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Joseph J. SarverView profile

Teaching Professor

• Biomechanics
• Orthopaedic Biomechanics
• Tendon Mechanics
• +5 more

Dr. Joseph J. Sarver serves as a Teaching Professor at Drexel University's School of Biomedical Engineering, Science & Health Systems since 2012, maintaining concurrent research affiliation with the University of Pennsylvania's McKay Orthopaedic Laboratory since 2001 where he progressed from post-doctoral fellow to Senior Research Investigator under Dr. Louis J. Soslowsky. His educational foundation includes: B.S. in Mechanical Engineering from Purdue University (1994) M.S. in Biomedical Engineering from Drexel University (1997) Ph.D. in Biomedical Engineering from Drexel University (2001) Postdoctoral training in Orthopaedic Surgery at the University of Pennsylvania (2003) Dr. Sarver's research program centers on orthopaedic biomechanics with emphasis on tendon mechanics, rotator cuff repair optimization, and neuromuscular adaptation to injury. His methodology integrates animal models (primarily rat and mouse), mechanical testing apparatus development, and advanced imaging techniques including MRI texture tracking and double-quantum-filtered NMR. Key investigations include the REMO project on remobilization after rotator cuff repair and studies of intervertebral disc degeneration. Analysis of his 15 most recent publications reveals consistent focus on shoulder biomechanics (2008-2011) examining rotator cuff repair outcomes, immobilization effects, and functional recovery in rat models, building upon earlier foundational work (1999-2007) in spinal biomechanics, neuromuscular fatigue quantification, and spinal cord injury rehabilitation. Dr. Sarver has actively mentored undergraduate students, Drexel co-op participants, and senior design teams, particularly in developing mechanical testing equipment like the planar biaxial system for soft tissue analysis. His collaborative research network extends to ultrasound experts (Dr. Seghal at Penn), NMR specialists (Dr. Wehrli), and muscle adaptation researchers (Sam Ward at UCSD). He maintains primary laboratory affiliation with the McKay Orthopaedic Laboratory at Penn while directing biomechanics instruction at Drexel, contributing to both institutions' research infrastructure through equipment development and collaborative projects spanning orthopaedic surgery, rehabilitation science, and biomedical engineering.

• Electrical Engineering
• Diagnostics of Electrical Machines
• Induction Motor Diagnostics
• +4 more

Jarosław Tulicki serves as a Lecturer at the Department of Electrical Engineering within the Faculty of Electrical and Computer Engineering at Cracow University of Technology. Having recently completed his doctorate in 2024, he specializes in electrical machine diagnostics with emphasis on induction motor fault detection through non-invasive techniques. His research focuses on rotor cage damage diagnostics using stator current spectrum analysis and axial flux signal processing under steady-state conditions. Key contributions include methodologies for detecting rotor asymmetries in double-cage induction motors and developing diagnostic systems for industrial applications like tramway drive systems. His work bridges theoretical electromagnetic modeling with practical condition monitoring solutions. Tulicki's publication record demonstrates consistent research progression from fundamental electromagnetic analysis (2015-2018) toward applied diagnostic systems (2019-2024). The most recent works emphasize double-cage motor diagnostics and real-time implementation, showing strong technical depth in signal processing and system integration. His research consistently targets industrial applicability while maintaining rigorous theoretical foundations. With 27 publications yielding an h-index of 4 in both Scopus and Web of Science, his scholarly impact is reflected in a total ministerial score of 497 within the Polish academic evaluation system. His ORCID profile (0000-0003-1720-3448) documents this active research trajectory.

