Find Academic Supervisors

Andreas NieklerView profile

Researcher

• Natural Language Processing
• Computational Social Science
• Digital Humanities
• +7 more

Andreas Niekler is a research associate and lecturer in Computational Humanities at the Institute of Computer Science, Leipzig University, Faculty of Mathematics and Computer Science. He develops computational methods for semantic language analysis and applies them to computational social science and humanities research, with focus on machine learning and data management methodologies. His research interests span Natural Language Processing , Computational Social Science , Digital Humanities , Text Mining , Machine Learning , and Conversational AI . Niekler specializes in developing algorithms including Bayesian Models, Topic Models, Deep Learning, and Support Vector Machines for text analysis applications. He is actively involved in the development of the interactive Leipzig Corpus Miner platform and researches how semantic representations can be applied to literary studies and scientometrics. Niekler's recent publications demonstrate expertise across computational linguistics, social science methodology, and practical applications of text mining. His work shows strong interdisciplinary connections between computer science, linguistics, and social sciences, with particular emphasis on developing robust methodologies for automated content analysis. As an educator, Niekler has extensive teaching experience in computational methods for humanities and social sciences, offering courses on text mining, computational linguistics, and programming in R and Python. He serves as a scientific staff member in the Computational Humanities research group led by Prof. Dr. Manuel Burghardt and represents scientific staff in the Faculty Council of Mathematics and Computer Science at Leipzig University.

Arnab NandiView profile

Professor

• Database Systems
• Human-in-the-Loop Data Analytics
• Next-Generation Query Interfaces
• +10 more

Arnab Nandi is a Professor in the Department of Computer Science & Engineering at The Ohio State University. His work bridges human interaction with data infrastructure, focusing on database systems, LLM-augmented analytics, and immersive query interfaces. Education: PhD in Computer Science & Engineering from the University of Michigan Leadership: Co-founder of OHI/O Hackathon Program and STEAM Factory interdisciplinary network Research spans human-in-the-loop data analytics , vibe querying (natural language + gestural interfaces), LLM integration into education, and climate response systems . Key projects include Omni (multimodal exploration), GestureDB , and Icarus (clinical pipelines). Recent publications analyze LLM-driven query stacks (HILDA 2025), video analytics (SIGMOD 2022), and data sunglasses for cognitive limits (HILDA 2025). Awards include NSF CAREER Google Faculty Research Award IEEE TCDE Early Career Award ACM Distinguished Member Advises students in database innovation , with alumni at Amazon, AWS, Roblox, and Meta. Teaches CSE 3241 (Database Systems), CSE 5889 (Software Startups), and CSE 5242 (Advanced Databases).

Tuomas Lauri Aleksanteri VäisänenView profile

Researcher

• Urban geography
• Regional studies
• Mobility research
• +7 more

Tuomas Väisänen is a Postdoctoral Researcher at the University of Helsinki's Faculty of Science and Department of Geosciences and Geography . He contributes to multiple research initiatives including the Helsinki Inequality Initiative (INEQ), Institute for Urban Studies (Urbaria), and Institute of Sustainability Science (HELSUS). His academic work bridges computational geography with urban studies, focusing on multilingualism, digital data sources, and mobility patterns. Doctor of Philosophy (2019–2023), Multidisciplinary Doctoral Programme in Environmental Studies, University of Helsinki Master of Philosophy (2016–2018), Department of Geosciences and Geography, University of Helsinki Bachelor of Science (2012–2016), Department of Geosciences and Geography, University of Helsinki His research explores urban diversity through linguistic landscapes , dynamic populations , and residential area changes . He integrates sociolinguistics with geographical methods , emphasizing mobile phone data , social media analysis , and machine learning . Recent projects like BORDERSPACE and MOBI-TWIN investigate cross-border interactions and European territorial integration . His 15 most recent publications highlight trends in computational urban geography , geospatial datasets , and linguistic diversity mapping , with a focus on Erasmus+ mobility , national park interactions , and transnational functional areas . Contributions to open datasets (e.g., Mobi-Twin, Erasmus+ flows) underscore his commitment to digital methods and European regional development . While no formal awards are listed, his peer-review activities for journals like Computers, Environment and Urban Systems and Biological Conservation demonstrate academic engagement. He teaches spatial data methods to Master's students and provides guest lectures on computer vision and GIS . Collaborations span the European Commission, Academy of Finland, and University of Tartu Foundation.

