Find Academic Supervisors

Hyung-Jo YoonView profile

Associate Professor

• Phraseology
• Computational Text Analysis
• Second Language Writing
• +3 more

Hyung-Jo Yoon is an Associate Professor in the Department of Linguistics/TESL at California State University, Northridge. His research focuses on phraseology, computational text analysis, second language writing, assessment, and task-based language teaching. He developed the Authorial Voice Analyzer , an automated tool for interactional metadiscourse analysis, which serves as a pedagogical resource for learners. Ph.D., Second Language Studies, Michigan State University (2013-2017) M.Ed., Teaching English to Speakers of Other Languages, University of Pennsylvania (2011-2013) B.A., English Education, Hankuk University of Foreign Studies (2004-2010) TEFL Certificate, University of California, Irvine (2009) His recent work investigates corpus-based measures as alternatives to traditional accuracy metrics in L2 writing, emphasizing pedagogical applications. Collaborative projects address professional development frameworks for ESL teachers, focusing on skill and knowledge requirements for effectiveness. Key research trends include automation of linguistic complexity metrics, validation of multi-word combinations for accuracy assessment, and cross-genre analysis of L2 writing. Subfields span syntactic complexity, metadiscourse profiling, genre differentiation, and cognitive task modeling. 2020 Research Article Award Winner: Cognitive validity of child English-language tests Yoon’s work bridges computational methods with language teaching, advocating for data-driven approaches to both assessment and pedagogy. His AVA tool exemplifies this integration, enabling systematic analysis of authorial voice in academic writing.

Dan GutfreundView profile

Researcher

• Machine Learning
• Natural Language Processing
• Computer Vision
• +3 more

Dan Gutfreund is a Principal Research Scientist and Senior Manager at the MIT-IBM Watson AI Lab, focusing on machine learning with applications to natural language processing and computer vision. He previously held managerial and technical roles at IBM's Haifa Research Lab and was involved in IBM Project Debater. Gutfreund earned his PhD in computer science from the Hebrew University in Jerusalem in 2005. His research spans Neuro-Symbolic AI , Computational Complexity , and Foundations of Cryptography , with notable contributions to datasets like Moments in Time and ObjectNet . His recent work includes multimodal models for the metaverse, generative AI for engineering design, and simulator-assisted training for interpretable systems. Gutfreund's publications reflect expertise in AI applications for supply chain prediction, avatar personalization, and reconciling virtual disputes. He has also explored evolutionary algorithms for software engineering and constraint-based generative models in design tasks.

• International Trade
• Globalization
• Services Offshoring
• +3 more

Dr. J. Bradford Jensen is the McCrane/Shaker Chair of International Business at the McDonough School of Business, Georgetown University. He also serves as a nonresident senior fellow at the Peterson Institute for International Economics and a research associate at the National Bureau of Economic Research. His research focuses on international trade, globalization, and the economic impacts of services offshoring, utilizing plant-level microdata to analyze firm heterogeneity and trade disputes. Ph.D. in Economics, Stanford University B.A., Kalamazoo College Jensen's work has influenced high-level trade policy, including citations in the U.S. Trade Representative's TiSA negotiations and the Economic Report of the President . His research on services trade and firm dynamics has been supported by grants from the National Science Foundation, Alfred P. Sloan Foundation, and MacArthur Foundation. He has held academic roles at Carnegie Mellon University and Dartmouth College, as well as leadership positions at the U.S. Census Bureau and Peterson Institute.

Mark D. HaywardView profile

Professor

• Life course origins of health disparities
• Morbidity and mortality
• Biodemography
• +4 more

