جست‌وجوی استاد و پژوهشگر

Chun Pengمشاهده پروفایل

استاد

• Female Reproduction
• Molecular Biology
• Cell Biology
• +۶ مورد دیگر

Dr. Chun Peng is a Full Professor and Tier 1 York Research Chair in Women's Reproductive Health in the Department of Biology at York University. He leads the Peng Lab, focusing on female reproduction, ovarian cancer, and placental development. His research employs molecular biology, biochemistry, and bioinformatics to explore growth factors, hormones, and miRNAs in reproductive health disorders like preeclampsia and ovarian cancer. Education: PhD from the University of Alberta, M.Sc. and B.Sc. from Zhongshan University, China. Research Interests: Molecular mechanisms of ovarian cancer, miRNA regulation in reproduction, and novel therapies targeting β-catenin inhibitors. Current projects include placental signaling networks, circular RNAs in cancer, and zebrafish ovarian development. Funding: Supported by Canadian Institutes of Health Research, NSERC, Cancer Research Society, and York University. The lab has produced over 100 publications and trained numerous graduate students and postdocs. Awards: Tier 1 York Research Chair (2022). Lab members have received scholarships like NSERC, OGS, and York Graduate Scholarships. Lab Infrastructure: Bioinformatics tools for miRNA analysis, ImageJ plugins for cell counting, and collaborations in plasmonic nanoparticle biosensors.

Morteza Namvarمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Natural Language Processing
• Large Language Models
• +۷ مورد دیگر

Dr. Morteza Namvar is a Senior Lecturer at The University of Queensland Business School and an Affiliate of both the Centre for the Business and Economics of Health and Centre for Enterprise AI. His work bridges business contexts with advanced computational techniques, focusing on practical applications of artificial intelligence in organizational and healthcare settings. Faculty of Business, Economics and Law - School of Business Centre for the Business and Economics of Health Centre for Enterprise AI - Faculty of Engineering, Architecture and Information Technology Dr. Namvar specializes in Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLMs), with research concentrated in three primary areas: leveraging NLP and LLMs for enhanced theory building in Information Systems research, text feature engineering using advanced language models, and personalization/user experience enhancement through contextual understanding. His work systematically analyzes unstructured text data to develop robust theoretical constructs and improve machine learning model capabilities. His recent publications reveal strong trends in healthcare applications of LLMs, hate speech detection, and cryptocurrency market analysis through social media. The research consistently applies NLP techniques to solve real-world problems across healthcare, finance, and e-commerce domains, with particular emphasis on sociotechnical aspects of technology implementation. Dr. Namvar has successfully secured competitive funding including the prestigious UQ Knowledge Exchange & Translation Fund grant for developing social media monitoring tools for small businesses, and multiple grants from Medical Protection Society Limited for machine learning applications in healthcare systems. 2021-2022: Developing a context-specific social media monitoring tool to empower Australian small business (UQKx&T Fund) 2021: Enhancing Education Development: Analysing Members' Feedback Using Machine Learning Techniques (Medical Protection Society) 2021: Investigating the effective use of data and analytics in Medical Protection Systems (Medical Protection Society) He currently supervises five PhD students working on NLP and LLM applications in healthcare, data privacy regulation, and patient outcomes. His research impacts include leading machine learning projects with industry partners like Medical Protection Society and PA Hospital, where his NLP techniques have helped improve organizational strategies through text data analysis.

Michael Colaresiمشاهده پروفایل

استاد

• International Relations
• Political Methodology
• Computational Social Science
• +۳ مورد دیگر

