Sandra Sampaio is a Lecturer in Information Management at the University of Manchester, actively contributing to research in data science and intelligent transportation systems. Her work bridges technical computing with practical applications in environmental sustainability and traffic management. Dr. Sampaio's research focuses on vehicular edge computing, resource allocation algorithms, and data wrangling techniques . Her work demonstrates particular expertise in applying computer vision to environmental monitoring and developing anomaly detection systems for traffic management. Her research portfolio shows a consistent trajectory toward solving complex problems at the intersection of data science and real-world transportation challenges. Analysis of her recent publications reveals a strong emphasis on practical applications of AI in transportation systems , with multiple papers addressing resource allocation in vehicular networks and edge computing environments. Her work shows increasing sophistication in handling spatial-temporal data patterns and developing efficient computational approaches for real-time traffic analysis. DRES ME - Data science to Recycle textiles for Environmental Sustainability - Manchester Engagement Study (2022) Dynamic Resource Management for Intelligent Transportation System Applications (2022) Dr. Sampaio has supervised multiple research projects and students, with evidence of collaborative work across disciplines. Her research has received notable external funding and recognition, including significant awards related to environmental sustainability and transportation systems. She maintains active collaborations with researchers across multiple institutions, particularly with Pedro Sampaio at the University of Manchester.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۶۱-۷۵ از ۹۲ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Austin Z. Henleyمشاهده پروفایل
عضو هیئت علمی
Assistant Teaching Professor at Carnegie Mellon University with expertise in AI-assisted programming, developer tools, and human-computer interaction. Previously held academic positions at the University of Tennessee (Assistant Professor 2018-2022) and research roles at Microsoft (Senior Researcher 2022-2023, Visiting Researcher 2019). Industry experience includes CTO at BYBE (acquired in 2023) and VP of Engineering at Swiftly. PhD from University of Memphis (Advisor: Scott Fleming) Over 40 publications in software engineering, AI tools, and educational technology Recipient of multiple best paper awards at EICS, SIGMOD, and VL/HCC Research focus on AI-enhanced programming interfaces and educational tool development Recent work explores transitioning academic research into industry applications through the OurCode framework, while educational efforts examine LLM-based programming assistants' impact on learning outcomes.
Kathleen Fisherمشاهده پروفایل
استاد مدعو
Kathleen Fisher is the Director of the Information Innovation Office at DARPA and an Adjunct Professor of Computer Science at Tufts University. She previously served as a full Professor and Chair of the Department of Computer Science at Tufts (2016–2021) and held roles as a Program Manager at DARPA (managing the HACMS and PPAML programs) and Principal Member of Technical Staff at AT&T Labs Research. She earned her PhD in Computer Science from Stanford University. Education: PhD in Computer Science, Stanford University. Research Interests: Kathleen’s work focuses on advancing programming languages theory and practice, with three core thrusts: (1) Domain-Specific Languages (DSLs) (e.g., Hancock, PADS, Forest), (2) Program Synthesis (e.g., generating programs from specifications), and (3) Formal Methods for provably correct software (e.g., the DARPA HACMS program). Her research emphasizes collaboration and interdisciplinary applications in cybersecurity and data wrangling. Publications: Her recent work spans DSL design, verified parsers, binary message format inference, and lens synthesis. Key trends include leveraging formal methods for security and advancing program synthesis techniques for practical systems. Awards: ACM Fellow (2011), Hertz Foundation Fellow, SIGPLAN Distinguished Service Award, and past leadership roles in SIGPLAN and the CRA-W Committee. Advising & Grants: Advised PhD students (e.g., Matthew Ahrens), led DARPA-funded projects, and contributed to open-source tools like Hoopl and ANTLR. Her lab, the TuPL Group , focuses on programming language innovations and education. Labs/Teams: Founded the Tufts Programming Languages (TUPL) research group and co-leads the Fisher Lab, advancing interdisciplinary applications of programming language techniques.
