Students

Riya Bangera is a Master’s student in Statistics at Columbia University, with experience in data science. She is currently focused on building and studying AI systems for real-world and socially impactful applications. Her interests include applying statistical thinking to understand data, designing responsible AI systems, and exploring how AI can support decision-making in domains such as healthcare, public policy, and community engagement.

Emily is a dual degree MSW/MPA student with a focus on international economics, social welfare and immigration. She is excited to work with the AI for Social Good and Society Initiative because the organization's values of centering community knowledge, lived experience and grassroots expertise called to her. Emily hopes to bring a new perspective to the AI models using social work values. In her free time she loves to travel, read and cook dinner for her friends. 

Kynzie (she/her) is a Fisher-Cummings Fellow and graduate student at the Columbia School of Social Work with policy and clinical concentrations. In her studies, she hopes to leverage her experience in advocacy, research, and policy to support human-centered systems change, recognizing the growing intersection between technology, law, and networks of care.

Kynzie also holds a B.A. in political science and Russian language from Yale University where she graduated with distinction in the major. 

Isabela is a C.P. Davis Scholar and an undergraduate student at the School of Engineering and Applied Sciences interested in bringing valuable experiences to end users and helping commercialize social and environmental solutions, especially in the context of emerging economies. Before Columbia, Isabela worked with a children's foundation to improve literacy rates through the use of AI in the classroom, led a product transformation at a personalized music startup, and started a community of low-income entrepreneurs in Bogota. 

Currently, she is providing product consulting at Ascend Consulting Group and serving as an analyst at the International Business Club as she builds a career that combines technical skills with market understanding. 

Amira Gbagba is a Statistics graduate student at Columbia University who focuses on making AI and data feel accessible, intuitive, and empowering, especially for beginners. She holds an Associate’s degree in Mathematics from Dutchess Community College and a Bachelor’s degree in Applied Mathematics & Statistics from Stony Brook University.

Her research experience includes work at Brookhaven National Laboratory, where she used Monte Carlo simulations to study radiation behavior and shielding, as well as a Mathematical Association of America research program analyzing public health and mobility data during COVID using statistical and dynamical modeling. Amira is also deeply committed to education and has supported historically underrepresented students in STEM through teaching and mentorship roles. She believes that everyone, regardless of background, deserves to feel confident navigating AI and data, and her work focuses on breaking down intimidating technical ideas into clear, practical explanations that others can use. Her current interests include AI literacy, statistics, economics, and using data to support education and public health research.

Wenze Li is an MA student in Statistics at Columbia University, with a prior background in insurance studies. He has a strong interest in applying statistical and data-driven methods to social research, especially for promoting health and well-being. His work draws on the HEAL study, using statistical methods to support evidence-based research and inform practical decision-making in health and social systems.

Xiaohui Ma is a Graduate Research Assistant in the AI for Social Good and Society (AI4SGS) program and an MA candidate in Statistics at Columbia University. Her work focuses on applying statistical modeling and machine learning to support data-driven decision-making in the public interest, with an emphasis on ethical and responsible AI. She is particularly interested in how AI can be developed to promote sustainable and equitable social outcomes.

Originally from Mexico City, Pablo earned his Bachelor's degree in Mathematics from The University of Texas at Austin before working as a Data Analyst at a financial institution. Now pursuing a Master's in Statistics at Columbia University, he combines his mathematical foundation with data science to create positive impact across different fields through evidence-based solutions.

Chi (Charlie) Zhang is a master’s student in Statistics at Columbia University in the Advanced Machine Learning track. His academic training focuses on statistical learning and applied machine learning, with an emphasis on rigorous model evaluation and methodological reliability in real-world text and social science data. He is particularly interested in the use of large language models for qualitative research, including topic modeling and automated coding of text data. His prior work includes developing retrieval-augmented generation (RAG)–based systems for document analysis and conducting empirical evaluations of machine learning models under noisy data conditions.

As part of the AI4SGS program, Chi looks forward to contributing to the ENVIO research team by supporting rigorous comparisons between LLM-based coding and human annotation, with the goal of improving analytic reliability and research workflow efficiency in social science and public health research.