Xin Li is a Professor in the Department of Electrical and Computer Engineering at Duke University and serves as the Associate Vice Chancellor at Duke Kunshan University. He holds a Ph.D. from Carnegie Mellon University (2005) and has held leadership roles in research consortia like the FCRP Focus Research Center and the Center for Silicon System Implementation (CSSI). His research bridges integrated circuits , machine learning , and cyber-physical systems , with applications in autonomous driving, battery lifetime prediction, and smart buildings. Education : Ph.D., Carnegie Mellon University (2005); M.S., Fudan University (2001); B.S., Fudan University (1998) His work emphasizes robust design methodologies for analog/RF circuits, data-driven predictive modeling , and Bayesian inference for high-dimensional variation spaces. Recent publications focus on generative adversarial networks for circuit design, multi-view imputation for incomplete data, and knowledge-driven autonomous systems . He has received numerous accolades, including the NSF CAREER Award (2012) , IEEE Donald O. Pederson Best Paper Awards (2013, 2016) , and IEEE Fellow (2017) . He has served as Editor for journals like IEEE Transactions on Biomedical Engineering and as Chair for conferences including ISVLSI and CAD/Graphics.
Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.
Jean-Christophe Burie is a Professor at the University of La Rochelle, serving as Vice-President for Digital Campus and Information Systems, Deputy Director of the L3i Laboratory (Informatics, Imaging, and Interaction), and Director of the joint research laboratory SAIL (Sequential Art Image Laboratory). His interdisciplinary work bridges computer science and humanities. His research focuses on image processing, pattern recognition, and artificial intelligence , with applications in historical manuscripts, comics indexing, and digital security. Key projects include enhancing palm-leaf manuscript analysis (AMADI/STIC ASIE), developing e-BDthèque for comics content extraction, and identity verification systems (MOBIDEM/IDECYS). He collaborates internationally with institutions like Leiden University and Vietnam's ICT Lab on medical imaging and cultural heritage preservation. He advises doctoral students in areas spanning ancient text analysis, document security, and manga character recognition. His recent publications demonstrate a strong emphasis on deep learning adaptations for document analysis, biometric security, and cross-modal recognition systems.
Laurence S. Magder, PhD, serves as Professor in the Department of Epidemiology and Public Health at the University of Maryland School of Medicine, where he has held continuous faculty positions since 1994 after progressing from Assistant to Associate to full Professor. With over 30 years of biostatistical expertise, he has contributed to nearly 200 biomedical publications through collaborative research across diverse health domains. Educational background includes: PhD in Biostatistics, Johns Hopkins University (1994) Master of Public Health, University of Michigan (1983) His research program centers on developing accessible statistical methodologies for real-world biomedical challenges. Key specialties include longitudinal data analysis, handling misclassified/missing data, transmission probability modeling, and systemic lupus erythematosus applications. Magder actively promotes a paradigm shift in statistical practice—advocating for evidence quantification over rigid hypothesis testing frameworks, which he argues renders traditional concerns like one-sided tests and multiple comparisons adjustments largely obsolete in scientific decision-making. Publication analysis reveals consistent methodological innovation across infectious disease modeling, diagnostic test evaluation, and missing data solutions. His work prioritizes practical applicability, translating complex statistical theory into tools usable by non-statisticians while maintaining rigorous evidence standards. Recurring themes include simplification of analytical approaches and contextual interpretation of statistical evidence within broader scientific judgment. As a collaborative biostatistician, Magder has supported numerous biomedical research projects throughout his career, though specific advising relationships and grant details remain undocumented in available sources. His role exemplifies the critical contribution of statistical expertise to advancing medical and public health research through both methodological development and direct project consultation.