Michael Colaresi is the Associate Vice Provost for Data Science and William S. Dietrich II Professor of Political Science at the University of Pittsburgh, affiliated with the Dietrich School of Arts and Sciences. He holds roles in the Center for Research Computing and Data, Computational Social Science program, and the Intelligent Systems Program at the School of Computing and Information. His research focuses on computational social science, leveraging machine learning and text analysis to study national security secrecy, political violence, and human rights dynamics. Colaresi co-directs the Responsible Data Science, Complexity, and AI for Peace (RedSCAIP) Lab, emphasizing interdisciplinary collaboration to address societal challenges. Education: PhD in Political Science from Indiana University (2002). He founded the Social Science Data Analytics initiative at Michigan State University and now leads efforts in Pitt’s Institute for Cyber Law, Policy, and Security. His work has been funded by NSF grants and foundations like Hillman and RK Mellon. Research Interests: International Relations, computational methods, conflict forecasting, and the intersection of technology with democratic governance. His 2014 book *Democracy Declassified* explores secrecy dilemmas in liberal democracies and was shortlisted for the Conflict Research Society Prize. Recent projects include the ViEWS political violence early-warning system and HR-VEAW human rights analytics platform. Awards: Co-winner of the 2017 Journal of Peace Research Best Visualization Award and 2006 Gosnell Prize for Political Methodology. He serves as co-editor of *International Interactions* and collaborates with institutions like the Peace Research Institute Oslo (PRIO) and Uppsala University’s Violence Early Warning Project. Teaching: Courses include Computational Social Science, Bayesian Statistics, and Text-as-Data methods. His pedagogical focus emphasizes bridging social science theory with modern computational tools.

Aron Culottaمشاهده پروفایل

استاد

• Natural Language Processing
• Social Network Analysis
• Artificial Intelligence
• +۹ مورد دیگر

Aron Culotta is a Full Professor of Computer Science at Tulane University, leading the Text Analysis in the Public Interest lab and serving as Director of the Tulane Center for Community-Engaged Artificial Intelligence and the Jurist Center for Artificial Intelligence. He holds a Ph.D. from the University of Massachusetts Amherst (2008), a M.S. from the same institution (2004), and a B.S. from Tulane University (2002). His work focuses on human-centered AI, leveraging NLP and social network analysis to address societal challenges in public health, emergency response, criminal justice, and civic transparency. Culotta has pioneered methods to track disease outbreaks, measure public health campaign effectiveness, and inform disaster response through social media analysis. Research interests include natural language processing, causal inference, domain adaptation, and crisis informatics. Notable contributions span frameworks for reducing algorithmic bias, developing community-driven AI platforms, and analyzing social media dynamics for public good. His interdisciplinary projects are supported by over $4.8M in funding from NSF, NEH, and Tulane initiatives, among others. Culotta has mentored 33 capstone teams and advised 5 graduated PhD students now in academia and industry. Awards: Barbara E. Moely Service Learning Award (2024-2025), Nayar Prize Finalist (2016), Teacher of the Year (2018), and multiple best paper recognitions at AAAI and CSCW. Leadership: Spearheads the Tulane CEAI Center, fostering collaborations across 7 universities and 3 non-profits. Hosts workshops like PACE 2024 and Gulf Coast AI Social at NeurIPS. Education: Ph.D. in Computer Science (UMass Amherst, 2008), M.S. (UMass Amherst, 2004), B.S. (Tulane University, 2002). His work emphasizes collaboration with community partners to ensure AI systems are inclusive, transparent, and equitable. Recent projects include a criminal court transparency platform and AI tools for tracking blight in New Orleans. Culotta’s research bridges technical innovation with societal impact, addressing urgent challenges like health equity and algorithmic fairness.

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

Andrew Taylor, MD, MHS is an Adjunct Associate Professor of Biomedical Informatics and Data Science, Emergency Medicine, and Biostatistics at Yale University. He leads the Yale Interdisciplinary AI & Medicine Lab (Y-IAML), focusing on integrating AI into healthcare delivery through cross-disciplinary collaboration. His work spans AI ethics, implementation science, and decision theory to create practical, patient-centered solutions. Education: MD from Emory University (2007), Emergency Medicine residency at University of Connecticut (2011), and Informatics Fellowship/MHS from Yale (2015). He also completed a point-of-care ultrasound fellowship. Research interests include machine learning for predictive analytics, EHR data mining, decision support tools for emergency care, and health equity. His team develops AI-driven solutions to reduce diagnostic errors, optimize clinical workflows, and address disparities in emergency department care. Recent work highlights AI's role in triage equity, opioid use disorder treatment, and deprescribing optimization. Key publications focus on AI-based decision support systems, lung nodule detection via NLP, and CMS quality measures. Notable Awards: University of St Andrews Global Fellow (2023) Grants: NIH (NIDA/NIA/NIMHD/NLM), AHRQ, SIDM, Gordon and Betty Moore Foundation, and industry partnerships fund his research. Current projects include ADAPT trial for buprenorphine initiation and EHR-driven causal analyses. Labs/Teams: Director of Artificial Intelligence and Data Science in Emergency Medicine, leading Y-IAML with 15+ collaborators across informatics, ethics, and clinical domains.

