Cathryn Mitchell is a Professor of Radio Science and Royal Society Industry Fellow at the University of Bath, specializing in ionospheric physics, position, navigation, and timing (PNT). She leads research in the Space & Telecoms Research Group (STAR), focusing on radio propagation, data assimilation, and space weather impacts on communication systems. Her work bridges theoretical, computational, and experimental approaches, with applications in satellite navigation, climate monitoring, and defense sectors. Her research interests include ionospheric tomography, HF communications, and the development of robust PNT systems. Mitchell collaborates extensively with industry partners like Spirent Communications on future navigation technologies and space weather resilience. She has held roles such as Academic Director of the Doctoral College and contributes to interdisciplinary projects like the DRIIVE initiative exploring ionospheric variability with EISCAT-3D radar. Recent work emphasizes ionospheric effects during geomagnetic storms (e.g., the 2024 Gannon Storm) and cooperative autonomous systems under communication constraints. Her projects are funded by the Royal Society, Natural Environment Research Council (NERC), and ESA, addressing challenges in space weather forecasting and PNT system reliability. Awards: Royal Society Industry Fellow (2022–present) Key Projects: Royal Society Industry Fellowship on Future PNT Technologies DRIVERS (DRIIVE): Ionospheric Variability Studies EISCAT-3D FINESSE: Ionospheric Structuring Analysis Mitchell’s lab, STAR, integrates academic and industrial partnerships to advance space weather applications and sustainable navigation systems, contributing to UN Sustainable Development Goals related to climate action and innovation.
Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.
Roxana Geambasu is an Associate Professor at Columbia University's Department of Computer Science, with affiliations to the Software Systems Lab and the Cybersecurity Committee . She specializes in systems security, differential privacy, and resource management for modern computing environments. PhD: University of Washington (2011) Undergraduate: Polytechnic University of Bucharest, Romania Her research focuses on integrating differential privacy as a first-class computing resource in infrastructure systems, with key contributions in: Privacy Budget Management (PrivateKube, DPack, Turbo) Web & Mobile Privacy (Cookie Monster, Big Bird) Security Abstractions (POSIX, Vanish, XRay) Article trends show a strong emphasis on differential privacy (8/15), resource scheduling (5/15), and privacy-preserving advertising (3/15). Key subfields include budget optimization, browser APIs, and ML pipeline privacy. Scientific awards include: Alfred P. Sloan Fellowship NSF CAREER Award Google Ph.D. Fellowship in Cloud Computing Best Paper Awards at SOSP, EuroSys She advises M.S. and undergraduate students, teaching courses in Distributed Systems and Privacy Curriculum . Her lab develops tools like PixelDP and Sunlight for operationalizing privacy in real-world systems.
Babak Hassibi is a Professor of Electrical Engineering and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). He obtained his B.S. from the University of Tehran (1989), M.S. and Ph.D. from Stanford University (1993, 1996), and has held positions at Caltech since 2001, including roles as Assistant Professor, Associate Professor, Professor, and Executive Officer. Education : University of Tehran, B.S. (1989) Stanford University, M.S. and Ph.D. (1993, 1996) Academic Roles : Assistant Professor, Caltech (2001–03) Associate Professor (2003–08) Professor (2008–13) Binder/Amgen Professor (2013–16) Bohn Professor (2016–) Executive Officer for Electrical Engineering (2008–15) Associate Director for Information Science and Technology (2010–12) Research Interests : Babak Hassibi’s work spans Communications , Signal Processing , Control Theory , and Machine Learning . He has contributed to wireless networks, genomic signal processing, multi-antenna systems, robust control, and high-dimensional statistics. His mathematical interests include Random Matrices and Group Representation Theory . Recent Publications highlight his focus on Adaptive Control , Stochastic Optimization , and Quantum Detection . Notable trends include Regret-Optimal Control , Stochastic Mirror Descent , and DNA Microarray Applications . Scientific Awards : Highly Cited Researcher Advising and Grants : He has advised numerous graduate students and postdocs, many of whom now hold prominent positions at institutions like MIT, USC, and Stanford. His research includes collaborations on patents and projects related to Wireless Communications and Genomic Technologies . Labs and Teams : Leads the Hassibi Group at Caltech, which explores nonlinear photonic systems, ultrafast optics, and quantum information processing.
