Viviana Acquaviva

About

Dr. Viviana Acquaviva is a professor who actively mentors both undergraduate and graduate students, having guided over 25 students throughout her career. She is currently recruiting a Ph.D. student and a post-baccalaureate researcher for the "Carbonara" project, indicating her ongoing involvement in research initiatives. Dr. Acquaviva values strong interests in programming, statistics, and machine learning, as well as proficiency in Python, as important skills for students wishing to work with her. While experience with climate data is helpful, it is not a requirement. She emphasizes the importance of enthusiasm, passion, humility, respect for all, and strong work ethics in her mentorship approach. Her past group members include a diverse range of students and researchers who have gone on to pursue advanced degrees and careers in physics, statistics, astronomy, data science, and software engineering.

Research topics

• Computer Security
• Computer Science
• Physics
• Data Mining
• Astronomy
• Astrophysics
• Data science
• Cartography
• Geography
• Database

Selected publications

• Ethics in climate AI: From theory to practice

  PLOS Climate · 2024-08-02 · 5 citations

  articleOpen access1st authorCorresponding

  Climate science, and climate artificial intelligence (AI) in particular, cannot be disconnected from ethical societal issues, such as resource access, conservation, and public health.An apparently apolitical choice-for example, treating all data points used to train an AI model equally -can result in models that are more accurate in regions where the density and quality of data is higher; these often coincide with the northern and western areas of the world (e.g., [1,2]).Inequity in the access t…

  PublisherOA PDFDOI

• From Data to Software to Science with the Rubin Observatory LSST

  arXiv (Cornell University) · 2022 · 3 citations

  The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collab…

  PublisherOA PDFDOI

• The Art of Measuring Physical Parameters in Galaxies: A Critical Assessment of Spectral Energy Distribution Fitting Techniques

  arXiv (Cornell University) · 2022-12-04 · 3 citations

  preprintOpen access

  The study of galaxy evolution hinges on our ability to interpret multi-wavelength galaxy observations in terms of their physical properties. To do this, we rely on spectral energy distribution (SED) models which allow us to infer physical parameters from spectrophotometric data. In recent years, thanks to the wide and deep multi-waveband galaxy surveys, the volume of high quality data have significantly increased. Alongside the increased data, algorithms performing SED fitting have improved, inc…

  PublisherOA PDFDOI

• Sensitivity of Ocean Carbon Sink Estimates to Rare Observations

  Geophysical Research Letters · 2025-10-10 · 2 citations

  articleOpen access

  Abstract We evaluate the impact of new data in the annual release of SOCATv2024 on air‐sea carbon dioxide (CO 2 ) flux reconstructions. Using the p CO 2 ‐Residual and LDEO‐HPD methods, we reconstruct surface ocean f CO 2 based on SOCATv2023 and SOCATv2024. For both products we find strong agreement in global mean air‐sea CO 2 fluxes for 1990–2017, but a notable divergence beginning in 2018. Reconstructions based on SOCATv2024 estimate substantially less ocean carbon uptake compared to those base…

  PublisherDOI

• Targeting bias in algorithm optimization improves reconstructions of surface ocean pCO₂

  Machine Learning Earth · 2025-07-30 · 1 citations

  articleOpen accessCorresponding

  In order to fully understand current and future climate impacts from rising carbon emissions, it is crucial to accurately quantify the air–sea CO _2 flux and the ocean carbon sink accross space and time. Air–sea flux estimates from observation-based data products used in the global carbon budget show a large spread and suggest a stronger carbon sink than global ocean biogeochemistry models (GOBMs) in the last decade. Output from GOBMs and Earth system models (ESMs) can be used as ‘testbeds’ to b…

  PublisherDOI

Frequent coauthors

• Yen‐Ting Lin

  66 shared

• Eric Gawiser

  62 shared

• Beth Reid

  49 shared

• M. Limon

  University of Pennsylvania

  47 shared

• Hy Trac

  Carnegie Mellon University

  47 shared

• Judy M. Lau

  Stanford University

  41 shared

• Eric R. Switzer

  38 shared

• Jonathan Sievers

  37 shared

Labs

• TerraD2IPI

Awards & honors

• Chambliss Astronomical Writing award (2024)
• Mentorship Award from Women Who Code (2023)
• One of 100 Technologists to Watch by Women Who Code (2023)
• Named one of 13 'Tecnovisionarie' Italian Women in AI by Wom…
• Listed as one of 50 women who are doing the history of Compu…

Similar researchers at Columbia University

• Robert F. Andersonh-103
• Robin Bellh-68
• O. Roger Andersonh-57