جست‌وجوی استاد و پژوهشگر

Harsha Gangammanavarمشاهده پروفایل

دانشیار

• Optimization under Uncertainty
• Stochastic Programming
• Large-Scale Computational Optimization
• +۳ مورد دیگر

Harsha Gangammanavar is an Associate Professor in the Department of Operations Research and Engineering Management (OREM) at Southern Methodist University (SMU), affiliated with the Data Science Institute. He holds a Ph.D. in Operations Research and M.S. in Electrical Engineering from The Ohio State University, and a B.E. in Electronics and Communication Engineering from Visvesvaraya Technological University, India. Education: Ph.D. in Operations Research, The Ohio State University M.S. in Electrical Engineering, The Ohio State University B.E. in Electronics and Communication Engineering, Visvesvaraya Technological University Research Interests: His work focuses on stochastic programming, large-scale computational optimization, and their applications in infrastructure systems, healthcare, and wireless communication. Key areas include optimization under uncertainty, stochastic decomposition methods, and scalable algorithms for power systems and renewable energy integration. Recent Activities & Awards: Recipient of NSF XTRIPODS grant for collaborative research in data science (2024). DOE Office of Science grant for multiscale stochastic optimization (2022). ONR grant for decomposition-based stochastic models in discrete-event systems (2022). Awarded INFORMS Undergraduate Student Paper Award (2021) and INFORMS Minority Affairs Poster Competition (2016). Advising & Grants: Current advisees include Ph.D. students Ishara A.A.D.H., Jackson Forner, Chhavi Sharma, and Zhiyuan Zhang. Former students Niloofar Fadavi, Sakitha Ariyarathne, and others have secured roles in industry and academia. Active in securing grants from AFOSR, ONR, DOE, and NSF. Labs & Teams: Leads research on stochastic optimization algorithms, power grid resilience, and healthcare applications through interdisciplinary collaborations. Open to motivated students for Ph.D. research in optimization and data science.

Hyune-Ju Kimمشاهده پروفایل

استاد

• Statistics
• Biostatistics
• Regression Analysis
• +۳ مورد دیگر

Hyune-Ju Kim is a Professor of Mathematics at Syracuse University's College of Arts & Sciences, affiliated with the Applied Statistics program. Her research focuses on change-point problems, resampling tests, regression model selection, sequential analysis, and statistical applications in genetics. She holds a Ph.D. in Statistics from Stanford University (1988) and a B.S. in Mathematics from Seoul National University (1983). Recent work emphasizes advancements in Joinpoint regression software, including model selection methodologies for health data analysis. Her contributions address complex survey data challenges and longitudinal medical trend studies. She has served on multiple departmental committees, including executive roles in faculty search committees and graduate programs. Teaching responsibilities include advanced statistics courses such as MAT 652 Probability & Statistics II, MAT 654 Linear Models, and MAT 750 Statistical Consulting. Her service roles include statistics liaison for Syracuse University Project Advance and leadership in curriculum development.

Andrea Scottمشاهده پروفایل

دانشیار

• Fluid Dynamics
• Machine Learning
• Remote Sensing
• +۸ مورد دیگر

Andrea Scott is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Faculty of Engineering. Her research focuses on fluid dynamics, remote sensing, and machine learning applications in environmental systems. She holds a Doctorate in Mechanical Engineering from the University of Waterloo (2008) and has taught courses such as ME 351 (Fluid Mechanics) and SYDE 621 (Numerical Methods). Notable awards include the 2022 Outstanding Performance Award and 2021 Distinguished Performance Award from the University of Waterloo. Research interests span turbulence modeling, data-driven approaches, and physically inspired neural networks. Her work includes developing algorithms for sea ice concentration estimation, SAR imagery analysis, and fluid flow simulations. She collaborates with groups like the Vision and Image Processing Lab and the Remote Sensing of Environmental Change group. Current projects involve small object detection in remote sensing and graph neural networks for unstructured grid problems. Education: PhD, Mechanical Engineering, University of Waterloo, Canada (2008) MASc, Mechanical Engineering, McMaster University, Canada (2001) BASc, Mechanical Engineering, University of Waterloo, Canada (1999) Teaching responsibilities include undergraduate and graduate courses in fluid mechanics and systems engineering. She actively mentors students through the IEEE GRSS Women-to-Women Mentorship program and serves as an Associate Editor for the AGU Journal of Machine Learning and Computation. Her lab oversees over 20 current and past graduate students, focusing on topics like space debris tracking, AI-driven environmental modeling, and sea ice dynamics. Research outputs include over 40 publications in journals such as Physical Review Fluids and IEEE Transactions on Geoscience and Remote Sensing .