Richard Hornbeck is the V. Duane Rath Professor of Economics at the University of Chicago Booth School of Business. He is an economic historian and applied-micro economist whose research focuses on the historical development of the American economy, examining why some places and people have become wealthier while others have remained poorer. Hornbeck is a research associate at the National Bureau of Economic Research, affiliated with programs on the Development of the American Economy, Development Economics, and Environmental and Energy Economics. Hornbeck's research spans multiple critical areas of economic history, with particular emphasis on slavery and emancipation economics, environmental history (especially the Dust Bowl migration), gender dynamics in 19th-century manufacturing, railroad development, and agricultural economics related to the Ogallala Aquifer. His work frequently utilizes newly digitized historical data to provide fresh insights into longstanding economic questions, connecting historical events to contemporary economic issues of wealth distribution and regional disparities. His publication record shows strong trends in American economic development research, with recent work focusing on the economic impacts of emancipation, historical manufacturing patterns by gender, and urban economic development. Many of his papers have been published as lead articles in top journals including the Journal of Political Economy, American Economic Review, and Review of Economics and Statistics. Hornbeck has received several prestigious honors including the Alfred P. Sloan Fellowship in 2014 and selection for the 2009 Review of Economic Studies Tour. His NBER affiliations demonstrate his standing in multiple subfields of economics research. Prior to joining Chicago Booth in 2015, Hornbeck was the Dunwalke Associate Professor of American History in the Economics Department at Harvard University. He received his PhD in economics from MIT in 2009 and a bachelor's degree in economics from the University of Chicago in 2004. His teaching includes courses on applied microeconomics in economic history and business in historical perspective, with course schedules extending through 2026.
Zita Vale is a Full Professor at the Institute of Engineering (ISEP) of the Polytechnic of Porto (IPP), where she holds the first Full Professor position since 2017. She is a co-founder of GECAD (1999) and coordinates GECAD's Power and Energy (PES) activities. GECAD is recognized by FCT since 2004 and classified as Excellent. She has served as GECAD director (2010-2017), vice-director (1999-2009, 2017-present), and is a member of the administration board. She is also co-founder and member of the coordination board of the National Associated Laboratory on Intelligent Systems. Her educational background includes a PhD (1993) and Agregação/Habilitation (2003) in Electrical and Computer Engineering from the University of Porto. She began her academic career at the University of Porto as a Teaching Monitor (1985), Assistant (1985-1993), and Professor (1993-1998) before moving to ISEP in 1998. Zita Vale's research focuses on the design and development of artificial intelligence-based models for Power and Energy Systems. Her work spans knowledge-based systems, multiagent systems, machine learning, metaheuristics, and semantics, with applications in smart grids, energy management, electricity markets, and renewable energy integration. She has an extensive international network and has participated in 80 R&D projects, raising over 22 million Euros for GECAD. Her recent publications demonstrate a strong emphasis on optimization techniques, explainable AI, energy storage systems, and the integration of distributed energy resources in power systems. She serves as Editor-in-Chief of Applied Energy (Elsevier), a leading journal in the field with an Impact Factor of 11.2. Her citation metrics are impressive, with over 17,000 citations on Google Scholar and an H-index of 64. Editor-in-Chief of Applied Energy (Elsevier) Over 17,000 citations on Google Scholar H-index of 64 Zita Vale has supervised 29 PhD students (25 completed) and 72 MSc students (66 completed), demonstrating her strong commitment to academic mentorship. She has also been involved in numerous international and national evaluation processes, including project proposals, faculty positions, and PhD juries across 15+ countries. She has contributed to over 225 evaluation processes from 2018-2023, including 150+ project proposals/execution, 50+ Faculty/Researchers positions, 2 Habilitation juries, and 25+ PhD juries. She leads GECAD's involvement in several major research initiatives, including the National Associated Laboratory on Intelligent Systems and various European projects such as IoTalentum, TRADERES, DOMINOES, and EcoRural-IoT from Horizon 2020, as well as PRODUTECH EU DIH from Horizon Europe. Her leadership extends to international organizations where she serves as President of Intelligent Systems Applications in Power (ISAP) and Technical Committee Program Chair of IEEE PES Analytic Methods for Power Systems Committee.
