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Nikos TzavidisView profile

Professor

• Small Area Estimation
• Official Statistics
• Outlier Robust Inference
• +7 more

Professor Nikos Tzavidis is a Professor of Statistical Methodology at the University of Southampton . He has held academic positions at institutions including University College London and the University of Manchester, and currently leads research initiatives integrating statistical methodology with geospatial and survey data. His work focuses on Small Area Estimation, official statistics, and poverty mapping, with applications across global development and public policy. Research interests: Small Area Estimation and Official Statistics Outlier Robust Inference Quantile and M-quantile Models Geospatial Data Analysis Poverty Mapping Machine Learning Applications in Statistics Recent research trends emphasize machine learning integration, geospatial data utilization, and advanced quantile modeling for poverty and demographic studies. His publications span journals like the Journal of Official Statistics and the Journal of the Royal Statistical Society . Scientific awards: ISI Fellow (2013) American Statistical Association (2004) Vice Chancellor's Teaching Award (2013) Johann von Spix Professor (2023) He supervises PhD students in social statistics, demography, and geography, and serves on editorial boards for journals including the Journal of Official Statistics . Externally, he has held leadership roles in the International Statistical Institute and contributed to United Nations and World Bank projects.

Scott SissonView profile

Professor

• Computational Statistics
• Bayesian Statistics
• Big Data
• +4 more

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

Sanjay PurushothamView profile

Assistant Professor

• Machine Learning
• Data Mining
• Optimization Theory
• +5 more

Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC), with a PhD in Electrical Engineering from the University of Southern California (USC) and a postdoctoral background in Computer Science at USC's Integrated Media Systems Center (IMSC). His research focuses on machine learning, data mining, and their applications in biomedical informatics, social network analysis, and multimedia data mining. Key contributions include survival analysis models using pseudo values and federated learning frameworks for healthcare data. He has received awards including the Best Paper Award at SIGSPATIAL 2014 and a Best Poster Runnerup at SCMLS 2016. Education: PhD in Electrical Engineering (USC), Postdoc in Computer Science (USC) His work spans interdisciplinary areas such as domain adaptation for remote sensing, thermal face translation, and interpretable neural networks for medical applications. Recent projects include federated survival analysis models and climate-informatics frameworks for cloud property retrieval. He teaches courses in artificial intelligence, healthcare informatics, and statistical learning at UMBC. Research highlights include developing MedFuseNet for multimodal medical question answering and VDAM for multi-sensor cloud data analysis. His work on fair survival analysis models addresses algorithmic bias in healthcare predictions. Current grants include a NSF CAREER award for trustworthy federated learning in computational healthcare.

Mohamed IbrahemView profile

Assistant Professor

• Security and Privacy in IoT Environments
• Applied Cryptography
• Privacy-preserving Machine Learning
• +5 more

Dr. Mohamed Ibrahem is an Assistant Professor at the School of Computer and Cyber Sciences, Augusta University, USA. His research focuses on Cyber-Security, IoT Privacy, and Machine Learning applications in Smart Grids and Cyber-Physical Systems. He received a Ph.D. in Electrical and Computer Engineering from Tennessee Tech University in 2021 and has been recognized with prestigious awards including the IEEE Senior Member (2024) and the Eminence Award for Best Ph.D. Paper (2021). Education: Ph.D., Electrical and Computer Engineering, Tennessee Tech University (2021) Research Interests: Security and Privacy in IoT, Applied Cryptography, Privacy-preserving Machine Learning, Secure Federated Learning, Smart Grid Cyber-Security, and Traffic Analysis Attacks. Publications: His work emphasizes privacy-preserving techniques in smart grids, federated learning for anomaly detection, and robust countermeasures against cyber-attacks. Recent trends include leveraging deep learning for real-time fraud detection in AMI systems and developing efficient decentralized learning frameworks. Awards: IEEE Senior Member (2024) Top 2% Scientists List (Stanford/Elsevier, 2024) Eminence Award for Best Ph.D. Paper (Tennessee Tech University, 2021) Advising & Grants: Supervises graduate research in Dissertation and Master's Thesis projects. Active in securing grants for IoT and Cyber-Security initiatives. Collaborates with industry partners on edge computing and secure AMI networks.

