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The Columbia-Amazon Summer Undergraduate Research Experience (SURE) program brings together a diverse group of undergraduates, many of whom come from backgrounds historically underrepresented in STEM fields, to do original scientific research under the supervision of Columbia University faculty, with additional professional training from Amazon mentors.
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SURE summer program to promote diversity in STEM a great success

Seventy-four percent of surveyed undergraduates in Columbia-Amazon program say it exceeded or far exceeded expectations.

By Prem Natarajan, Shih-Fu Chang
November 23, 2021
4 min read
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In September 2020, Columbia University and Amazon partnered to create the Columbia Center of Artificial Intelligence Technology (CAIT), a multiyear collaboration between Columbia Engineering and Amazon to support research, education, and outreach programs.

Out of the discussions around CAIT emerged the idea for a summer research internship that would provide opportunities for students from historically underrepresented backgrounds to pursue careers in STEM fields. And in the summer of 2021, we launched the Columbia-Amazon Summer Undergraduate Research Experience (SURE) program.

SURE is an eight-week summer program in which a diverse group of undergraduates, many of whom come from backgrounds historically underrepresented in STEM fields, do original scientific research under the supervision of Columbia University faculty, with additional professional training from Amazon mentors. Students and faculty are encouraged to select projects that reflect mutual interests, including those that confront societal challenges such as resource conservation and management, equal access for people with disabilities, and the diagnosis and treatment of disease.

Amazon and Columbia Engineering cosponsor the program, which includes workshops, trainings, and panel discussions on topics related to career management, graduate study and academic research opportunities, diversity and inclusion in tech, and scientific culture in both academia and industry, at an average of one a week during the internship period.

The initial SURE class comprised 26 students from 12 U.S. states and territories (including Guam and Puerto Rico), 54% of whom identified as Black or multiracial, 19% of whom identified as Hispanic, and 58% of whom identified as female. In a survey conducted at the end of the program, 74% of respondents said that SURE either exceeded or far exceeded their expectations, and 100% of respondents said that they are “extremely likely” to recommend the program to classmates and peers.

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Members of the initial Columbia-Amazon SURE (Summer Undergraduate Research Experience) class, together with resident advisors and Columbia University faculty.
Credit: Jane Nisselson

After the session, the SURE team also polled faculty mentors about their experiences, and 100% of respondents said that they would recommend participation in the SURE program to their faculty colleagues. The surveys were conducted by Libby Goss, manager of the SURE program, and Shavonna Q. Hinton, assistant dean for Diversity, Equity, and Inclusion at Columbia Engineering.

“I was able to get much more involved in the projects than I had initially expected,” one of the students told the survey team. “I felt like the work I was doing was useful and that I was making a significant contribution to the team.”

“Columbia SURE has been one of the most enriching experiences in my undergraduate degree,” said another. “I was afforded a fast-paced experience in the BME [biomedical engineering] niche I am most interested in, cell and tissue engineering. Within four weeks, I was fully immersed in working with nanoparticles, cancer cells, and engineered cell lines.”

Student projects

Another measure of the program’s success is the breadth and quality of the projects in which the students participated. In some cases, students made contributions to large, ongoing projects in their mentors’ research groups; in other cases, they developed their own, small-scale projects that could be completed in a summer and complemented or enhanced ongoing projects.

Several students worked on problems in biomedicine, using both biological and computational tools: identifying the genetic pathways that lead to overactivation of the immune response; using CRISPR to help understand organ formation in embryos and how it goes awry; investigating the mechanism whereby the COVID virus infects cells; automatically interpreting OCT breast scans; and developing models that predict preterm birth from uterine ultrasound data, among others.

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Left to right: 2021 SURE fellow Imani Phillips, 2021 SURE fellow Sarah Flanigan, and Shih-Fu Chang, the interim dean of Columbia Engineering and an Amazon Scholar.
Credit: Jane Nisselson

Two projects focused on water management — using computer vision to gauge the extent of irrigation in sub-Saharan Africa and evaluating small-scale, decentralized approaches to wastewater treatment — and another focused on the effect of urban tree planting on surface temperatures. Other students concentrated on problems in chemistry and materials science.