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).
Brandon M. Stewart is an Associate Professor of Sociology at Princeton University with extensive interdisciplinary affiliations. He serves as Director of the Statistics Core at the Office of Population Research and maintains formal connections with the Politics Department, Princeton Institute for Computational Science and Engineering, Center for Information Technology Policy, and Center for the Digital Humanities. Stewart holds editorial leadership as Co-Editor-in-Chief of Political Analysis and Associate Editor at Sociological Methods & Research . His educational background includes: Ph.D. in Government from Harvard University (2015) Master's degree in Statistics from Harvard University (2014) Stewart's research pioneers innovative quantitative methods for social science applications, specializing in automated text analysis and modeling complex heterogeneity in regression. His methodological frameworks enable researchers to uncover hidden structures in large datasets that were previously too costly or impossible to analyze. While his recent work has focused on using newspaper archives to study propaganda mechanisms in contemporary China, his tools are deliberately designed for broad applicability across diverse domains including education, human trafficking, forced migration, international relations, constitutional law, and psychology. His publication record demonstrates consistent innovation at the intersection of statistics, machine learning, and social inquiry. Stewart's work shows a clear trajectory from foundational methodological development to practical implementation across numerous substantive areas, with recurring themes of enhancing causal inference with textual data, developing robust topic modeling techniques, and creating accessible computational tools for social scientists. Stewart's scholarly excellence has been recognized through multiple prestigious awards: 2024 Leo Goodman (Early Career) Award from the Methodology Section of the American Sociological Association 2023 Emerging Scholar Award from the Political Methodology Society Edward R Chase Dissertation Prize Gosnell Prize for Excellence in Political Methodology Political Analysis Editor's Choice Award Recognition for Excellence in Mentoring Graduate Students As a mentor, Stewart has guided several successful graduate students to faculty positions at institutions including UCLA and Georgetown. His collaborative approach is evident in numerous multi-author projects spanning disciplines from political science to computational linguistics. His leadership extends to the Sociology Statistics Reading Group, which he founded to foster interdisciplinary methodological exchange, and his summer methods camp that trains social scientists in advanced quantitative techniques.
Henrik Hansen is a Professor and Head of Department at the Department of Economics , University of Copenhagen , where he has held various academic and administrative roles since 1995. He is associated with the Development Economics Research Group (DERG) and affiliated with the Copenhagen Center for Disaster Research (COPE) network. Education : MSc in Economics (1991) and PhD in Economics (1995) from University of Copenhagen. His research focuses on development economics , with emphasis on development aid effectiveness , applied econometrics , and education research . He has conducted extensive fieldwork in Myanmar and Vietnam , analyzing enterprise surveys, household data, and policy impacts. Recent publications highlight productivity in manufacturing sectors, gender wage gaps, and credit access determinants. He teaches "Applied Research Methods" in the Global Development program and supervises development economics and applied econometrics students. Professional roles include editorial positions at Review of Development Economics and Asia & The Pacific Policy Studies , alongside advisory work for Statistics Denmark and Vietnamese Academy of Social Sciences . He has organized international conferences and contributed to 25+ media discussions on topics like corruption in Africa and development financing .
