At Stanford Data Science, the strength of our community lies not just in groundbreaking research, but in the people who bring it to life. As we reflect on the journeys of three incredible postdoctoral fellows—Emily, Ellie, and Ambarish—we’re reminded of the vibrant, collaborative spirit that defines SDS.
From interdisciplinary projects and impactful mentorship to lasting friendships and meaningful career transitions, each of them shares a unique perspective on how SDS shaped their path. Whether advancing methods in causal inference, building inclusive communities in science, or exploring the real-world impact of data, Emily, Ellie, and Ambarish embody the curiosity, warmth, and excellence at the heart of our program.
Emily Gordon
Fondest Memory
For Emily, two of the most memorable experiences during her time in the Stanford Data Science program were attending the Women in Data Science and Women in Data-Driven Discovery conferences. Unlike many data science gatherings, these events featured a majority of women participants—something she found both rare and deeply inspiring. “It felt like a welcoming, supportive environment to discuss data science,” she shared, “and a powerful reminder of how important gender diversity is to scientific discovery.”
SDS Impact on Growth
The interdisciplinary spirit of the SDS program has profoundly shaped Emily’s academic growth. Through the program, she’s had the opportunity to engage with researchers across a broad spectrum of disciplines—learning how data science serves as a common language that connects varied domains of knowledge. “SDS has shown me what data science means across all facets of scientific discovery,” she said. “I’ve learned how to find common points of interest and communicate meaningfully across fields.”
Favorite Project
A major highlight of Emily’s time at SDS has been her research project applying machine learning to predict the onset of abrupt summertime warming under climate change. Tackling this complex and highly challenging problem has required creativity and persistence—everything from sourcing the right data and setting up the modeling framework to refining metrics and interpreting ambiguous results. “This sort of research wouldn’t have been possible without the SDS fellowship,” Emily noted. “It’s been invaluable to have the freedom to try—and fail—at many different approaches.”
Now in the final stages of the project, Emily and her collaborators are preparing their findings for journal submission. She’s hopeful that the work will not only contribute to the growing field of extreme event prediction but also lay the foundation for her future research agenda. “This project will be a cornerstone of my research,” she said. “And SDS made it possible.”
Looking Ahead
Join us in congratulating Emily on her faculty position at the University of Auckland as Lecturer in Climate Physics!
Ellianna Abrahams
Fondest Memory
Ellianna Abrahams's (for friends, Ellie) most cherished experience in the Stanford Data Science (SDS) Scholars Program was the opportunity to connect with an extraordinary community of faculty, staff, fellows, and scholars. She found the SDS environment to be filled with intellectually curious individuals, all deeply passionate about their research and eager to engage across disciplines. From her very first day on campus, SDS provided Ellie with a welcoming and collaborative network, enabling her to form lasting professional relationships and friendships that will extend far beyond her time in the program.
SDS Impact on Growth
Ellie credits the SDS program with shaping her approach to academic data science, especially its interdisciplinary nature. She notes that researchers often face similar methodological challenges, even when working in vastly different domains. The weekly "Data & Donuts" gatherings were especially valuable, offering an informal and generative space to connect with early-career researchers and refine ideas during early project stages. She also highlighted the Sustainability Data Science Earth ML group as a particularly meaningful part of her experience, where she regularly engaged with a vibrant community focused on applying machine learning to Earth Observation. These sessions helped deepen her thinking and refine her contributions to a multidisciplinary field.
Favorite Project
At Stanford, Ellie has been working at the intersection of geoscience and machine learning, with a focus on improving Earth Observation of polar ice sheets. Her research addresses one of the most pressing challenges in climate science: the significant uncertainty surrounding future sea level rise. Ice sheets, which are difficult to monitor due to harsh environmental conditions and limited accessibility, are the primary source of this uncertainty. Ellie’s work has demonstrated that embedding geoscientific context into machine learning models—either implicitly or explicitly—can dramatically improve their performance in these unique environments. By tailoring computer vision algorithms to account for the physical characteristics of ice sheets, she has enhanced the accuracy and robustness of satellite-based monitoring.
