Vision

The NSF-funded AI Advancements and Convergence in Computational, Environmental and Social Sciences (AI-ACCESS) National Research Traineeship (NRT) program at Washington University in St. Louis aims to build a cohort of new investigators, trained at the intersection of computational science (specifically AI), environmental science, and social sciences, with the skills to capitalize on the enormous synergistic potential in the convergence of AI and environmental social science.

Rationale

The very nature of important environmental and social science questions are shifting as social systems are increasingly embedded within computational platforms that mediate daily human activity. Huge datasets are rapidly becoming commonplace, but the right methods for understanding data generated by human behavior, as well as accessible tools for studying them, are lacking. The questions raised by the huge quantities of data generated by daily environmental conditions and social behavior are engaging and profound. These questions pose special challenges for the practice of data science.

These challenges are even more profound in environmental science, a multidisciplinary field spanning computational, social, and natural sciences. The importance of this area is never more critical! The UN warns of worsening human-induced climate change, heatwaves, and wildfires, even with the most ambitious efforts to curb greenhouse gas emissions. Wildfires cause both significant economic burden and adverse human health impacts. To make matters worse, socially vulnerable populations have unjustly and disproportionately bore the brunt of these adverse environmental and social impacts.

Our training program is premised on the belief that these challenges — building new AI and computational methodologies that both meet the needs and address the complexity of environmental and social sciences — can be tackled only using a truly transdisciplinary approach. Adequate solutions require combining deep knowledge of AI techniques with the domain expertise to understand how and when they may apply to the core tasks of environmental and social sciences and their intersections.

Recruitment

We are targeting new Ph.D. students who have research interests in the intersection of AI and environmental social science to become a trainee (only U.S. citizens and permanent residents are eligible) or a participant (all Ph.D. students are eligible) in the AI-ACCESS team.

Join us and you will:

  • Be uniquely trained to use data to understand human behavior, including how we affect and react to the changing environment, and harness that knowledge to improve environmental and social conditions.
  • Understand what it means to do team science and transdisciplinary research, and possess the communication and leadership skills necessary to effect change in organizations.
  • Fill a growing need for organizations that aspire to develop data-driven policies and computational algorithms to address environmental and social challenges.

Both trainees and participants will have access to newly-developed AI-ACCESS resources, such as interdisciplinary mentoring, professional development workshops, and summer internship opportunities. Trainees will also receive an AI-ACCESS fellowship for two years, which includes a competitive stipend of $34k per year and support to attend national conferences. See Application page for more information.

Training

To train our Ph.D. students to be computationally-oriented data scientists working towards the public good, we have developed a new interdisciplinary curriculum that includes remedial courses, core cohort-building courses, and domain depth courses. See Coursework page for more information.

Students will be exposed to research in different areas through “rotations,” starting in fall of their admission year, after which they are expected to identify one of three tracks (computational, environmental, or social sciences) that best aligns with their research interests. Reflecting the interdisciplinary nature of the program, students will also need to identify two faculty from different tracks to serve as their co-advisors.