https://www.google.com/search?client=safari&rls=en&q=xu+yi+uva&ie=UTF-8&oe=UTF-8

Optimizing photonic devices for quantum applications

Quantum computing, communications, and sensing have the potential to revolutionize science and technology. The photonic-based quantum platform is one of the leading candidates for quantum technology, and quantum properties in photonic systems can be well maintained at room temperature. Our research group leverages integrated photonics to provide scalable solutions to photonic quantum applications. In this project, students will design and simulate photonic components for quantum computing and sensing applications. Students shall have good knowledge of EM field or quantum mechanics.

https://virginia.joinhandshake.com/edu/jobs/8199138

Pay (the lab would pay 100% of the wages), Volunteer

Advanced Computing, Engineering Technologies for a Sustainable and Connected World

https://engineering.virginia.edu/faculty/xu-yi

High-performance computing is widely needed in machine learning, AI and fundamental science research. Photonic computation approaches have key-advantage over conventional computers in terms of power efficiency because of the near-zero power consumption in the system except for the generation of the information carrier (lasers) and carrier detection (photodiodes). In this research, students will design and simulate photonic components for a novel nanophotonic computing architecture for high-performance computing of multiply-accumulate (MAC) operations to reach an unprecedented combination of fast computation speed and high power efficiency.

https://virginia.joinhandshake.com/edu/jobs/8199143

Pay (the lab would pay 100% of the wages), Volunteer

Advanced Computing, Engineering Technologies for a Sustainable and Connected World

https://engineering.virginia.edu/faculty/anil-vullikanti

Modeling, Detection and Control of Hospital Associated Infections

Antimicrobial resistance, and hospital associated infections (HAIs), such as Methicillin-resistant Staphylococcus aureus (MRSA), are becoming a significant burden on our healthcare system. This is particularly important in facilities such as nursing homes and long term care centers. The general goal of this project is to understand the risk of HAIs in such facilities, predicting infection rates, and modeling transmission from and to the community. Students will use a large scale health insurance claims dataset for this study. Students will work on developing models of infection risk at such facilities, and a community transmission model to study these questions.

https://virginia.joinhandshake.com/edu/jobs/8255178

Pay (the lab would pay 100% of the wages), Work Study (the lab would pay 25% of the wages of qualified students), Research for credit, Volunteer

Engineering for Health, Data Sciences and Machine Intelligence

Engineering education research: how do we troubleshoot?

Engineering education research asks questions like how instructors can teach better, how can we shape our curriculum to meet objectives, and how students learn. We are working on an education project to investigate how (or if!) engineering students learn to troubleshoot systems or debug code. The cross-disciplinary research involves qualitative approaches like interviews and systematic literature review methodologies. These methods are less common in engineering, and they will expose you to a different way of thinking about knowledge and likely your own learning. There are multiple opportunities to contribute to this project including background research, data analysis, and project planning. If you are interested, we will work with you to determine a reasonable level of effort and goals. Background expectations are minimal; we expect you to be curious, to have a willingness to learn and try new things, and to be motivated to work independently.

https://virginia.joinhandshake.com/edu/jobs/8255316

Research for credit, Possibility for pay in the future

Science, Technology, and Society

Heterogeneous Data Pipeline for Scientific Computing

The surge in sensors, internet-linked devices, and social media has led to an unprecedented influx of data. Scientific data, sourced from various outlets, has grown more intricate with diverse attributes, high dimensions, and complex interrelationships among variables. The process of managing, structuring, and preparing this data, essential for applying deep learning - the prevailing approach in large-scale data science, can be a bottleneck. The transfer of data across systems for model training also presents challenges. These issues affect scientific domains such as genomics, climate modeling, accelerator physics, astronomy and neuroscience. For instance, genomics generates over 200GB of data per genome sequencing, while climate simulations yield up to 10PB, necessitating more efficient data analysis techniques. One approach to address these challenges on modern high-performance computing (HPC) platforms involves integrating scalable runtime tools with data frameworks. Parallel computing and distributed communication protocols play a crucial role in resolving these sizable issues.

Google Pathways offers a comparable distributed execution environment for its LLMs and deep learning models, though it remains proprietary. Establishing a common data processing pathway with a diverse pipeline presents technical hurdles. RADICAL-Pilot, a Python runtime engine, efficiently manages various workloads on HPC machines. Our prior work, Cylon, a high-performance distributed memory data parallel library, further adds to the complexity. Combining these components is intricate due to differences in system architectures, technologies, programming languages, and communities.