Pamela P MartinezView profile

Assistant Professor

• Computational Biology
• Host-Pathogen Interactions
• Infectious Diseases
• +3 more

Pamela P Martinez serves as an Assistant Professor at the University of Illinois Urbana-Champaign within the College of Liberal Arts & Sciences, holding concurrent appointments in Microbiology, Statistics, and the Center for Latin American and Caribbean Studies. Her research program integrates computational modeling with epidemiological data to address critical questions in infectious disease dynamics. Her primary research focuses on the interplay between ecological and evolutionary processes in pathogens, specifically investigating how climatic and demographic factors shape spatial-temporal disease patterns and how pathogen diversity influences public health interventions. She employs mathematical and computational approaches applied to longitudinal time-series data, with particular emphasis on SARS-CoV-2, dengue, malaria, and rotavirus. Her work bridges theoretical epidemiology with practical public health applications, often incorporating climate science and socioeconomic variables. Analysis of her 15 most recent publications (2021-2025) reveals consistent themes across computational biology, climate-infectious disease interactions, and pandemic response. Key trends include the development of novel modeling frameworks for pathogen evolution (e.g., ortholog refinement algorithms), quantification of climate drivers on tropical diseases, and rigorous assessment of intervention strategies during the COVID-19 pandemic. Her research demonstrates increasing integration of high-resolution mobility data, immune history analysis, and equity considerations in infectious disease modeling. No scientific awards are documented in the available information. Details regarding student mentorship, grant funding, laboratory infrastructure, or collaborative research teams are not provided in the source materials.

Jing YangView profile

Professor

• Data Visualization
• Visual Analytics
• High Dimensional Data Analysis
• +7 more

Jing Yang is a full Professor and Director of MS CS Program in the Computer Science Department at the University of North Carolina at Charlotte (UNCC). She has been actively involved in data visualization and visual analytics research since joining UNCC in 2005 after completing her PhD at Worcester Polytechnic Institute. Dr. Yang earned her Bachelor's degrees in Engineering Mechanics and Computer Science from TsingHua University in 1997 and completed her Ph.D. in Computer Science from Worcester Polytechnic Institute in May 2005 under advisors Matthew O. Ward and Elke A. Rundensteiner. Her research focuses on developing visual analytics techniques for abstract data including multidimensional data, time-oriented data, networks, hierarchies, text documents, and trajectory data. She conducts design studies for application domains such as sports, bioinformatics, finance, network security, and health, while exploring fundamental visualization topics like interactions, insight management, clustering-based approaches, and animations. Her recent work emphasizes sports analytics, urban data, and multivariate time series visual analytics. Analysis of Dr. Yang's publication record reveals a strong focus on practical applications of visualization techniques across diverse domains. Her work consistently bridges theoretical visualization frameworks with real-world data challenges, particularly in transportation (taxi trajectories), sports (tennis match analysis), financial transactions, and bioinformatics. The publications demonstrate an evolution from foundational visualization techniques to increasingly sophisticated domain-specific applications. Dr. Yang has secured substantial research funding from NSF, EPA, DHS, and industry partners including Google and Bank of America. Her grants portfolio includes significant projects like TrajAnalytics (NSF, $200,950), Visualizing Event Dynamics (NSF EAGER, $75,317), and Visualizing High Dimensional Categorical Datasets (Google Faculty Research Award, $60,000), among others totaling over $4 million in research support. As an educator, Dr. Yang has taught numerous courses including Visual Analytics, Information Visualization, and Database Design. She directs the Charlotte Visualization Center and has mentored numerous students through research projects. Her collaborative approach is evident in her extensive co-authorship network spanning multiple institutions and disciplines. Current research directions include narrative animation for streaming text visualization and advanced techniques for exploring high-dimensional categorical datasets.

