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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 centers on health applications of AIML, using foundational models in natural language processing and computer vision. I teach Applied ML 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
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News
- [2025] I am excited to contribute to the Oxford-Berkeley Summer Doctoral Program by presenting my AIML-related research and leading a workshop on healthcare applications of foundational models, with a focus on classification and generative approaches.
- [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
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)
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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)#
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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
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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
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*=equal contributions, #=senior author, student co-authors
Research (in progress)
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
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Patient Quality of Life and Benefits of Leptin Replacement Therapy (LRT) in Generalized and Partial Lipodystrophy.
study funded by Aegerion Pharmaceuticals Inc., 2018
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Effect of Leptin Replacement Therapy (LRT) on Survival and Disease Progression in Generalized and Partial Lipodystrophy.
study funded by Aegerion Pharmaceuticals Inc., 2018
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Litigation consulting
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)
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Teaching
DATASCI 267: Foundamentals of Generative AI, UC Berkeley
DATASCI 207: Applied Machine Learning, UC Berkeley
DATASCI 210: Capstone, UC Berkeley
AAE 875: Fundamentals of OOP and Data Analytics using Python, UW-Madison: Summer 2019
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