Mark D. Hayward is a Professor of Sociology at the University of Texas at Austin, holding the Centennial Commission Professorship in the Liberal Arts #2. He is also a faculty research associate at the Population Research Center. With a Ph.D. from Indiana University Bloomington, Dr. Hayward has established himself as a leading scholar in population health, demography, and biodemography. Dr. Hayward has held numerous leadership positions in professional organizations, including serving as president of the Interdisciplinary Association of Population Health Science and the Southern Demographic Association. He has also chaired sections of the American Sociological Association and served on boards of the Population Association of America and the Society of Biodemography and Social Biology. His extensive service includes membership on the Committee on Population at the National Academy of Sciences and the Board of Scientific Counselors at the National Center for Health Statistics, as well as the National Advisory Committee for the Robert Wood Johnson Foundation's Health and Society Scholars Program. His research focuses on two primary questions: (1) How life course exposures from childhood into adulthood influence the health and mortality of older adults, examining the 'long arm of childhood' perspective that investigates how early life events potentially impact health decades later; and (2) How inequalities in adult health and mortality arise from educational experience, with particular emphasis on how education influences trends and disparities in dementia, disability, and mortality. His work has substantially expanded current knowledge about these relationships and has examined how US state policies shape educational inequalities and health outcomes. Dr. Hayward's scholarly output demonstrates significant trends in understanding the social determinants of health across the lifespan. His recent work has increasingly focused on dementia epidemiology, racial disparities in health outcomes, and the role of regional contexts (particularly the Southern US) in shaping health trajectories. His publications span high-impact journals including the American Journal of Public Health, Demography, JAMA, the Journals of Gerontology: Social Sciences, the Journal of Health and Social Behavior, and Social Science and Medicine. Throughout his career, Dr. Hayward has been actively involved in enhancing the measurement and collection of population health data, particularly longitudinal studies. He has served on advisory boards for numerous national health studies and provided scientific guidance to universities, NIH, private foundations, and federal agencies. His research has been consistently funded by the National Institute on Aging and the Eunice Kennedy Shriver National Institute on Child Health and Human Development. Dr. Hayward teaches courses including H S 378 • Seminar In Health & Society and SOC 389K • Human Mortality, reflecting his expertise in population health, mortality patterns, and the social determinants of health across the life course.

Xiongcai CaiView profile

Adjunct Associate Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Dr. Xiongcai Cai is an Adjunct Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW). With expertise in Artificial Intelligence , Machine Learning , and Computer Vision , he contributes to advancing Recommender Systems , Natural Language Processing , and Health Informatics . His work bridges theoretical and applied research in technology for human-centric applications. Current roles: Adjunct Associate Professor, UNSW School of Computer Science and Engineering Key research areas: Machine Learning, Recommender Systems, Computer Vision, Generative AI Dr. Cai's research portfolio demonstrates a consistent focus on recommender systems and machine learning over the past decade. His technical contributions span graph convolutional networks , temporal bilinear models , and embedding techniques for collaborative filtering. Recent work in 2025 addresses knowledge distillation for GCNs-based recommenders, while earlier studies tackled cold-start transitions and matrix factorisation boosting. His publication history (2 book chapters, 7 journal articles, and 36 conference papers) reveals a strong emphasis on real-time applications in domains like gait recognition (2020), health data analytics (2016), and social network recommendation (2010-2015). The research applies mathematical rigor to practical challenges in online dating platforms , medical decision support , and object tracking systems . Contact details: Email: x.cai@unsw.edu.au Phone: +61 2 9385 8858

Steven ZhouView profile

Assistant Professor

• leadership
• personality
• vocational psychology
• +10 more

Steven Zhou is an Assistant Professor of Psychological Science at Claremont McKenna College, where he leads the STATS Lab (www.statslabatcmc.com). His academic work bridges quantitative methods with psychological research, focusing on leadership, personality, and vocational psychology. With methodological expertise in psychometrics, machine learning, and data visualization, he actively contributes to both academic and practitioner communities through research, teaching, and consulting. Dr. Zhou's educational background includes: PhD in Organizational Psychology + Certificate in Computational Social Sciences from George Mason University BA in Organizational Psychology + Certificate in Conflict Resolution from Pepperdine University His research program centers on developing and refining psychological assessments while investigating bias in measurement systems. Dr. Zhou specializes in forced-choice personality assessments, leadership evaluation methodologies, and career calling research. His work consistently addresses the gap between academic research and practical application in organizational settings, with recent projects incorporating large language models and agent-based simulations to study complex behavioral phenomena. Dr. Zhou's publication record shows a clear trajectory toward increasingly sophisticated methodological approaches to organizational psychology questions. His recent work demonstrates growing integration of computational techniques (machine learning, NLP) with traditional psychological research, while maintaining strong connections to practical organizational applications. The research consistently addresses measurement challenges in leadership assessment and career development. His notable scientific achievements include: 2025 Jablin Dissertation Award from the International Leadership Association and Jepson School of Leadership Studies 2025 Chapman Dissertation Award from the Network of Leadership Scholars and Academy of Management 2023 Dissertation Research Award from the American Psychological Association 2022 Free Inquiry Grant from the Foundation for Individual Rights and Expression 2022 Graen Grant for Student Research on Leaders and Teams from SIOP 2021 Kenneth E. Clark Student Research Award from ILA and Center for Creative Leadership 2021 Outstanding Graduate Student Instructor from GMU Department of Psychology Dr. Zhou actively mentors both graduate and undergraduate students, with numerous student co-authors across his publication record. He has secured nearly $40,000 in external research funding to support his work on leadership assessment and psychometric methodology. His professional service includes editorial roles for multiple journals and leadership positions within the Society for Industrial and Organizational Psychology. As director of the STATS Lab, Dr. Zhou leads a research team focused on applying quantitative methods to understand leadership, career development, and personality. The lab's five research pillars include Psychometrics & Measurement Science, Bias in Measurement & Analytics, Leadership Assessment & Team Dynamics, Integrating Novel Methods into Research, and Career Development & Vocational Psychology, all emphasizing both methodological rigor and practical application.

