procurement sectors. No scientific awards were documented in the source materials. Professor Ritz actively supervises academic development through bachelor’s and master’s theses, doctoral research, and collaborative projects. Current initiatives include the “Winfy” AI chatbot (v4.0, 2025), AI-based feedback systems for educational content (Freiraum 2025 grant), the frits intelligent tutoring project with Prof. Kammer, and doctoral research on AI adoption in SMEs. His work bridges theoretical research with practical implementation in SAP environments and cloud platforms. He operates within THM’s MNI department infrastructure, collaborating through the Business Informatics Working Group (AKWI) and contributing to the Digital Classroom communication platform for online education.

• Business Informatics
• Artificial Intelligence
• Business Intelligence
• +۱۰ مورد دیگر

Supriyo Ghosh is a Senior Researcher at Microsoft Research, India. Prior to this role, he held positions at IBM Research AI Lab (2019–2021) and the Institute of Infocomm Research (I2R), A*STAR. He completed his PhD in Information Systems at Singapore Management University (2017) under Prof. Pradeep Varakantham and conducted postdoctoral research at MIT's SMART and LIDS centers (2016–2017). His research focuses on data-driven decision analytics, including algorithmic optimization, reinforcement learning, urban logistics, and network resilience in cyber-physical systems. His work has addressed cloud incident management, proactive decision-making under uncertainty, and applications of large language models (LLMs) in system reliability. Notable contributions include developing automated root-cause analysis frameworks and improving incident response strategies in large-scale cloud environments. He has also explored reinforcement learning applications in healthcare treatment optimization and air traffic control systems. Award-winning research includes the Best Paper Award at ACM SoCC'22 for an empirical study on high-severity cloud service incidents. He actively serves as a PC member for top conferences like AAAI, NeurIPS, and ICML, demonstrating his leadership in advancing AI and optimization fields. His academic background includes a graduate exchange at Carnegie Mellon University (CMU) and collaborations with MIT faculty like Prof. Patrick Jaillet. His work bridges theoretical foundations with real-world applications in transportation, cybersecurity, and enterprise systems.

• Algorithmic Foundations of Mathematical Optimization
• Reinforcement Learning
• Data-centric Learning
• +۵ مورد دیگر

Holly Branigan is a Professor of Psychology of Language and Cognition at the University of Edinburgh, affiliated with the School of Philosophy, Psychology and Language Sciences. Her research focuses on language production, dialogue, child language development, and bilingualism. She holds a Personal Chair and has led numerous grants including projects on conversational alignment in children with autism, language experience effects on development, and syntactic priming in dialogue. Her teaching includes undergraduate courses on Psychology of Language and postgraduate modules on Dialogue. She supervises PhD students in topics like syntactic processing, bilingualism, and interactional linguistics. Branigan has secured over £10M in research funding from bodies like the ESRC and EU, leading projects such as 'How does language experience support language development?' (2018–2023) and 'AThEME: Advancing European Multilingual Experience' (2014–2019). Her work explores how speakers adapt language based on social context, interlocutor traits, and cognitive factors. Key achievements include a 7-year ESRC fellowship (1999–2006), and recognition for contributions to understanding syntactic processing mechanisms. She currently leads research on child language imitation in social contexts and bilingual syntactic representation.

• Language Production
• Dialogue
• Language Development
• +۶ مورد دیگر

Alberto Monge Roffarello is an Assistant Professor (RTDb) at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, where he is a member of the e-Lite Research Group. He teaches courses including Human-AI Interaction, Digital Wellbeing, and Web Applications across multiple degree programs such as Computer Science and Systems Engineering, Computer Engineering, and Film and Media Engineering. His research centers on Human-Computer Interaction, with a strong focus on Digital Wellbeing and End-User Development in the Internet of Things . He investigates how users can personalize smart environments through trigger-action programming and how design patterns in digital interfaces—such as Infinite Scroll and Never-Ending Autoplay—exploit psychological vulnerabilities to capture attention. His work aims to empower users with tools for self-control, meaningful interaction, and habit mitigation. The recent publications highlight a consistent trajectory in digital self-control tools , attention-aware design , and semantic recommendation systems for IoT. His research bridges theoretical analysis with practical tools, including conversational agents (HeyTAP), debugging platforms (EUDebug, My IoT Puzzle), and ontologies (EUPont) to lower the barrier for non-technical users. Scientific contributions include: Systematic review of digital self-control tools (TOCHI 2022) Typology of Attention-Capture Damaging Patterns (CHI 2023) Design frameworks for meaningful interactions (AVI 2022) Behavioral interventions for smartphone habits (TiiS 2021) He actively supervises PhD students—Francesca Russo, Robert Everett Schwartz, and Luca Scibetta—on topics ranging from AI for education to digital wellbeing in high-schools. His work is supported by competitive research projects such as EMPATHY - Empathetic Mobility Platform . He has contributed to multiple national and international collaborations, notably with Santa Clara University (USA). Alberto leads and participates in research initiatives within the e-Lite - Intelligent and Interactive Systems group, focusing on intelligent interfaces, user empowerment, and ethical design. His lab develops tools that translate high-level user intentions into executable IoT rules, promote digital literacy, and support conscious technology use.

