Chen Chen - US grants

PROJECT SUMMARY Transposable elements (TEs) are ?genomic parasites? that can replicate and re-integrate into the host cell genome. Uncontrolled TE activity in germ cells leads to DNA damage, disruption of gametogenesis, and infertility. In mammalian male germ cells, the PIWI- piRNA pathway uses small RNAs as a guide to silence mobile TEs to protect genome integrity and sustain fertility. The proper production of piRNAs is critical for TE silencing and spermatogenesis. However, the mechanisms governing piRNA biogenesis are not well understood. In particular, the roles of many RNA binding proteins during piRNA biogenesis remain elusive. By studying a subgroup of Tudor domain proteins that also harbor LOTUS domains, we discovered that TDRD5 is a novel RNA binding protein critical for piRNA biogenesis in mice. Strikingly, we have discovered a novel RNA binding property of LOTUS domains that is conserved in bacteria, plants and animals. This binding property is different from reported protein binding feature of some animal LOTUS domains. We hypothesize that animal LOTUS domains have both RNA and protein binding activities and that the LOTUS group of Tudor domain proteins together play critical roles in mammalian piRNA biogenesis. To test this hypothesis, we will use biochemical approaches and mouse models to: 1) Clarify the RNA and protein binding activities of the LOTUS domain superfamily; 2) Determine the mechanism of a specific LOTUS domain-RNA interaction; and 3) Define the functional involvement of LOTUS domain proteins in piRNA biogenesis and Vasa regulation in mice. These studies will provide valuable new insights into the mechanisms of piRNA biogenesis that safeguard germline genome integrity to sustain male fertility and expand our knowledge in broader RNA biology and developmental biology.

Penetration of technologies such as wireless broadband and artificial intelligence (AI) is propelling a rapid adoption of network cameras across the household, industrial, and commercial sectors. These cameras such as surveillance cameras, dash cameras, and wearable cameras can capture voluminous amounts of visual data that can be turned into valuable information for public safety, autonomous driving, service robots, augmented/mixed reality, assisted living, etc. To reach the potential, new methods are needed for efficiently and effectively extracting, transferring, and sharing useful information from ubiquitous cameras while preserving user privacy. This project uses techniques and perspectives from wireless networking, computer vision, and edge computing to analyze and solve the problems in ubiquitous camera systems, fosters interdisciplinary research, provides a unique training program for undergraduate and graduate students, and has a high potential to introduce transformative technologies that enable new real-life products and services.

This project aims to realize ubiquitous machine vision (UbiVision) and enable efficient utilization of networked cameras for information extraction and sharing. Toward this end, three fundamental research problems are investigated: 1) how to dynamically manage highly coupled resources and functions across multiple technology domains: camera functions, network resources, and computation resources on edge servers; 2) how to design adaptive and efficient machine vision algorithms for resource-constrained smart cameras; and 3) how to engineer reliable machine learning frameworks for robust vision analysis on edge servers. First, a new model-free end-to-end resource orchestration method is designed to improve the efficiency of wireless networking and computing by combining the merits of conventional optimization and emerging machine learning techniques. Second, a novel universal convolution neural network (CNN) and corresponding CNN optimization methods are developed for efficient multi-task feature learning on smart cameras. Third, a teacher-student network learning paradigm is innovated to develop memory and computation efficient machine vision algorithms that are able to achieve robust performance under various adverse conditions caused by varying network conditions and limited server computation budgets.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PROJECT SUMMARY Pachytene piRNAs are a population of small regulatory RNAs unique to mammals that regulates spermatogenesis and fertility. In mice, pachytene piRNAs are generated from long piRNA precursors through cleavage by the piRNA processing machinery and loaded onto two cytoplasmic PIWI proteins MIWI and MILI. However, the mechanism by which PIWI proteins are recruited to piRNA processing machinery to participate in piRNA biogenesis remains elusive. Here we provide preliminary data that reveal a novel genetic separation of MIWI and MILI to differentially enter into the piRNA processing machinery. This segregation is mediated by a mitochondria-anchored protein complex that specifically interacts with MIWI but not MILI. We hypothesize that distinct mitochondria- anchored complexes differentially direct different PIWI proteins into the piRNA pathway during pachytene piRNA processing. To test this hypothesis, we will use biochemical and mouse genetic approaches to: 1) understand the functional importance of specific mitochondria-based protein interactions in directing piRNA biogenesis and germ cell function; 2) define the coupling mechanism for PIWI protein recruitment and downstream piRNA processing; 3) explore a specific MILI recruiting mechanism to enter into the piRNA pathway. These studies will provide novel insight into the organizing principle of the piRNA processing machinery and the understanding of mechanism underlying pachytene piRNA biogenesis and its relevance to normal spermatogenesis and fertility.

Federated learning (FL) has emerged as a key technology for enabling next-generation privacy-preserving AI at-scale, where a large number of edge devices, e.g., mobile phones, collaboratively learn a shared global model while keeping their data locally to prevent privacy leakage. Enabling FL over wireless multi-hop networks, such as wireless community mesh networks and wireless Internet over satellite constellations, not only can augment AI experiences for urban mobile users, but also can democratize AI and make it accessible in a low-cost manner to everyone, including people in low-income communities, rural areas, under-developed regions, and disaster areas. The overall objective of this project is to develop a novel wireless multi-hop FL system with guaranteed stability, high accuracy and fast convergence speed. This project is expected to advance the design of distributed deep learning (DL) systems, to promote the understanding of the strong synergy between distributed computing and distributed networking, and to bridge the gap between the theoretical foundations of distributed DL and its real-life applications. The project will also provide unique interdisciplinary training opportunities for graduate and undergraduate students through both research work and related courses that the PIs will develop and offer.

This project proposes to use concepts of federated learning and multi-agent reinforcement learning to provide optimal solutions for training DL models over wireless multi-hop networks that have communication constraints due to noisy and interference-rich wireless links. The main thrusts include: 1) developing a novel hierarchical FL system architecture with layered federated computation, semi-asynchronous model aggregation, and regularized objective function to significantly improve system scalability, communication efficiency, and stability; 2) fine-tuning the FL system via multi-agent reinforcement learning to maximize the FL accuracy with the minimum convergence time under the computing constraints of edge devices; 3) finding high-gain computation-light robust federated computing strategies for resource-constraint edge devices, including efficient DL model design and resource-aware model adaptation; and 4) developing an open-source wireless FL framework (OpenWFL) for fast prototyping, deploying, and evaluating the proposed FL algorithms in both an emulator and physical testbeds.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.