J. Khai Tran Email & Phone Number

Santa Monica, California, Us

Santa Monica, Ca, Us

Managed and guided a team of statisticians/data scientists to ensure rigor of analyses for diverse field and lab studies generated across multiple regions globally. Consistently exceeded or greatly exceeded expectations and promoted from Director to Senior Director in 2022.• Oversaw experimental designs and data collection procedures, from small-scale experiments to massive-scale trials, in collaboration with regional field and lab teams. Greatly reduced experimental costs through the use of mathematical optimization and advanced statistical approaches for reducing the impact of confounding variables.• Ensured that analysis requests were fulfilled to the highest standards, enforcing best practices and leading the development and implementation of novel approaches (e.g. applying Gaussian Processes and a Hierarchical Bayesian framework utilizing a custom MCMC sampling method to address spatial and temporal correlations in field data and enabling meta-analysis across multiple trials).• Managed computational resources and established a solid proprietary statistical code library, including optimization for GPU acceleration both locally and in AWS.• Facilitated cross-functional collaboration between chemistry and field biology, allowing implementation of mechanistic statistical models, (e.g. dose-responses to environment-dependent release-diffusion-decay processes), to answer business-critical questions (e.g. proposed ways to achieve the same level of efficacy using a fraction of the amount of active ingredient, drastically reducing the cost).• Helped establish a software team to implement a data visualization app and auditable data management system.• Leveraged ecological expertise to introduce novel concepts for product design and evaluation.

Basel, Basel, Ch

Oversaw delivery of model-based risk assessments as part of regulatory dossiers for the registration of insecticidal traits and to inform business decisions regarding the evolution of pesticide resistance. Received a Syngenta Special Recognition Award in 2014.• Set the strategic direction and led the adoption of new rigorous methods for quantifying risk, using limited trials to predict regional longevity of products with regards to resistance, while minimizing reliance on questionable assumptions.• Designed all aspects of experiments (e.g. sampling schemes, proper controls, etc.) to determine critical parameters used for forecasting. Improved sampling efficiency by introducing novel adaptive designs.• Utilized innovative statistical methods that make better use of available information and allow the study of previously unaddressable questions (e.g. estimating allele frequencies from the shape of dose-response curves using a mixture model), often leveraging high performance computing and modern machine learning techniques. • Established internal modeling capabilities by developing modular solutions and putting in place scalable hardware/software, efficiently satisfying both immediate and future needs.• Represented Syngenta as an expert through various modes of communication, establishing a strong reputation for scientific integrity and retaining a high level of credibility with regulatory agencies and academics worldwide.

Performed risk analysis and utilized predictive modeling of biological systems to inform decisions regarding insect resistance management. Received two DuPont Recognition Awards for work with the Bureau of Plant Industry in the Philippines (2011) and work with AcreMax registrations (2011)• Analyzed data from a wide range of experimental designs to inform models for predicting rates of resistance development in insect pests. Contributed insights and guidance to biologists in the generation of laboratory and field-based data.• Developed mathematical models to project future outcomes. Incorporated statistics and probability into predictions by propagating uncertainty, providing probabilistic results, using Markov transition models, Bayesian statistics, Monte Carlo methods, etc.• Applied modeling results to evaluate strategies for managing insect resistance, utilizing expertise in Game Theory and optimization. Communicated results through presentations and written reports.• Optimized algorithms for efficient execution of models and develop code to execute models in Matlab, R, and Python, (e.g. sped up execution of a previously existing model by as much as 10,000-fold).• Instructed and supervised interns as well as mentored junior scientists.

Stony Brook, Ny, Us

Taught Applied Ecology and Conservation Biology. Topics included:• Population viability analysis and scientifically informed decision-making (creating value criteria, using probabilistic risk assessments, and optimization)• Exponential and density-dependent growth in continuous and discrete time (including chaos)• Stochastic population dynamics• Age, stage, and spatially structured populations• Community dynamics (interacting populations of different species)

Hanover, Nh, Us

Investigated the lower lethal temperature of southern pine beetles and its effects on the pest's population dynamics across the southeastern U.S.• Used kriging methods to determine temperatures at forest sites from distant weather station data.• Estimated degree of thermal buffering from time series of under-bark vs. air temperature using a mechanistic Newtonian cooling model.• Measured supercooling points of various life stages of southern pine beetles.• Examined the relationship between reconstructed microclimate conditions and empirically measured population growth rates using quantile regression and compared that relationship to one predicted from measured physiological tolerances.• Collected field populations near the northern distributional limit to determine stage structure as it relates to physiological tolerances.

Doctor Of Philosophy - Phd, Ecology And Evolution With Extensive Coursework In Applied Mathematics And Statistics

Bachelor Of Arts - Ba, Ecology And Evolutionary Biology With Minors In Anthropology And Environmental Science