El Kindi Rezigمشاهده پروفایل
استادیار
- Data Management
- Data Quality
- Data Discovery
- +۵ مورد دیگر
El Kindi Rezig is an Assistant Professor at the Kahlert School of Computing, University of Utah, specializing in data management and data quality. He holds a Ph.D. in Computer Science from Purdue University, advised by Walid Aref and Mourad Ouzzani. Previously, he was a research scientist/postdoc at MIT CSAIL under Turing Award recipient Michael Stonebraker. His research focuses on automating data science workflows through systems addressing data discovery, preparation, and debugging. Collaborations with Intel and healthcare institutions drive real-world applications. Research interests include scalable data cleaning (e.g., Horizon system), causal explanations for AI (CausalExplain), and visual data wrangling (Buckaroo). Major grants include an NSF-Simons Institute award (PI: Stella Offner) and University of Utah funding. He has advised 8 students (Ph.D., MS, and undergraduate researchers) since 2021. Key awards include Purdue's Raymond Boyce Teaching Excellence Award (2017) and teaching fellowship (2015-2016). Courses taught include Data Management for ML (University of Utah) and Systems Programming at Purdue. He is active in conference service, serving on PC for VLDB 2025, SIGMOD 2025, and KDD 2024. His Erdős number is 3 via a path linking through Michael Stonebraker and Patrick E. O'Neil. Notable systems include Dagger (data debugging), DICE (example-based data discovery), and SeerCuts (explainable discretization). Recent work emphasizes healthcare analytics and heterogeneous data management, as co-editor of a Springer LNCS volume on the topic (2021).
Nabeel Siddiquiمشاهده پروفایل
استادیار
Nabeel Siddiqui is an Assistant Professor of Communications at Susquehanna University. His work bridges computational methods with humanities scholarship, focusing on digital humanities and computer science applications. Ph.D., College of William and Mary MA, George Mason University BA, University of Southern Mississippi Research interests include Digital Humanities , Machine Learning , Statistical Modeling , and Reproducibility in Research . He develops tools for data wrangling and digital scholarship workflows, emphasizing cross-cultural and multilingual approaches. Recent publications examine computer vision datasets , neural networks for image classification , and statistical validity in humanities research . His work also addresses inclusivity challenges in academic conferences and the materiality of data in scholarly ecosystems.
Tianyi Zhangمشاهده پروفایل
استادیار
Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.
Michael Braunمشاهده پروفایل
استادیار
Michael Braun is an Assistant Professor in the Department of Management & Entrepreneurship at DePaul University's Driehaus College of Business. His research focuses on organizational behavior, computational modeling, and multilevel dynamics. Prior to joining DePaul in 2019, he served as an instructor at the University of South Florida. Education PhD, Organizational Psychology, Michigan State University (2012) MA, Organizational Psychology, Michigan State University (2009) BA, Psychology, Purdue University (2006) Research Areas Braun's work spans emergent leadership, team cohesion, and knowledge emergence in dynamic organizational contexts. He employs computational modeling, longitudinal data analysis, and sociometric methods to study multilevel dynamics in teams and decision-making processes. His publications address methodological challenges in organizational research, including spurious relationships in longitudinal data, dominance analysis accuracy, and construct validity in big data studies. Current projects integrate computational models with social theory and examine temporal patterns in team cognition and performance. Scientific Awards 2013 Organizational Research Method Best Paper Award 2015 Owens Scholarly Achievement Award 2017 Journal of Business and Psychology Reviewer of the Year Award Braun also works with Starling Trust Sciences since 2016, focusing on sociometric data analysis and psychometric assessments. His methodological expertise is reflected in contributions to journals like Journal of Applied Psychology , Organizational Research Methods , and Journal of Vocational Behavior .