Recognizing the global importance of this work, Ellie has also prioritized building these approaches into open source software libraries and facilitating access to computational tools, ensuring that researchers worldwide can benefit from and build upon her contributions.
Looking Ahead
As of July 2025, Ellie serves as a Research Software Specialist and Computational Scientist at Stanford’s Doerr School of Sustainability, where she continues to explore the frontiers of climate data science.
Ambarish Chattopadhyay
Fondest Memory
Ambarish’s time at Stanford Data Science (SDS) was marked by a vibrant mix of community, mentorship, and intellectual discovery. Looking back on his three years in the program, he finds it impossible to choose a single favorite memory. Instead, what stands out is the constellation of experiences that made his time at SDS so meaningful.
From his very first day at Wallenberg Hall to his last at CoDa, Ambarish was struck by the environment of excellence and warmth. “The structure at SDS made it easy to connect with brilliant students and faculty across so many disciplines,” he recalls. Whether it was listening to John and Naras share their infectious enthusiasm for research or participating in the many interdisciplinary events, Ambarish found constant inspiration.
A particular highlight was his involvement with the Stanford Causal Science Center (SC²), where he participated in and helped organize events like the CausaliTea mixer, the Bay Area Tech Economics Seminars, and the Stanford Causal Science Conference. “Those experiences were not only enriching, they were also just a lot of fun,” he says, giving a special shout-out to Elizabeth Wilsey for her camaraderie and leadership.
Ambarish also formed strong connections with the SDS administrative staff, describing them as “the warmest and most helpful admin team I’ve encountered at any university.” Over the years, friendly conversations with Chris, Lynn, Elizabeth, Laura, and Maddy became a joyful part of his routine. And then there was Wallenberg—the old SDS space—which, for Ambarish, felt like a second home (except, as he jokes, “I got work done there instead of watching YouTube or falling asleep”).
Of course, many of the most lasting memories are tied to friendships forged during his time at SDS. One standout is SDS alum Yan Min—an “all-rounder” whom Ambarish mentored and who became a close friend and cherished part of his Stanford journey.
SDS Impact on Growth
Being part of SDS broadened Ambarish’s perspective as a researcher. “Seeing how data science was being applied across so many areas was truly eye-opening,” he says. The program’s unique positioning between academia and industry gave him insight into real-world challenges and helped shape his interdisciplinary interests.
His postdoc fellowship also served as a pivotal bridge between student and faculty life. “I’m deeply grateful to my advisor, Guido Imbens, for giving me space to grow into an independent thinker,” Ambarish says. He also highlights the rewarding experience of mentoring younger scholars, which helped prepare him for the road ahead in academia.
Favorite Project
During his time at Stanford, Ambarish focused on methodological problems in causal inference—specifically, design-based approaches to concluding data in randomized experiments and observational studies. His work explored how to quantify uncertainty in causal estimates without relying on modeling assumptions, especially in complex experimental settings or when individuals interact in unpredictable ways.
“These methods matter,” he explains, “because in both social and medical sciences, randomized experiments are the gold standard for evaluating policies, treatments, and interventions.” His research offers new tools to navigate the complexities of modern experiments and extends to settings where true randomization isn’t possible—opening the door to more robust, transparent causal analysis.
Throughout this journey, the conversations and feedback he received from colleagues at SDS and SC² were instrumental. "Those interactions shaped the direction of my work and helped me grow not just as a researcher, but as a member of a vibrant, thoughtful community."
Looking Ahead
Ambarish will soon begin a new chapter as an Assistant Professor at the Indian Statistical Institute (ISI), Kolkata. He is excited to return to India and join one of the most respected research institutions in the region, where he looks forward to continuing his work in causal inference and mentoring the next generation of data scientists.
