Name: Talita Perciano
Pronouns: she/her/hers
Biography:
Talita Perciano is a Research Scientist at Lawrence Berkeley National Laboratory. She is a member of both the Machine Learning and Analytics group and the Computational Biosciences group. She conducts research in the areas of machine learning, quantum image processing, quantum algorithms, image analysis, and high-performance computing motivated by the incredible challenges around scientific data generated by computational models, simulations, and experiments. Her research focuses on mathematical foundations for new methods, on the implementation of scalable methods, and on platform-portability. Her goal is to develop powerful, mathematically-grounded, scalable algorithms that meet the requirements needed to analyze current and future scientific datasets acquired in user data facilities. She has built a diverse collaboration network throughout the years in fields such as materials science, biosciences, chemistry, among others. She earned her doctorate in Computer Science from the University of São Paulo in 2012.
Institution/Lab: Lawrence Berkeley National Laboratory
Website: https://crd.lbl.gov/divisions/scidata/mla/staff/talita-perciano/
SRP Collaboration Topic/Title: Project 1: Quantum Algorithms for Scientific Data, Project 2: Probabilistic Graphical Deep Learning Field
Field or research area: Quantum computing, computer science
Please select all the topical areas that apply to your project:
Data Science (i.e., data analytics, data management & storage systems, visualization); Machine Learning and AI; Quantum Computing and Information Science
Brief Abstract:
P1: Quantum computing has received a great amount of attention in the last few years. In this project, we aim to take advantage of quantum computing theory to develop quantum data analysis and quantum machine learning tools suitable to the analysis of scientific data. This includes the development of new quantum circuits for quantum data representation and for analysis algorithms (feature extraction, template matching). We also aim to develop innovative quantum machine learning algorithms targeting/combining NISQ devices and HPC. We aim to develop concrete proof-of-concept tools that run on NISQ devices. P:2 A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. Commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. The main advantages are its ability to use prior information (physical constraints) related to the data. This becomes very important when analyzing scientific data. This project aims to develop efficient PGM-based algorithms to tackle problems such as image segmentation, image denoising, feature tracking, data reduction, data fusion, etc. We use mainly Markov Random Fields and Conditional Random Fields, and some of our approaches combine these methods with deep learning algorithms.
Desired relevant skills, background, or interests:
Project 1 Related relevant skills: Python, C++ programming languages, image processing, quantum computing, machine learning. Project 2 Related interests: Python and C++ programming languages, data analysis, statistics, applied mathematics, image processing, deep learning, machine learning.
Other comments:
Do any special requirements apply? other
Other, specify: None
Keywords:
Quantum Image Processing; Quantum Algorithms; Quantum Machine Learning; Deep Learning; Probabilistic Graphical Models
Lightning Talk Title: Quantum Algorithms and Deep Learning for Scientific Data Analysis