Deep Learning

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.

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...

Aniket Kesari, Ph.D.

Research Fellow
Berkeley Law

Aniket is a postdoctoral scholar at the D-Lab. He earned his Ph.D. from Berkeley Law, where he specialized in Law & Economics. He also holds a BA from Rutgers University – New Brunswick in Political Science and History and is a JD candidate at Yale University. His research focuses on privacy and cybersecurity law, and he is generally interested in using data science to tackle public policy problems. During his graduate career, he was a Google Public Policy Fellow, a Data Science for Social Good (DSSG) Fellow at the University of Chicago, and a Technology Policy Analyst Intern at...

Abhishek Roy

IUSE Undergraduate Advisory Board
Economics
Data Science

I'm Abhishek Roy and I'm double majoring in Economics and Data Science. I've been a part of D-Lab's IUSE project since Spring 2020 and have truly found an organization that is not only passionate about Data Science but also strives to expand its reach equitably to all communities. I am involved in Research and Project Management roles in various departments and labs at Berkeley and I'm an Editor at the Berkeley Economic Review. I love diving into anything at the intersection of Data Science, Economics, Business, and Computational Social Science. Whenever I'm free, I love writing...

Spencer Le

Data Peer Consultant, UTech
Computer Science
Data Science

I am a senior majoring in Computer Science and minoring in Data Science. I love crunching down big data and analyzing it in order to help solve real-life issues. In my free time, I like jamming out to music, drawing, studying history, and posting on my foodstagram. If you have any questions regarding Computer Science or Data Science, please stop by!

R Introduction to Deep Learning: Parts 1-2

November 17, 2021, 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.

Nikita Samarin

Data Science Fellow
Electrical Engineering and Computer Science (EECS)

Nikita Samarin is a doctoral student in Computer Science in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley advised by Serge Egelman and David Wagner. His research focuses on computer security and privacy from an interdisciplinary perspective, combining approaches from human-computer interaction, behavioral sciences, and legal studies. Samarin is a member of the Berkeley Lab for Usable and Experimental Security (BLUES) and an affiliated graduate researcher at the Center for Long-Term Cybersecurity (CLTC) and the...