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Ping ZhongView profile

Associate Professor

• Free Probability
• Random Matrix Theory
• Operator Algebras
• +2 more

Ping Zhong is an Associate Professor in the Department of Mathematics at the University of Houston. He holds a Ph.D. from Indiana University Bloomington and joined UH in 2024 after serving as Assistant Professor at the University of Wyoming (2018-2024) and completing postdoctoral work at the University of Waterloo. His research explores free probability theory, operator algebras, and applications of random matrix theory to high-dimensional statistics. Educational background includes: Ph.D. Mathematics, Indiana University (2014) M.S. Mathematics, Peking University (2008) B.S. Applied Mathematics, Huazhong University of Science and Technology (2005) Research focuses on fundamental mathematical structures in probability with emerging applications in quantum physics and statistical learning. Current investigations bridge theoretical frameworks with computational approaches for high-dimensional data analysis. Recent publications demonstrate consistent contributions to spectral analysis of random operators and convergence properties in non-commutative probability spaces. Works frequently appear in premier mathematics journals including Transactions of the AMS and Journal of the European Mathematical Society.

• Combinatorial group theory
• Geometric semigroup theory
• Algorithmic problems in algebra
• +6 more

Robert D. Gray is a Professor of Mathematics at the School of Mathematics, University of East Anglia. His research focuses on combinatorial and geometric group and semigroup theory, algorithmic problems in algebra, decidability, homological finiteness properties, and group actions on graphs and topological spaces. EPSRC Research Fellow Editorial Board: International Journal of Algebra and Computation Available for PhD supervision in semigroup and inverse monoid theory His recent work explores: Topological finiteness properties of monoids Undecidability in one-relator inverse monoids Algorithmic properties of inverse monoids Maximal subgroups in special inverse monoids Key article trends include: Geometric and algorithmic aspects of inverse monoids Homological properties of semigroups Connections between group theory and semigroup theory Applications to graph theory and automata Scientific Awards: EPSRC Fellowship EP/V032003/1 EPSRC grant EP/N033353/1 EPSRC Postdoctoral Fellowship EP/E043194/1 Contact: Room S1.29, School of Mathematics, University of East Anglia, Norwich NR4 7TJ. Email: Robert.D.Gray@uea.ac.uk . Phone: +44 1603 591443.

Vladimir PodolskiiView profile

Associate Professor

• Computational complexity
• logical foundations of computer science
• tropical geometry

Vladimir Podolskii serves as an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering and holds a concurrent appointment in the Department of Mathematics within the School of Arts and Sciences. His academic work bridges theoretical computer science and mathematical foundations, with a focus on computational boundaries and algebraic structures. He completed his doctoral studies at Lomonosov Moscow State University in Moscow, Russia, receiving his PhD in 2009. Podolskii's research program centers on computational complexity theory, examining the inherent difficulty of computational problems through decision trees and threshold functions. His investigations extend to the logical underpinnings of computation and the application of tropical geometry in optimization contexts. This interdisciplinary approach connects discrete mathematics with theoretical computer science frameworks. His 2022 publications reveal concentrated exploration of decision tree efficiency for threshold functions and classification systems for ontology-mediated queries, demonstrating methodological rigor in analyzing computational limits and knowledge representation structures. Podolskii actively mentors graduate researchers through Dissertation Research courses while teaching core curriculum including Algorithms and specialized seminars on computational complexity toolkits, shaping the next generation of theoretical computer scientists.

• Obesity Research
• Type 2 Diabetes Management
• Nutrition and Dietetics
• +6 more

Dr. Grace Farhat is a Senior Lecturer at Manchester Metropolitan University (MMU), affiliated with the Faculty of Health and Education. She holds roles as Admissions Lead and Placement Co-Lead for the MSc Dietetics Programme. Her academic qualifications include a BSc in Nutrition and Dietetics, an MSc in Human Nutrition, and a PhD in Public Health Nutrition. She is a registered dietitian (HCPC, UK), registered nutritionist (Rnutr), and a Senior Fellow of the UK Higher Education Academy (SFHEA). Her research focuses on obesity, Type 2 diabetes, dietary interventions, and the effects of antioxidants (e.g., pomegranate extract) on inflammation and cardiometabolic health. Current projects include studies on dietary interventions for South Asian populations and the antimicrobial potential of plant extracts. She has led projects such as a randomized controlled trial on pomegranate extract’s effects in older adults and explored frozen foods’ role in hunger prevention. Dr. Farhat serves as an external examiner for programs at the University of Chester and Exeter, reviews for journals like Nutrients and Frontiers in Nutrition , and guest-edited a special issue on dietary approaches for obesity and diabetes. She has supervised PhD projects on topics ranging from e-waste scavengers in Nigeria to climate change mitigation in Sri Lankan industries. Her work emphasizes culturally tailored interventions, antimicrobial research, and public health strategies. Collaborations with industry partners like Birds Eye UK and Iceland highlight her applied research focus. Awards include the SFHEA fellowship, underscoring her contributions to higher education.

Yizao WangView profile

Professor

• Probability Theory
• Stochastic Processes
• Extreme Value Theory
• +4 more

Yizao Wang is a Professor in the Department of Mathematical Sciences at the University of Cincinnati. He holds a Ph.D. from the University of Michigan (2012) and specializes in Probability Theory, Stochastic Processes, and their applications, with a focus on extreme value theory, long-range dependence, and random fields. His research includes studies on asymmetric exclusion processes, fractional Brownian motion, and self-similar processes. Education : Ph.D. in Mathematics, University of Michigan, 2012. Research Interests : Probability theory, stochastic processes, extreme value theory, long-range dependence, random fields, limit theorems, and applications in finance and statistics. His work frequently explores the interplay between combinatorial structures and stochastic models, such as random partitions and operator-scaling processes. Grants : He has led and co-led multiple grants, including: DoD Army Research Laboratory: Advances in Extreme Value Theory with Long-Range Dependence ($84,229, 2020–2023) National Security Agency: From Random Partitions to Self-Similar Processes ($40,000, 2016–2018) National Science Foundation: Cincinnati Symposium on Probability Theory and Applications ($20,000, 2018–2019) Students and Advising : Mentored numerous graduate and undergraduate students in capstone projects and independent studies, focusing on topics like random walks, branching processes, and stochastic differential equations. Notable advisees include Connor McClellan, Yiyang Yu, and Weiqing Yu (recipient of the Charles Phelps Taft Senior Thesis Fellowship). Labs/Teams : Active in collaborative research groups within the department, contributing to interdisciplinary projects in stochastic modeling and mathematical finance. Teaches advanced courses in probability, stochastic processes, and financial mathematics.

Osama MahmoudView profile

Lecturer

• Predictive Modelling
• Health Data Science
• Machine Learning
• +6 more

Dr. Osama Mahmoud is a Lecturer (Assistant Professor) in Data Science and Statistics at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science (SMSAS) and the Department of Mathematical Sciences. He holds dual roles as the Director of the BSc Data Science and Analytics programme and Deputy Director of Research at SMSAS. His academic journey includes a PhD in Statistics from the University of Essex and an Honorary Senior Researcher position at the University of Bristol Medical School (2020–2024). Dr. Mahmoud's research focuses on Predictive Modelling, Health Data Science, Machine Learning, Bio and Medical Statistics, and Explainable AI. He pioneered the Slope-Hunter method (Nature Communications, 2022) for bias correction in genome-wide studies and developed tools like the Proportion Overlapping Score (POS) and open-source packages on CRAN/GitHub. His work bridges statistical methodology with applications in healthcare, environmental epidemiology, and industrial collaboration. Key contributions include studies on sleep-breast cancer mortality linkages (2025), lung function-cardiovascular risk relationships (2024), and smoking's role in depression recovery (2022). He has secured grants supporting collaborations with UNDP, Rolls Royce, and Recruitment Smart. Teaching innovations include pioneering programming and text analytics modules. Education: PhD in Statistics, University of Essex Affiliations: UK Reproducibility Network (UKRN) Institutional Lead, British Data Science Society Member Grants: Projects in Data Science, theoretical/applied Statistics, and industrial partnerships Tools: ESKNN, OTE, propOverlap packages on CRAN His work emphasizes reproducibility, with over 50 peer-reviewed publications and international teaching engagements at ICTP (Italy), EuADS (Luxembourg), and UK institutions.

