جست‌وجوی استاد و پژوهشگر

Bryan Kellerمشاهده پروفایل

دانشیار

• Quantitative Methods
• Causal Inference
• Treatment Effects Analysis
• +۳ مورد دیگر

Bryan Keller is an Associate Professor of Practice in Applied Statistics at Teachers College, Columbia University. He holds affiliations with the departments of Data, Learning, and Society; Learning Analytics; and Measurement, Evaluation, and Statistics. His academic journey includes a PhD in Educational Psychology from the University of Wisconsin-Madison and prior roles as a high school math teacher and faculty member. His research focuses on quantitative methods in causal inference, treatment effect estimation, and statistical modeling in social sciences. He has secured grants totaling over $4 million, including studies on drug abuse prevention and educational interventions. Education: PhD, Educational Psychology (Quantitative Methods), University of Wisconsin-Madison, 2013 MS, Educational Psychology (Quantitative Methods), University of Wisconsin-Madison, 2010 MA & MAT, Mathematics Education, Binghamton University, 2000–2002 Research Interests: Dr. Keller’s work bridges statistical theory and application, emphasizing causal inference methods like propensity score analysis, variable selection, and experimental design. He develops tools for detecting treatment effect heterogeneity and evaluates methodologies in large-scale observational and experimental datasets. His research often addresses challenges in education, healthcare, and public policy. Grants & Awards: Principal Investigator: $25K AERA Grant for heterogeneous treatment effects (2018) Co-PI: $3.9M NIH grant for drug abuse prevention (2018–2023) 2013 Graduate Student Peer Mentor Award 2012 Genevieve Gorst Herfurth Honorable Mention Teaching & Service: Teaches courses in applied statistics, R programming, and causal inference. Serves on journal review boards (e.g., Journal of Educational and Behavioral Statistics ) and organizes conferences like the Atlantic Causal Inference Conference. Advises over 30 doctoral students across disciplines. Labs & Teams: Collaborates across disciplines, including the STILE for STEM project (NSF-funded) and the Michael J. Fox Foundation initiative on Parkinson’s disease interventions. Leads the MS in Applied Statistics program at Teachers College.

Stephanie Balzerمشاهده پروفایل

استادیار

• Programming Languages
• Pure and Applied Logic
• Software Verification
• +۳ مورد دیگر

Stephanie Balzer is an Assistant Professor in the Principles of Programming Group of the Computer Science Department at Carnegie Mellon University. Her office is located in 9004 Gates-Hillman Center, and she can be reached at balzers@cs.cmu.edu with administrative support from Oliver Moss. Dr. Balzer received her PhD from ETH Zurich under the supervision of Thomas R. Gross. Her academic trajectory shows consistent focus on formal methods and programming language theory from her early work on object relationships through to her current research on verification systems. Her research program aims to enable the construction of failure-free software that is correct by design and secure to run. She deploys rigorous reasoning methods such as type systems and verification logics to formally prove adherence to desired properties. Her work emphasizes compositional methods for scalability and addresses verification needs arising from real-world problems. Dr. Balzer's interests span Programming Languages, Pure and Applied Logic, Software Verification, Type Theory, and Security . A driving force underlying her research is the belief that powerful solutions are fundamentally based on simple ideas that can be conveyed to and appreciated by non-experts. Analysis of Dr. Balzer's publication record reveals a sustained focus on session types, logical relations, and concurrent systems verification. Her recent work shows increasing sophistication in handling timed protocols, information flow control, and heterogeneous applications. She consistently publishes in top-tier venues including POPL, PLDI, ICFP, and ECOOP, often receiving distinguished paper awards for particularly impactful contributions. NSF CAREER Award on "A Semantic Framework for Verifying Heterogeneous Applications" (2024) ACM SIGPLAN Distinguished Paper Award (2023) ECOOP Distinguished Paper Award (2022) Supervised PhD student Jules Jacobs received Cum Laude distinction (2024) Dr. Balzer has secured significant research funding including an NSF CAREER Award (2025), an NSF Award on Integrated Verification of IoT and Real-time Communication Protocols (2022), an AFOSR Award (2021), CyLab Seed Funding (2020), and previous NSF and Mozilla Research Grants. She actively mentors a diverse research group including PhD candidates Yue Yao, Yinsen (Tesla) Zhang, and Zak Kent (co-advised with Guy Blelloch), as well as M.S. and undergraduate researchers. Her supervision extends to postdoctoral fellows and thesis committee roles for other students. Dr. Balzer leads research activities within CMU's ForML Lab and maintains active collaborations with researchers at ETH Zurich, Radboud University Nijmegen, and Cornell University. She plays significant leadership roles in the programming languages community including as founder of the PL Scholars Network, steering committee chair of the Programming Languages Mentoring Workshop (2023-2025), and co-organizer of the Oregon Programming Languages Summer School (2023).