Manohar N. Murthi serves as an Associate Professor in the Department of Electrical & Computer Engineering at the University of Miami's College of Engineering. His academic profile shows active engagement across both technical engineering domains and social science research, with recent publications spanning quantum computing applications, neural network models for biomedical signal processing, and political conspiracy theories. Dr. Murthi's research interests bridge multiple disciplines, with primary focus areas including machine learning, quantum computing, signal processing, belief theory, and conspiracy theory research. His work demonstrates a unique interdisciplinary approach that connects electrical engineering methodologies with social science applications, particularly in analyzing belief systems and misinformation patterns. The breadth of his research is evident in publications ranging from technical algorithms for Dempster-Shafer belief theory to sociological studies on White Replacement theory and QAnon conspiracy beliefs. Analysis of his recent publications (2020-2024) reveals two distinct but complementary research trajectories: technical work in quantum tensor networks, graph neural networks, and belief theory frameworks; and social science applications examining conspiracy theories, political extremism, and misinformation. His technical papers often develop novel computational frameworks for uncertainty quantification and data analysis, while his social science work applies these methodologies to understand belief formation and political behavior. This dual focus creates a distinctive research profile that connects engineering rigor with social science insights. Dr. Murthi maintains active research collaborations, particularly with Kamal Premaratne, across multiple publications in both engineering and political science journals. His work has been published in venues including IEEE transactions, The Journal of Politics, and Politics, Groups and Identities, demonstrating successful cross-disciplinary scholarship. His research appears to be supported by collaborative grants, though specific funding sources aren't detailed in the available information. While specific laboratory information isn't provided in the source material, Dr. Murthi's research suggests involvement in computational laboratories focused on machine learning, signal processing, and data analysis. His work with sEMG signals for gesture recognition indicates potential connections to biomedical engineering labs, while his belief theory research suggests computational theory groups. His interdisciplinary approach likely involves collaboration across multiple research teams within and beyond the College of Engineering.
Professor Donna Slonim is a leading computational biologist at Tufts University with dual appointments in the Department of Computer Science and Department of Immunology , and membership in the Genetics, Molecular and Cellular Biology program. She holds a Ph.D. from MIT (1996) and focuses on integrating genomic data with algorithmic approaches to advance disease diagnosis and treatment. Education Ph.D., MIT, 1996 M.S., University of California, Berkeley, 1991 B.S., Yale University, 1990 Research Interests include: Algorithm development for biological network analysis Precision medicine applications in human development Pharmacogenomics and drug discovery Temporal gene expression modeling Machine learning for biomedical data Scientific Awards : Best Student-Led Paper Award at ACM-BCB 2022 Academic Leadership : Teaches courses like Computational Biology , Statistical Bioinformatics , and Biological Networks Co-leads the BCB Group at Tufts On sabbatical during 2025-26 but remains active in research
Joe Chick is a historian specializing in urban society across the medieval and early modern periods, affiliated with the University of Warwick. His research explores monastic lordship, social networks, and urban adaptation post-Dissolution of the Monasteries. He combines traditional historical methods with digital humanities tools like social network analysis. PhD in History (University of Warwick, 2016-2020) Early Career Fellowship (Institute of Advanced Study, Warwick, 2020-2021) Research Assistant (Wellcome-Trust project, King's College London) His work spans topics including the 1381 Revolt, parish governance, and the intersection of ceremony and authority in medieval towns. He has presented at conferences across Europe and contributed to academic publications in these fields. Scientific awards include the Pickering Prize for highest medieval dissertation mark (2016) and an ESRC Doctoral Studentship (2016-2020). He has taught undergraduate modules on medieval history, folklore, and urban identity since 2018.
Kota Saito is a Professor of Economics at the California Institute of Technology. He holds a Ph.D. from Northwestern University (2011) and previously served as an Assistant Professor at Caltech from 2011–16 before becoming a full professor in 2017. Education: B.A. (Keio University, 2005), M.A. (University of Tokyo, 2007), Ph.D. (Northwestern University, 2011) His research focuses on decision theory , developing mathematical models that explain behavioral regularities in experimental economics and psychology. Key areas include stochastic intertemporal preferences , random utility models , and discrete choice analysis . Recent work explores non-separable time preferences in dynamic discrete choice models and axiomatizations for random utility with incomplete datasets. The 15 most recent publications span decision theory , discrete choice , and behavioral economics , with particular attention to stochastic intertemporal choice , mixed logit models , and risk-time preference interactions . These studies often involve collaborations with Federico Echenique, Jay Lu, and Taisuke Imai. Scientific Awards NSF Grant SES-1919263 (PI) NSF Grant SES-1558757 (Co-PI) Pension Research Council/TIAA Institute Partnership Grant (2017-18) At Caltech, Saito is affiliated with the Ronald and Maxine Linde Institute of Economic and Management Sciences and the Center for Theoretical and Experimental Social Sciences (CTESS). He teaches graduate courses in economic theory, including Behavioral Decision Theory and Theory of Value .
