Deep Learning

María Martín López

Data Science Fellow
Psychology

María Martín López is a PhD student in the Cognition area within the Department of Psychology. Her research relates to cognitive computational and quantitative models of individual differences in behaviors, thoughts, and emotions. She is particularly interested in how we can create and leverage novel algorithms to understand, measure, and predict processes relating to externalizing psychopathology (e.g. impulsivity, aggression, substance use). She answers these questions using a range of computational and quantitive models including AI, NLP, SEM, time series analysis, multi-level...

Demystifying AI

May 5, 2025, 2:30pm
In this workshop, we provide a basic and relatively non-technical introduction to the foundational concepts underlying contemporary AI tools. First, we’ll cover the the fundamentals of AI, Machine Learning, and Neural Networks/Deep Learning. Then, we’ll examine the capabilities and limitations of contemporary AI tools such as ChatGPT, Claude, and Perplexity, and outline best practices for the use of such tools.

Sharing Just Enough: The Magic Behind Gaining Privacy while Preserving Utility

April 15, 2025
Sohail Khan
by Sohail Khan. Netflix knows what you like, but does it need to know your politics too? We often face a frustrating choice: share our data and be tracked, or protect our privacy and lose personalization. But what if there was a third option? This article begins by introducing the concept of the privacy-utility trade-off, then explores the methods behind strategic data distortion, a technique that lets you subtly tweak your data to block sensitive inferences (like political views) while still maintaining useful recommendations. Finally, it looks ahead and advocates for a future where users, not platforms, shape the rules, reclaiming control of their own privacy.

Python Deep Learning

March 4, 2025, 2:00pm
In this workshop, we will convey the basics of deep learning in Python using keras on image datasets. You will gain a conceptual grasp of deep learning, work with example code that they can modify, and learn about resources for further study.

Nicolas Nunez-Sahr

Consultant
Statistics

I lived in Santiago, Chile until I graduated from high school, and then moved to the US for undergrad at Stanford, where I obtained a Bachelor’s degree from the Statistics Department. I then worked as a Data Scientist in an NLP startup that was based in Bend, OR, which analyzed news articles. I love playing soccer, volleyball, table tennis, flute, guitar, latin music, and meeting new people. I want to get better at mountain biking, whitewater kayaking, chess and computer vision. I find nature astounding, and love finding sources of inspiration.

Gaby May Lagunes

Consultant
ESPM

Hello! I’m Gaby (she/her). I am PhD student at the ESPM department, I hold a masters in Data Science and Information from the Berkeley ISchool and I have 5+ years of industrial experience in different data roles. Before that I got a masters in Engineering for International Development and an undergraduate degree in Physics from University College London. And somewhere between all that I got married, survived the pandemic, and had two awesome boys. I’m very excited to help you use data to enhance your work and your experience here at Berkeley!

Python Deep Learning: Parts 1-2

September 24, 2024, 2:00pm
The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results.

Python Deep Learning: Parts 1-2

November 18, 2024, 9:00am
The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results.

Iñigo Parra

Availability: By appointment only

Consulting Areas: Python, R, LaTeX, Data Manipulation and Cleaning, Data Science, Data Visualization, Deep Learning, Digital Humanities, Machine Learning, Natural Language Processing, Social Network Analysis, Regression Analysis, Means Tests, Bash or Command Line, Excel, Gephi, Git or Github, Qualtrics, RStudio, Overleaf

Kurt Soncco Sinchi

Consultant
Civil Engineering

First generation student and looking to improve and apply Data Science core concepts into social impactful projects, as well as trying to leverage the information from previous cases for better insights of society. Focused on infrastructure and its impact under natural disasters.