Oscar Moll

Data Systems and Applied ML Researcher

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I’m a postoctoral researcher at the Data Systems Group at MIT CSAIL, working with Sam Madden and Michael Cafarella. I enjoy working on tough problems with great people.

I am currently in the job market looking to work on machine learning model development, especially language and vision-language models on impactful real-world deployments.

My expertise spans applied machine learning work during graduate school and data-systems work in industry prior to grad school. During my PhD, also here at the MIT Data Systems group, I focused on problems around the data needed for developing AI applications, such as the need for retrieval from large databases of video and images. Solutions I developed mixed algorithmic insights (eg. clever sampling techniques, human-in-the-loop interactive retrieval algorithms, supervised and semi-supervised learning techniques) with system-building and implementation work. To know more about my work, you can take a look at the selected publications below.

Before grad school, I worked as a software engineer at Amazon Web Services, on the Amazon Aurora DB team, specifically on its innovative cloud storage component.

In my spare time I enjoy hiking and sight seeing in New Hampshire and Maine with the MIT outing club, running along the Charles in Cambridge, and lately strength training the basic four lifts. I am originally from Cobán, a city in northern Guatemala. I am also a Li Po Chun United World College high-school alum, a Guatemalan science olympiad alum, and an alum of American School of Guatemala’s PBA scholarship program.

selected publications

  1. SeeSaw: Interactive Ad-hoc Search Over Image Databases
    Oscar Moll, Manuel Favela, Samuel Madden, Vijay Gadepally, and Michael Cafarella
    Proc. ACM Manag. Data, Dec 2023
  2. ExSample: Efficient Searches on Video Repositories through Adaptive Sampling
    Oscar Moll, Favyen Bastani, Sam Madden, Mike Stonebraker, Vijay Gadepally, and Tim Kraska
    In 2022 IEEE 38th International Conference on Data Engineering (ICDE) , Dec 2022