Dr Miao Xu is a Research Fellow at the University of Queensland (UQ), affiliated with the School of Electrical Engineering and Computer Science within the Faculty of Engineering, Architecture and Information Technology. She holds an Australian Research Council DECRA Fellowship (ARC DECRA), recognizing her early-career research excellence. Her research focuses on machine learning, data science, and time series analysis, with applications in healthcare, materials science, and algorithmic fairness. Dr Xu's work addresses challenges in noisy label handling, unlearning mechanisms, and adaptive modeling for irregular data. Education: She earned a Doctor of Philosophy (PhD) from Nanjing University. She is actively involved in supervising research and contributes to the Centre for Enterprise AI at UQ. Research Interests: Dr Xu’s expertise spans machine learning , time series analysis , deep learning , and unsupervised learning . Her recent work emphasizes robust learning with noisy or incomplete labels, model unlearning, and applications in alloy design and medical informatics. She explores methods like instance-attention GNNs for irregular time series and confidence-guided techniques for adversarial attack detection. Publications: Her recent work includes advancements in GNN-based time series modeling, bias mitigation in text classification, and active learning for alloy design. Key themes include improving generalization, reducing algorithmic bias, and enhancing model transparency. Awards: Her ARC DECRA fellowship (202X–202X) supports her research on data-driven methodologies. Supervision & Grants: Available for PhD supervision in machine learning and data science. Her grants include funding for projects in unlearning mechanisms and spatiotemporal modeling. Labs/Teams: Affiliated with the Centre for Enterprise AI at UQ, collaborating on enterprise-scale AI applications and interdisciplinary research.
Dr. Luc Rocher is a UKRI Future Leaders Fellow and Senior Research Fellow at the Oxford Internet Institute (OII), University of Oxford. They are also affiliated with Kellogg College and Imperial College London’s Data Science Institute. Their work focuses on algorithmic accountability, privacy-enhancing technologies, and the societal impacts of AI. Rocher leads the Synthetic Society Lab, investigating how to make technology accountable to the public, and the Observatory of Anonymity, an international tool assessing re-identification risks. Their research bridges technical and social science approaches, including statistical modeling, adversarial machine learning, and interactive tools. Education: PhD from Université catholique de Louvain (2019), prior roles at Imperial College London, ENS de Lyon, and MIT Media Lab. Awards include the UKRI Future Leaders Fellowship and recognition from institutions like the European Commission and OECD. Rocher has published in top journals/conferences (Nature Communications, Usenix Security, WWW) and contributed to policy discussions on AI regulation. Advising: Current students include Andrew Bean (DPhil), Lujain Ibrahim (DPhil), Juliette Zaccour (DPhil), and others. Grants funded by UKRI, EPSRC, and the John Fell Fund. Research emphasizes privacy risks in digital data, pricing algorithms, and public sector AI systems. Labs/Teams: Synthetic Society Lab (public interest AI), Observatory of Anonymity (global re-identification risks). Recent work includes demonstrating flaws in traditional anonymization methods and advocating for privacy-preserving frameworks.
Yue Li is an Associate Professor at the School of Computer Science, Nanjing University, where they co-run the PASCAL Research Group with Tian Tan. Their work focuses on static program analysis techniques and tools for programming languages, software engineering, security, and hardware verification. PhD in Computer Science from UNSW Sydney (2016) Postdoctoral research at Aarhus University (Denmark) and UNSW Sydney B.Eng and M.Eng from Northwestern Polytechnical University (2010, 2012) Research interests center on Program Analysis and Programming Languages , with a focus on: Pointer analysis for database-backed applications Context sensitivity optimization Reflection analysis in Java/Android Operational semantics for hardware languages Distributed dataflow analysis frameworks Developer-friendly static analysis tools Key publication trends (2016-2025) span static analysis , pointer precision , reflection handling , and tool frameworks across conferences like OOPSLA, PLDI, ICSE, ISSTA, and journals including TOPLAS and IEEE TSE. Notable artifacts include Tai-e and Chianina systems. 2025: ICSE Best Artifact & Distinguished Paper Awards 2024: IEEE TSE Publication on Generic Sensitivity 2023: OOPSLA Distinguished Artifact, SPLASH/ECOOP committees 2021: National Youth Talent Support Program, ZiJin Scholar 2016: ECOOP Distinguished Paper, CGO Best Paper As co-PI of PASCAL Research Group, they lead projects on precision-guided analysis, microservice systems, and cloud-based dataflow frameworks, with teaching awards for SICP and Software Analysis courses.