Vasant Dhar is the Robert A Miller Professor of Business and Professor of Data Science at the Leonard N. Stern School of Business at New York University. He serves as Director of Industry Relations and specializes in Technology, Operations, and Statistics. Joining Stern in 1983, Professor Dhar has established himself as a leading expert in artificial intelligence, data science, and financial technology. Professor Dhar's educational background includes: Ph.D. in Artificial Intelligence from the University of Pittsburgh (1984) M.Phil. from the University of Pittsburgh (1982) B.Tech. in Chemical Engineering from the Indian Institute of Technology, Delhi (1978) His research focuses on how risk influences our trust in AI systems, demonstrating the existence of an "automation frontier" that expresses a tradeoff between how often machines will be wrong and the consequences of their errors. Professor Dhar examines how innovations such as Artificial Intelligence impact our lives, and how we can create technology and policy for a better future in a world of increasingly intelligent machines. His work spans financial applications of AI, where he was among the first to bring machine learning to Wall Street in the 1990s, founding the machine-learning-based hedge fund SCT Capital Management. Professor Dhar's recent publications reveal a strong focus on the practical applications and societal implications of AI. His work addresses critical issues including AI reliability in financial document analysis, the governance of AI companies, ethical considerations in biometric payments, and the evolving relationship between humans and increasingly intelligent machines. His research demonstrates how AI is transforming various sectors while raising important questions about trust, accountability, and the future of work. Among his notable recognitions is the Robert A Miller Professorship, an endowed chair position at NYU Stern. His research has been funded by grants from industry and government agencies such as the National Science Foundation. Professor Dhar teaches courses on Systematic Investing, Data Science, Prediction, and Tech Innovation. He has written over 100 research articles and is the host of the "Brave New World" podcast, which explores how technology and virtualization in the post-COVID era is transforming humanity. He publishes fortnightly at vasantdhar.substack.com and is a frequent speaker in academic and industrial forums.
Christoph Stadtfeld is Associate Professor of Social Networks at ETH Zurich's Department of Humanities, Social and Political Sciences and co-director of the ETH Social Networks Lab. His research examines social network dynamics, focusing on tie formation processes, network effects on individuals, and advanced statistical methodologies for longitudinal network analysis. Education: PhD from Karlsruhe Institute of Technology (2011) Postdoctoral researcher and Marie-Curie fellow at University of Groningen, University of Lugano, and MIT Media Lab (2011-2014) His work bridges sociology, statistics, and computer science to address fundamental questions about how social structures evolve and influence behavior. Key interests include relational event modeling, co-evolution of networks and attributes, and applications in mental health, political polarization, and scientific collaboration. He develops innovative methods for analyzing dynamic networks using cutting-edge computational approaches. Recent publications reveal strong emphasis on methodological rigor in temporal network analysis, with significant contributions to relational event modeling and dynamic network actor frameworks. His work increasingly addresses societal challenges including political polarization, mental health impacts of social isolation, and innovation dynamics in healthcare. Scientific awards: Raymond Boudon Award of the European Academy of Sociology (2017) Freeman Award of the International Network for Social Network Analysis (2021) As co-director of the ETH Social Networks Lab, Stadtfeld leads interdisciplinary research teams developing novel network methodologies. His work has been supported by prestigious fellowships including Marie-Curie funding, and he actively mentors graduate students in network science methodology and applications across diverse domains. The ETH Social Networks Lab serves as a hub for advancing network theory and methodology, with ongoing projects examining student networks during crises, scientific collaboration dynamics, and innovation ecosystems through the lens of network science.