Riccardo RosatiView profile

Professor

• Knowledge Representation
• Description Logics
• Semantic Web
• +7 more

Riccardo Rosati is a Full Professor at the Department of Computer, Control, and Management Engineering, Sapienza University of Rome, affiliated with the Faculty of Information Engineering, Computer Science and Statistics. He obtained his PhD in Computer Science from Sapienza University of Rome in 1997. His research focuses on Knowledge Representation, Description Logics, Semantic Web, Ontologies, Information Integration, Databases, and Artificial Intelligence. Specifically, he has made significant contributions to the integration of description logics and logic programming, and is one of the creators of the DL-Lite family of description logics, which became a standard in the Semantic Web (OWL 2 QL). His recent publications center around controlled query evaluation, ontology-based data access, and applying knowledge representation in healthcare informatics. The trend shows strong emphasis on both theoretical foundations and practical applications. Scientific awards and recognitions include: Distinguished Paper Award at IJCAI 2024 AAAI 2021 Classic Paper Award He has advised numerous students and been involved in research projects including OPTIQUE (Scalable End-user Access to Big Data), ACSI (Artifact-Centric Service Interoperation), TONES (Thinking Ontologies), INFOMIX (boosting information integration), QuOnto (Querying Ontologies), and HYPER (How Your Peers Exchange Resources). He leads the Artificial Intelligence and Knowledge Representation research group and collaborates with the Data Management and Service-Oriented Computing group at DIAG.

Curtis HuttenhowerView profile

Professor

• Computational Biology
• Bioinformatics
• Human Microbiome
• +4 more

Dr. Curtis Huttenhower is a Professor of Computational Biology and Bioinformatics at Harvard T.H. Chan School of Public Health , with dual appointments in the Department of Biostatistics and Department of Immunology and Infectious Diseases . His research focuses on computational methods for microbial community analysis, human microbiome public health implications, and machine learning applications in genomics. Education: B.S. (2000) from Rose-Hulman Institute of Tech, M.S. (2003) from Carnegie Mellon, Ph.D. (2008) from Princeton Major grants: NIH R21CA299494 (cancer virome), U24HL175772 (HVP consortium), OT2CA297578 (early-onset colorectal cancer prevention) His work spans functional metagenomics , microbiome diagnostics , and structured biological knowledge in machine learning . Recent studies include strain-level microbiome mapping and microbiome links to depression, diabetes, and cardiovascular disease . He contributes to open-source tools like MaAsLin and WAAFLE , and leads the Human Microbiome Project sub-cohort for inflammatory bowel disease microbiome characterization.

• Medical Assistive Technology
• Robotics and Intelligent Systems
• Design Engineering
• +3 more

Eiichiro Tanaka is a Professor at Waseda University's Faculty of Science and Engineering , specializing in Medical Assistive Technology , Robotics , and Design Engineering . His research focuses on developing wearable robotic systems for gait training, neuro-rehabilitation, and elderly mobility assistance. Key Research Areas : 1. Human-Robot Interaction for emotion-adaptive assistive devices. 2. Biomechanical Modeling of lower-limb assistance. 3. Ontological Frameworks for academic emotion analysis. 4. Non-Powered Exosuits for muscle fatigue reduction. His work integrates deep neural networks and physiological signal processing to create emotion-aware walking aids. Recent studies (2022-2020) demonstrate 24% fatigue delay and 16% walking distance improvement using RE-Gait® devices. Earlier projects include guide-dog robots and self-contained gear diagnostics . All publications employ 3D motion analysis , Wearable Sensors , and torque control algorithms .