Daniel Martin Katzمشاهده پروفایل

استاد

• Legal Informatics
• Artificial Intelligence & Law
• Blockchain Technology
• +۵ مورد دیگر

Professor Daniel Martin Katz is a Full-Time Faculty member at Chicago-Kent College of Law, Illinois Institute of Technology, and Director of the Law Lab. He holds a PhD, JD, M.P.P., and B.S., with expertise in applying computational methods to legal systems. His research focuses on Legal Informatics, AI in Law, Blockchain, and Complexity Science. Notable awards include the Fastcase 50 and ABA Journal's Legal Rebels. He co-founded LexPredict and advises legal tech startups. His work bridges law, technology, and innovation, emphasizing data-driven legal practices. Education: PhD (University of Michigan), JD (University of Michigan Law School), M.P.P. (University of Michigan), B.S. (University of Oregon). Research interests include AI ethics, legal analytics, and computational governance. He edits journals like the International Journal of Law and Information Technology and contributes to global legal tech discourse through media appearances and conferences. His articles explore AI's impact on legal assessment (e.g., GPT-4 passing the bar exam), legal complexity analysis, and NLP applications. Awards highlight his transformative role in legal innovation. Advising includes roles at LexPredict and NextLaw Labs. Labs include the Law Lab and collaborations with Stanford CodeX.

Federico Sianoمشاهده پروفایل

استادیار

• Accounting
• Financial Analysis
• Textual Analysis
• +۳ مورد دیگر

Federico Siano is an Assistant Professor of Accounting at the University of Texas at Dallas (UT Dallas), affiliated with the Jindal School of Management. His research focuses on the intersection of accounting disclosures, textual analysis, and macroeconomic factors, leveraging machine learning and big data methods to explore topics like systematic risk, investor behavior, and financial reporting practices. His work investigates how firms' disclosures influence market perceptions and risk premiums, with a particular emphasis on applying advanced analytical techniques to small datasets. Recent studies include analyses of earnings announcements, macroeconomic discourse in corporate communications, and challenges in financial statement replication. Dr. Siano's research has been published in leading accounting and finance journals, addressing themes such as narrative-driven investing, LLM applications in financial text analysis, and the primacy of numerical data in disclosures.

Ryan Boydمشاهده پروفایل

استادیار

• Computational Social Science
• Psychology
• Personality Assessment
• +۳ مورد دیگر

Ryan Boyd is an Assistant Professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas. As a computational social scientist, he specializes in analyzing everyday language (social media, speeches, writings) to uncover psychological patterns in personality, emotion, and cognition. Affiliation: University of Texas at Dallas Research Focus: Language analysis, mental health detection, AI psychology His work leverages computational linguistics and NLP to study: Personality through text Emotion regulation via social media Mental health indicators in digital language Historical and political discourse analysis Recent publications emphasize AI integration in psychological assessment, radicalization detection, and cultural language frameworks like MR-LIWC2015 for Marathi NLP. He develops accessible tools like LIWC, BUTTER, and WhisperingWizard for non-technical researchers, bridging complex analytics with user-friendly interfaces.

Heather Desaireمشاهده پروفایل

استاد

• Mass Spectrometry
• Bioanalytical Chemistry
• Physical Organic Chemistry
• +۳ مورد دیگر