• formal semantics
• syntax-semantics interface
• semantic variation
• +5 more

Dorota Klimek-Jankowska serves as an Assistant Professor in the Department of English and Comparative Linguistics at Wrocław University, where she has established herself as a prominent researcher in formal linguistics with specialization in Polish language structures. Her academic career spans over fifteen years since completing her PhD in 2008, during which she has developed a robust research program combining theoretical linguistics with experimental methodologies. Her educational background includes: MA in English Philology, Institute of English Studies, Wrocław University (2003) MA in Linguistics CUM LAUDE, Utrecht Institute of Linguistics, Netherlands (2006) PhD in Linguistics, Wrocław University (2008) Dr. Klimek-Jankowska's research focuses on the intricate relationship between formal syntax and semantics, particularly examining how grammatical structures interface with cognitive processing mechanisms. She has pioneered experimental approaches to studying Polish aspectual systems, semantic variation, and the syntax-semantics interface using neurolinguistic methods including ERP studies and eye-tracking experiments. Her work bridges theoretical linguistics with cognitive science, demonstrating how abstract grammatical categories manifest in real-time language processing. Analysis of her publication record reveals consistent focus on Polish linguistic structures through experimental methodologies, with particular emphasis on aspectual phenomena, temporal interpretation, and category boundaries. Her research trajectory shows increasing sophistication in methodology, moving from theoretical analyses to sophisticated experimental designs incorporating multiple psycholinguistic techniques. Her scientific recognition includes: Ministry of Science scholarships for outstanding young researchers (2015-2017) Rector's award for organizational achievements (2015) National Science Center Opus 5 program stipend (2014-2017) Multiple stipends from the Foundation for Polish Science (2007-2013) Huygens Scholarship Program at Utrecht Institute of Linguistics (2004) Dr. Klimek-Jankowska has secured substantial research funding through competitive grants and actively participates in international collaborations, particularly with researchers at Utrecht University. Her training in advanced experimental methodologies including eye-tracking, ERP recording, and statistical analysis has positioned her at the forefront of experimental linguistics in Poland. She has contributed significantly to academic service through conference organization and participation in national initiatives for social sciences and humanities development. Her international research collaborations include work with Humboldt University corpus linguists on collocation strength measures and with Utrecht University researchers on definiteness translation. She maintains active participation in major linguistic conferences worldwide and has contributed to significant edited volumes in her field.

Vijay ChidambaramView profile

Associate Professor

• Computer Systems
• Storage Systems
• Distributed Systems
• +5 more

Vijay Chidambaram is an Associate Professor in the Department of Computer Science at The University of Texas at Austin, where he leads the UT Systems and Storage Lab. His research focuses on building next-generation storage systems with higher performance and stronger reliability, developing both storage systems and testing frameworks to ensure rigorous development. He is also part of the UT Data Systems group and the LASR research group at UT Austin. Ph.D. in Computer Science, University of Wisconsin–Madison M.S. in Computer Science, University of Wisconsin–Madison B.E. in Computer Science, College of Engineering, Guindy Chidambaram's research interests center on computer systems, particularly storage systems, distributed systems, and databases. His work addresses critical challenges in crash consistency, persistent memory file systems, and storage reliability. His group has made significant contributions to understanding and solving crash-consistency bugs, developing innovative testing frameworks, and building open-source storage systems that impact both academia and industry. They focus on innovation at both the data-structure level and systems level, with recent work exploring CXL-based database architectures and verification tools for storage systems. His research group has produced numerous influential publications in top-tier conferences including OSDI, FAST, ATC, and EuroSys, with several best paper awards. Their work spans from theoretical foundations of crash consistency to practical implementations of high-performance storage systems, with a consistent emphasis on open-sourcing their software to maximize impact. Distinguished Artifact Award at OSDI 2025 Best Paper Award at EuroSys 2023 Best Paper Award at ATC 2018 Best Paper Award at FAST 2018 NSF CAREER Award 2018 Microsoft Research Graduate Fellowship 2014 ACM SIGOPS Dennis M. Ritchie Dissertation Award 2016 Chidambaram actively mentors PhD students, with current advisees including Minh Phan, Talia Chen, Nitin Mathai, and Shurang Wu. His former students have gone on to positions at Microsoft Research, HP Labs, Apple, and academia. He has secured significant research funding, including NSF grants and industry collaborations. Beyond research, he serves on program committees for major systems conferences and co-founded the open-access Journal of Systems Research. He also teaches courses including Virtualization, Advanced Operating Systems, and Distributed Systems, and authored the CS Assistant Professor Handbook, a guide for new computer science faculty. His UT Systems and Storage Lab maintains close industry collaborations while pursuing fundamental research questions in storage systems. The lab is known for its rigorous approach to systems research, combining theoretical insights with practical implementations, and has developed multiple open-source systems that have influenced both academic research and industry practice.