Patricia J. BrooksView profile

Professor

• Language Development
• Language Learning
• Individual Differences in Cognition
• +12 more

Patricia J. Brooks is a Professor in the Department of Psychology at the College of Staten Island, City University of New York (CUNY). She serves as Director of the Language Learning Laboratory and is an active member of the Doctoral Faculty at The Graduate Center, CUNY, contributing to PhD programs in Psychology, Educational Psychology, and Speech-Language-Hearing Sciences. Education: PhD, New York University MA, New York University BA, Johns Hopkins University Her research focuses on individual differences in language learning across the lifespan, with an emphasis on adult second language acquisition and child language development. She employs experimental methods and computer-based games to study speech perception and production in both typical and atypical development. She also has a strong interest in pedagogy, particularly active learning, mentoring, and the use of technology in education. Her recent publications span diverse areas including statistical learning in developmental disorders, narrative evaluation in children, phonological processing, and innovative teaching practices such as Wikipedia editing in the classroom. These works reflect a consistent theme of understanding cognitive and social mechanisms in language development and applying that knowledge to educational contexts. Scientific Service and Leadership: Faculty Advisor, Graduate Student Teaching Association, American Psychological Association (Division 2) She has authored and edited key academic texts such as Language Development (2012), Encyclopedia of Language Development (2014), and Cognitive Development in Digital Contexts (2017). Her work bridges experimental psychology, linguistics, and educational practice. She is actively involved in research mentorship and graduate training, contributing to the development of future scholars in psychology and education. Her laboratory supports interdisciplinary research on language and cognition, and she collaborates widely across institutions and disciplines.

Daniel AsenView profile

Associate Professor

• Modern China
• History of Science
• History of Technology
• +12 more

Daniel Asen is an Associate Professor and Undergraduate Director in the Department of History at Rutgers University–Newark. He specializes in modern Chinese history, with research interests in the history of science, technology, medicine, and legal history, particularly forensic science in 20th-century China. Dr. Asen earned his Ph.D. from Columbia University in 2012. His research explores the role of fingerprinting and dermatoglyphics in criminal investigation, state surveillance, and scientific research during the Republican and People's Republic of China eras. He is currently working on a biography of Frank Yee (Yu Xiuhao), an American-trained police expert in Nationalist China. His publications span history, law, medicine, and science studies, reflecting interdisciplinary engagement with forensic pluralism, identity, race, and biometrics. The articles show a consistent focus on the development of forensic systems in China, the intersection of science and state power, and the global circulation of forensic knowledge. Themes include professionalization of forensic experts, the use of probability in pre-DNA paternity testing, and the racial implications of dermatoglyphic research in postwar East Asia. Standard Grant: Fingerprinting in Twentieth Century China from Racial, Political and Scientific Perspectives. NSF Science, Technology, and Society Program Award #1654990 (2017-2021) Henry Luce Foundation/ACLS Program in China Studies Postdoctoral Fellowship (2013) Dr. Asen advises undergraduate students in the History major and minor and is actively involved in teaching and public outreach, including a dedicated website for teaching the history of fingerprinting. He has no known graduate advisees listed, but his grants indicate active research leadership. He is not affiliated with a named lab but conducts independent and collaborative historical research.

• Plant Genetic Resources
• Plant Genetics
• Agricultural Biodiversity Conservation
• +3 more

Dr. Gaetano Laghetti is a Senior Researcher at the Institute of Biosciences and BioResources (IBBR) of the National Research Council of Italy (CNR) in Bari. He has served as the curator of the CNR's Gene Bank in Bari since 2011 and as the Italian representative in the 'Solanaceae' working group since 2012. Since October 2013, he has been deputy director of IBBR and heads the CNR work order on 'Biodiversity for the sustainability of Mediterranean agricultural systems' since 2011. Dr. Laghetti's educational background includes: 1984: Degree in Agricultural Science (cum laude) from the University of Bari, Faculty of Agriculture 1986: Passed State Examination for qualification to practice as an agronomist 1987: Awarded international scholarship by the International Institute of Tropical Agriculture (IITA) His research focuses on the safeguarding of plant genetic resources , with particular emphasis on Mediterranean agricultural systems. Dr. Laghetti has conducted approximately 50 exploration and collecting missions across Italy and internationally, gathering over 5,000 plant accessions. His work encompasses germplasm characterization using agronomical, morphological and biochemical traits, statistical analysis of characterization data, and taxonomic studies to identify crop origins and wild ancestors. He has significant expertise in obtaining EU quality marks for local products and holds two plant patents. Dr. Laghetti's recent publications demonstrate a strong focus on agrobiodiversity conservation , with particular attention to traditional Italian crop varieties, ethnobotanical knowledge, and the documentation of crop wild relatives. His research spans plant systematics, germplasm characterization, and the application of statistical methods to genetic resource data. He has made significant contributions to documenting ancient cereal varieties in Southern Italy and studying the biogeography of edible halophytes in the Apulia region. As an active member of the scientific community, Dr. Laghetti serves on the Editorial Board of the International Journal Genetic Resources and Crop Evolution and is a peer reviewer for multiple international journals in botany and biodiversity. He has coordinated four books and authored approximately 200 scientific papers. Dr. Laghetti has supervised academic work including a Research Doctorate, a Degree Thesis, and six scholarship recipients. He has served as the scientific responsible for six internal CNR projects and seven external projects, while collaborating on 21 additional research initiatives. His field work includes establishing and managing an experimental field for scientific trials since 2003. At the institutional level, Dr. Laghetti contributes to biodiversity conservation through his leadership roles at the CNR Gene Bank in Bari and his representation of Italy in international working groups focused on plant genetic resources.