Mine Çetinkaya-Rundel is a Professor of the Practice and Director of Undergraduate Studies in the Department of Statistical Science at Duke University, with an affiliation in the Computational Media, Arts, and Cultures program. Her work emphasizes innovative pedagogy in statistics and data science, focusing on reproducible research, open-source tools, and student-centered learning. She actively integrates computational methods into undergraduate curricula and teaches courses like STA 199 (Introduction to Data Science) and STA 313 (Advanced Data Visualization). Her research interests span reproducible research methodologies, the use of real-world datasets in education, and the development of open-source educational resources. Key contributions include the infer R package, the OpenIntro Statistics textbooks, and initiatives like DataFest. She advocates for accessible and equitable education through projects like CURV (Connecting, Uplifting, and Recognizing Voices). Her teaching and research reflect a commitment to bridging theory and practice, emphasizing computational literacy and ethical data use. Collaborations with platforms like Posit (formerly RStudio) further her mission of advancing reproducible workflows in education and industry.
Lisa Elonمشاهده پروفایل
عضو هیئت علمی
- Biostatistics
- Public Health
- Environmental Health
- +۵ مورد دیگر
Lisa Elon is an Associate Research Professor in the Department of Biostatistics and Bioinformatics at Emory University. She specializes in biostatistical applications across public health, environmental health, and clinical research. Her work includes analyzing data on mental stress and myocardial ischemia, indoor air pollution, social contact networks, and immunity response studies. She has led data management teams for major trials and contributed to studies on workplace physical activity and vaccine provision. Education: MPH from Emory University and MS from North Carolina State University (NCSU). Research interests focus on leveraging biostatistics to address public health challenges, including environmental exposures, clinical trial design, and data-driven policy recommendations. Her expertise spans data wrangling, survey methodology, and longitudinal study design, as demonstrated by her role in the Healthy Doc Project tracking medical students' health. Awards include the 1997 RSPH Charles C. Shepard Award and a Rollins Merit Scholarship. She teaches data analytics courses at the Emory School of Nursing and advises the Applied Practical Experience program. Her grants and projects include collaborations on vaccine policy, emergency department physician performance evaluations, and interventions to improve workplace health. While specific labs/teams are not listed, her work involves interdisciplinary teams across public health, clinical, and environmental domains.
Flavio De Paoli is an Associate Professor affiliated with the Insid&s LAB. His research focuses on semantic web technologies, data enrichment, and interactive tools for data management. Key projects include the enrichMyData initiative and the development of frameworks like SemT and Protobject . His work spans geospatial data analysis, services-oriented computing, and scalable data solutions. Notable contributions include tools for semantic table interpretation (e.g., MantisTable ), API-based data enrichment, and event-driven analytics. He has also contributed to cloud computing frameworks and semantic wiki integration for knowledge management. His research emphasizes human-in-the-loop systems, automated data processing, and tool development for both academic and industrial applications. Projects like EW-shopp and BEEO demonstrate his focus on applying semantic technologies to real-world challenges in marketing, urban planning, and smart systems.
Amelia McNamara is an Associate Professor in the Department of Computer and Information Sciences at the University of St Thomas. She holds a PhD in Statistics from UCLA and a BA in Mathematics and English from Macalester College. Her research focuses on statistical computing, statistics education, and data visualization, with notable contributions to tools like R and pedagogical strategies such as vocalizing code. She has been recognized with awards including the Waller Education Award (2021) and the Accessibility Award (2022). Her teaching spans courses like Introductory Statistics, Applied Regression Analysis, and Data Communication and Visualization. She has led curriculum development for high school data science programs and contributed to initiatives like the Mobilize project, impacting over 10,000 students. McNamara actively participates in professional service roles, including editorial work for journals like the Journal of Statistics and Data Science Education and membership in the American Statistical Association. Her research interests also include spatial statistics, data journalism, and reproducible research practices. She collaborates on projects exploring coding conventions in R and the impact of syntax on learning. McNamara maintains an active presence in academic communities through conferences, workshops, and social media, advocating for equitable and transparent data practices.