Hemanshu KaulView profile

Associate Professor

• Discrete Mathematics
• Operations Research
• Graph Theory
• +5 more

Hemanshu Kaul is an Associate Professor of Applied Mathematics at Illinois Institute of Technology (IIT), part of the College of Computing. He serves as Co-Director of the M.S. in Computational Decision Science and Operations Research (CDSOR) program. His expertise spans Discrete Mathematics, Operations Research, Graph Theory, and Network Optimization, with applications in transportation, computer science, and engineering. Education: PhD in Mathematics from the University of Illinois at Urbana-Champaign (UIUC), MS in Mathematics from the Indian Institute of Technology Bombay. He has held roles including Distinguished Teaching Fellow (2016–2018) and AMS Project NExT Fellow (2007–2008). Research Interests : Focus on Graph Packing, DP-coloring, List Coloring, and algorithmic solutions for discrete optimization problems. His work bridges theoretical foundations with practical applications such as transportation networks and computer science systems. Publications & Grants : Over 50 publications in combinatorics and optimization, including NSF/NSA-funded projects like the EXCILL III Conference (2016–17). Recent work explores spectral Turán problems, DP-coloring algorithms, and longitudinal network models. Awards : Board of Trustees Award for Excellence in Teaching (2019) Excellence in Teaching Award (2017, College of Science, IIT) Interdisciplinary Research Grant (2009–2010, Transportation Networks) Advising & Leadership : Co-advisor for IIT's SIAM Student Chapter. Led restructuring of the Applied Math M.Sc. program (2018–19). Advised teams in the Mathematical Contest in Modeling (MCM), including a 2019 Meritorious Winner team for a disaster response system design. Labs & Collaborations : Involved in interdisciplinary projects combining applied math with computer science and engineering, including work on equitable public transit systems and network optimization.

Joel E. CohenView profile

Professor

• Mathematical Biology
• Population Dynamics
• Demography
• +7 more

Joel E. Cohen is the Abby Rockefeller Mauzé Professor at The Rockefeller University, where he leads the Laboratory of Populations. With over five decades of research experience, Cohen has pioneered innovative mathematical approaches to study biological populations and variability. His work bridges mathematics, biology, and environmental science, fundamentally changing how scientists understand population dynamics and the significance of biological variability. Dr. Cohen's research focuses on developing new mathematical tools to address population problems in demography, epidemiology, and ecology. He has made seminal contributions to the understanding of heavy-tailed distributions that describe extreme events like hurricanes and disease outbreaks, challenging traditional statistical approaches. His laboratory has conducted groundbreaking research on the spatial distribution of human populations in relation to geophysical factors, with unexpected practical applications ranging from soap formulation to semiconductor manufacturing. Cohen has also developed mathematical models for Chagas disease control in rural Argentina and created algorithms to predict international migration patterns. Analysis of Cohen's recent publications reveals a sustained focus on Taylor's law of fluctuation scaling, population dynamics, and ecological statistics. His work consistently demonstrates how abstract mathematical concepts can transform our understanding of biological systems, from cellular processes to global population trends. The research spans theoretical mathematics to practical applications in disease control, conservation biology, and environmental management. Olivia Schieffelin Nordberg Prize for excellence in writing in the population sciences (March 1997) Gheorghe Lazar Prize of Romanian Academy (December 2000) As director of the Laboratory of Populations, Cohen has led research on human population growth, infectious diseases, food webs, and international migration. His methods for assessing the uncertainty of population projections have been applied in court cases for predicting future claimants of asbestos-related diseases. Cohen's laboratory has collaborated with the United Nations Population Division on migration studies and developed mathematical models that account for more than half of the variability in annual migration numbers among 229 countries. Current research directions include understanding how demographic, economic, and cultural changes interact with Earth's physical, chemical, and biological environments. The Laboratory of Populations employs a multidisciplinary approach that combines mathematical modeling, statistical analysis, and field studies to address complex population issues. Their work exemplifies how basic quantitative research on populations frequently yields unexpected practical applications, demonstrating the profound connections between theoretical mathematics and real-world challenges in public health, environmental science, and resource management.

Stephen HinshawView profile

Professor

• Developmental Psychopathology
• Clinical Psychology
• Attention-Deficit Hyperactivity Disorder (ADHD)
• +4 more

