Dr. Matthew Brookhouse is a Senior Lecturer at the Fenner School of Environment & Society, part of the Australian National University's Institute for Climate, Energy & Disaster Solutions. With a PhD in Dendroclimatology from ANU, he specializes in using forest structural complexity and tree-ring analysis to understand climate interactions and ecological responses in Australian subalpine environments. Research Focus: Sub-alpine ecology, Dendrochronology, CO2 responsiveness in eucalypt species Teaching: First-year research methods with emphasis on statistical application, advanced modeling and field botany Projects: Leading collaborative snow-gum dieback research and dendrochronological monitoring initiatives His publications span 2006-2025 with recent emphasis on machine learning applications for forest monitoring, tropical tree-ring chronologies for climate change, and climate sensitivity in Australian alpine ecosystems. Key collaborations include institutions like Australian Nuclear Science and Technology Organisation and University of Canberra researchers. Current projects focus on snow-gum woodland dieback mechanisms, high-resolution dendrometric monitoring, and integrating dendrochronology with environmental policy frameworks. He maintains active supervision of research students and contributes to both undergraduate and postgraduate curriculum development.
Heng Ji is a Professor at the Siebel School of Computing and Data Science , affiliated with the Department of Computer Science , Electrical and Computer Engineering Department , and multiple research labs including the Coordinated Science Laboratory and Carl R. Woese Institute for Genomic Biology at the University of Illinois Urbana-Champaign. She serves as an Amazon Scholar and Founding Director of the Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE) and CapitalOne-Illinois Center on AI Safety and Knowledge Systems (ASKS) . B.A. and M.A. in Computational Linguistics from Tsinghua University M.S. and Ph.D. in Computer Science from New York University Her research bridges Natural Language Processing with Vision-Language Models , Knowledge-Enhanced LLMs , and AI for Science (e.g., chemical language modeling). She leads major multi-institutional projects such as DARPA ECOLE MIRACLE , KAIROS RESIN , and DEFT Tinker Bell , while advising governments (U.S. Air Force Data Analytics Expert Panel) and industry (Amazon, Google, IBM). Her work on multimodal reasoning, agent-based systems, and chemical language models (e.g., mCLM ) has been supported by NSF, DARPA, and corporate partners. Recent publications (2025) focus on LLM agents , vision-language integration , and scientific knowledge acquisition . Awards include NSF CAREER , IEEE Intelligent Systems' AI's 10 to Watch , and multiple Outstanding Paper Awards at ACL/NAACL. She advises students like Chi Han (ACL/NAACL awardee) and post-docs Xiusi Chen and Yuji Zhang , and leads the BLENDER Lab , which develops frameworks like WiNELL (Wikipedia updating) and ProteinZero (protein generation). She has also served as NAACL Secretary and Program Co-Chair for ACL-IJCNLP2022. Outstanding Paper Award at ACL2024 Two Outstanding Paper Awards at NAACL2024 Young Scientist by World Laureates Association (2023-2024) AI's 10 to Watch by IEEE (2013) NSF CAREER (2009) Google/IBM/Bosch Research Awards
Pardis Emami-Naeini is an Assistant Professor of Computer Science at Duke University, with joint appointments in the Sanford School of Public Policy and the Department of Electrical and Computer Engineering. She serves as the Director of the Duke Interdisciplinary Security, Privacy, and Interaction Research (InSPIre) lab and is a Duke Science and Technology Scholar. Her interdisciplinary work bridges computer science, public policy, and electrical engineering, with a focus on developing usable privacy and security solutions that empower individuals from diverse sociodemographic backgrounds. Dr. Emami-Naeini earned her Ph.D. in Computer Science from Carnegie Mellon University in 2020, followed by postdoctoral research at the University of Washington (2020-2022). Her research sits at the intersection of security, privacy, and human-computer interaction, with particular expertise in IoT security, technology-enabled abuse, reproductive health privacy, and smart city security. She has published extensively at flagship venues including IEEE S&P, CHI, CSCW, and SOUPS, with her work covered by major media outlets such as Wired and The Wall Street Journal. Her recent publications reveal a clear trajectory toward examining the human dimensions of security and privacy in emerging technologies, from LLM chatbots for mental health to social robots and period-tracking apps in the post-Roe v. Wade landscape. Her work consistently emphasizes the need for privacy-aware design that accounts for diverse user needs and contexts, particularly for vulnerable populations. Google Systems and ML Research Gift Award (2025) Google AI Research Scholar Program Award (2024) Top 5% Instructor in Duke Trinity College (2024) ORAU Ralph E. Powe Junior Faculty Enhancement Award (2023) Duke Science and Technology Scholar (2022) IEEE S&P paper highlighted in IEEE Security and Privacy Magazine (2021) CyLab Presidential Fellowship (2019) Dr. Emami-Naeini actively mentors several Ph.D. students including Jabari Kwesi, Jessie Cao, and Hiba Laabadli, as well as undergraduate and master's students. Her research has influenced key organizations including the National Institute of Standards and Technology (NIST), Consumer Reports, and the World Economic Forum in creating usable security and privacy labels for smart devices. She serves on numerous program committees including USENIX Security and CHI, and has participated in NSF grant review panels, demonstrating her growing leadership in the security and privacy community. Her InSPIre lab conducts user-centered research to uncover security and privacy needs of diverse stakeholders, with a particular focus on marginalized communities. The lab's work spans multiple domains including intimate partner violence, reproductive health, virtual reality, and smart cities, always with a strong emphasis on translating research findings into practical tools and policy recommendations.