Our goal is to create a seamless integrated approach deployable on clouds, supercomputers, and HPC platforms. This approach also supports heterogeneous federated distributed systems and accommodates accelerators like GPUs alongside CPUs. Employing RADICAL-Pilot to encapsulate Cylon or other deep learning frameworks establishes a heterogeneous runtime environment capable of managing scalable compute and data-intensive workloads including islands of data-parallel(MPI) jobs. Pathways illustrates the need to support workflows that link together components of deep learning with those of the pre and post-processing data engineering steps. The proposed design consists of multiple masters with thousands of workers with function based task scheduling and resource allocation. The independent underlying jobs don’t need to concern about resource allocation and releasing when the task is finished. This allows us to use homogeneous tasks to be executed in a set of allocated nodes that combine multiple heterogeneous data pipelines to be executed in the same run and leverage the hyperparameter parallelism along with distributed operation. The optimization of heterogeneous systems with compiler-based technologies like MLIR shows promise. Our approach aims to excel in both scientific and engineering research HPC systems, scaling up to exascale performance, while also demonstrating robust performance on cloud infrastructures commonly used in commercial and distributed applications. This dual capability serves as a gateway to foster collaboration and innovation within the open-source scientific research community.

https://virginia.joinhandshake.com/edu/jobs/8302723

Research for credit, Volunteer

Advanced Computing, Data Sciences and Machine Intelligence, Science, Technology, and Society

ErinRaymond@virginia.edu

Anil Vullikanti, Madhav Marathe

https://biocomplexity.virginia.edu/institute/divisions/network-systems-science-and-advanced-computing/computing-global-challenges

Students work with faculty mentors on ongoing research projects, which teach them about the process of research and team science and how they can make meaningful contributions to solving real-world problems. Throughout the summer, students learn about cutting-edge software technologies and methods in machine learning, network science, agent-based simulation, data science, and computational biology. The students gain a theoretical understanding as well as practical experience with how these methods work and how they can be applied to meet the demands of problems in different domains.

Undergraduate students in any discipline are encouraged to apply. Preference will be given to enthusiastic applicants who want to take full advantage of this amazing opportunity; demonstrated analytical and computational skills are a bonus.

https://virginia.joinhandshake.com/jobs/8791809/share_preview

Pay (the lab would pay 100% of the wages)

Even though this is a full time, 8 week internship, work schedules can be adjusted depending on student and mentor needs.

Engineering for Health, Systems Biology and Biomedical Data Sciences, Engineering for the Cyber Future, Cyber-Physical Systems, Cyber-Social (Learning) Systems, Advanced Computing, Data Sciences and Machine Intelligence, Engineering for Environmental and Energy Applications, Science, Technology, and Society

https://engineering.virginia.edu/faculty/b-brian-park

As newer vehicles can obtain and share their geo-location data, researchers and entrepreneurs are interested in using these data to improve urban transportation mobility. Our team started working on this big data in Charlottesville areas to see if we can identify daily commuting patterns to see if car-sharing or carpooling is feasible. We are to reduce this data to have a set of vehicles commuting to the University of Virginia and classify their patterns (e.g., flexible hours, route choices, and home locations). This will help determine the feasibility of car-sharing based on similarity in home location, arrival and departure times, etc. This project requires knowledge of Python and related package, and experience in handling big data.

Pay (the lab would pay 100% of the wages)

Cyber-Physical Systems, Cyber-Social (Learning) Systems, Data Sciences and Machine Intelligence, Engineering for Environmental and Energy Applications, Next Generation Transportation

The recent emergence of programmable switches enables the execution of more complex operations along the end-to-end path at line rate. However, due to resource scarcity and the lack of computing operators, it’s still difficult for modern programmable switches to support deep neural network operations, which is at the core of many widely used LLM applications such as ChatGPT and Google Gemini. Recently, photonic computing has emerged as a new area with the potential to perform computation. In this project, students will use photonic devices and programmable switches to design the first-ever data plane prototype that serves ML inference queries at line rate using photonic computation.

Basic knowledge of computer networks is required. Knowledge of in-network computing and photonics is a big plus. Starts in Fall, 2024.

Engineering for the Cyber Future, Engineering Technologies for a Sustainable and Connected World