Peng ZhaoView profile

Assistant Professor

• High-dimensional statistics
• Statistical network models
• Multivariate statistics
• +7 more

Peng Zhao is a tenure-track Assistant Professor in the Department of Applied Economics & Statistics at the University of Delaware, with affiliations to UD's Data Science Institute. His academic career includes a postdoctoral research position at Texas A&M University's Department of Statistics (August 2020–June 2023), following his Ph.D. training at Florida State University. Education Ph.D. in Statistics, Florida State University, 2020 B.S. in Statistics, Beijing Institute of Technology, 2015 Dr. Zhao's research focuses on cutting-edge statistical methodologies, including: High-dimensional statistical modeling and inference Network-based statistical analysis Multivariate data processing techniques Scalable Bayesian computational methods Nonparametric Bayesian approaches Dependency-aware statistical learning frameworks Optimization algorithms for complex models Regularization mechanisms in statistical learning He teaches graduate-level courses in regression analysis (STAT611) and mathematical statistics (STAT602) at the University of Delaware. Office location: Room 214, Townsend Hall, 531 S. College Avenue, Newark, DE 19716.

Zhiyong HuangView profile

Associate Professor

• Data Analytics
• Machine Learning
• Computer Vision
• +6 more

Zhiyong Huang is an Associate Professor at the National University of Singapore (NUS) School of Computing. He holds multiple leadership roles, including Deputy Director of the NUS Business Analytics Centre, Director of the Computing Translational Research & Development (C-TReND) Centre, and Assistant Dean (Industry Relations). He is also a Senior Principal Investigator at the NUS Chongqing Research Institute. Education: PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), MEng and BEng in Computer Engineering from Tsinghua University. Leadership: Senior Member of ACM and IEEE, Pioneer Member of ACM SIGGRAPH, and Chair of the Singapore ACM SIGGRAPH Chapter. Research Interests: His work spans Data Analytics, Machine Learning, Computer Vision, Human-Robot Interaction, and Computer Graphics. Key projects include the NUS Digital Twin initiative and secure data analytics pipelines. Article Trends: Recent publications focus on time series generation, cryptocurrency benchmarks, medical image registration, and phishing detection. These works integrate machine learning, computer vision, and multimodal systems. Scientific Awards: Finalist, World Technology Summit & Awards (Entertainment, 2010) Bronze, National Science and Technology Progress Award (1992) Tsinghua 12.9 Distinguished Young Teacher Award (1989) Grants & Service: Extensive involvement in Singapore's IT Standards Committee, review panels for EDB SIIRD projects, and editorial roles. He has served as PC co-chair, local chair, and reviewer for numerous conferences and journals.

• Data Visualization
• Human-Computer Interaction
• Big Data Analytics
• +13 more

Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.

• Pharmacology
• Traditional Chinese Medicine
• Cancer Therapy
• +7 more

Wing Lam is an Associate Research Scientist in the Department of Pharmacology at the Yale School of Medicine. He holds a BSc in Molecular Biology and a PhD in Biochemical Pharmacology from City University of Hong Kong, followed by postdoctoral training at Yale. His research focuses on developing traditional Chinese medicine (TCM) formulations as adjuvants for cancer therapy, notably YIV-906, which enhances chemotherapy efficacy and mitigates intestinal toxicity. Lam also pioneered the STAR database for herbal drug discovery and the Mechanism-Based Quality Control (MBQC) platform for botanical drug standardization. Education: BSc (Hons) Molecular Biology, City University of Hong Kong, 1995 PhD Biochemical Pharmacology, City University of Hong Kong, 1999 Postdoc, Pharmacology, Yale University, 1999-2002 His research interests span cancer pharmacology, TCM modernization, and mitochondrial toxicity mechanisms. Key projects include YIV-906’s role in enhancing anti-PD1 and CAR T-cell therapies, developing L-nucleoside analogs like troxacitabine, and investigating tylophorine analogs’ antitumor effects. Lam has co-chaired sessions at multiple Consortium for Globalization of Chinese Medicine (CGCM) meetings and contributed to patents on herbal drug formulations and quality control methods. Recent work explores YIV-906’s potential for inflammatory bowel disease (IBD) and phase II clinical trials for colon and liver cancers. Lam’s publications highlight synergistic drug interactions, mitochondrial DNA depletion mechanisms, and TCM’s evidence-based application in chronic diseases. His grants include studies on PHY906 as an adjuvant in rectal cancer therapy and collaborations with Yiviva, Inc. He maintains active roles in editorial boards, including a special issue on herbal drug quality control in Frontiers in Pharmacology . Lam’s lab is embedded within Dr. Yung-Chi Cheng’s group, focusing on translational pharmacology and botanical drug innovation.

