Los Angeles, California, United States
PhD student in Environmental Engineering with deep expertise in climate risk and biodiversity analysis, focusing on the integration of geospatial analysis, econometrics, and advanced machine learning techniques. Demonstrated success in developing and refining innovative strategies for climate and biodiversity risk management, in alignment with international frameworks such as TNFD, SBTi, and CSRD. Skilled in assessing climate hazard risk models, exploring the feasibility of novel data integration, and leading interdisciplinary research initiatives. Proficient in collaborating with cross-functional teams to design actionable solutions that enhance sustainability efforts and mitigate climate-related risks. Committed to advancing environmental resilience through research, innovation, and strategic consultation.
+ Acts as a domain expert in climate risk and biodiversity, providing strategic guidance on risk management projects + Collaborates with internal stakeholders, including sustainability teams and risk managers, to develop comprehensive climate and biodiversity risk strategies + Ensures that strategies align with international standards and frameworks, such as the Taskforce on Nature-related Financial Disclosures (TNFD), Science-Based Targets initiative (SBTi), and Corporate Sustainability Reporting Directive (CSRD) + Conducts in-depth reviews of existing climate hazard risk modeling approaches, assessing their strengths, weaknesses, and applicability to specific projects + Evaluates the accuracy, scalability, and relevance of various models for predicting and managing climate-related risks + Provides recommendations on the most effective modeling approaches for different types of climate hazards, such as extreme weather events, sea-level rise, and biodiversity loss + Scopes and assesses the feasibility of integrating climate data with cutting-edge machine learning (ML) techniques for advanced risk analysis + Works with data scientists and ML engineers to identify opportunities for leveraging ML to enhance climate risk predictions and scenario modeling + Explores the potential of boundary-pushing ML approaches, such as deep learning and reinforcement learning, to improve the accuracy and granularity of climate risk assessments
+ Researched previous studies and research to develop a novel study around people's motivations for participating in Citizen Science. + Established relationships with project coordinators for local marine survey projects to use their programs for my study + Created an online survey instrument for project coordinators to distribute to their volunteers that asked them questions regarding their motivations for participation and their perceived benefits from the program. + Attended training sessions on marine survey projects to push my survey instrument and meet with some volunteers + Crated brief presentations to explain my project in the hopes of boosting participation + Compiled survey data in excel to compare two marine survey projects in a comparative case study + Performed basic data analysis to describe patterns that exist between the programs and also build visual graphics for presentation + Defended my project to a panel at the University of Illinois Springfield and distributed my thesis to the project coordinators to assist them in their recruiting and retention efforts
+ Conducted geospatial analyses to identify areas at high risk for extreme climate events, such as floods, droughts, and heat waves + Utilized Geographic Information Systems (GIS) to map and visualize climate vulnerabilities across different regions + Integrated spatial data with socio-economic datasets to assess the impact of climate risks on vulnerable communities + Applied econometric techniques to analyze the economic impact of climate risks and vulnerabilities + Developed and refined decision analysis models to support climate adaptation and mitigation strategies + Collaborated with economists and policy analysts to incorporate model outputs into broader climate risk assessments + Analyzed high-resolution datasets, including remote sensing data and satellite imagery, to monitor environmental changes and climate trends + Utilized CMIP6 climate model outputs to project future climate scenarios and assess potential risks + Utilized reanalysis datasets to understand historical climate patterns and inform predictive models + Integrated multiple sources of data, including climate models, socio-economic data, and remote sensing outputs, to create comprehensive risk assessments + Interpreted complex data sets to produce actionable insights for policymakers and stakeholders + Prepared detailed reports and presentations to communicate research findings to diverse audiences.
+ Designed and developed analytical tools to monitor and optimize global energy programs + Supported the creation of dashboards and reporting systems to track the performance of demand- response initiatives + Collaborated with the development team to implement and test new tools and features aimed at enhancing program efficiency + Assisted in the continuous monitoring of global energy programs to ensure compliance with performance metrics + Analyzed data from various sources to identify trends and areas for improvement in energy usage and demand-response strategies + Analyzed current data management and reporting processes to identify inefficiencies and potential areas for improvement + Assisted in implementing systemic changes to data management and reporting practices based on identified improvement opportunities + Collaborated with IT and engineering teams to ensure changes were effectively integrated into existing systems and tools + Supported the development of best practices for data management and reporting within the organization
+ Analyzed large datasets to ensure accuracy and consistency in utility and industrial energy data + Validated data by cross-referencing multiple data sources and correcting discrepancies + Monitored and documented business workflows related to data processing and management + Assisted in developing and implementing process improvements in data management workflows + Investigated the root causes of detected anomalies, such as irregular consumption patterns or errors in data collection + Collaborated with cross-functional teams to resolve data issues and prevent future occurrences