• Human-Computer Interaction
• Digital Wellbeing
• End-User Development
• +۷ مورد دیگر

Yuxing Wu is an Assistant Teaching Professor at the College of Information Science and Technology, Pennsylvania State University, specializing in human-computer interaction with a focus on health, family, and child-centered design. Her academic journey spans Indiana University Bloomington and the University of Washington. Her educational background includes: Ph.D. in Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington B.S. in Informatics (Human-Computer Interaction), Information School, University of Washington, Seattle Wu's research investigates family-food interactions to unpack complexities in domestic activities and chronic health challenges, aiming to support family health and developmental goals through design. She employs feminist theory to examine care work in family meals and develops technologies for family wellness, food allergy management, and inclusive online communities. Her work bridges theoretical frameworks with practical design solutions for real-world family dynamics. Her publication record demonstrates a cohesive trajectory in family-centered HCI, evolving from foundational studies on smart speaker interactions to sophisticated investigations of family meal tensions and trans community support. Recent work increasingly addresses health equity through inclusive design methodologies and critical examinations of power dynamics in technology use. Key recognitions include: Luddy PhD Instructor Award Luddy PhD Teaching Fellowship Award Luddy Ph.D. Summer Scholarship Award DigiStars Scholar designation As an educator, Wu teaches Health Informatics, Social Informatics, UX Design, and AI Ethics courses. Her grant portfolio includes the $4,300 Luddy Ph.D. Summer Scholarship for family mealtime research. She actively mentors through conference workshops and curriculum development initiatives focused on computing ethics. Wu's research ecosystem spans the Proactive Health Lab at Indiana University (dissertation work on family technologies), the User Empowerment Lab at UW (smart speaker studies), and the CHiLL Lab (health information management for older adults). Current work expands into AI ethics education and trans-inclusive community design.

• Human-Computer Interaction
• Health Informatics
• Family Informatics
• +۳ مورد دیگر

Alina Maria Dulipovici is an Associate Professor in the Department of Information Technology at HEC Montréal. She also serves as Deputy Director of Training at the Multidisciplinary Institute for Cybersecurity and Cyberresilience (IMC 2 ) and is a member of the Information Systems Research Group (GReSI). Additionally, she works as an associate researcher at the French-speaking Center for the Computerization of Organizations (CEFRIO). Her educational background includes: M. Sc. (Management) from HEC Montréal PhD in Computer Information Systems from Georgia State University Professor Dulipovici's research focuses on cybersecurity and information security, with particular emphasis on employee safety behaviors, organizational culture related to information asset protection, and data protection risk management. Her work also explores knowledge sharing in organizations, ethics and privacy considerations, knowledge management systems, and strategic IT alignment. She employs qualitative research methods to investigate how organizations can better protect their information assets through understanding employee behaviors and organizational culture. Her recent publications demonstrate a strong focus on the intersection of human behavior and cybersecurity. She examines how values shape knowledge sharing practices and how age affects innovation with IT. Her work bridges theoretical frameworks with practical applications in organizational settings, particularly in the Canadian context where privacy laws like Quebec's Law 25 are shaping organizational practices. Professor Dulipovici actively supervises graduate students, having directed 3 master's theses and 17 supervised project management activities in the past five years. Her students' work covers cybersecurity governance, security posture visualization, privacy-enhancing technologies, conversion funnel development, and various aspects of cybersecurity implementation in organizational contexts. She is currently involved in a major cybersecurity research project called the "Insider Threat Alliance," which received nearly $5.4 million in funding from the Natural Sciences and Engineering Research Council of Canada and MITACS. Through the IMC 2 , she collaborates with research teams from Université de Montréal and Polytechnique Montréal on developing the Insider Threat Sentinel tool, which combines proactive access control with reactive malicious behavior detection using AI techniques. Her contribution focuses on the economic and sociotechnical dimensions of insider threat management.

• Cybersecurity / Information Security
• Employee safety behaviors
• Organizational culture favorable to the protection of information assets
• +۶ مورد دیگر

Ilhan Aslan is a researcher affiliated with the University of Augsburg and DFKI GmbH , focusing on Human-Computer Interaction , Machine Learning , and Affective Computing . His work explores emotional speech interfaces , tangible interaction , and playful multimodal systems . Research Themes : Speech emotion recognition, proxemic-aware interaction, AI ethics, and user experience design. Recent Contributions : 2025 studies on proactive AI agents , voice emotion conversion , and solo role-playing AI ; 2024 work on audio enhancement and emotional trajectory modeling . His publications with Elisabeth André , Timothy Merritt , and Niels van Berkel demonstrate cross-institutional collaboration. Current projects emphasize sustainable creativity support , haptic feedback systems , and bias detection in speech AI .

• Human-Computer Interaction
• Machine Learning
• Affective Computing
• +۴ مورد دیگر

Dirk K.J. Heylen serves as Full Professor at the Digital Society Institute within the University of Twente, holding a dual appointment in the Human Media Interaction department. His academic profile demonstrates extensive engagement in artificial intelligence research with particular focus on human-centered applications. Research interests span Artificial Intelligence, Autonomous Agent Systems, Natural Language Processing , and Knowledge Representation , with significant applications in healthcare, well-being, and social robotics. His work bridges technical AI development with societal context considerations, particularly examining human-AI interaction patterns in daily life scenarios. Recent publications reveal strong emphasis on conversational agents, virtual human interaction, and predictive health technologies. Notable scientific recognition includes the Best Student Paper award at the 22nd ACM International Conference on Multimodal Interaction (2018). His research activities demonstrate consistent output across conferences, journals, and datasets, with particular growth in healthcare applications since 2020. Professional activities include editorial work for major conferences (ICMI 2020), organization of specialized events (Narrative Matters 2018), and hosting academic visitors. His supervised work encompasses 22 research projects and multiple datasets supporting human-AI interaction studies.

• Artificial Intelligence
• Autonomous Agent Systems
• Natural Language Processing
• +۶ مورد دیگر