Feng ChenView profile

Associate Professor

• Nonparametric and semiparametric statistics
• Point processes and self-exciting processes
• Statistical computing
• +3 more

Associate Professor Feng Chen is a faculty member at the School of Mathematics & Statistics, University of New South Wales, specializing in statistical methodology development and applications. His research bridges theoretical statistics and practical implementations across financial modeling, spatiotemporal processes, and public health analysis. PhD in Statistics from University of Hong Kong (2008) MSc in Applied Probability & Statistics from Lanzhou University (2004) BSc in Mathematics from Lanzhou University (2001) Research focuses include: Nonparametric and semiparametric statistical methods Point process modeling with emphasis on Hawkes processes Statistical computing and algorithm development Applications to financial data, earthquake analysis, and public health Recent publications demonstrate methodological advances in: Hawkes process estimation with complex data structures Renewal process applications in seismology GARCH modeling with missing data Spatiotemporal clustering analysis Scientific recognition includes: UNSW Science Staff Impact Award (2023) Professional roles: Director of Research Postgraduate Studies (2023--) Associate Editor for multiple journals Statistics Honours Coordinator (2013-2018) Active participant in statistical societies

Ben K. GrunwaldView profile

Professor

• criminal law
• criminal procedure
• constitutional law
• +4 more

Ben K. Grunwald serves as Professor of Law at Duke University School of Law, where he has been a faculty member since 2017 after completing a Bigelow Fellowship at the University of Chicago Law School. His scholarly work bridges legal theory and empirical analysis to address critical issues in criminal justice systems. JD, PhD in Criminology, AM in Statistics, and BA from the University of Pennsylvania Clerked for the Honorable Thomas Ambro on the U.S. Court of Appeals for the Third Circuit Bigelow Fellow at the University of Chicago Law School (2016-2017) Professor Grunwald's research program centers on evidence-based criminal justice reform, with particular emphasis on prosecutorial power dynamics, sentencing fairness, juvenile justice boundaries, and police accountability mechanisms. His methodological approach integrates advanced statistical techniques with legal scholarship to produce actionable insights for policymakers. Current projects examine racial bias in criminal records, optimal decarceration strategies, and the labor market consequences of police discipline. His recent publications demonstrate a consistent focus on translating empirical findings into practical legal reforms, particularly in law enforcement practices and juvenile justice systems. Through interdisciplinary collaborations with criminologists and statisticians, his work challenges conventional wisdom using rigorous data analysis while maintaining strong connections to constitutional principles and procedural justice. Professor Grunwald teaches foundational courses including Criminal Law and Criminal Procedure: Investigation, along with specialized seminars in Criminology and Criminal Procedure that emphasize empirical methodologies. His curriculum development reflects his commitment to training future lawyers in evidence-based legal practice and policy analysis.

Sharon HowardView profile

Research Fellow

• Early Modern History
• British History
• Women's and Gender History
• +3 more

Dr. Sharon Howard is a historian and Honorary Research Fellow at the University of Sheffield, specializing in early modern crime and law, British history, and women's and gender history. Her work bridges traditional historical research with digital humanities methodologies, including data analysis and visualization. Her research focuses on community dynamics, legal authority, and digital tools for historical inquiry. She maintains the Early Modern Resources website (http://earlymodernweb.org), her personal blog (http://sharonhoward.org), and engages with digital history communities via Twitter (@sharon_howard). Her projects include Criminal Tattoos (2018), Digital Panopticon (2013), and London Lives (2009), among others. These initiatives emphasize accessible historical data and interdisciplinary collaboration.

Ronald J. GlotzbachView profile

Associate Professor

• Web Programming
• Dynamic Content Delivery
• Database Integration
• +6 more

Ronald J. Glotzbach is an Associate Professor at Purdue University's West Lafayette campus within the School of Applied and Creative Computing . His work focuses on web programming and development , leading projects that integrate dynamic content, databases, and educational technologies . He has taught courses such as CGT 356 (Web Programming), CGT 353 (Interactive Media), and CGT 456 (Advanced Web Programming). B.S. in Computer Graphics Technology M.S. in Technology Ph.D. in Curriculum and Instruction (pursuing) His research explores leading-edge web technologies for delivering interactive content, emphasizing web-enabling software, dynamic media integration, and mobile programming . Key trends in his publications include RSS technologies in education , web-based evaluation systems , and geospatial data tools for environmental sciences. Scientific awards include: Outstanding Professor (2003, 2004) CGT Dwyer Award for Outstanding Undergraduate Teaching (2005) CGT Outstanding Un-Tenured Faculty Award (2008) Professor Glotzbach has led numerous student teams in CGT projects , served as SIGGRAPH Student Volunteer Chair (2003-2005) , and collaborated with industry partners like Boeing (F-15 Distributed Mission Trainer) and Microsoft (XML Documents Testing Team) . He also provided expert testimony in a copyright case (2006) for Wargo & French, LLP.