Gudrun Hillerمشاهده پروفایل

استاد

• Particle Physics
• Flavor Physics
• Beyond the Standard Model (BSM)
• +۱۲ مورد دیگر

Prof. Dr. Gudrun Hiller is a faculty member in the Department of Physics at the Technical University of Dortmund, where she conducts research in theoretical particle physics. She is affiliated with the HEP theory group and actively involved in the IMAPP international master program in particle physics. Her research spans flavor physics, rare decays of beauty and charm quarks, physics beyond the Standard Model, asymptotic safety, and SMEFT. University: Technical University of Dortmund School: Faculty of Physics Department: Department of Physics Office: Room P1-04-307 Email: gudrun.hiller@tu-dortmund.de Phone: +49 (0)231 755-3580 Her research focuses on flavor physics, emphasizing rare semileptonic and radiative decays of beauty and charm quarks, precision tests with top quark observables, and the development of novel particle physics models with controlled high-energy behavior (asymptotic safety). She leads the BSM seminar, covering topics such as flavor, Higgs, dark matter, quantum gravity, and asymptotic safety. The recent publications and workshop activities indicate a strong focus on rare charm decays, CP violation, flavor anomalies, leptoquarks, dark matter models, and the interplay between flavor physics and collider phenomenology. Her group actively participates in international collaborations and workshops, including those at CERN, DESY, and Aspen Center for Physics. DFG Research Unit: Theoretical Flavor Physics Dortmund/Siegen BMBF Theorieverbund (2012-2015) Particle Theory Group at Dortmund: R&D on EOS Member of the HEP theory group at Dortmund Organizer of workshops on asymptotic safety and flavor physics Prof. Hiller supervises students and postdoctoral researchers, with recent seminar topics indicating active advising in areas like flavored dark matter, leptoquarks, proton decay, and flavor anomalies. She has taught courses including Introduction to Elementary Particle Theory, Theoretical Physics II, and General Relativity, demonstrating a strong commitment to education and mentorship.

Florian Diekertمشاهده پروفایل

استاد

• Environmental Economics
• Resource Economics
• Socio-ecological Systems
• +۷ مورد دیگر

Professor Florian Diekert is a Professor of Environmental Economics at the University of Augsburg's Faculty of Business and Economics and a member of the Centre for Climate Resilience. He leads the Environmental Economics Lab, which focuses on designing, analyzing and testing strategies for sustainable and resilient development, with particular emphasis on socio-ecological systems under pressure and group decisions under strategic and natural uncertainty. Professor Diekert received his education from the Technical University Dresden (International Relations) and the University of Oslo (MPhil in Economics, PhD in Economics in 2011). His research employs economic experiments, theoretical modeling, and statistical analysis, often in interdisciplinary collaborations with natural scientists. Prior to joining the University of Augsburg in 2023, he was a Junior Professor in Heidelberg and conducted research visits at Columbia University and UC Santa Barbara. His research spans Environmental Economics , Resource Economics , and Behavioral Economics , with specific focus on socio-ecological systems, group decision-making under uncertainty, lab-in-the-field experiments, social norms, and early warning systems for ecological tipping points. His work integrates theoretical insights with practical applications for sustainable development policy, often through interdisciplinary collaborations with natural scientists. Professor Diekert's publication record reveals a consistent trajectory of high-impact research on climate resilience, fisheries management, and collective action problems. His recent work increasingly incorporates experimental methods to investigate how environmental conditions shape economic behavior across different cultural contexts (Chile, Norway, Tanzania), and how policy interventions can improve sustainability outcomes through behavioral insights. ERC Starting Grant 'NATCOOP' for research on how nature shapes preferences and incentives of economic agents and how this affects common-pool resource management Professor Diekert advises multiple doctoral students and leads a vibrant research team including postdoctoral researchers and doctoral candidates. His Environmental Economics Lab actively participates in major international conferences such as the Global Tipping Points Conference, Nordic Annual Environmental and Resource Economics Workshop, and European Association of Environmental and Resource Economists conference, demonstrating strong international engagement in his field. The Environmental Economics Lab at the University of Augsburg brings together economists, natural scientists, and policy experts to address pressing sustainability challenges through interdisciplinary research. The lab conducts both theoretical and experimental work, with field studies in various international contexts including Chile and Tanzania, and maintains strong connections with other research institutions across Europe and beyond.

Ian Rennerمشاهده پروفایل

مدرس ارشد

• Ecological Statistics
• Species Distribution Models
• Statistical Theory
• +۳ مورد دیگر

Dr. Ian Renner is a Senior Lecturer in the School of Information and Physical Sciences at the University of Newcastle, specializing in Data Science and Statistics. He holds a PhD in Statistics from the University of New South Wales, complemented by a Master of Statistics from the University of Utah and a Bachelor of Science in Mathematics from Valparaiso University. His research focuses on species distribution models (SDMs), particularly leveraging presence-only data and point process models. He developed the PPM-LASSO approach and maintains the R package 'ppmlasso' for model implementation. Education: PhD (Statistics), University of New South Wales Master of Statistics, University of Utah Bachelor of Science (Mathematics), Valparaiso University Research Interests: Dr. Renner's work bridges statistics and ecology, emphasizing the development of robust SDMs. Key areas include: Unifying MAXENT and Poisson point process models Observer bias correction in ecological data Integration of regularization techniques (e.g., LASSO) for predictive accuracy Application of citizen science data in conservation His methodologies address challenges like taxonomy changes and sampling biases in species distribution studies. Publications: His recent work highlights advancements in SDM stability, citizen science applications, and regularization methods. Key themes include improving model reliability through penalized likelihoods and addressing ecological data complexities. Awards: JB Douglas Award (2011) Runner-up for Best Student Talk (2011) EJG Pitman Prize (2010) Grants & Supervision: He has secured $12,838 in internal grants, including a visiting fellowship at CNRS (France) and conference funding. He currently co-supervises a PhD on deep learning for speech depression recognition and has guided two other students in statistical ecology and methodology. Labs/Teams: Leads the development of the 'ppmlasso' R package, collaborating with researchers like Olivier Gimenez and Eric Beh to advance ecological statistics.

Marie-Christine Dükerمشاهده پروفایل

استادیار

• high-dimensional statistics
• time series analysis
• functional data analysis
• +۱۱ مورد دیگر

Marie-Christine Düker is an Assistant Professor in the Department of Statistics and Data Science at Friedrich-Alexander University (Germany). Her research focuses on high-dimensional statistics, time series analysis, functional data analysis, and extreme value theory with applications in economics, psychology, chemistry, and ecology. Previously, she was a postdoctoral associate at Cornell University's Department of Statistics and Data Science under David Matteson. She earned her PhD in Mathematics from Ruhr-University Bochum under Herold Dehling and spent part of her doctoral studies at the University of North Carolina at Chapel Hill with Vladas Pipiras. Current Position: Assistant Professor, Department of Statistics and Data Science, Friedrich-Alexander University Previous Academic Affiliation: Postdoctoral Associate, Cornell University Education: PhD in Mathematics, Ruhr-University Bochum; Part-time research at University of North Carolina Research Interests: Her work spans high-dimensional time series under long-range dependence and nonstationarity, discrete data modeling, nonlinear dynamics, dimension reduction, and change-point analysis. Applications include econometrics, neuroscience, chemical data analysis, and ecological forecasting. Recent Publications: Her 2025-2024 work covers Hilbert space-valued linear processes, kernel estimation for nonlinear dynamics, confidence interval approximations, and latent Gaussian count time series. Earlier papers address simultaneous diagonalization, long-run variance matrices, and transition rate estimation challenges. Contact: marie.dueker@fau.de