Byeong U. Park is a Professor in the Department of Statistics at Seoul National University, College of Natural Sciences. He has held this position since 1999 and previously served as Assistant Professor (1988–1992) and Associate Professor (1992–1999) at the same university. His research focuses on nonparametric structured models, semiparametric inference, and non-Euclidean data analysis. 1982: B.Sc., Department of Computer Science and Statistics, Seoul National University 1984: M.Sc., Department of Computer Science and Statistics, Seoul National University 1987: Ph.D., Department of Statistics, University of California at Berkeley (supervised by Peter J. Bickel) His research spans nonparametric regression, density estimation, and analysis of complex data on manifolds. Key methodologies include smooth backfitting, local likelihood estimation, and handling of errors-in-variables. He has developed techniques for additive models, varying coefficient regression, and high-dimensional data analysis. Notable scientific awards include the 2019 Incheon Award of Science and Technology, 2018 Carver Medal from IMS, 2017 Seoul National University Research Award, and fellowships from IMS, ASA, and KAST. He has served as Editor-in-Chief for multiple journals and held leadership roles in the Bernoulli Society and ISI. 2019–2023: Vice President, International Statistical Institute 2013–2017: Elected Council member, Bernoulli Society 2002–2004: Editor-in-Chief, Journal of Korean Statistical Society As head of the Nonparametric Inference Lab at Seoul National University, he leads research on infinite-dimensional statistical models and nonparametric methods, emphasizing applications to real-world problems in economics and biomedical data.
Xiaoyang Wang is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at the University of New South Wales (UNSW). He holds a Bachelor's and Master's degree in Computer Science from Northeastern University, China, and earned his PhD from CSE UNSW. Dr. Wang's research focuses on database systems with a special emphasis on query processing and data mining on large-scale graph, spatial, and streaming data. His expertise extends to data-driven machine learning, smart contract analysis on blockchain, and FinTech with financial network analysis. His work spans Graph Processing, Graph Neural Networks, Spatial Data Processing, AI for Databases (AI4DB), Database for AI (DB4AI), and FinTech applications. His publication record shows significant contributions to the field with 7 book chapters, 56 journal articles, 61 conference papers, 7 edited conference proceedings, and 4 conference abstracts. Recent publications (2022-2025) demonstrate his strong research trajectory in advanced graph processing techniques, neural network applications, and innovative database approaches. Key themes include hierarchical contrastive learning, robust attack frameworks, temporal graph processing, influence maximization, knowledge graph-enhanced reasoning, and rumor mitigation. Dr. Wang actively recruits PhD students interested in pursuing research in related fields and encourages current undergraduate and master's students at UNSW to contact him about research opportunities. He maintains an active research agenda with practical implications for industries dealing with large-scale network data, financial technology applications, and data-intensive systems. He can be reached at xiaoyang.wang1@unsw.edu.au and is located in Engineering building K17-501D at UNSW.
Dr. Kiril Kuzmin is a Lecturer in the Department of Computer Science at Georgia State University, where he teaches Data Structures, Algorithms, Data Science, and Machine Learning. He holds a Ph.D. in Computer Science (2024) with a concentration in Bioinformatics from Georgia State University and a Ph.D. in Mathematics (2009) from the National Academy of Sciences of Belarus. His academic journey includes roles as Assistant and Associate Professor at Belarusian State University and a postdoctoral fellowship at the University of Turku, Finland. Dr. Kuzmin’s research focuses on Bioinformatics, Machine Learning, Discrete Optimization, and Graph Theory. He has published over 50 papers, with notable contributions in stability analysis of combinatorial optimization problems and applications of machine learning in genomics. His work includes predicting host specificity of coronaviruses and developing algorithms for heterogeneous genomic population analysis. Dr. Kuzmin has received the Scopus Award in Mathematics (2013) and served as PI/co-PI on three international projects. His teaching spans Java programming, Data Structures, and advanced mathematical courses like Calculus and Algebra. He is affiliated with Georgia State’s bioinformatics research group and has actively contributed to academic and political advocacy in Belarus.
Dave Tompkins is an Associate Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. He holds a PhD in Computer Science and a MASc in Electrical Engineering from the University of British Columbia, along with a BESc and BSc from Western University. His primary research focuses on Stochastic Local Search (SLS) algorithms for the Satisfiability Problem (SAT) and MAX-SAT. Research Interests: His work spans Stochastic Local Search Algorithms SAT and MAX-SAT problem solving Dynamic Local Search techniques Heuristic algorithm design and optimization Data compression and image coding Genetic algorithms and game theory Scientific Awards: He has received Best Paper Award (2006, CCAI) Best Poster Awards (2003, ASI; 1999, ASI) Gold and Silver Medals in SAT Competitions (2004) Incomplete Solver Track Awards (2012, MAX-SAT) Projects: He is known for developing the UBCSAT framework and the Captain Jack SAT solver. He has also contributed to standards like JBIG2 and JPEG-2000.