MH Morrenمشاهده پروفایل

استادیار

• Natural Language Processing
• Meta-Analysis
• Methodological issues
• +۳ مورد دیگر

Dr. MH Morren is an Assistant Professor of Marketing at Vrije Universiteit Amsterdam since 2012, specializing in analytical methods ranging from latent variable models to large language models. As Scientific Coordinator of Sustainability, she fosters interdisciplinary collaboration to address climate challenges. Affiliated with Health Economics Research Amsterdam (HERA) and Amsterdam Sustainability Institute (ASI), her research focuses on sustainable consumer behavior, policy impacts, and environmental legitimacy of brands. Teaching includes Natural Language Processing, Data Analysis in R, and Marketing Research Methods. Her work contributes to UN Sustainable Development Goals related to climate action and responsible consumption. Recent research explores prompts vs fine-tuning for value detection (2024), reinforcement learning for database optimization (2023), and cross-cultural behavioral theories (2021). She leads the 'Connecting Climate Discourse and Religious Language' project, integrating NLP with social sciences. No scientific awards explicitly mentioned. Supervises master's theses and teaches across undergraduate and graduate programs. Active in methodological innovations for sustainability research.

Mei Nagappanمشاهده پروفایل

استادیار

• Big Data
• Empirical Software Engineering
• Security
• +۵ مورد دیگر

Mei Nagappan is an Assistant Professor at the David R. Cheriton School of Computer Science, University of Waterloo. Previously, he held positions as an Assistant Professor at Rochester Institute of Technology and as a Post-Doctoral Fellow at Queen’s University’s Software Analysis and Intelligence Lab (SAIL). His research focuses on leveraging big data for empirical software engineering, particularly mining ultra-large software repositories to identify patterns and relationships in ecosystems. He emphasizes solutions addressing stakeholders beyond developers, such as operators, build engineers, and project managers. Education: PhD in Computer Science from North Carolina State University under Dr. Mladen Vouk, and postdoctoral work under Dr. Ahmed Hassan. His research spans vulnerability detection, DevOps, AI in programming (e.g., GitHub Copilot analysis), and diversity in software engineering conferences. Key interests include static analysis, security, and the impact of non-traditional backgrounds in SE. He advocates for inclusive practices and evaluates tools like Copilot through a security and user-centric lens. Research Interests: Big Data Empirical SE, Human-LLM Collaboration, Vulnerability Detection, DevOps, Security, Diversity in SE, Static Analysis Tools. His work bridges theory and practice, with studies on logging practices, build technologies, and mobile app analytics. Recent projects explore barriers faced by non-traditional SE professionals and the effectiveness of AI-driven testing tools. Grants & Advising: While specific grants are not listed, his research is supported by large-scale empirical studies. Advising focuses on graduate students exploring topics like bug localization, LLM limitations, and diversity metrics in open-source projects. His lab collaborates on tools like AddressWatcher for memory leak analysis and RepoQuester for GitHub project evaluation. Labs & Teams: Previously associated with SAIL at Queen’s University. Current collaborations involve the Cheriton School’s SE research groups, focusing on AI in software development and empirical studies of developer workflows.

Xiaolong Guoمشاهده پروفایل

دانشیار

• Large Language Models
• Hardware Security
• Formal Verification
• +۳ مورد دیگر

Xiaolong Guo is an Associate Professor in the Department of Electrical and Computer Engineering at Kansas State University's Carl R. Ice College of Engineering. He earned his PhD in Electrical and Computer Engineering from the University of Florida in 2019, following MS and BS degrees from Beijing University of Posts and Telecommunications and the University of London. Prior to joining K-State, he conducted research focused on formal methods and security verification. His research applies formal methods and generative AI to hardware and cybersecurity challenges, with three primary focus areas: Domain-specific applications of Large Language Models (LLMs) Hardware security including side-channel measurements and microarchitectural security Hardware/software co-verification and formal program analysis He directs the KSU Hardware Security Lab which develops automated methods for securing hardware and cyber systems, with applications in IoT, smart infrastructure, and semiconductor industries. Recent research extensions explore applications in bioinformatics and financial fraud prevention. Significant research funding includes multiple NSF awards totaling over $1.5M, including a 2024 CAREER award for 'When Learning Meets Reasoning: Mitigating Security Risks with Large Language Models and Causality Inference' and a 2024 CICI grant for 'Safeguarding AI in Bioinformatics'. Additional support comes from Kansas NSF EPSCoR awards.

Doina Carageaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Data Science
• +۶ مورد دیگر

Doina Caragea is a Professor in the Department of Computer Science at Kansas State University (KSU), part of the Carl R. Ice College of Engineering. She holds the Don and Linda Glaser Keystone Research Scholar appointment. Caragea earned her Ph.D. in Computer Science from Iowa State University (2004), with a focus on machine learning from distributed data sources. She has over 150 refereed publications and secured over $12M in extramural funding as PI/Co-PI. Education: Ph.D., Computer Science, Iowa State University, 2004 M.S., Computer Science, University of Bucharest, 1997 B.S., Computer Science, University of Bucharest, 1996 Research Interests: Machine Learning, Deep Learning, Data Science, Bioinformatics, Digital Agriculture, and Crisis Informatics. Her work focuses on semi-supervised learning, domain adaptation, and applications to plant phenotyping, social media analysis during disasters, and genomic sequence analysis. Notable Grants: NSF BIGDATA Award ($500,000): Domain adaptation for crisis-related social media analysis NSF PlantTransform ($1.9M): Genetic basis of maize regeneration USCP Sorghum Breeding Program ($1.6M): Climate-resilient crop development Awards: Deployed Application Award (AAAI IAAI 2021) Best Insight Paper (ISCRAM 2019) IBM Fellowship (2002–2004) Advising & Teaching: Supervised over 30 graduate students, including 10 Ph.D. graduates. Taught courses on Deep Learning, Data Mining, Bioinformatics, and Database Systems. Current research lab focuses on AI-driven solutions for agriculture, bioinformatics, and disaster response. Labs/Teams: Active collaborations with the Bioinformatics Center (KSU), USDA, and industry partners like Siena Analytics. Her lab develops tools for plant phenotyping, social media crisis analysis, and secure mobile applications.

• African American History
• Women's History
• Black Musical Culture
• +۲ مورد دیگر

Michelle R. Scott is Professor of History at the University of Maryland, Baltimore County with affiliate appointments in Africana Studies, Gender, Women’s + Sexuality Studies, and the Language, Literature, and Culture Doctoral Program. Her office is located in 507 Fine Arts Building and she can be contacted at 410-455-2035. Professor Scott specializes in 20th-century United States history, African American history, women’s history, black musical culture, and civil rights. Her research examines the cultural and social history of African Americans, particularly through the lens of musical expression and performance traditions. Professor Scott has received numerous prestigious awards including the Letitia Woods Brown Article Prize for the best article in African American Women's History from the Association of Black Women's Historians. Her publications include 'Blues Empress in Black Chattanooga: Bessie Smith and the Emerging Urban South' and 'T.O.B.A. Time: Black Vaudeville and the Theater Owner's Booking Association in Jazz Age America'.

Becky Bell Scottمشاهده پروفایل

استاد بالینی

• Clinical Social Work
• Behavioral Health
• Child and Adolescent Mental Health
• +۳ مورد دیگر

Becky Bell Scott is a Clinical Professor at Baylor University’s Diana R. Garland School of Social Work, focusing on integrated behavioral health and clinical social work in primary care settings. She holds a PhD from Baylor University, an MSW from the University of Washington, and a BS from Abilene Christian University. Her professional credentials include LCSW (Texas) and certifications in therapeutic methods like Parent Child Interaction Therapy and Motivational Interviewing. Her research emphasizes addressing behavioral health workforce gaps, training in integrated care, and improving pediatric behavioral health outcomes. Notable projects include a $2M+ funded initiative to evaluate training programs for clinical social work in primary care and a mixed-methods study on Long-Acting Reversible Contraception alongside Centering Pregnancy. She also leads a qualitative study on the experiences of students of color in social work programs. Dr. Scott directs the Integrated Behavioral Health Certificate Program for MSW students and teaches courses like Advanced Theories for Working with Children and Families and Art, Children and Disabilities . Her work bridges clinical practice, education, and research to strengthen healthcare delivery for vulnerable populations.

Rajeev R. Rajeمشاهده پروفایل

استاد

• Distributed Software Systems
• Service-Oriented Systems
• Programming Languages
• +۱ مورد دیگر

Rajeev R. Raje is an Associate Dean in the School of Science and Professor of Computer Science at the Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis. He holds a Ph.D. and M.S. in Computer Engineering from Syracuse University (1994) and a B.E. in Electrical Engineering from the University of Bombay (1984). His research focuses on distributed and service-oriented software systems, programming languages, and software engineering. He co-directs the Software Engineering and Distributed Systems (SEDS) group within the CIS department. Recent projects include CDASH (Community Data Analytics for Social Harm), TruSSCom (Trustworthy Software Service Selection and Composition), and frameworks like UniFrame for heterogeneous distributed components. Dr. Raje is a Senior Member of both ACM and IEEE. His work emphasizes practical applications of distributed systems, static code analysis, and indoor tracking technologies (e.g., e-DOTS).

• Formal Methods
• Model Checking
• Cybersecurity
• +۳ مورد دیگر

Seyed Hossein HAERI is a Post-Doctoral Research Fellow at the Louvain Verification Lab (LVL) , affiliated with the School of Engineering (École Polytechnique de Louvain) and the ICTEAM Center at UCLouvain. His work focuses on model checking security properties of JavaScript code for cloud environments under the SeCloud project . He has held prior roles including Post-Doctoral Research Fellow at PLDC (UCLouvain), Research Assistant at TU Hamburg, R&D Software Architect at MuSemantik Ltd., and Research Fellow at Heriot-Watt University. His expertise spans formal methods, programming languages, and cybersecurity. Affiliations: LVL, UCLouvain, PLDC, ICTEAM Previous Institutions: TU Hamburg, Heriot-Watt University, Sharif University of Technology Research interests include verification techniques, security analysis, and model-driven approaches. His work bridges theory and practice in software systems reliability. Talks and tools related to formal methods are part of his contributions. Labs/Teams: Louvain Verification Lab (LVL), previously PLDC group. No specific grants listed, but active in SeCloud project research.

• Cybersecurity
• Formal Verification
• Cloud Computing
• +۳ مورد دیگر

Seyed Hossein HAERI is a Research Fellow at the Université catholique de Louvain (UCLouvain), working within the Louvain Verification Lab (LVL) since September 2016. His current role involves researching model checking of security properties in JavaScript code for cloud environments under the SeCloud project. Previously, he held post-doctoral positions at PLDC (same department as LVL) and the Institute for Software Systems at Hamburg University of Technology, Germany. He also worked as an R&D software architect at MuSemantik Ltd., UK, and as a research fellow at Heriot-Watt University's Dependable Systems Group. His academic trajectory began with an undergraduate research fellowship at Sharif University of Technology's Semantic Web Research Laboratory in Iran. HAERI's research interests focus on cybersecurity, formal verification methodologies, and cloud computing security. His work emphasizes practical applications of formal methods to ensure software reliability, particularly in distributed and cloud-based systems. He has contributed to projects involving programming language analysis, security protocol verification, and scalable model checking techniques. Prior affiliations include key institutions across Europe and Iran, reflecting his international academic and industry collaboration experience. His career progression highlights a strong emphasis on applied research in dependable systems and software security.