Cristian Gómez CanelaView profile

Professor

• Environmental Chemistry
• Analytical Chemistry
• Mass Spectrometry
• +5 more

Cristian Gómez Canela is a Full Professor in the Department of Analytical and Applied Chemistry at the School of Engineering, Ramon Llull University (IQS). He serves as Coordinator of the Master's Degree in Analytical Chemistry and is an active member of the Catalan Chemical Society (SCQ), representing SCQ in EuChems-EYCN. His academic journey includes a PhD in Chemistry from the University of Barcelona (2014), followed by postdoctoral research at IDAEA-CSIC and King's College University. Dr. Gómez Canela's research focuses on environmental analytical chemistry, particularly the optimization and validation of analytical methods based on liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and high-resolution mass spectrometry (HRMS) for determining organic pollutants in environmental samples. His work extends to metabolomics applied to aquatic organisms and the analysis of neurotoxic compounds in water systems. His research fingerprint reveals strong expertise in zebrafish models (100%), neurotransmitter analysis (66%), Daphnia magna studies (64%), and neurotoxicity assessment (21%). His recent publications (2024-2025) demonstrate a clear trend toward environmental neurotoxicology, with emphasis on the effects of pharmaceuticals and industrial pollutants on aquatic organisms. His work integrates advanced analytical techniques with biological endpoints to assess environmental risks, particularly focusing on neurological and cardiovascular impacts. The research spans method development for pollutant detection, environmental monitoring, and mechanistic studies of neurotoxic effects. Dr. Gómez Canela leads multiple significant research projects including CHEMIPARK (2024-2027) on passive sampling methodologies for environmental pollutants, GESPA (2022-2025) as part of the Environmental Process Engineering and Simulation Group, and several projects on neuroactive compounds in water systems. He has an impressive research output with 91 scientific publications from 2011-2025 and an h-index of 27 with over 2,000 citations. As a dedicated educator, he contributes to multiple academic programs including the Master in Analytical Chemistry, Master in Pharmaceutical Chemistry, and undergraduate degrees in Chemistry and Chemical Engineering. His research group GESPA represents a multidisciplinary team combining chemical engineering, biotechnology, and chemical analysis to advance environmental sustainability through theoretical and experimental approaches.

Heather BatteyView profile

Professor

• Statistical Theory
• High-Dimensional Inference
• Sparsity in Statistics
• +3 more

Heather Battey is a Professor in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. Her work bridges foundational statistical theory with practical scientific applications, focusing on parametrization effects, sparsity, and high-dimensional inference. Education PhD, University of Cambridge (2008-2011) Research Interests Battey's research examines how model structure and parametrization influence inferential procedures, particularly in high-dimensional settings. She investigates the equivalence between sparsity and reparametrization, and challenges traditional Fisherian statistical abstractions through modern practices. Her publications reveal a pattern of innovation in high-dimensional regression, covariance matrix analysis, and statistical methodology for complex data. Collaborations span disciplines including machine learning, economics, and biomedical research. Scientific Awards Fellow of the Institute of Mathematical Statistics (2023) EPSRC Early Career Research Fellowship (2020-2026) EPSRC Postdoctoral Research Fellowship (2017-2020) Advising and Grants Battey supervises PhD students Charlotte Edgar, Jakub Rybak, and Rebecca Lewis, with informal guidance to Henrique Hoeltgebaum. Over 15 pre-doctoral researchers have been mentored in topics ranging from support vector machines to spatial point processes. Current funding includes an EPSRC grant for theoretical foundations of inference with nuisance parameters and prior support for covariance matrix inference.