Samuel Madden is the Faculty Head of Computer Science and Distinguished College of Computing Professor at MIT's Department of Electrical Engineering and Computer Science (EECS). He joined MIT in 2004, earning a PhD from UC Berkeley (2003) and BS/MEng from MIT (1999). His research focuses on database systems, machine learning integration, and high-performance data processing. He co-leads the Data Systems for AI Lab and Data Systems Group. Key achievements include co-founding Cambridge Mobile Telematics, receiving ACM Fellow (2020), and awards like the NSF CAREER Award and SIGMOD Codd Innovations Award. His work bridges database systems with modern AI applications, emphasizing scalable and efficient data processing. Research interests span database analytics, query processing, cloud computing, and applying machine learning to system design. He has published extensively on topics like query optimization (e.g., Abacus, Kairos), AI-driven data systems (Palimpzest, Symphony), and scalable analytics (RITA, Cackle).
Kamilla Kopec-Harding is a Senior Research Data Scientist at the University of Birmingham's Advanced Research Computing department, funded by the Centre for Artificial Intelligence in Government (CAIG). She holds an MSci in Chemistry and Pharmacology from the University of Birmingham and a PhD in computational chemistry from the University of Manchester. Her career includes roles as a Research Assistant in the Manchester Asthma and Allergy Study, Scientist/Health Data Manager at the Arthritis Research UK Centre for Epidemiology, and Research Software Engineer at the University of Manchester (2019–2023). Education: MSci in Chemistry and Pharmacology, University of Birmingham PhD in Computational Chemistry, University of Manchester Her research interests span data science, interdisciplinary collaboration (e.g., Computer Science and Earth Sciences), and health data management. She specializes in using R and Python for data wrangling, visualization, and analysis. Kamilla actively contributes to computational education through Carpentries-style workshops, teaching R, Unix shell, and Git. She leads projects in research software engineering and data science, emphasizing reproducibility and open science practices. Her work bridges academic disciplines to enable data-driven discoveries. Labs/Teams: Research Software Group at the University of Birmingham.
Syed Hussain Ali Naqvi is an Instructor at the University of St. Thomas, specializing in Software Engineering and Data Science. He teaches courses such as SEIS 603 (Foundations of Python) and SEIS 631 (Data Preparation and Analysis). His expertise includes software process management, data-driven decision making, and integrating AI tools into educational practices. Education: MS in Software Engineering, University of St. Thomas BS in Computer Information Systems, Minnesota State University, Mankato BS in Physics and Mathematics, University of Karachi His research interests focus on foundational programming concepts, statistical analysis, and leveraging Python for data management and AI-driven education tools. He emphasizes practical applications of programming, including unit testing with PyTest and data wrangling with NumPy and Pandas. No scientific awards or grants are explicitly mentioned in the provided text.
- Statistics
- Data Science
- Bayesian Statistics
- +۵ مورد دیگر
Peter Kramlinger is affiliated with the University of California, Davis, specifically within the Department of Statistics under the College of Letters and Science. He is actively involved in teaching and developing course materials for undergraduate and graduate-level statistics and data science courses. University: University of California, Davis School: College of Letters and Science Department: Department of Statistics Role: Lecturer His research and instructional interests center on modern statistical computing, Bayesian methods, and data science education. He emphasizes computational tools and reproducible workflows in teaching. The recent course repositories and tutorials reflect a strong focus on practical data analysis, with an emphasis on MCMC methods, statistical programming, and large-scale data processing. These materials span topics in both foundational and advanced data science, indicating a commitment to evolving pedagogical practices in statistics. Although no formal scientific awards are listed, his open-source contributions to educational materials demonstrate scholarly engagement and a dedication to accessible learning resources. Peter Kramlinger advises no listed students, but his instructional role suggests mentorship of students through coursework. There is no mention of external grants or funded research projects in the available data. He maintains an active GitHub presence with publicly shared teaching materials, indicating participation in open academic communities and collaborative learning environments.