Stephen Hinshaw is Distinguished Professor of Psychology at the University of California, Berkeley, and Professor of Psychiatry and Behavioral Sciences at UC San Francisco. His research focuses on developmental psychopathology, ADHD mechanisms, and stigma reduction in mental health. With over $20 million in NIH grants and equivalent foundation funding, he has led longitudinal studies on ADHD in girls and women since 1992 as Principal Investigator for the MTA Study's Berkeley site. Degree: Ph.D. (UCLA), A.B. summa cum laude (Harvard) Leadership: Former Psychology Department Chair (2004-2011), Co-director of Schwab Dyslexia Center and Child Teen Family Center Research priorities include: Longitudinal ADHD studies in females Stigma reduction through youth programs Mechanisms of therapeutic change Comorbidity patterns in psychopathology Neuropsychological risk factors Preventive mental health interventions Recent publications demonstrate interests in: ADHD underrepresentation in girls (2022) Self-harm risk in ADHD females (2021) Anti-stigma high school clubs (2020) Developmental pathways of ADHD (2018) Scientific recognition includes: APA Distinguished Scientific Contributions Award (2020) Sarnat Prize (2019) SRCD Distinguished Contributions (2017) APS James McKeen Cattell Lifetime Award (2016) SSCP Distinguished Scientist (2015) As mentor and lab director, he has trained numerous researchers while maintaining active media presence in major outlets like New York Times and Wall Street Journal . His 2022 book Straight Talk about ADHD in Girls addresses critical gaps in female-specific ADHD understanding.

• Chemical Engineering
• Materials Science
• Nanotechnology
• +3 more

Prof. Robert Grass is a Lecturer at the Department of Chemistry and Applied Biosciences at ETH Zurich, affiliated with the Institute for Chemical and Bioengineering Sciences. His research focuses on innovative applications of nanotechnology, DNA-based storage systems, and sustainable catalytic processes for CO2 valorization. Grass has pioneered silica-encapsulated DNA technologies for traceability in healthcare, environmental monitoring, and anti-counterfeiting measures. His work bridges chemical engineering with information technology, addressing challenges in long-term data preservation and molecular-level security. Current projects include developing compostable DNA storage materials and designing catalysts for methanol synthesis from CO2, contributing to both environmental sustainability and energy systems. Grass's interdisciplinary approach integrates nanomaterials design, enzymatic processes, and machine learning to advance next-generation storage and sensing technologies. Research Interests: Development of DNA-based storage systems with error-correction mechanisms Nanoparticle engineering for medical and environmental applications Catalytic materials for CO2 conversion and green chemistry Bio-inspired security systems using molecular randomness Sustainable materials for long-term data preservation His recent work highlights advancements in silica-encapsulated DNA tracers for tracking pathogen transmission dynamics, as well as low-nuclearity catalysts enabling efficient methanol synthesis from CO2. Grass actively explores the intersection of nanotechnology and digital information, including cryptographic applications leveraging DNA's inherent complexity.

Danielle MatthewsView profile

Professor

• Child Language Development
• Pragmatic Development
• Deafness and Communication
• +4 more

Professor Danielle Matthews is a Professor of Psychology at the University of Sheffield's School of Psychology and an academic at the Interdisciplinary Centre of the Social Sciences (ICOSS). Her research focuses on how children learn language, particularly pragmatic development (e.g., communicative repair, referential adaptation) and the impact of deafness and socio-economic factors on language acquisition. She has pioneered interventions to support communication in deaf infants and families from disadvantaged backgrounds, collaborating with organizations like the BBC’s Tiny Happy People initiative. Research Interests: Pragmatic abilities in communication, language development in deaf children, socio-economic influences on language, and word learning mechanisms. Grants: Nuffield Foundation, BBC Education, Leverhulme Trust, and UKRI GCRF grants for projects on language interventions and communication development. Teaching: Undergraduate courses on Developmental Psychology and Pragmatic Development; postgraduate supervision and ethics training. Her work emphasizes practical interventions, such as video-based communication strategies for parents of deaf infants and randomized controlled trials to improve caregiver responsiveness. She has also explored how conversational experience shapes pragmatic skills and mitigates disparities linked to hearing loss or socio-economic status. Recent studies include investigations into the cognitive underpinnings of conversational skills in autistic children and the long-term mental health implications of early language delays. Her research bridges theory and practice, aiming to inform educational and clinical practices globally.