• Educational Technology
• Learning Support Systems
• Generative AI
• +3 more

Shintaro Uchiyama serves as a Research Associate at Waseda University's Faculty of Human Sciences since April 2025. Previously, he held positions as a Part-time Lecturer at National Institute of Technology, Toyota College (2021-2024) and teaching assistant roles at Toyohashi University of Technology. His academic foundation includes Bachelor's and Master's degrees in Engineering from Toyohashi University of Technology, completed in 2020 and 2022 respectively. Bachelor of Engineering (2020), Toyohashi University of Technology Master of Engineering (2022), Toyohashi University of Technology Graduate Coursework Completed (2025), Toyohashi University of Technology Uchiyama's research focuses on educational technology innovation, particularly in generative AI applications for learning support systems and flipped classroom methodologies. His work bridges speech recognition technologies with educational applications, developing systems that enhance student engagement through immediate feedback mechanisms and annotation tools. Current projects include STEAM learning environments using XR technology and learning behavior analysis systems for correspondence high schools. His publication record demonstrates consistent output in educational technology conferences and journals, with recent work emphasizing large language models for educational feedback and mixed reality applications. Research trends show progression from foundational VR/Hololens educational tools (2018) toward sophisticated AI-driven learning systems (2023-2025), maintaining focus on practical classroom implementation. 特別研究報告会最優秀特別研究発表賞 (Toyohashi University, 2022) 優秀講演賞 (ATS2020 Symposium, 2021) Rinnai Scholarship Foundation Award (2022) Japan Student Services Organization Half-Repayment Exemption (2022) Multiple student support awards from Toyohashi University (2021-2022) Uchiyama actively contributes to educational research through JSPS-funded projects including 'Establishing STEAM learning environments utilizing generative AI' (2024-2029) and 'Learning behavior analysis for correspondence high schools' (2025-2027). His committee work includes publicity chair roles for International Conferences on Advanced Informatics. Professional memberships span the Japanese Society for Information and Systems in Education and Japan Society for Educational Technology since 2019-2020.

• Site Investigation
• Experimental Geomechanics
• Machine Learning
• +5 more

Nima Baghbani is an Associate Lecturer in Engineering at La Trobe University, Melbourne, Australia, with concurrent appointments as a senior geotechnical engineer/manager in GHERG at Federation University Australia. His academic career spans multiple institutions including La Trobe University (2020-present), Federation University (2021-present), and Deakin University (2020-present), where he serves in teaching, research, and supervisory roles. His educational background includes: PhD in Geotechnical Engineering from Deakin University (2020-2023) Master's degree in Geotechnical Engineering from Amirkabir University of Technology, Tehran (2014-2016) Bachelor's degree in Civil Engineering from Ferdowsi University of Mashhad (2009-2013) Baghbani's research operates at the intersection of geotechnical engineering and artificial intelligence, with principal interests in site investigation, experimental geomechanics, machine learning applications in soil dynamics, and development of intelligent geotechnical sensors. His interdisciplinary approach combines traditional geotechnical methods with cutting-edge computational techniques to solve complex engineering problems involving soil behavior prediction, desiccation crack analysis, and sustainable material applications. His publication record demonstrates a clear trend toward integrating AI methodologies with conventional geotechnical engineering challenges. The most recent articles reveal growing emphasis on recycled materials in construction, hybrid prediction models for soil properties, and vibration mitigation in infrastructure projects. His work frequently employs machine learning techniques like XGBoost, genetic programming, and grey-box models to address geotechnical challenges with improved accuracy and efficiency. Baghbani teaches across multiple institutions with courses including: Geotechnical Design at La Trobe University Soil Mechanics at La Trobe University Geotechnical laboratory at both La Trobe and Federation Universities Field investigation at Deakin University With over 13 years of professional engineering experience complementing his academic work, Baghbani has contributed to more than 50 international and national papers. His dual role as Senior Geotechnical Engineer at FMG Engineering since 2023 provides practical context that enriches both his research and teaching, creating a valuable bridge between theoretical knowledge and real-world engineering applications.