Nurul Alam is a Lecturer in Accounting at the University of Sydney Business School. His research focuses on machine learning applications in finance and accounting, corporate distress prediction, and financial reporting. He holds a PhD from the University of Sydney, where he investigated machine learning models for US corporate bankruptcies. His teaching excellence has earned over 15 awards, including multiple Dean’s Citations and nominations for the Wayne Lonergan Award for Teaching Excellence. Education Master of Commerce (Finance & Accounting), University of Sydney Bachelor of Business Administration (Finance & Banking), University of Rajshahi, Bangladesh Research Interests Machine Learning and Deep Learning in accounting and finance Big Data analytics for corporate financial decision-making Accounting fraud detection mechanisms Financial reporting standards and regulations Current Projects Corporate bankruptcy prediction using survival models and AI Deep Learning applications in panel data analysis for firm distress Machine learning variable selection in corporate finance Accounting fraud patterns among lower/mid-level employees Teaching Contributions Financial Accounting B (ACCT3011) Quantitative Methods for Accounting (QBUS5002) Quantitative Business Analysis (BUSS1020)
Professor Donald Robertson is a faculty member at the University of Cambridge , holding the position of Professor of Economics and Director of Graduate Studies and PhD Programme within the Faculty of Economics . He is affiliated with Pembroke College and contributes to econometric research and graduate education. Research Interests : His work focuses on Econometrics , Applied Macroeconomics , and Financial Economics , with methodological expertise in Time Series Analysis , Panel Data Analysis , and Predictive Modeling . His publications address topics like cross-sectional dependence, unit root testing, and instrumental variable estimation. Teaching : He instructs modules such as Introduction to Probability and Statistics , Time Series Methods , and MPhil Prep Course - Statistics . Publications : Recent contributions include work on R² bounds for predictive models, factor residuals in panel data, and fiscal fatigue in debt ratios, reflecting his focus on econometric theory and macroeconomic applications. Contact : Email dr10011@cam.ac.uk or phone +44(0)1223 335270. Office hours by email appointment in Room 70.
Deborah Balk is a Professor at the Marxe School of Public and International Affairs at Baruch College, part of the City University of New York (CUNY). She also serves as Director of the CUNY Institute for Demographic Research and holds appointments in the CUNY Graduate Center's Sociology and Economics programs, as well as the CUNY School of Public Health's Epidemiology Program. Her expertise lies in spatial demography, integrating earth and social science data to address policy challenges related to urbanization, climate change, and population dynamics. Dr. Balk has led significant roles in climate assessments, including Co-Chair of the New York City Panel on Climate Change’s 4th Assessment (2019–2024) and membership in the U.S. National Climate Assessment’s 6th Health Chapter (2025). She holds a PhD in Demography from UC Berkeley and degrees from the University of Michigan (MPP and AB in International Relations). Her research focuses on urbanization, migration, poverty, health, and environmental interactions, particularly climate adaptation and equity. Notable projects include analyzing population vulnerability in coastal zones and developing spatial demographic tools for global health and policy. Awards include the Andrew Carnegie Fellowship (2016–2018) and the William and Flora Hewlett Foundation Fellowship (1991). Dr. Balk has secured grants exceeding $6 million from NSF, NASA, and others, supporting work on urbanization, climate justice, and demographic data integration. She advises multiple institutions, including the U.S. Census Bureau and National Academy of Sciences. Her teaching spans spatial demography, urban policy, and statistical methods, reflecting her commitment to bridging demographic science and real-world applications.
Yang Luo is a Kennedy Trust Senior Research Fellow in Data Science at the University of Oxford's Kennedy Institute of Rheumatology. His research bridges statistical genomics and computational immunology to unravel genetic contributions to immune-mediated traits, with a focus on the major histocompatibility complex (MHC) region. His work leverages large biobank datasets (UK Biobank, Biobank Japan), gene expression resources (GTEx), and proteomic data to decode molecular mechanisms linking genetic variation to disease risk. Specific interests include tuberculosis genetics, multi-ancestry polygenic risk scores, and single-cell eQTL modeling. Recent publications highlight expertise in HLA association studies, evolutionary immunogenetics, and disease-specific cell state dynamics. Key contributions include constructing a global HLA haplotype panel and developing novel statistical methods for admixed population genetics. Scientific Awards: Kennedy Trust Senior Research Fellow in Data Science His lab integrates computational and experimental approaches to translate genetic findings into clinical applications for immune disorders.