• Financial Econometrics
• Volatility Modeling
• Time Series Analysis
• +4 more

Andrew J. Patton is a Professor in the Department of Economics at Duke University's Trinity College of Arts & Sciences, with a secondary affiliation at Singapore Management University's School of Economics. His office is located at 213 Social Sciences Building, Box 90097, Durham, NC 27708-0204, United States, and his professional website is available at http://econ.duke.edu/~ap172/. Dr. Patton's research focuses on financial econometrics, with particular expertise in volatility modeling, high-frequency data analysis, and dependence structures in financial markets. His work spans several key areas including realized variance methods, copula modeling for financial time series, risk management techniques, and forecast evaluation methodologies. He has made significant contributions to understanding market microstructure, hedge fund risk exposures, and the dynamics of financial correlations. His publication record demonstrates consistent output in top finance and econometrics journals, with recent work exploring machine learning applications in volatility forecasting, granular risk measures, and the impact of high-frequency data on asset pricing models. The research shows a clear trajectory toward increasingly sophisticated modeling of financial dependencies and risk structures. Dr. Patton has received recognition for his scholarly contributions, ranking among SSRN's top 1,427 authors by total paper downloads and top 1,090 by total paper citations. His work is frequently cited in the field of financial econometrics, indicating substantial influence on contemporary research. He maintains active research collaborations with leading economists including Tim Bollerslev, Kevin Sheppard, Tarun Ramadorai, and Robert Engle, among others. These collaborations span multiple institutions and have produced influential work on volatility modeling, risk measurement, and financial market dynamics.

• Premenstrual Dysphoric Disorder
• Neuroscience
• Stress Research
• +2 more

Celine Bencker is an External Lecturer and Academic researcher in the Department of Clinical and Health Psychology at the University of Vienna. Her research focuses on psycho-biological interactions in premenstrual symptomatology and stress responses during daily life. Her primary research interests include Premenstrual Dysphoric Disorder, stress neuroscience, and psychobiological interactions across the menstrual cycle. She employs ecological momentary assessment to investigate real-time psychological and biological stress responses in clinical populations. She has received prestigious awards including the Early Career Award (2025) SBSM Scholar Award (2025) She leads the research project: Investigating psycho-biological interactions in premenstrual symptomatology and stress during daily life , funded through the ÖAW-DOC Fellowship.

• Epidemiology
• Systematic Review Methods
• Low Back Pain Management
• +4 more

Dr. Jill Hayden is a Professor in the Department of Community Health and Epidemiology at Dalhousie University's Faculty of Medicine. She leads the BACK Program, focusing on prognosis research and systematic reviews, funded by multi-year CIHR grants. Her work addresses improving healthcare decision-making through evidence synthesis and low back pain management. She contributes to the Cochrane Collaboration and serves as Nova Scotia Science Lead for the Maritime SPOR SUPPORT Unit. Education: PhD (University of Toronto) Chiropractic Clinical Sciences Residency (Canadian Memorial Chiropractic College) DC (Canadian Memorial Chiropractic College) BSc Honours (Dalhousie University) Research Interests: Dr. Hayden's research integrates clinical epidemiology with systematic review methodologies, emphasizing prognosis studies, individual participant data (IPD) analyses, and network meta-analyses. She investigates opioid prescribing practices, exercise therapy efficacy, and knowledge translation strategies to bridge research evidence and clinical practice. Key Awards: Researcher of the Year (Canadian Chiropractic Association, 2014) CIHR/CCRF New Investigator Award (2007–2012) Research Professorship in Epidemiology (2011–2016) Grants & Advising: Her CIHR-funded research supports prognosis studies and systematic review innovations. She advises on spinal manipulative therapy efficacy and has collaborated across institutions on pain management protocols. Labs/Teams: Leads the BACK Program and co-convenes the Cochrane Prognosis Methods Group, fostering interdisciplinary collaboration in evidence-based healthcare.

Catherine WolframView profile

Assistant Professor

• Complex Analysis
• Probability Theory
• Geometry
• +3 more

Catherine Wolfram is a Gibbs Assistant Professor at Yale University and a National Science Foundation (NSF) postdoctoral fellow. She holds a PhD in Mathematics from MIT (2024), advised by Scott Sheffield, and an undergraduate degree from the University of Chicago. Her research focuses on complex analysis, probability, and geometry, with applications to statistical mechanics and random surfaces. She has presented her work at institutions including the University of Michigan, Columbia University, and international conferences such as the World Congress on Probability and Statistics. Her recent activities include talks on dimer models, holography in mathematical physics, and Teichmüller theory. Catherine has also engaged in teaching roles at MIT, leading recitations in probability and multivariable calculus. Her research has been supported by NSF postdoctoral funding, and her work has been published in journals like International Mathematics Research Notices (IMRN). She actively collaborates with researchers in probability, geometry, and mathematical physics, evidenced by her co-authored papers and participation in workshops at IHES, CIRM, and the Fields Institute.