Heather Desaire is a University Distinguished Professor and holds the Keith D. Wilner Chair in Chemistry at the University of Kansas, Department of Chemistry. Her research focuses on mass spectrometry applications in bioanalytical and physical organic chemistry, including structural analysis of glycoproteins and organic reaction mechanisms. She pioneered methods to characterize glycopeptides using tandem mass spectrometry and developed rules for dissociation reactions in mass spectrometry. Her recent work integrates machine learning for biomarker discovery, Alzheimer’s disease studies, and detecting AI-generated academic texts. Key tools developed by her group include GlycoPep DB and GlycoPep ID for glycopeptide analysis. Education: B.A. in Chemistry from Grinnell College (1997), Ph.D. in Chemistry from University of California, Berkeley (2001). Postdoctoral work at Quintiles Inc. (2002). Research Interests: Mass spectrometry of glycoproteins and small organic molecules. Machine learning applications in proteomics and biomarker validation. Forensic analysis of latent fingerprints and sebum via mass spectrometry. Structural studies of HIV envelope glycoproteins and vaccine development. Publications highlight advancements in AI detection (over 99% accuracy distinguishing ChatGPT text), Alzheimer’s biomarkers, and lipidomic studies using non-invasive sampling. Her work has been supported by grants including the NSF Career Award and the ASMS Research Award. Awards: William T. Kemper Award for Excellence in Teaching (2009), NSF Career Award (2007-2012), and ASMS Research Award (2006). Lab: Desaire Research Group develops cutting-edge tools for glycoproteomics and mass spectrometry data analysis. Collaborations focus on translational research in virology, neurodegenerative diseases, and forensic science.

Tae-Hoon Kimمشاهده پروفایل

استاد

• Cybersecurity Education
• Network Security
• Data Science
• +۸ مورد دیگر

Tae-Hoon Kim is a Professor in the Department of Computer Information Technology and Graphics at Purdue University Northwest. He holds a Ph.D. in Information Science from the University of Pittsburgh, an M.S. in Interdisciplinary Telecommunications from the University of Colorado Boulder, and a B.S. in Mechanical Engineering from Oklahoma State University. His research focuses on cybersecurity education, wireless sensor networks, data-driven analysis using machine learning, and AR-based educational tool development. Current projects are funded by the Army Research Office, NSF, NSA, and industry partners. Research interests span network security, data science, and innovative educational technologies. He actively develops interactive tools for K-12 and college cybersecurity education using augmented reality and data visualization techniques. Teaching specialties include network protocols, IoT, blockchain, and big data analytics. His publications address topics ranging from biomass energy prediction models to cultural heritage glyph analysis. Teaching responsibilities include courses on LAN/WAN design, network security, AI, and mobile applications. Over 20 years of academic experience includes advising student projects in cybersecurity and data science.

• Digital Marketing
• Consumer Behavior
• Influencer Marketing
• +۵ مورد دیگر

Andreas Bayerl is an Assistant Professor of Marketing at the Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Quantitative Marketing from the University of Mannheim and conducts interdisciplinary research at the intersection of digital behavior, consumer psychology, and data science. His research focuses on how individuals generate, process, and are influenced by digital information, particularly in the context of online reviews and influencer marketing. Using a mixed-method approach—combining large-scale observational data, text and image analysis, and field and laboratory experiments—Andreas investigates behavioral patterns in digital ecosystems. His work has been published in leading journals including Journal of Marketing , Harvard Business Review , MIT Sloan Management Review , and Nature Human Behavior . The most recent publications reveal a consistent focus on digital influence, credibility, and consumer decision-making. Key themes include the effectiveness of micro-influencers, the psychological impact of fake reviews, multimodal analysis of visual and textual content, and experimental validations of marketing strategies across platforms. His methodological rigor and real-world relevance are evident across these works. Andreas has received significant recognition for his contributions, including: H. Paul Root Award for groundbreaking research in influencer marketing He actively teaches in the area of data science and marketing analytics, contributing to the next generation of analytically skilled marketers. While no formal advisees are listed, his role as an assistant professor suggests involvement in student supervision and academic mentorship. His research program appears to be supported by empirical rigor and industry relevance, though specific grants or lab affiliations are not mentioned in the provided text.

• Semantic Computing
• Big Data Integration
• STEM Education
• +۳ مورد دیگر

Srividya Bansal is an Associate Professor and Program Chair of the Software Engineering program at Arizona State University's School of Computing and Augmented Intelligence. She joined ASU in 2010 and previously held a visiting assistant professorship at Georgetown University. Her research focuses on semantics-based approaches for Big Data integration, knowledge graphs, and outcome-based STEM education design. She currently co-leads the NSF-funded 'SUDOKN: Supply and Demand Open Knowledge Network' project under the Proto-OKN program. Education: B.Tech in Computer Science, National Institute of Technology Warangal (1999) MS in Computer Science, Texas Tech University (2002) PhD in Computer Science, University of Texas at Dallas (2007) Industry experience: 5 years as a software engineer at SAP Labs India and Tyler Technologies Research interests include semantic computing, web service composition, and educational technology. She has over 75 publications and actively teaches courses like Semantic Web Engineering and Software Enterprise . Current NSF grants include IUSE-funded work on step-based tutoring systems for circuit analysis and Proto-OKN's open knowledge networks. Professional affiliations include IEEE Computer Society and ACM. She leads the Instructional Module Development (IMOD) System project to enhance outcome-based curriculum design in STEM fields.