Shelly Gulatiمشاهده پروفایل

استاد

• Engineering Education
• Microfluidics
• Biological Fluid Flow
• +۳ مورد دیگر

Shelly Gulati is a Professor of Bioengineering in the School of Engineering and Computer Science at the University of the Pacific, where she bridges technical bioengineering research with transformative educational practices. Her academic credentials include: Post-doctoral training at Imperial College London (2007-2009) PhD in Bioengineering from the University of California, Berkeley and San Francisco (2006) BS in Chemical Engineering from John Hopkins University (2000) Professor Gulati's research spans Microfluidics , Biological Fluid Flow , and Engineering Education . Early work focused on micro-droplet dynamics, drug dissolution testing, and biological fluid applications using microfluidic platforms. Her recent trajectory emphasizes pedagogical innovation, including curriculum redesign, advising models, and initiatives supporting underrepresented students. She integrates entrepreneurial-minded learning into bioengineering education while developing frameworks for self-regulated learning and online community building. Publication trends reveal a strategic pivot from bioengineering fundamentals (2016-2018) toward educational scholarship (2021-2024). Recent work addresses first-year experience transformation, integrative advising, and equity-focused learning practices, reflecting her commitment to reshaping engineering education through evidence-based approaches. Gulati actively reimagines academic advising through initiatives like the "Thriving Tigers" model, which unifies faculty, academic, and career services. She designs targeted interventions such as book clubs for female engineering students and remote rapport-building activities, demonstrating holistic investment in student development beyond traditional classroom boundaries. While earlier microfluidics research likely involved experimental laboratory work, her current leadership centers on educational program development and assessment within the School of Engineering and Computer Science.

Mitzi Boltonمشاهده پروفایل

عضو هیئت علمی

• Sustainable Development
• Public Policy
• Public Administration
• +۷ مورد دیگر

Dr. Mitzi Bolton is a Senior Research Fellow at the Monash Sustainable Development Institute (Monash University), specializing in public policy, governance, and sustainable development. She holds external roles such as Academic Advisor to the Australia and New Zealand School of Government's National Regulators Community of Practice and former Chair of the IPAA Sustainability Community of Practice (Vic). Her research focuses on integrating science and policy to address complex sustainability challenges, particularly leveraging AI for public decision-making and hazardous waste management. Education: PhD in Public Policy & Sustainable Development (Australian National University, 2020) MComLaw in Environmental Law (Monash University) BSc (Hons) in Botany/Ecology (University of Melbourne) Research Interests : Mitzi’s work emphasizes optimizing public decision-making frameworks, sustainable development governance, and the application of AI in policy. She collaborates across disciplines to advance transdisciplinary solutions, particularly in environmental policy and regulatory practices. Her projects include designing digital tools for prison leavers and developing frameworks for hazardous waste management. Recent Projects : IP3.04 – Management of Hazardous Waste (2024–2025) AI Techniques for Hazardous Waste Reuse (2022–2023) SDG Governance Scoping (2022) Awards : Recipient of the Geoff Anderson Prize (2022) for contributions to sustainable development research. Advising & Grants : Supervises PhD students focusing on public sector practices for sustainable development. Leads grants exploring AI’s role in governance and environmental policy. Collaborations : Engages with institutions like the Australasian Land and Groundwater Association and the Australia and New Zealand School of Government. Active in global sustainability networks addressing SDG implementation challenges.

Jayce Robert Getzمشاهده پروفایل

دانشیار

• Langlands Functoriality
• Automorphic Representations
• Trace Formulae
• +۵ مورد دیگر

Jayce Robert Getz is an Associate Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. His research focuses on Langlands functoriality, nonabelian Fourier transforms, and relative trace formulae, with applications to automorphic representations and arithmetic geometry. He holds a Ph.D. from the University of Wisconsin (2007), an M.A. from the same institution (2006), and an A.B. from Harvard University (2004). Getz has held visiting positions at Pohang University of Science and Technology (2021, 2024), and collaborated internationally at institutions like the Hebrew University and the Institute for Advanced Study. His work bridges number theory, representation theory, and algebraic geometry. Getz has been awarded prestigious fellowships, including the NSF Postdoctoral Research Fellowship (2007–2010) and the NDSEG Graduate Fellowship (2004–2007). He is a Principal Investigator on multiple NSF grants, including projects on summation formulae and L-functions. His research interests include trace formulae, L-functions, algebraic cycles, and nonabelian harmonic analysis. Recent publications highlight advancements in summation formulae for quadrics, nonabelian circle methods, and automorphic kernel functions. Teaching includes courses on number theory, algebraic structures, and advanced topics in mathematics. Getz collaborates globally, with affiliations at the Postech Mathematics Institute and contributions to international workshops. His work emphasizes foundational questions in arithmetic geometry and the Langlands program.