• Climate Change
• Ecology
• Evolution
• +4 more

Dag Olav Hessen is a Professor at the University of Oslo , leading the Center for Biogeochemistry in the Anthropocene (CBA) . The center integrates expertise from the Department of Life Sciences, Earth Sciences, and Chemistry to investigate climate-carbon-ecosystem interactions in northern latitudes. Research Focus: Climate change impacts on freshwater and marine ecosystems Key Areas: Ecological stoichiometry, genome size, pelagic food webs, permafrost thaw Methodology: Interdisciplinary approaches combining fieldwork, lab experiments, and modeling His recent publications address critical issues like greenhouse gas dynamics in thawing permafrost , tipping points in lake ecosystems , and global change effects on zooplankton communities . Collaborative work spans Arctic char vulnerability, DOM-contaminant interactions, and climate governance frameworks.

Taro MienoView profile

Associate Professor

• Precision Agriculture
• Water Resource Management
• Groundwater Economics
• +7 more

Taro Mieno is an Associate Professor in the Department of Agricultural Economics at the University of Nebraska-Lincoln, specializing in precision agriculture and water resource economics. His work bridges agricultural policy, spatial econometrics, and environmental sustainability. University: University of Nebraska-Lincoln Department: Agricultural Economics Academic Rank: Associate Professor Research Interests: Precision agriculture and variable rate technologies Groundwater economics and drought resilience On-farm experimentation and data-driven decision making Agricultural policy and environmental externalities Machine learning applications in agronomic optimization Soil-water interactions and resource management Email: tmieno2@unl.edu

Hye-Chung KumView profile

Professor

• Health Informatics
• Data Science
• Privacy-Preserving Record Linkage
• +4 more

Hye-Chung Kum is a Professor in the Department of Health Policy & Management at Texas A&M University, where she pioneers Population Informatics to transform digital data into evidence-based health policy solutions. Her work bridges computer science, public health, and social work to address critical data challenges in healthcare systems. Education: PhD, University of North Carolina at Chapel Hill (UNC-CH), 2004 MSW (Master of Social Work), UNC-CH, School of Social Work, 1998 MS, UNC-CH, Department of Computer Science, 1997 BS, Yonsei University, Seoul, Korea, Department of Computer Science, 1995 Research Vision: Dr. Kum develops privacy-preserving human-computer hybrid systems for cleaning and integrating chaotic real-world data (e.g., EHRs, administrative records). Her Population Informatics framework enables ethical large-scale analysis while addressing data genocide in marginalized communities through innovations like the Secure Decoupled Linkage (SDLink) system. Publication Trends: Recent work (2024-2025) reveals three dominant threads: (1) Mental health service utilization dynamics during/post-pandemic, (2) Racial/ethnic disparities in emergency department use and cancer outcomes, and (3) Ethical data sharing frameworks for vulnerable populations. Her Texas-focused studies on Medicaid payment models and AI-driven record linkage demonstrate practical policy applications. Research Infrastructure: As director of the Population Informatics Lab, she leads cross-disciplinary teams developing Privacy-by-Design tools that balance public health needs with data sovereignty—particularly for American Indian and Alaska Native communities. Her grant-funded work consistently addresses Medicaid transformation and healthcare cost drivers through secondary data analytics.