Ivan Smirnovمشاهده پروفایل

استادیار

• Computational Social Science
• Generative AI
• Artificial Intelligence
• +۱۰ مورد دیگر

Ivan Smirnov is a computational social scientist currently serving as the AI Lead in Research and Researcher Training at the Graduate Research School, University of Technology Sydney (UTS). He is also an External Faculty Member at the Complexity Science Hub in Vienna. His work spans the intersection of AI, sociology, and education, with a focus on Generative AI's role in research and doctoral training. University: University of Technology Sydney School: Graduate Research School Previous Positions: Assistant Professor, University of Mannheim; Group Leader, Higher School of Economics, Moscow Smirnov’s research explores how digital traces and machine learning can illuminate social dynamics such as inequality, gender bias, and academic performance. He employs computational methods to study topics like online toxicity, student wellbeing, and the digital representation of gender. His methodological expertise includes natural language processing, social network analysis, and predictive modeling using social media data. His recent publications reveal a consistent trend in using large-scale digital data to address pressing social questions, particularly in education and mental health. He frequently publishes in leading interdisciplinary journals such as Proceedings of the National Academy of Sciences , EPJ Data Science , and Royal Society Open Science , and presents at major conferences including IC2S2 and ICWSM. His scientific contributions have been recognized through media coverage in Nature , MIT Technology Review , and The Times , as well as a Best Course Award at HSE for his pioneering teaching in computational social science. Best Course Award at Higher School of Economics Media features in Nature, MIT Technology Review, The Times, ABC TV Smirnov is actively involved in research leadership and training, having led AI initiatives at UTS and co-organized the Summer Institute in Computational Social Science. He has secured funding from the European Commission and the Russian Science Foundation. He supervises research students and has developed the open educational course Getting Started with Generative AI in Research , reflecting his commitment to empowering the next generation of researchers. He also contributes to the academic community through peer review for top conferences and journals. He is affiliated with professional groups including the Human-AI Collaborative Knowledgebase for Education and Research (HACKER) and the SDG Classification Expert Group, and leads initiatives focused on integrating AI into research training and supporting HDR student wellbeing.

Noémie Elhadadمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Biomedical Informatics
• +۳ مورد دیگر

Noémie Elhadad is an Associate Professor in the Department of Biomedical Informatics at Columbia University, affiliated with the Data Science Institute and Computer Science. She leads research at the intersection of machine learning, natural language processing, and healthcare, focusing on deriving insights from clinical and patient-generated data. Her work includes developing systems like HARVEST for patient record summarization and the Citizen Endo project for endometriosis phenotyping. She holds the role of Graduate Program Director in DBMI and has mentored numerous students and postdocs. Education: Ph.D. in Computer Science (Columbia University, 2006) Affiliations: Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center Her research emphasizes actionable knowledge extraction from electronic health records (EHRs) and online health communities. Key projects include probabilistic phenotyping models (Phenome) and mHealth applications for chronic disease management. She has secured grants from NSF, NIH, and NLM, and her work has been recognized with the ACMI Fellowship (2016). Publications span over 100 peer-reviewed articles, focusing on clinical NLP, predictive modeling, and participatory health research. Current initiatives explore AI-driven solutions for endometriosis and maternal health using wearable and self-tracking data.

Jaafar Alghazoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۷ مورد دیگر

Dr. Jaafar Alghazo is an Associate Professor in the Math, Science and Technology Department at the University of Minnesota. He currently teaches courses in Information Technology Management and Software Engineering, including ITM 3900-E90: Internship, SE 2200-001: Intro to Software Engineering, and SE 4500-001: Senior Project I. Research Focus: Machine Learning, Deep Learning, Medical Image Analysis, Cybersecurity, IoT, UAV Communications Key Publication Trends: 15 most recent works span 2025–2023, emphasizing neural network optimization, Arabic sign language recognition, flood detection, and secure medical imaging techniques