Mauro PezzèView profile

Professor

• Software Engineering
• Software Testing and Analysis
• Self Healing Systems
• +1 more

Mauro Pezzè is a Professor of Software Engineering at Università della Svizzera italiana (USI), Constructor Institute of Technology (CIT), and Università degli Studi di Milano Bicocca. He coordinates the STAR - Software Testing and Analysis research Lab, a joint research team at USI and CIT. Pezzè has held significant editorial roles including Editor-in-Chief of ACM TOSEM and service on the editorial boards of IEEE TSE and STVR. He has chaired major conferences including ICSE 2012 and ISSTA 2006/2013. Professor Pezzè's research focuses on Software Engineering, particularly Software Testing and Analysis, Self Healing Systems, and Self Adaptive Systems. His work bridges theoretical foundations with practical applications, emphasizing automated testing techniques, program analysis, and self-adaptive software architectures. He is known for his influential book 'Software Testing and Analysis: Process, Principles and Techniques' which has shaped the field. Analysis of Pezzè's recent publications reveals a consistent focus on software testing and failure prediction, with increasing integration of AI and machine learning techniques. His work spans traditional program analysis, GUI testing, distributed systems reliability, and the application of large language models to testing challenges. The research demonstrates a trajectory from foundational testing techniques toward more complex, adaptive, and AI-enhanced approaches to software quality assurance. Professor Pezzè has received significant recognition through leadership roles in the software engineering community: Editor-in-Chief of ACM TOSEM (Transactions on Software Engineering and Methodology) Member of the editorial board of IEEE TSE (Transactions on Software Engineering) Member of the editorial board of STVR (International Journal of Software Testing, Analysis and Verification) Program Chair of ICSE (International Conference on Software Engineering) 2012 Program and General Chair of ISSTA (International Symposium on Software Testing and Analysis) 2006 and 2013 As coordinator of the STAR research lab, Professor Pezzè leads a collaborative effort between USI and Constructor Institute focused on advancing software testing and analysis methodologies. The lab's work spans theoretical foundations and practical applications, with particular emphasis on automated testing techniques, program analysis, and self-healing systems. This research environment fosters innovation in software quality assurance and trains the next generation of software engineering researchers.

Ricardo DuniaView profile

Lecturer

• Process Control
• Fault Detection
• Data Analytics
• +3 more

Ricardo Dunia serves as a Lecturer in the Department of Chemical Engineering at the University of Texas at Austin, focusing on process control education and industrial applications. His work bridges theoretical frameworks with practical operational solutions for chemical processes. Education: Ph.D. in Chemical Engineering, University of Texas, Austin (1997) M.S. in Chemical Engineering, University of Maryland, College Park (1992) His research centers on predictive algorithms for dynamic systems, abnormal condition identification, training system design for operational safety, and data analytics for supply-demand optimization. Key methodologies include principal component analysis, fault detection systems, and real-time predictive control frameworks that enhance industrial productivity and reliability in competitive global markets. Publication trends from 1996-2008 reveal an evolution from foundational statistical process control techniques toward integrated educational frameworks and real-time nonlinear control applications. His work consistently emphasizes practical implementation of data-driven solutions for chemical process industries, with growing emphasis on educational tools in later publications. Dunia teaches core courses including ChE 360: Process Control and ChE 356: Optimization: Theory and Practice, directly incorporating his research on sensor fault identification and predictive control systems into curriculum development.