And, of course, several projects dealt with information technology and electrical engineering. Two students worked on different projects that involved interpreting data from the Internet of things; another student measured the coverage of several experimental long-range data networks in the urban environment; others created a Web interface to provide access to a system that uses machine learning to sharpen MRI images; and one student did a project on using machine learning to manage the operation of lithium-ion battery packs, based on measurements of voltage, temperature, and other factors.

In light of the enthusiastic response to the inaugural instance of the Columbia-Amazon SURE internship program, we plan to expand the program in 2022 — increasing enrollment to 50 students and extending the program length to 10 weeks. The program will also provide graduate fellowships for students from historically underrepresented populations to pursue Columbia master’s degrees in STEM fields.

Please visit the program page to learn more about the SURE program and to apply for a SURE fellowship.

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About the Author
Prem Natarajan
Prem Natarajan is a former Alexa AI vice president.
Shih-Fu Chang
Shih-Fu Chang is the interim dean of Columbia Engineering, the Richard Dicker Professor in both the Computer Science Department and the Electrical Engineering Department at Columbia University, and an Amazon Scholar.

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This is a unique opportunity to lead a business-critical applied science initiative from its inception—defining the scientific charter, establishing foundational research pillars, and building a multi-year science roadmap for transformative impact. As the single-threaded applied science leader, you will build and guide a dedicated team of applied scientists, research scientists, and machine learning engineers, working closely with cross-functional engineering and product partners, to research, develop, and deploy agentic AI systems that fundamentally reimagine the advertiser journey. Your charter will begin with advancing the science behind intelligent agents that simplify campaign creation, automate optimization decisions through autonomous reasoning and planning, and deliver personalized advertising strategies at scale. You will pioneer novel approaches in areas such as LLM-based agent architectures, multi-step planning and tool use, retrieval-augmented generation, reinforcement learning from human and business feedback, and robust evaluation methodologies for agentic systems. You will expand to proactively identify and tackle the next generation of AI-powered advertising experiences across the entire SPB portfolio. This high-visibility role places you as the science leader driving our strategy to democratize advertising success—making it effortless for advertisers of all sizes to achieve their business goals while delivering relevant experiences for Amazon customers. Key job responsibilities Build, mentor, and lead a new, high-performing applied science organization of applied scientists, research scientists, and engineers, fostering a culture of scientific excellence, innovation, customer obsession, and ownership. Define, own, and drive the long-term scientific and product vision and research strategy for agentic AI-powered advertising experiences across Sponsored Products and Brands—identifying the highest-impact research problems and charting a path from exploration to production. Lead the research, design, and development of novel agentic AI models and systems—including LLM-based agent architectures, multi-agent orchestration, planning and reasoning frameworks, tool-use mechanisms, and retrieval-augmented generation pipelines—that deliver measurable value for advertisers and create delightful, intuitive experiences. Establish rigorous scientific methodology and evaluation frameworks for assessing agent performance, reliability, safety, and advertiser outcomes, setting a high bar for experimentation, reproducibility, and offline-to-online consistency. Partner closely with senior business, engineering, and product leaders across Amazon Advertising to translate advertiser pain points and business opportunities into well-defined science problems, and deliver cohesive, production-ready solutions that drive advertiser success. Drive execution from research to production at scale, ensuring models and agentic systems meet high standards for quality, robustness, latency, safety, and reliability for mission-critical advertising services operating at Amazon scale. Champion a culture of scientific inquiry and technical depth that encourages bold experimentation, publication of novel research, relentless simplification, and continuous improvement. Communicate your team's scientific vision, research breakthroughs, strategy, and progress to senior leadership and key stakeholders, ensuring alignment with broader Amazon Advertising objectives and contributing to Amazon's position at the forefront of applied AI. Develop a science roadmap directly tied to advertiser outcomes, revenue growth, and business plans, delivering on commitments for high-impact research and modeling initiatives that shape the future of AI-powered digital advertising.
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