Cornelia Paulik

I am an Assistant Professor of Practice at UC Berkeley's School of Information and currently a Visiting Scholar at Stanford University.

Before this, I was a Research Scientist at Stanford University and a Postdoctoral Fellow in the Data-Intensive Development Lab at UC Berkeley.

My research sits at the intersection of health and climate, leveraging foundational models and other machine learning architectures in natural language processing (NLP) and computer vision (CV). I teach Applied Machine Learning and Generative AI to Master’s students in the School of Information.

Note to prospective PhD students: due to the high volume of emails I receive, I may not be able to respond to your questions individually. If you are interested in having me as a co-advisor, please apply through the iSchool PhD application process and mention this in your application.


Email: cornelia.paulik [at] berkeley.edu  /  Office: TBD  /  Curriculum Vitae


News

  • [2025] I am excited to contribute to the Oxford-Berkeley Summer Doctoral Program by giving a research talk on my AI-related work and leading a workshop on classification and generative applications using foundational AI models.

  • [2024] Excited to lead the Women in MIDS initiative at UC Berkeley!

  • [2023] I am giving a talk on October 27 in the statistics department at UC Davis on Ped-BERT, my latest research.

  • [2023] Excited to give a talk at UC Berkeley's School of Information on Transformers with an application to Electronic Medical Records [slides].

  • [2023] My MIDS students won the Hal Varian award for their Curie.AI Capstone project (jointly advised with A. Todeschini)

  • [2022] My MIDS students won the Hal Varian award for their HealthCAir Capstone project (jointly advised with A. Todeschini)

  • [2022] My 5th-year MIDS students won the Hal Varian award for their Wildfire-RX Capstone project (jointly advised with F. Nugen)

  • [2020 - present] I am teaching Applied Machine Learning for the MS in Data Science program at UC Berkeley.

  • [2020] I have been working on deploying ML models at scale lately. These are my notes on how to set up a Spark cluster using Hadoop/HDFS from scratch.

  • [2019] Excited to teach a new class on Fundamentals of OOP and Data Analytics using Python for the MS in Applied Economics at UW-Madison.


Journal publications

Estimating Health and Economic Impacts of Global Behavior Change During the COVID-19 Pandemic, under review, Nature (2022)
J.Tseng*, K.C. Coy*, C. Ilin (Paulik)*, A.C. Ewing, T. Chong, S.M. Marks, I. Bolliger, N.M. Gonzalez, K.Bell, A.J. Hakim, S. Hsiang

Utilizing Prenatal Data for Early Detection of Pediatric Health Risks: An Exploratory Approach for Improved Clinical Outcomes, Nature Scientific Reports, volume 14, article number 15350 (2024)
C. Ilin (Paulik)

Improving Nonalcoholic Fatty Liver Disease Classification Performance With Latent Diffusion Models, Nature Scientific Reports, volume 13, article number: 21619 (2023)
R. Hardy, J. Klepich, R. Mitchell, S. Hall, J. Villareal>, C. Ilin (Paulik)#

Public Mobility Data Enables COVID-19 Forecasting and Management at Local and Global Scales, Nature Scientific Reports, volume 11, article number: 13531 (2021)
C. Ilin (Paulik)*, Sebastien Annan-Phan*, Xiao Hui Tai*, Shikhar Mehra, S. Hsiang, J. Blumenstock

Competition, Price Dispersion and Capacity Constraints: The Case of the U.S. Corn Seed Industry, European Review of Agricultural Economics, volume 1 (2021)
C. Ilin (Paulik), G. Shi


*=equal contributions, #=senior author, student co-authors
Research (in progress)

The Aerial History Project and the Mapcasting Project, joint with the Global Policy Lab at Stanford University.

Contribution to manuscripts and posters

Longitudinal Matching. A Method for Generating Comparable Samples of Treatment and Treatment-Naive Patients with Progressive Conditions.
K. Cook, O. Ali, D. Gupta, C. Ilin (Paulik), D. Holmqvist, D. Lee, E. Tuttle, P. Bradt, 2018

Patient Quality of Life and Benefits of Leptin Replacement Therapy (LRT) in Generalized and Partial Lipodystrophy.
study funded by Aegerion Pharmaceuticals Inc., 2018

Effect of Leptin Replacement Therapy (LRT) on Survival and Disease Progression in Generalized and Partial Lipodystrophy.
study funded by Aegerion Pharmaceuticals Inc., 2018


Litigation consulting

Analysis of claims data related to mental health and substance abuse disorders.
Des Roches, et. al v. Blue Shield and Magellan

Analysis of claims data related to emergency department orthopaedic services.
Confidential v. Blue Shield


TAs and Mentees

Current TAs

  • Matthew Holmes (UC Berkeley)
  • Thomas Lai (UC Berkeley)
  • Stone Jiang (UC Berkeley)
  • Henry Caldera (UC Berkeley)

Past Mentees:

  • Romain Hardi (UC Berkeley, now PhD in Bioinformatics student at Harvard)
  • Nicole Lin (Stanford)
  • Nathanel Jo (Stanford)
  • Liza Peckham (UW Madison)
  • Jingyi Tong (UW Madison)
  • Yuxuan Li (UW Madison)

Teaching

DATASCI207: Applied Machine Learning, UC Berkeley: Spring 2025, Summer 2024, Fall 2023, Summer 2023, Spring 2023, Fall 2022, Summer 2022, Spring 2022, Fall 2021, Summer 2021, Spring 2021, Fall 2020, Summer 2020

DATASCI210: Capstone, UC Berkeley

AAE875: Fundamentals of OOP and Data Analytics using Python, UW-Madison: Summer 2019