SURF Testimonials
Hi all! My name is Jaishnoor, and I worked at the Zhang Lab at Notre Dame this summer through the C-CAS SURF program. My work focused on using neural networks, specifically LSTMs and Transformers, for retrosynthetic prediction. This work contributed to the larger C2D project, one of whose main components is an open-source online handbook for data chemistry.
Having only recently started working with these neural networks, I was a bit nervous but also excited to get my hands dirty creating the architecture and writing the actual implementation code. Not only was I able to experiment with different components and learn extensively in the process, but I was also able to utilize this learner’s perspective to write content for the handbook’s chapter. I am extremely proud to have contributed my writing to something that is live and publicly available.
I loved the independence and room to explore that were afforded to me by my mentor, while also being able to rely on her and the PhD students’ experience and expertise when navigating difficult challenges. I had been interested in pursuing a PhD, but this experience truly solidified my resolution. Beyond the research, Notre Dame fostered a welcoming atmosphere and created a full, enjoyable experience for its visiting scholars, which made this experience all the more complete. I will remember this summer for its new and meaningful experiences, as well as the opportunity to meet people from around the world and form lasting friendships.
— Jaishnoor Kaur, 2025 Participant
Hi everyone! My name is Grace Perna, and this summer I had the opportunity to work in the Nelson lab at Caltech through the C-CAS DATA SURF program. Between meeting some of the most incredible friends and mentors, and exploring new and upcoming areas of chemistry, I truly wouldn’t exchange the past 10 weeks for the world (plus Pasadena is beautiful!).
My undergraduate studies are in biotechnology, but my interests in organic synthesis and catalysis have grown substantially in the past year. In that way, I wanted my summer to reflect my own journey in exploring areas of the field that I had little exposure to. My project focused on methods development for biocatalytic transformations that give access to vinyl cation intermediates—i.e., engineering enzymes to catalyze an organic pathway different than that of the original enzyme’s function.
Going into the internship, I had absolutely no experience with programming and very little with synthetic chemistry. Now, in August, I feel confident enough in the skills I’ve learned to seek further opportunities in computational disciplines for my field. I was exposed to molecular docking and modeling software, and gained a better understanding of how important high throughput screening is for upcoming fields—like biocatalysis. Not only did my chemistry wet-lab skills grow, but the sole act of being around people of different disciplines—especially synthetic and structural chemists—meant that I could learn new techniques and principles beyond just my own project.
Most importantly, however, this program provided me with the resources and support to better understand the graduate school process and experience. I have always had an inkling that I’ve wanted to pursue a PhD, but I wasn’t fully aware of the other options and career paths, nor the steps to take to get there. The C-CAS program has been invaluable in informing my future career decisions, and this summer as a whole has had an insurmountable effect on expanding my wet lab skills, data management and analysis techniques, and exposing me to new areas of chemistry.
— Grace Perna, 2024 participant
Hi all! My name is Mary Lavin and I worked in the Doyle Lab at UCLA as a Data SURF student through C-CAS this summer. My project focused on building machine learning models for structure-property relationship modeling of organic photocatalysts. Leading up to this internship, I was nervous about my lack of machine learning experience and felt uncertain about how much I would be able to accomplish. Reflecting on the past couple months, I’m very proud of how much I have grown as a computational chemist. I created an end-to-end machine learning pipeline for important photocatalyst properties such as absorption/emission (nm), ground state redox potential (eV), and excited state redox potential (V), all with mean absolute errors comparable to DFT calculations. I also synthesized novel photocatalysts for experimental validation.
This experience has helped me further my wet lab skills and learn new computational techniques. Furthermore, I received invaluable advice from the graduate students in the Doyle Lab, which will continue to inform how I approach my graduate studies. Outside of work, I enjoyed the new experience of living in LA, especially as someone who grew up around the East coast. I went on hikes, explored the city, and spent time at the beach. Thank you to Professor Abigail Doyle, my mentor Jason Wang, Courtney Holland, and C-CAS for this experience! I had a great summer and I’m excited to see what C-CAS continues to accomplish.
—Mary Lavin, 2023 participant