Subhashis GhoshalView profile

Professor

• Bayesian Inference
• Nonparametric Bayesian Inference
• High-Dimensional Models
• +4 more

Subhashis Ghoshal is a Goodnight Distinguished Professor in the Department of Statistics at North Carolina State University (NCSU). He holds a Ph.D. in Statistics from the Indian Statistical Institute (1995). His research focuses on Bayesian nonparametrics, high-dimensional models, asymptotic theory, and functional data analysis. He has authored influential books like *Fundamentals of Nonparametric Bayesian Inference* (2017) and contributed to methodologies in image processing and statistical inference. Key awards include the Goodnight Distinguished Professorship (2021), Dr. Cavell Brownie Mentoring Award (2014-15), and the De Groot Prize (2019). He has held editorial roles in journals like *Statistical Science* and *Annals of Statistics*. His work bridges theory and applications, addressing challenges in modern statistical problems such as uncertainty quantification and causal inference. He advises on graduate programs and actively contributes to academic leadership at NCSU.

• Financial Analysis
• Equity Valuation
• Life Insurance
• +3 more

Demetris Christodoulou is an Associate Professor in Accounting, Governance and Regulation at the University of Sydney. He holds a BEcon from Piraeus University, an MSc(Fin) from the University of York (UK), and a PhD from Athens University of Economics and Business (AUEB). His research focuses on applying data analytics, econometrics, and visualization techniques to financial analysis, equity valuation, life insurance, and financial advice. He co-directs the PEMA research group, specializing in productivity and performance measurement analytics, and previously led the MEAFA research group (2007–2022). He has collaborated extensively with industry partners including Deloitte and Australian insurers, and developed training programs for over 1,000 executives. His work includes open-source contributions to Stata software and the Graph Workflow platform, alongside $662k in workshop-generated funds supporting academic programs. He has advised multiple PhD students and taught at leading universities globally. Education: BEcon in Economics (Econometrics), Piraeus University MSc in Finance, University of York (UK) PhD in Accounting and Financial Analysis, Athens University of Economics and Business His research interests span financial reporting models, life insurance underwriting strategies, and behavioral finance. Recent projects address dishonesty mitigation in insurance disclosures and the adviser effect on customer disclosures. He has published widely in top journals like the Review of Accounting Studies and Stata Journal , and his work was featured in The Australian for insights on insurance fraud reduction. He maintains international collaborations, including visiting roles at Columbia Business School and the University of Cyprus, and serves on advisory boards for organizations like Behaviour.ai. Publications highlight methodological innovations in econometrics and visualization, with 2025's upcoming Stata Journal paper advancing time-series analysis techniques. His grants include partnerships with industry on longitudinal studies of insured lives, aiming to improve risk modeling and public policy insights.

Xiaoxiao LiView profile

Assistant Professor

• Federated Learning
• Medical Imaging Analysis
• AI Fairness and Trustworthiness
• +7 more

Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), with joint appointments in Computer Science (Associate Member) and the School of Medicine at Yale University (Adjunct Assistant Professor). She is also a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on enhancing trustworthiness, fairness, and efficiency in AI algorithms and foundation models, particularly in healthcare applications. Education: B.S. (Honors) in Zhejiang University (2015), Ph.D. in Biomedical Engineering from Yale University (2020), Postdoc at Princeton University (2020-2021). She leads the Trusted and Efficient AI (TEA) Lab at UBC, which develops algorithms for federated learning, medical imaging analysis, and interpretable AI systems. Research interests include federated learning, generative models, medical image analysis, AI fairness, and graph-based methods for neuroimaging. Recent projects include GMValuator (data valuation for generative models), FairMedFM (fairness benchmarking in medical AI), and FedTextGrad (textual gradient-based FL optimization). Grants: Canada Foundation for Innovation Grant (2023), UBC Green Lab Fund (2023), Vector Institute funding Teaching: Courses on machine learning, federated learning, and AI ethics at UBC Awards & Recognition: Best Paper Award at FL@FM WWW 2024, Editorial Board Member of Medical Image Analysis , multiple top-tier conference acceptances (NeurIPS, ICLR, CVPR, MICCAI). Lab & Teams: TEA Lab collaborates with industry and hospitals to translate AI research into clinical tools. Current projects address AI fairness in healthcare, federated learning for medical data, and multimodal medical analytics.

Daniel M. KaneView profile

Professor

• Number Theory
• Combinatorics
• Complexity Theory
• +3 more

Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.

Katerina MarcoulidesView profile

Associate Professor

• Data Mining
• Structural Equation Modeling
• Longitudinal Data Analysis
• +3 more

Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.