Anthony Man-Cho So is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He currently serves as Dean of the Graduate School and Deputy Master of Morningside College . With a BSE from Princeton University and a PhD in Computer Science from Stanford University, his career at CUHK began in 2007. Academic Leadership: Dean, Graduate School (2023–present); Deputy Master, Morningside College (2019–present) Education: BSE (Princeton), MSc/PhD (Stanford) His research focuses on optimization theory and its interdisciplinary applications in computational geometry, machine learning, signal processing, and statistics. Key projects include non-convex optimization for wireless networks, robust graph learning, and decentralized learning algorithms. His publications span high-impact journals like Mathematical Programming , SIAM Journal on Optimization , and conferences such as NeurIPS and ICML . Recent work emphasizes dynamic regret analysis , low-rank matrix recovery , and stochastic beamforming . He has authored over 50 refereed papers and a monograph on semidefinite programming. Awards include IEEE Fellow (2023), CUHK Research Excellence Award (2016–17), and multiple IEEE/INFORMS best paper and teaching accolades. He has served on editorial boards of journals like Mathematical Programming and SIAM Journal on Optimization , and as Lead Guest Editor for IEEE Signal Processing Magazine . Teaching roles include courses on optimization, discrete mathematics, and machine learning. Scientific Awards IEEE Fellow (2023) CUHK Outstanding Fellow (2019) Multiple IEEE/INFORMS Best Paper Awards (2010–2022) IEEE/UGC Teaching Awards (2008–2022) His methodology integrates theoretical rigor with practical applications, particularly in wireless communication systems, sensor networks, and financial engineering. Collaborations span institutions in Hong Kong, mainland China, and the U.S., reflecting a global academic influence.
Dr. Friedrich Götz is an Associate Professor of Psychology in the Department of Psychology at the University of British Columbia (Faculty of Arts). His research focuses on geographical psychology, exploring the causes and consequences of regional personality differences through an interdisciplinary Big Data approach. PhD, University of Cambridge (UK), 2021 MPhil, University of Cambridge (UK), 2017 BSc, University of Konstanz (Germany), 2016 Dr. Götz’s work bridges social and personality psychology with behavioral science, examining topics like mobility, migration, wanderlust, courage, and entrepreneurship. He co-developed large-scale survey studies with TIME Magazine, attracting over 3 million participants. His research often involves experience sampling methods and open science. His recent publications emphasize geographical psychology, personality-environment interactions, and methodological advancements. Articles span 2020–2025, with key themes including regional personality differences, misinformation susceptibility, and the psychological impact of environmental features. Rising Star Award (Association for Psychological Science), 2025 SAGE Emerging Scholar Award (Society for Personality and Social Psychology), 2025 Top 40 under 40 – Germany (CAPITAL Magazine), 2024 President’s New Researcher Award (Canadian Psychological Association), 2024 Best Dissertation Prize (German Psychological Society), 2021 Leading Scholar (Green College), 2021 Dr. Götz teaches undergraduate and graduate courses, including Personality Psychology and Contemporary Conceptual Issues in Personality, with a focus on geographical dimensions. He is based in the Personality and Geographical Ambiance (PANGEA) Lab, which prioritizes inclusivity and collaboration in studying person-environment interactions.
Roy Dong is an Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges Control Theory Economics Statistics Optimization to address challenges in cyber-physical systems and the Internet of Things, focusing on data manipulation, privacy, and strategic behavior in interconnected systems. His academic journey includes a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2017) and dual B.S. degrees in Economics and Computer Engineering from Michigan State University (2010). At Illinois, he teaches courses ranging from Control Systems to Convex Optimization , with multiple teaching excellence awards. Roy's research explores Closed-loop effects of machine learning Causality in decision systems Incentive design for strategic agents Privacy-utility tradeoff optimization Human behavior modeling with applications in smart grids, transportation networks, and semi-autonomous vehicles. His work formulates privacy-preserving mechanisms as optimization problems, balancing data utility against user privacy in dynamic systems. Article trends show expertise in Game theory for strategic data sources Energy disaggregation techniques Nonlinear basis pursuit algorithms Privacy-aware control systems with a focus on cyber-physical systems and human-in-the-loop applications. Scientific recognition includes 'Teacher Ranked as Excellent' awards (ECE 120, ECE 486, ECE 515) Contributions to smartSDH building control and CPRL compressive sensing Roy leads the Privacy-aware Control Systems research group, collaborating with institutions like UC Berkeley and Michigan State University , and directs projects funded by grants including the New USDA NIFA grant for agricultural robot autonomy .
