AI News, Lily Hu
My research is at the intersection of applied mathematics and energy.
I study statistics, optimization, and machine learning to improve energy generation and efficiency.
Most recently, I developed a deep learning solution for the computer vision problem of image segmentation on overlapping DNA chromosomes.
EECS at UC Berkeley
new 5-year research project that recognizes the shift from transistor-scaling-driven performance improvements to a new post-scaling world where whole-stack co-design is the key to improved efficiency.
Building on the success of the completed Par Lab project, it uses deep hardware and software co-tuning to achieve the highest possible performance and energy efficiency for future mobile and rack computing systems.
BAIR includes over two dozen faculty and more than a hundred graduate students pursuing research on fundamental advances in the above areas as well as cross-cutting themes including multi-modal deep learning, human-compatible AI, and connecting AI with other scientific disciplines and the humanities.
It serves as a nexus for interactions with companies for long-term research collaborations and knowledge transfer, as well as access to faculty and students who are building the technological foundation for future electronic devices and information systems.
Berkeley Institute for Data Science (BIDS) Our initiatives are designed to bring together broad constituents of the data science community, including domain experts from the life, social, and physical sciences and methodological experts from computer science, statistics, and applied mathematics.
Berkeley Laboratory for Automation Science and Engineering UC Berkeley's Laboratory for Automation Science and Engineering, directed by Professor Ken Goldberg of IEOR and EECS, is a center for research in robotics and automation, with current projects in networked telerobotics, computer assisted surgery, automated manufacturing, and new media art forms.
Berkeley Vision and Learning Center (BVLC) BVLC center PIs conduct research at the intersection of computer vision and machine learning, driven by three core principles: rich and adaptive representations, large-scale semantics, and 3-D demonstration and interaction.
The distance between these principal areas of investigation is closing fast, and a unified core of algorithms, data, and implementations will drive future advances in intelligent media and active interactive systems.
Components are fabricated using state-of-the-art processes and evaluated in a realistic test environment Biomimetic Millisystems Lab The objective of the Biomimetic Millisystems Lab is to harness features of animal locomotion to enable high performance mobile millirobots.
Center for Augmented Cognition (CAC) The Center for Augmented Cognition supports Berkeley faculty and students in their research on new computing paradigms and methodologies of human cognition modeling, human-computer interaction, and human-robot collaboration through augmented and virtual reality technologies.
Center for Cell Control (CCC) The Center for Cell Control is an NIH Nanomedicine Development center focusing on the use of systems control, with therapeutic intent, to determine the parameters for guiding the cell to a directed phenotype/genotype which will then be followed by in depth study, using nanoscale modalities, of the path by which this desired state is achieved.
Center for Integrated Access Networks (CIAN) The vision of CIAN is to create transformative technologies for optical access networks where virtually any application requiring any resource can be seamlessly and efficiently aggregated and interfaced with existing and future core networks in a cost-effective manner.
Center for Neural Engineering & Prostheses (CNEP) We integrate cutting-edge engineering with world class basic and clinical neurosciences to develop technology to restore sensory, motor, and cognitive function in patients suffering from disabling neurological conditions.
Center for Research in Energy Systems Transformation (CREST) CREST’s mission is to develop frameworks, tools, and new technology research projects aimed at overcoming the challenge of transforming the global energy system while creating sustainable opportunities for economic growth.
We expect to accelerate development of phone apps, increase the reliability of distributed programs, enable development of high-performance GPU mobile apps, and facilitate robotics programming to end users.
Using approaches from many disciplines (including biophysics, chemistry, cognitive sciences, computer science, genetics, mathematics, molecular and cell biology, and physiology), we seek to understand how the brain generates behavior and cognition, and to better understand, diagnose, and treat neurological diseases.
Industrial Cyber-Physical Systems Center (iCyPhy) iCyPhy is a research consortium formed to identify and develop new engineering techniques that will make it easier to successfully build products and services that combine complex software, hardware and mechanical components.
Industrial Engineering and Operations Research (IEOR) In IEOR, we invent, analyze and teach tools and approaches for design, analysis, risk management, and decision-making in complex real-world systems like supply chains, energy systems, healthcare systems, and financial systems.
With its unique focus on international collaboration and through its established international programs, ICSI brings together scientists from all over the world and at all stages of their career to work with established staff researchers, UC Berkeley professors, and their networks of academic, government, and industrial partners.
Networked Systems Lab (NETSYS) The Networked Systems Lab (NetSys) addresses a broad set of issues relating to networking and systems, ranging from traditional networking topics (e.g., congestion control and routing) to more systems-oriented topics (e.g., data analytics and disaggregated datacenters).
The center is funded by Intel, and includes both academics and Intel researchers working together collaboratively to make computing safer for users Simons Institute for the Theory of Computing (SITC) The Institute aims to promote fundamental research on the foundations of computer science, as well as to expand the horizons of the field by exploring other scientific disciplines through a computational lens.
Software Defined Buildings (SDB) Software Defined Buildings (SDB) seeks to design, engineer, and evaluate the foundational information substrate for cyberphysical systems in a concrete, canonical form - creation of efficient, agile, model-driven, human-centered building systems.
Through the combined effort of its researchers, partners and Industry Members, SBI is developing the standards and technologies needed to create transformative applications in energy, materials, pharmaceuticals, chemicals, food products, security, and other industries that affect our daily lives.
Team for Research in Ubiquitous Secure Technology (TRUST) Team for Research in Ubiquitous Secure Technology (TRUST) TRUST is devoted to the development of a new science and technology that will radically transform the ability of organizations (software vendors, operators, local and federal agencies) to design, build, and operate trustworthy information systems for our critical infrastructure.
As a platform for innovative research and graduate-student education with parallel functionality on the two partner campuses, the institute will integrate research programs at both UC Berkeley and Tsinghua University to address societal needs and global challenges.
Berkeley Berkeley Academic Guide: Academic Guide 2017-18
About the Program Bachelor of Science (BS) Materials Scientists and Engineers are involved in every aspect of technology, ranging from the design of materials appropriate for use in integrated circuits and biological applications to those materials needed for energy generation (both conventional energy sources and green sources) and for building bridges, roads, and buildings.
The objectives of the undergraduate program in Materials Science and Engineering (MSE) are to educate graduates who have the following skills: Knowledge of the fundamental science and engineering principles relevant to materials design, development and engineering application Understanding of the relationship between nano/microstructure, characterization, properties and processing and design of materials Have the experimental and computational skills for a professional career or graduate study in materials Possess a knowledge of the significance of research, the value of continued learning and environmental/social issues surrounding materials Ability to communicate effectively, to work in teams, and to assume positions as leaders This major program leads to a Bachelor of Science (BS) degree.
Five-Year BS/MS Program The five-year combined Bachelor of Science/Master of Science program augments the existing four-year undergraduate program with a fifth year of graduate study that provides a professionally oriented component, preparing students for careers in engineering or engineering management within the business, government, and/or industrial sectors.
- On 20. januar 2021
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