Stefan SiebertView profile

Professor

• Crop Production
• Sustainable Resource Use
• Climate Change Effects on Agriculture
• +5 more

Professor Stefan Siebert is Head of the Department of Crop Sciences at the Agricultural Faculty of the University of Göttingen, a position he has held since October 2017. His academic career spans multiple prestigious institutions including the University of Bonn, University of Frankfurt, and University of Kassel, where he completed his doctoral studies. University of Göttingen (2017-present): Professor and Head of Crop Science/Agronomy University of Bonn (2016-2017): Temporary Head of Chair Crop Science University of Bonn (2009-2015): Habilitation in Crop Science and Resource Conservation University of Frankfurt (2004-2009): Postdoctoral Scientist University of Kassel (2002-2005): Ph.D. Student and Scientist Professor Siebert's research focuses on sustainable resource use in crop production, climate change impacts on plant growth, plant growth modeling, and drought risk assessment. His work integrates large-scale data analysis with modeling approaches to understand the complex interactions between resource use, crop management, and productivity. He has developed comprehensive datasets on water, soil, and nutrient use in agriculture and pioneered methods for analyzing irrigation systems globally. His recent publication trends reveal a strong emphasis on irrigation systems, climate change impacts on agriculture, and advanced crop modeling techniques. The analysis of his 15 most recent articles shows consistent focus on water resource management, spatial analysis of agricultural systems, and the effects of climate variability on crop production. His work frequently combines remote sensing data with ground-based measurements to create high-resolution datasets for agricultural decision-making. Professor Siebert leads several significant research projects including ZERN (Future of Nutrition in Lower Saxony, 2024-2029), Collaborative Research Center 1502 on Regional Climate Change (2022-2025), and OUTLAST (Development of a global drought hazard forecasting system, 2022-2025). He has been continuously developing and maintaining a global dataset on irrigated areas since 1997, establishing himself as a leading authority in agricultural water management. His teaching portfolio is extensive, covering scientific writing, field production exercises, general crop production, methodical work, plant biology, crop production and breeding, sustainability of production systems, and applications of data analysis to agronomy. Professor Siebert's work bridges theoretical research with practical applications in sustainable agriculture, making significant contributions to our understanding of how to maintain food security in the face of climate change.

Tolga SinmazdemirView profile

Senior Lecturer

• Political Conflict
• Political Violence
• Middle East Politics
• +2 more

Dr. Tolga Sinmazdemir is a Senior Lecturer in Political Methodology at the Department of Politics and International Studies, SOAS University of London. His research focuses on political conflict and violence, with a special emphasis on the Middle East. Previously, he served as an Assistant Professor at Bogazici University, Istanbul, and held a fellowship at the London School of Economics and Political Science (LSE) School of Public Policy. Current Affiliation: SOAS University of London Prior Affiliation: Bogazici University Research Network: Evidence in Governance and Politics (EGAP) His work rigorously analyzes original data to identify causes and consequences of conflict, including studies on Syrian refugee flows, host society attitudes in Turkey, and cross-border dynamics. Tolga holds a Ph.D. in Politics from New York University. His recent publications examine refugee integration in co-religious societies, threat perceptions among refugees, and geographical influences on foreign policy preferences. His methodological expertise includes survey experimentation and collaborative cross-border research. Tolga supervises doctoral students including Yun-Tzu Chang, Anna E. Kensicki, and Rodi Yilmaz, focusing on transnationalism, narrative constructs, and ideological electoral decline in Turkey.

Sushil PrasadView profile

Professor

• Data Intensive Computing
• Energy-Efficient Deep Learning
• Parallel Algorithms
• +3 more

Sushil Prasad is a Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on data-intensive computing, energy-efficient deep learning models, parallel algorithms, and high-performance software systems. He holds a Ph.D. from the University of Central Florida, an M.S. from Washington State University, and a B.Tech. from the Indian Institute of Technology, Kharagpur. His work emphasizes integrating parallel and distributed computing into early computer science curricula. Key research interests include geospatial data analysis using ICESat-2 and Sentinel-2 imagery, edge device-optimized neural networks, and scalable polygon processing algorithms. He has contributed to frameworks like MPI-GIS and Crayons for high-performance geospatial computing. His educational initiatives include NSF-funded curriculum modernization efforts in parallel computing education. Recent work trends show a focus on climate science applications (e.g., polar sea ice classification), energy-efficient AI, and GPU/OpenMP parallelization. He has organized workshops like EduHPC and EduPar to advance HPC education strategies. Notable recognition includes the TCPP Outstanding Service Award (2012). His projects span cloud-based GIS systems, distributed ML training, and big spatial data processing. Collaborations include NSF-funded research on colocation mining, trajectory analysis, and curriculum development for undergraduate HPC education.