Dong Jae KimView profile

Professor

• Software Engineering
• Software Testing
• Mining Software Repositories

Dong Jae Kim is a Professor at DePaul University in the United States, actively contributing to the software engineering community through research and conference service. He serves on program committees for major venues including ICSE, ESEC/FSE, ASE, and CASCON, and maintains a professional presence via his personal website and Twitter account. His academic profile reflects deep engagement with empirical software engineering research and emerging AI methodologies. Kim's research centers on Software Engineering with specialized focus on Software Testing and Mining Software Repositories. He investigates critical challenges in test code maintenance, particularly how object-oriented features like inheritance and interfaces impact test quality. His work increasingly integrates artificial intelligence, especially large language models, for tasks including log analysis, fault localization, and code generation. This dual emphasis on foundational testing principles and cutting-edge AI applications defines his scholarly identity. Analysis of Kim's publication trajectory from 2020-2026 reveals a strategic evolution toward AI-enhanced software engineering. Early work established empirical foundations in test smell evolution, while recent publications demonstrate sophisticated application of large language models to log parsing (LibreLog, LLMParser), fault localization (Order Matters!), and code generation (SOEN-101). The consistent thread through his research is rigorous empirical validation of practical tools, with growing emphasis on benchmarking open-source AI solutions for real-world software engineering problems. No scientific awards, fellowships, or medals were documented in available sources. Similarly, no information was found regarding graduate students advised by Kim or details of research grants secured. Laboratory affiliations, research teams, and collaborative projects remain unspecified in current records. Future research directions appear oriented toward refining AI-driven approaches for software maintenance tasks, particularly in log analysis and test code optimization, though explicit future work statements were not identified.

Jared KutzinView profile

Adjunct Associate Professor

• simulation-based education
• educational technologies
• behavior change in healthcare
• +6 more

Jared Kutzin serves as an Adjunct Associate Professor at the MGH Institute of Health Professions and holds a dual appointment as Associate Professor of Emergency Medicine and Medical Education at the Icahn School of Medicine at Mount Sinai, where he concurrently serves as Senior Director of the Simulation, Teaching and Research Center at The Mount Sinai Hospital. His educational foundation includes: Doctor of Philosophy, Nursing (candidate), Texas Woman’s University MS in Medical Education Leadership, University of New England - College of Medicine DNP in Public Health Nurse Leadership, UMASS Amherst MPH in Health Policy and Management, Boston University BS in Nursing, Columbia University BS in Community Health Education, Hofstra University Dr. Kutzin's research centers on leveraging simulation-based education and educational technologies to drive behavior change that enhances healthcare quality and safety. His specialized focus spans emergency medicine, nursing practice, pre-hospital systems, and disaster response protocols. Current investigations examine virtual reality educational environments and the critical relationship between built clinical environments and clinician performance. His methodological approach integrates human factors engineering with educational theory to optimize simulation design and implementation. Analysis of his 15 most recent publications reveals a dominant trajectory toward technology-enhanced simulation methodologies, particularly virtual reality and telemedicine applications, with increasing emphasis on global health contexts and economic evaluations of simulation programs. The work consistently bridges clinical practice with educational innovation across emergency medicine, nursing, and prehospital care domains. His distinguished recognition includes: Society for Simulation in Healthcare Educator of the Year (2022) MedEdPORTAL Associate Editor of the Year (2020) Fellow of the Institute for Medical Education Mount Sinai Health System (2020) Fellow of the New York Academy of Medicine (2018) Inaugural Fellow of the Society for Simulation in Healthcare Academy (2017) Long Island Business News 40 under 40 (2015) As an educator and leader, Dr. Kutzin has developed and implemented simulation curricula across multiple clinical settings, significantly impacting staff competency development and patient safety initiatives. His grant-funded work focuses on simulation technology integration and healthcare systems improvement, though specific funding sources aren't detailed in available materials. His leadership extends to the REBEL Lab and Center of Excellence in Healthcare Simulation Research, where he drives innovation in simulation operations, educator development, and cross-disciplinary collaboration to transform clinical education and practice.

Siobahn Day GradyView profile

Associate Professor

• Artificial Intelligence
• Human-Computer Interaction
• Algorithmic Bias
• +7 more