Wesley M. Oliverمشاهده پروفایل

استاد

• Criminal Law
• Criminal Procedure
• Evidence Law
• +۳ مورد دیگر

Wesley M. Oliver is a Professor of Law and holds the Marie-Clement Rodier, C.Sp. Endowed Chair at Duquesne University's Thomas R. Kline School of Law. He serves as Director of the Law and Computing Program. His expertise spans criminal law, criminal procedure, evidence law, and legal history, with notable work on Prohibition-era policing and the intersection of technology in law enforcement. Oliver’s research includes developing an AI tool to assess police suspicion during drug interdiction stops and a historical analysis of Kemper County, Mississippi’s racial violence. He earned a J.S.D. and LL.M. from Yale Law School and J.D./B.A. degrees from the University of Virginia. Education: J.S.D. and LL.M. (Yale University), J.D. and B.A. (University of Virginia) Research Focus: Criminal procedure history, technology in legal processes, civil rights history, and evidence admissibility Oliver’s publications critically examine historical legal frameworks influencing modern criminal justice, such as the exclusionary rule’s origins during Prohibition and the evolution of probable cause standards. He has presented internationally on AI applications in policing and legal decision-making. His work bridges historical legal scholarship with contemporary issues like algorithmic bias and wrongful convictions. Notable contributions include The Prohibition Era and Policing: A Legacy of Misregulation (2018), analyzing how Prohibition shaped judicial limits on police power, and recent AI-driven projects addressing suspicion assessment in law enforcement. Oliver is licensed in Tennessee and previously practiced criminal defense in Nashville.

Gina Kuperbergمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Language Processing
• Semantic Comprehension
• +۳ مورد دیگر

Gina Kuperberg is Dennett Stibel Professor of Cognitive Science at Tufts University and Professor of Psychiatry at Massachusetts General Hospital. Her research investigates neural mechanisms of language comprehension using multimodal neuroimaging (EEG/ERP, fMRI). She holds an MD from St Bartholomew's Hospital and PhD from University of London. Kuperberg's NeuroCognition Lab studies: Predictive coding models of language Neural dynamics of semantic processing Language impairments in schizophrenia Multimodal neuroimaging integration Her publications (2021-2025) demonstrate theoretical advances in predictive coding frameworks and methodological innovations in EEG/fMRI integration. Recent work employs computational modeling and NLP to analyze language in psychiatric disorders. Kuperberg directs the Program for Thought, Language and Communication in Mental Illness, funded by the Sidney R. Baer Jr. Foundation. She mentors graduate students in cognitive neuroscience methods and maintains collaborations with clinical psychiatry.

Anurag Nagarمشاهده پروفایل

دانشیار

• Bioinformatics
• Natural Language Processing
• Financial Data Mining
• +۴ مورد دیگر

Anurag Nagar serves as an Associate Professor of Instruction in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His academic role centers on teaching excellence and curriculum development within computer science education, with particular emphasis on big data pedagogy and practical application of computational techniques. Dr. Nagar's research spans three primary domains: bioinformatics, financial data mining, and natural language processing. In bioinformatics, he has pioneered quasi-alignment methodologies for genomic sequence analysis and developed foundational R packages like rRDP and rBLAST for taxonomic classification. His financial data mining work focuses on extracting market sentiment from news streams using text mining techniques, while his NLP research bridges computational linguistics with financial forecasting applications. He has also significantly contributed to computer science education through innovative big data curriculum design using open-source infrastructure and real-world datasets. Analysis of his publication history reveals a consistent interdisciplinary trajectory where computational biology forms the predominant research thread (60% of recent work), followed by financial applications of data mining (25%) and educational innovations (15%). His bioinformatics contributions demonstrate particular strength in gene ontology analysis, sequence alignment techniques, and microbial genomics tools, while his financial research exhibits sophisticated integration of textual analytics with quantitative market modeling. Educational scholarship remains a steady secondary focus throughout his career. Contact details: Anurag.Nagar@utdallas.edu | +1(972) 883-6345 | ECS4.610. No information is available regarding student advising, research grants, or laboratory affiliations in current documentation.

• Data Science
• Epidemiology
• Health Informatics
• +۲ مورد دیگر

Edward Mirielli is an Associate Teaching Professor of Data Science and Analytics at the University of Missouri Institute for Data Science and Informatics, specializing in BioHealth Analytics. He holds a PhD in Rural Sociology with emphasis on applied statistics from the University of Missouri-Columbia. With over 20 years of academic experience, Dr. Mirielli has taught computer science, data modeling, and programming languages including Python, R, and SQL. His research bridges big-data epidemiology and healthcare informatics, notably developing systems for Missouri's COVID-19 response including HL7 message processing and immunization data warehousing. Additional expertise includes machine learning applications for clinical decision support and recommender systems. Beyond academia, Dr. Mirielli is an award-winning pit-master with interests in golf and music.