• Water Security
• Climate Change
• Public Health
• +۱۱ مورد دیگر

Corinne Wallace is an Adjunct Assistant Professor in the Department of Civil Engineering at McMaster University , with research expertise at the intersection of water security, climate change, public health, and gender equity. Her work spans both technical engineering analysis and community-engaged studies, particularly focusing on marginalized populations in Canada, East Africa, and South America. Dr. Wallace's research interests include: Water-Health Nexus Climate Change Impacts on Disease Gender Equity in Water Access Indigenous Water Governance Mosquito-Borne Disease Modeling Rainwater Harvesting Systems Her publication trends show increasing focus on climate-health interactions (2025), gender-water linkages (2024), and machine learning applications for water quality analysis (2025). She employs mixed-methods approaches and works closely with First Nations communities and international development organizations. Dr. Wallace utilizes art-science communication strategies like the Virtual Water Gallery to transform water and climate knowledge dissemination. Her recent work explores EDI (Equity, Diversity, Inclusion) implementation in large research networks and conceptual frameworks for climate-water equity.

• Human-Computer Interaction
• User Experience Design
• Cognitive Psychology
• +۳ مورد دیگر

Demetrios Karis serves as an Adjunct Lecturer in Experience Design at Bentley University while operating his independent consultancy Karis User Experience Evaluation. His academic foundation includes a BA from Swarthmore College, PhD from Cornell University, and Post Doctoral Fellowship at the University of Illinois, Urbana-Champaign, with current office location in Smith Technology Center (Room 121). Education background: BA, Swarthmore College PhD, Cornell University Post Doctoral Fellowship, University of Illinois, Urbana-Champaign Dr. Karis's research spans four decades with two distinct phases: early foundational work in Human-Computer Interaction focusing on speech recognition interfaces and cognitive psychology (1980s-2000s), followed by a radical pivot toward existential climate collapse analysis (2018-2025). His current scholarship integrates biophysical, political, economic, military, health, and psychological perspectives to model civilizational risks, while earlier contributions established key principles in usability testing, remote collaboration systems, and speech interface design. This evolution reflects both technical expertise in user experience methodologies and growing urgency regarding planetary-scale crises. Analysis of his 15 most recent publications reveals a clear methodological continuity—applying rigorous human factors frameworks from his HCI work to complex societal systems in his climate research—while demonstrating extraordinary disciplinary range from psychophysiology to geopolitical forecasting. Regarding academic service, no specific awards or honors are documented in available sources. His position as Adjunct Lecturer suggests primary industry engagement through his consultancy, though he maintains active teaching responsibilities in Bentley's Experience Design program. The absence of listed advisees or grants indicates his academic role may be primarily instructional rather than research-mentorship focused. Dr. Karis's independent research practice through Karis User Experience Evaluation represents his primary operational base for advancing both technical UX work and macro-scale collapse modeling.

Valerie Venturaمشاهده پروفایل

استاد

• Computational Neuroscience
• Time Series Forecasting
• Graphical Models
• +۶ مورد دیگر

Valerie Ventura is a Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. She serves as Co-Director of the Ph.D. Program and maintains dual affiliations as affiliated faculty in the Machine Learning Department and the Center for the Neural Basis of Cognition (CNBC). As a graduate advisor for the Program in Neural Computation (PNC), she bridges statistical methodology with neuroscience applications. Her research focuses on computational neuroscience, particularly time series forecasting, networks, algorithms, and nonparametric methods. Key research areas include computational neuroscience, biostatistics & epidemiology, graphical models & networks, and DELPHI lab projects. Her work integrates advanced statistical techniques to solve complex problems in neural data analysis, motor control, and neural decoding. Analysis of her publication record reveals consistent contributions to neural decoding methodologies, spike train analysis, and cortical covariance modeling. Her recent work emphasizes practical applications in brain-machine interfaces and neural prosthetics, with a strong focus on improving decoding accuracy through novel statistical approaches to spike sorting and waveform analysis. The integration of machine learning with traditional statistical methods characterizes her interdisciplinary approach. Scientific Awards: 2006 Canadian Journal of Statistics Best Paper Award As an educator, Ventura serves as Co-Director of the Ph.D. Program in Statistics and Data Science while advising students through the CNBC's Program in Neural Computation. Her collaborations span multiple departments and research centers, particularly through the DELPHI Lab Group which focuses on computational neuroscience applications. Current research directions suggest continued innovation in neural decoding algorithms and applications to motor control systems.