Fred MorstatterView profile

Researcher

• Machine Learning
• Natural Language Processing
• Computational Social Science
• +3 more

Fred Morstatter is a Research Assistant Professor at the Thomas Lord Department of Computer Science, University of Southern California. He serves as Principal Scientist at the USC Information Sciences Institute and Associate Director for USC Data Science, bridging academia and applied research in AI ethics and social media analysis. Research Interests include: Mitigating algorithmic bias in NLP systems Developing robust social media content analysis frameworks Creating hybrid human-machine forecasting models for geopolitical events Studying causal relationships in online-offline event dynamics Advancing crowdsourcing methodologies with ethical AI Recent Article Trends examine: Temporal knowledge graph forecasting without explicit training data Gender bias quantification in named entity recognition Characterizing misinformation through network analysis Developing fair decision-making attribution mechanisms Mapping moral valence in crisis-related social media discourse Student Supervision includes advising PhD candidates exploring: Implicit biases in LLMs Computational social science Hate speech detection Persuasion modeling in forecasting systems Contact: fred@isi.edu | Google Scholar | USC ISI

• Geoinformatics
• Artificial Intelligence
• Environmental Research
• +7 more

Peter Mandl is an Associate Professor affiliated with the Institute for Computer Science Education at the University of Klagenfurt. His research focuses on Geoinformatics, Artificial Intelligence, Environmental Research, Remote Sensing, and Computer Simulation. He explores topics such as socio-ecological systems, spatial agent-based modeling, land use change, and urban and regional analysis. His work integrates interdisciplinary approaches, combining GIS technology with environmental policy, social sciences, and engineering. Key research interests include the application of geospatial tools for sustainable land use planning, renewable energy assessment, and modeling complex geographic systems. He has contributed to studies on forest fuel markets, satellite image interpretation, and fog phenomena in climate research. His publications emphasize spatial decision support systems, agent-based simulation frameworks, and the development of open geoinformatics platforms like RadWeb Mittelkärnten. His articles demonstrate a consistent focus on bridging theoretical modeling with practical applications, often using agent-based methods to analyze environmental, economic, and social systems. While no scientific awards are explicitly mentioned, his contributions to geoinformatics education and research are evident through his extensive publication record and teaching roles. Collaborative projects and grants are not detailed here, but his work suggests involvement in interdisciplinary teams addressing environmental and urban challenges. Labs and teams associated with his research are not explicitly listed, though his affiliations imply participation in geoinformatics and environmental science groups at the University of Klagenfurt.

• Data Science
• Natural Language Processing
• Machine Learning Applications
• +3 more

Chief Assistant Professor Gloria Hristova, PhD candidate in Data Science, holds a dual academic role at Sofia University's Department of Statistics and Econometrics within the Faculty of Economics and Business Administration. Her research focuses on applying machine learning and NLP techniques to business analytics, digital transformation assessment, and public sentiment analysis. She teaches courses on text analytics, machine learning for finance, and quantitative management methods. Active in open-source contributions, her GitHub repositories showcase projects in data science competitions and Python-based analytical tools development. Education: PhD Studies in Data Science (2019–present), Department of Statistics and Econometrics, Sofia University. Professional memberships include the Data Science Society. Research Interests: Business Applications of Machine Learning Sentiment Analysis in Public Services Automated Analytics Tools Development Text Mining for Customer Insights Published works analyze digital transformation in SMEs, public opinion on e-government services, and banking chat data analytics. Her recent projects employ transformer-based language models and BERTopic for topic modeling in crisis contexts like pandemic education shifts. Professional Affiliations: Representative of FEBA in the Assistant's Club at Sofia University. Collaborates with academic and industry partners on data-driven solutions for enterprise digitalization challenges.