Iona Cheng is a Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco (UCSF), where she conducts groundbreaking research in cancer epidemiology. She serves as co-Investigator of the SEER Greater Bay Area Cancer Registry and is Principal Investigator of multiple NIH- and foundation-funded projects examining genetics, lifestyle factors, and neighborhood characteristics in relation to cancer risk. Dr. Cheng has developed an extensive research program focused on racial/ethnic differences in cancer risk and leads population-based cancer surveillance studies that document variations in cancer incidence and mortality patterns across diverse racial and ethnic groups. University of California, Davis, BS, 1990–1994, Physiology Yale University, MPH, 1999–2001, Chronic Disease Epidemiology University of Southern California, PhD, 2001–2005, Epidemiology University of California, San Francisco, Postdoc, 2006–2008, Genetic and Molecular Epidemiology Dr. Cheng's research spans multiple disciplines within cancer epidemiology, with particular emphasis on understanding how environmental exposures, genetic factors, and social determinants interact to influence cancer risk and outcomes across different racial and ethnic populations. Her work frequently examines the impact of air pollution, endocrine-disrupting chemicals, and neighborhood characteristics on cancer development and survival. She has made significant contributions to understanding cancer disparities among Asian American, Native Hawaiian, and Pacific Islander populations, bringing attention to the unique cancer risks and outcomes within these understudied groups. Her research often leverages the Multiethnic Cohort Study, one of the largest prospective studies of cancer incidence and mortality across diverse racial/ethnic populations. Analysis of Dr. Cheng's recent publications reveals a consistent focus on environmental and social determinants of cancer risk across multiple organ sites. Her work demonstrates a sophisticated integration of epidemiological methods with environmental exposure assessment, genetic analysis, and health disparities research. Many of her studies examine the intersection of environmental exposures and racial/ethnic disparities in cancer outcomes, particularly regarding breast cancer, lung cancer, and other malignancies. She has published extensively on the impact of air pollution on cancer risk and survival, as well as the effects of endocrine-disrupting chemicals like bisphenol A, parabens, and phthalates. American Association for Cancer Research Scholar-in-Training Award (2007) National Institutes of Health Loan Repayment Award (2007) National Institutes of Health Loan Repayment Renewal Award (2009) American Association for Cancer Research Faculty Scholar Award (2011) National Institutes of Health Loan Repayment Renewal Award (2011) National Institutes of Health Loan Repayment Renewal Award (2013) American Journal of Epidemiology/Society of Epidemiology Research Top 10 manuscripts (2014) Cancer Prevention Institute of California Mentoring Award (2015) American Society of Human Genetics Top poster As Principal Investigator of multiple NIH-funded projects, Dr. Cheng oversees substantial research grants focused on cancer epidemiology and health disparities. Her work often involves large interdisciplinary collaborations with researchers across multiple institutions, including the Multiethnic Cohort Study which follows over 200,000 participants from diverse racial/ethnic backgrounds. She has demonstrated leadership in mentoring junior researchers, particularly those from underrepresented backgrounds in science, as evidenced by her Cancer Prevention Institute of California Mentoring Award. Her research program integrates data from cancer registries, electronic health records, and geospatial information to provide comprehensive insights into cancer patterns and risk factors. Dr. Cheng's research is closely connected to the UCSF Helen Diller Family Comprehensive Cancer Center and leverages collaborations with Lawrence Berkeley National Laboratory, which provides advanced technological resources for cancer research. Her work benefits from access to extensive cohort data, sophisticated exposure assessment methods, and interdisciplinary expertise in genetics, environmental science, and computational biology available through these institutional partnerships. She frequently collaborates with researchers studying the genetic and environmental determinants of cancer across multiple organ systems, contributing to a more comprehensive understanding of cancer etiology and prevention strategies.