Dr. Siobahn Day Grady serves as Associate Professor in the School of Library and Information Sciences at North Carolina Central University (NCCU) and founding director of the Institute for Artificial Intelligence and Emerging Research (IAIER). As the first woman to earn a Ph.D. in Computer Science from North Carolina Agricultural and Technical State University (2018), she has established herself as a national leader in AI ethics, digital equity, and STEM education. Her leadership extends to the UNC AI Faculty Learning Community, Governor's AI Council, and multiple advisory boards including the Winston-Salem State University Foundation and National Girls Collaborative Project. Dr. Grady's research centers on human-centered artificial intelligence, with particular focus on algorithmic bias mitigation, healthcare applications, and misinformation combat strategies. Her work bridges computer science, information systems, and social justice through projects like the Digital Empowerment Leadership Program (DELP) and STEM-It-Yourself initiative. She has secured over $3 million in grants from organizations including Google.org, NIH, and NSF to advance AI literacy and workforce development at HBCUs. Her publication portfolio demonstrates consistent impact across AI governance, health equity, and inclusive education. Recent articles examine operationalizing fairness in health systems, expanding AI literacy at HBCUs, and digital empowerment for marginalized communities. The research trajectory reveals increasing emphasis on practical implementation frameworks that connect technical innovation with social impact, particularly for underrepresented populations. 2025 Graduate College Hall of Fame Inaugural Inductee (NC A&T) AAAS IF/THEN Ambassador with permanent statue at NC A&T College of Engineering 2023 Distinguished Alumni Award from Winston-Salem State University Multiple Grace Hopper Celebration Faculty Scholarships (2019-2024) 2022 Trailblazer & National STEM Advocate recognition Caroline L. Lattimore Award for Excellence in Education (2021) As faculty fellow for e-Learning Excellence at NCCU, Dr. Grady has secured six merit-based STEM scholarships across all UNC System HBCUs. She leads the Laboratory for Artificial Intelligence and Emerging Research (LAIER), which has established NCCU as a national leader in responsible AI through initiatives like the AI Emerging Scholars Program and IAIER Seed Grant program. Her leadership philosophy integrates innovation, service, and strategic collaboration to build systems preparing learners for a technologically empowered future.

Jamal A. Al-KhatibView profile

Professor

• Ethical and social issues in marketing
• Cross-cultural consumer behavior
• Marketing education

Jamal A. Al-Khatib serves as Professor of Marketing and Director of the Small Business Institute at the University of St. Thomas' Opus College of Business. With over two decades of academic experience since joining in 2002, he maintains active research and teaching roles while leading student consulting initiatives for local businesses. Education: PhD in Marketing, University of Mississippi MBA in International Business, Ball State University BS in Business Administration, King Abdul Aziz University Research Focus: His scholarly work critically examines ethical dilemmas in global marketing practices, cross-cultural consumer behavior patterns (particularly between Middle Eastern, Latin American, and Western contexts), and innovative approaches to marketing education. Publications in top journals like Journal of Business Ethics and Journal of International Marketing demonstrate his interdisciplinary approach bridging marketing theory with cultural anthropology and business ethics. Publication Trends: Recent research (2016-2017) reveals consistent focus on sales-marketing interface dynamics, cross-national ethical comparisons, and country branding effects. His work increasingly integrates psychological frameworks like temporal construal theory with traditional marketing concepts, reflecting evolving methodological sophistication in studying global consumer behavior. Professional Engagement: As Director of the Small Business Institute, he oversees student consulting teams providing pro-bono marketing solutions to local enterprises. His industry background includes marketing communications leadership at Saudi Arabia's National Commercial Bank, creating valuable practitioner perspective in both research and classroom instruction.

• Cardiology
• Heart Failure
• Cardiac Amyloidosis
• +3 more

Finn Gustafsson serves as an Affiliate Professor at the Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, specializing in Internal Medicine with a Cardiology focus. His clinical work is based at Blegdamsvej 3 in Copenhagen, Denmark, where he maintains an active research profile with 433 publications to date. His research interests center on cardiac amyloidosis, heart failure management, and infectious complications in transplant recipients. Current projects investigate the obesity paradox in heart failure, thoracentesis efficacy in acute decompensation, and aspirin therapy optimization post-LVAD implantation. His work frequently addresses diagnostic challenges in coronary artery disease among amyloidosis patients and phenotypic characterization of asymptomatic cardiac infiltration. Analysis of his recent publications reveals strong emphasis on Danish population studies, multicenter collaborations across Europe, and clinical trials addressing therapeutic disparities in advanced heart failure. His research output demonstrates consistent focus on translational cardiology with immediate clinical applications. Professor Gustafsson actively collaborates with international research teams, as evidenced by his co-authorship on studies involving institutions across 15 countries. His work has generated significant attention with coverage in 9 news outlets, 114 social media posts, and 126 academic reader engagements in the past year. He maintains clinical practice through the Copenhagen University Hospital system (Region Hovedstaden), where his email finn.gustafsson@regionh.dk serves as primary professional contact. His research program receives support from multiple Danish health research councils and international cardiology foundations.

Juho KimView profile

Associate Professor

• Human-Computer Interaction
• Human-AI Interaction
• Interactive Systems
• +5 more

Juho Kim is an Associate Professor in the School of Computing at KAIST, where he directs KIXLAB (the KAIST Interaction Lab). He is also affiliated faculty in the Kim Jaechul Graduate School of AI at KAIST. His academic journey includes a Ph.D. from MIT, M.S. from Stanford University, and B.S. from Seoul National University. From 2015-2016, he served as a Visiting Assistant Professor and Brown Fellow at Stanford University. His educational background includes: Ph.D. in EECS from MIT M.S. in Computer Science from Stanford University B.S. in Computer Science and Engineering from Seoul National University Professor Kim's research in human-computer interaction and human-AI interaction focuses on building interactive and intelligent systems that support interaction at scale, aiming to improve the ways people learn, collaborate, discuss, make decisions, and take action online. His work spans several key areas including educational technology, online collaboration platforms, AI-powered learning systems, and tools for human-AI collaboration. He is particularly interested in how interactive systems can scale to support large numbers of users while maintaining quality interactions. His recent publications show a strong trend toward human-AI interaction, with particular emphasis on educational applications, collaborative systems, and addressing challenges in AI reliability and user experience. His work often combines technical innovation with rigorous user studies to evaluate real-world impact. Professor Kim has received numerous scientific awards including a KIISE/IEEE-CS Young Computer Researcher Award, KAIST's Songam Distinguished Research Award, Grand Prize in Creative Teaching, Q-Day Creative Education Award, and Excellence in Teaching Award. He has also received nearly 20 paper awards from top conferences including ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP. KIISE/IEEE-CS Young Computer Researcher Award KAIST's Songam Distinguished Research Award Grand Prize in Creative Teaching Q-Day Creative Education Award Excellence in Teaching Award Nearly 20 paper awards from ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP KAIST Global Research Cooperation Award Outstanding Ethics Professor Award from KAIST Graduate Student Association Professor Kim actively mentors students at all levels, with a substantial roster of Ph.D. and M.S. students. His recent sabbatical year was spent as a chief scientist at Ringle Inc. to transfer his research on AI-powered analysis and diagnosis of English learners' proficiency into real products. He is also an Educational Innovation Advisor at KAIST, with the mission to promote AI-applied teaching and learning in the university. Professor Kim directs KIXLAB (the KAIST Interaction Lab), which focuses on human-AI interaction research. The lab brings together students and researchers from diverse backgrounds to work on projects that bridge interactive systems and artificial intelligence. KIXLAB has established collaborations with industry partners and regularly contributes to top-tier conferences in HCI and AI.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +1 more

Rebecca Hwa is a Professor in the Department of Computer Science at the University of Pittsburgh. She was on leave during the 2020-2021 academic year, serving as a Program Director at the National Science Foundation. Her research focuses on Natural Language Processing, Machine Learning, Artificial Intelligence, and Human-Computer Interaction. She co-advises PhD students with Prof. Adriana Kovashka and has mentored graduates now at tech companies like Facebook, Microsoft, LinkedIn, and Apple. Her work bridges computational methods with social and educational applications. Recent research trends include advancing NLP techniques for detecting bias in social media analysis, developing domain-robust visual question answering systems, and creating interfaces to enhance student writing through AI-driven tools. Her contributions span interdisciplinary areas, integrating machine learning with human-centric design. Affiliated with the Intelligent Systems Program, she emphasizes collaborative projects between academia and industry. Her advising record reflects a commitment to preparing students for impactful careers in tech and research. No scientific awards are explicitly mentioned in the text.