Elena Mäder-Baumdickerمشاهده پروفایل

استاد

• Differential Geometry
• Geometric Variational Problems
• Minimal Surfaces
• +۴ مورد دیگر

Prof. Dr. Elena Mäder-Baumdicker holds a Tenure Track position in Geometry at TU Darmstadt since 2019. She was previously a postdoc at KIT and conducted research at Princeton University. Currently, she is organizing conferences such as the BIRS workshop on Non-linear Critical Point Theory and the Women in Geometry 3 conference. Her research focuses on global and local questions in differential geometry, particularly geometric variational problems involving minimal surfaces and Willmore surfaces. She has supervised two active PhD students (Nils Neumann and Jona Seidel), along with several Master’s and Bachelor’s theses. She is a member of the SPP 2026 'Geometry at infinity' and actively engages in academic outreach, including the Meet&Math initiative and public lectures for school students. Her teaching includes courses on differential geometry and seminars on geometric analysis. Education: PhD in Mathematics (2014, Freiburg), Diploma in Mathematics (2010, Freiburg) Research Interests: Minimal Surfaces, Willmore Surfaces, Geometric Flows (e.g., Mean Curvature Flow), Alexandrov Spaces, Variational Problems in Geometry Recent Conferences: Co-organizer of the Ernst Kuwert 60th Birthday Conference (2021), Geometric Analysis meets Geometric Topology (2019), and multiple BIRS events (2023–2025) Grants & Fellowships: DFG-funded research position (2017–2019), Leopoldina Fellowship (2018–2019) Labs/Teams: Leads the 'Variational Problems in Geometry' team at Women in Geometry 3, co-runs the RMU Seminar 'Solution Concepts in PDEs' Her work emphasizes the interplay between geometric analysis and topology, with a focus on singularities in geometric flows and applications of variational methods. She actively participates in promoting gender equality in academia through her role in the AK-Gleichstellung committee.

Joseph Ibrahimمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Survival Analysis
• Prior Elicitation
• +۶ مورد دیگر

Joseph Ibrahim is an Alumni Distinguished Professor in the Department of Biostatistics at the Gillings School of Global Public Health, University of North Carolina at Chapel Hill, where he has served since 2002. He currently holds dual leadership roles as Director of Graduate Studies for the Department of Biostatistics and Director of the Biostatistics for Research in Genomics and Training Grant. His methodological innovations in Bayesian survival analysis and missing data methodologies have significantly advanced public health research, particularly in cancer genomics applications. Professor Ibrahim's research program centers on developing statistical frameworks for complex clinical and genomic data. His seminal contributions include Bayesian cure rate models, prior elicitation techniques, and diagnostic tools for high-dimensional survival analysis. Current work focuses on integrating multi-omics data with longitudinal tumor burden metrics and refining adaptive clinical trial designs for biomarker-driven populations. These methodologies directly address critical challenges in precision oncology and pharmacovigilance, enabling more robust inference from real-world evidence. His 2025 publications reveal three dominant trends: (1) Advancements in cure rate modeling for joint longitudinal-survival data with change points, (2) Computational innovations for high-dimensional penalized models using autoencoders and R packages like hdbayes, and (3) Methodological refinements for Bayesian trial design incorporating historical controls. These works consistently bridge theoretical statistics with cancer research applications, particularly in tumor phylogeny inference and signal detection for adverse events. Scientific awards include: Samuel S. Wilks Memorial Award (2024) from the American Statistical Association for distinguished contributions to biostatistics Professor Ibrahim has mentored 48 pre-doctoral students and 8 postdoctoral fellows, with exceptional thesis publication records in top statistical journals. As principal investigator of the T32 Cancer Genomics Training Grant since 2004, he has secured funding for 35 doctoral students. His curriculum leadership includes modernizing eight graduate courses and establishing new data science computing sequences since 2015. Current advising focuses on Bayesian methodology development for cancer genomics applications. He directs the department's Biostatistics for Research in Genomics initiative and leads the T32 Cancer Genomics Training Grant team, which integrates statistical methodology development with translational cancer research across UNC's Lineberger Comprehensive Cancer Center and clinical partners.