Fariya Sharmeen is an Associate Professor of Mobility and Urban Planning at KTH Royal Institute of Technology's School of Architecture and the Built Environment (ABE), affiliated with the Digital Futures Faculty. She holds a PhD from Eindhoven University of Technology and has previously served as Assistant Professor at Radboud University, Lecturer at Bangladesh University of Engineering and Technology (BUET), and research fellow at institutions including TU Delft and Imperial College London. Her research focuses on sustainable mobility transitions, social network dynamics in travel behavior, and policy responses to emerging transport technologies like MaaS and cycling innovations. Notable honors include the 2017 Piet Rietveld Award for transport research and a 2013 Royal Geographic Society award for transport geography. Sharmeen advises doctoral and master’s students on topics such as urban transformation and mobility governance. She coordinates courses like Sustainable Mobility (FAG3187) and leads projects like Bicification and ENCom. Her work integrates quantitative methods with policy analysis, addressing challenges in both global north and south contexts.
Xuan Liang is a Lecturer in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), Australian National University. With a PhD from Peking University and postdoctoral experience at Monash University, his research focuses on spatial statistics, nonparametric modeling, and environmental data analysis. Education: PhD in Statistics (Peking University, 2017), BSc in Statistics (Zhejiang University, 2012) His work addresses methodological challenges in spatial panel data analysis, network modeling, and air pollution quantification. He has developed novel techniques for meteorological confounder adjustment in air quality assessments and contributed to distributed data analysis methods. Recent research trends include: Advancing quasi-score matching for spatial econometric models Improving subbagging algorithms for big data Creating robust distributed data aggregation frameworks Refining spatial autoregressive panel data methodologies Scientific contributions include: ANU Vice-Chancellor’s Citation for Outstanding Contribution to Student Learning (Early Career), 2022 CBE Teaching Commendation for Outstanding Teaching, 2020 Co-development of the ggmatplot R package for matrix visualization Co-inventor of Chinese patent 201811183512.0 for air quality assessment He teaches advanced courses in time series analysis, regression modeling, and mathematical statistics at ANU, while maintaining active research collaborations in econometrics and environmental statistics.
Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.
Prof. Ivan Cole is an Adjunct Professor at RMIT University's School of Engineering, specializing in rapid materials discovery for corrosion protection, nanostructures, and additive manufacturing. His work integrates computational modeling with high-throughput experimentation, focusing on corrosion inhibitors, biocompatible surfaces, and additive manufacturing process optimization. With over 30 years of experience across academia and industry (including leadership roles at CSIRO and Centro-Svilluppo Materiali), he leads the Rapid Discovery & Fabrication Team (RDF) to advance these research areas. Research Interests: Corrosion science, microbially induced corrosion (MIC), additive manufacturing surfaces, nanostructure sensing, multiscale modeling, and green materials discovery. His team addresses challenges in corrosion protection, biomedical implants, and environmental remediation through innovative methodologies. Awards: 2019 Australian Corrosion Medal 2016 CSIRO Lifetime Achievement Award 2013 Best Paper in NACE Corrosion Supervision & Projects: Active in mentoring PhD/Master’s students across corrosion inhibition, additive manufacturing, and nanostructure design. Notable projects include developing quorum sensing inhibitors for biofilm control, in-situ monitoring for metal AM, and eco-friendly corrosion inhibitors. Labs & Collaborations: Leads the Rapid Discovery & Fabrication Team and collaborates with industry partners to translate research into practical solutions for materials durability and sustainability.
Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.
Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.
Christine Eckert is a Professor of Marketing Analytics at the TUM School of Management (Technische Universität München). She holds a doctorate in economics from Goethe University Frankfurt am Main and has previously held academic positions at University of Technology Sydney and EBS University of Business and Law. Education: Mathematics (Johannes Gutenberg University Mainz, Christian-Albrechts University Kiel); Economics (Goethe University Frankfurt am Main) Her research focuses on quantitative modeling of market participants' decisions, spanning consumer financial behavior, strategic innovation decisions, and corporate social responsibility. She also explores methodological advancements in management research, particularly causal inference techniques. Notable contributions include serving as co-editor for Big Data and Business Analytics (Journal of Business Research) and receiving an Australian Research Council Discovery Grant (2019-2021). Her work has been recognized with the Center for Financial Planning's Best Paper Award (2021). Key journals: Journal of the Academy of Marketing Science, Journal of Management, Journal of Marketing Research She contributes to academic governance through roles like Panel Member for New Zealand's Performance Based Research Fund (2018) and advisory board membership with Super Consumers Australia (since 2022).
Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.
