Deep Learning

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.

Emily Grabowski

Senior Data Science Fellow, Senior Instructor, Senior Consultant
Linguistics

I am a Ph.D. student in Linguistics. My research interests include understanding how our speech production and speech perception systems constrain linguistic variation, especially as it applies to the larynx. I am also interested in integrating theoretical representations of language with speech. I approach this using a broad variety of tools/methodologies, including theoretical work, experiments, and modeling. Current projects include developing a computational tool to expedite the analysis of pitch and an online perception experiment on the relationship between pitch and perceived...

Chirag Manghani

Consultant
School of Information

Chirag is a 2nd year graduate at the I-School. Proficient in Python, Java, R, and SQL, he navigates software application development, machine learning and data science. His keen interest lies in data analysis and statistical methods, driving him to bridge theory and practice seamlessly. Chirag's dedication to excellence, adaptable mindset, and innate curiosity define him as a dynamic problem solver in the ever-evolving tech landscape.

Deibi Sibrian

Data Science for Social Justice Fellow 2024
Deibi is a Ph.D. student in the Department of Environmental Science, Policy, and Management, centering critical interdisciplinary ecology and multispecies justice. Deibi coined the term "Cryptonocene," an interdisciplinary framework, to study the socio-environmental health impacts of cryptocurrencies and related technologies, such as AI. With over two years of experience as a graduate instructor, Deibi now is a Graduate Student Researcher, NSF Digital Transformation Fellow, and Mentored Research Fellow. Before joining Berkeley, Deibi was the project manager for an interdisciplinary team...

Hellina Hailu Nigatu

Data Science for Social Justice Senior Fellow 2024
Electrical Engineering and Computer Science (EECS)

I am a PhD student at UC Berkeley in the EECS department co-advised by Prof. Sarah Chasins and Prof. John Canny. My research interest broadly lies in the intersection of AI and HCI, with a focus on making usable AI tools accessible to end users.

I am currently looking into making NLP tools usable and accessible for low-resourced languages. I am also interested in the impact of AI on society, specifically in how it affects Global Majority countries and communities. Outside of research, I like to read books, make and drink traditional Ethiopian coffee, knit,...

Violet Davis

Data Science for Social Justice Senior Fellow 2024
MIDS

I am a Masters student studying Data Science with the School of Information. My research involves computational social science projects focused on social justice and equity.

Python Deep Learning: Parts 1-2

June 12, 2023, 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

March 12, 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

April 11, 2023, 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.

R Introduction to Deep Learning: Parts 1-2

March 29, 2022, 10:00am
This workshop introduces the basic concepts of Deep Learning — the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels.