We aim to empower students to use their theses as a pathway to impact.

Join a community of students and researchers tackling the world's most pressing challenges.

As a student, you'll invest hundreds, if not thousands, of hours into your thesis.

What if all of those hours contributed to solving the world’s biggest challenges?

Commit to a high-impact thesis and career today.

Resource Library

Empower your research journey with our resource library.

These resources are designed to support you at every stage, from conceptualization to publication, ensuring your research reaches its full potential for impact.

Our Featured Research Areas

  • AI Safety

    Explore the critical challenges of ensuring artificial intelligence systems remain beneficial to humanity as they grow more powerful and autonomous.

  • Global Health

    Tackle pressing issues in healthcare accessibility, disease prevention, and medical innovations that can improve lives worldwide.

  • Animal Welfare

    Address ways to improve animal health, foster ethical treatment and protection of animals across various industries and ecosystems.

  • Biosecurity

    Examine potential biological threats and strategies to safeguard humanity’s long-term future.

Explore Research Topics

Effective Thesis Success Stories

  • "Cultured Meat: Do Chinese Consumers have an Appetite" by Chloe Dempsey

    This study explored the future of cultured meat in China, focusing on consumer acceptance and preferences. A survey of 1,020 Chinese respondents revealed that 70% are willing to try cultured meat, and 58% are ready to buy it, with processed meat products being the preferred format.

  • “Artificial Intelligence in Nuclear Command and Control” by Tao Burga

    This study explored the risks of integrating AI decision support systems (DSSs) into nuclear command, control, and communication systems, focusing on automation bias, where users over-rely on AI.

  • “White-Box Adversarial Policies in Deep Reinforcement Learning” by Stephen Casper

    This paper explores adversarial policies in reinforcement learning (RL), focusing on white-box attacks that utilize a target agent’s internal state. Unlike black-box approaches, which treat the target agent as part of the environment, white-box policies exploit detailed knowledge of the agent’s internal workings.

  • "Beyond the ban: A comprehensive approach to the governance of autonomous weapons” by Jia Yuan Loke

    Current debates about autonomous weapons tend to see a establishing a ban or developing regulations as mutually exclusive options. This research argues that effective governance must incorporate both mechanisms.

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