- talks - lists.cs.princeton.edu

Borislav Hristov will present his FPO, "Deciphering Disease Genomes in a Network Context" on Thursday, 8/29/2019 at 11am in CS 402
by Nicki Mahler 21 Aug '19

21 Aug '19

Borislav Hristov will present his FPO, "Deciphering Disease Genomes in a Network Context" on Thursday, 8/29/2019 at 11am in CS 402. The members of his committee are as follows: Mona Singh (Adviser); Examiners: Ben Raphael and Bernard Chazelle; Readers: Olga Troyanskaya and Barbara Engelhardt. A copy of his thesis, is available upon request. Everyone is invited to attend his talk. The abstract follows below: Despite the incredible influx of sequencing data pinpointing the gene variants responsible for the development of heterogeneous diseases remains a particularly hard task because the same phenotypic outcome (disease) can result from a myriad of combinations of different alterations across the genome. A promising avenue is to consider genome alterations within the context of pathways because different alterations within any of several genes comprising the same pathway can have similar consequences with respect to disease development. Large-scale biological networks provide a helpful proxy for biological pathway knowledge as genes that participate in the same pathway tend to interact with each other and form modules within the larger network. In this dissertation, I introduce two novel methods that further our ability to computationally highlight potential disease-causing genes by examining disease genomes in the context of biological networks. First, in Chapter 2, I present a novel network-based approach which tackles cancer mutational heterogeneity by utilizing per-individual mutational profiles. I provide an intuitive formulation relying on balancing the size of a connected subgraph within the larger network with covering many patients. I describe a machine learning-like schema for selecting the value of the single required parameter and both an integer linear programming framework and a fast heuristic for optimizing the objective function. I demonstrate the outstanding performance of my method in identifying cancer-relevant genes, especially those mutated at very low rates. Next, in Chapter 3, I propose a general computational framework that uses prior knowledge of disease-associated genes to guide a network-based search for novel ones based upon newly acquired information. I use a graph di↵usion kernel to spread the signal from the set of already known disease genes and then use it to bias a random walk originating from the newly implicated genes to move closer to the known ones. I demonstrate that integrating the two types of information is better than using either one of them alone. I show, in the context of cancer, that my method readily outperforms other network-based methods. Finally, I apply my approach to several complex diseases, thereby demonstrating its versatility in a broad range of settings.

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IEEE SSCS Distinguished Lecture Colloquium at Princeton - August 22, 2019 - E-Quad B205 - Prof. Zhihua Wang, Tsinghua University
by Emily Lawrence 20 Aug '19

20 Aug '19

Sent on behalf of Prof. Kaushik Sengupta **Refreshments at 1:00 pm and a chance to talk with speaker ** IEEE Princeton/Central Jersey Section - Distinguished Lecture Colloquium, August 2019 Speaker: Prof. Zhihua Wang, Tsinghua University Talk Title: Binaural Hearing Aid System and the Intelligent Acoustic Signal Processing Date: Thursday, August 22, 2019 Time: 1:30 - 2:30 p.m. Location: Princeton University, Department of Electrical Engineering, Engineering Quadrangle, Room B205 Abstract: Nowadays, the hearing aid technology is facing a new horizon based on advanced digital signal processing, wireless communication and artificial intelligence. In this lecture, new methodology with a systematic solution covering both the auditory periphery and the cognitive system is given. The up to date and rapidly evolves technologies for the binaural hearing aid system are well addressed. The multi-channel wide dynamic range compression, active noise reduction, self-adaptable directivity, acoustic scene analysis, and the wireless linking with other audio or communication systems are presented. The key technologies including the ultra-low power chip design, the advanced digital signal processing (DSP), and the wireless system integration and connectivity are discussed. The micromechanics and high performance electro-acoustics, the miniaturized antenna, the user interface, and the fitting system development are also the important aspects for the hearing aid technologies are shown in this lecture. A smart binaural hearing aid technology, which simultaneously processes the acoustic signals from the four microphones in both ears are indicated which utilizing the computing power from the smart phone to finish the advanced binaural DSP algorithms. Bio: Zhihua Wang (M'99-SM'04-F'17) received the B.S., M.S., and Ph.D. degrees in Electronic Engineering in 1983, 1985 and 1990, respectively, from Tsinghua University, Beijing, China, where he has served as full professor and Deputy Director of the Institute of Microelectronics since 1997 and 2000. He was a visiting scholar at CMU (1992-1993) and KU Leuven (1993-1994), and was a visiting professor at HKUST (2014.9-2015.3). His current research mainly focuses on CMOS RFIC and biomedical applications, involving RFID, PLL, low-power wireless transceivers, and smart clinic equipment combined with leading edge RFIC and digital image processing techniques. He has co-authored 12 books/chapters, over 183 (480) papers in international journals (conferences), over 244 (29) papers in Chinese journals (conferences) and holds 123 Chinese and 8 US patents. Prof. Wang has served as the chairman of IEEE SSCS Beijing Chapter (1999-2009), an AdCom Member of the IEEE SSCS (2016-2019), a technology program committee member of the IEEE ISSCC (2005-2011), a steering committee member of the IEEE A-SSCC (2005-), the technical program chair for A-SSCC 2013, a guest editor for IEEE JSSC Special Issues (2006.12, 2009.12 and 2014.11), an associate editor of IEEE Trans on CAS-I, CAS-II and IEEE Trans on BioCAS, and other administrative/expert committee positions in China's national science and technology projects. Contact Info: Nagi Naganathan, <mailto:nagisub@gmail.com> nagisub(a)gmail.com or Kaushik Sengupta, <mailto:kaushiks@princeton.edu> kaushiks(a)princeton.edu

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Mihir Kulkarni will present his general exam "Training models for NLP tasks on novel domains using transfer learning" on Wednesday, August 7, 2019 at 3pm in Friend 006
by Nicki Mahler 01 Aug '19

01 Aug '19

Mihir Kulkarni will present his general exam "Training models for NLP tasks on novel domains using transfer learning" on Wednesday, August 7, 2019 at 3pm in Friend 006. The members of his committee are as follows: Karthik Narasimhan (advisor), Sanjeev Arora, and Olga Russakovsky. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Abstract: Recent advances in machine learning for NLP have shown that there are great benefits to unsupervised pre-training followed by finetuning to specific tasks (BERT, GPT): in particular, BERT was able to obtain state of the art results on 11 NLP tasks using this method. However, having these models perform well on domains that are different from the one where they were trained is still a challenge. In our work, we explore methods to transfer representations learnt by BERT to different models for improved performance on NLP tasks in a different domain-- medical papers. We generate a dataset from medical reviews obtainable on the Cochrane Database of Systematic Reviews, to train models to learn representations on technical documents that match representations learnt by BERT on plain text summaries of those documents. We experiment with different methods to perform section-wise matching between documents to create our parallel corpus, both for performing the matching and transferring representations. Performance on the domain transfer task is reported on both intrinsic and extrinsic evaluation metrics including named entity recognition on the EBM-NLP and sentence similarity on the MED-STS datasets. Reading list: 1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016. 2. Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 3. Peters, Matthew E. and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke: Deep contextualized word representations, Proc. of NAACL, 2018. 4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I: Attention is all you need. In Advances in neural information processing systems, 2017. 5. Jiaqi Mu, Suma Bhat, Pramod Viswanath: All-but-the-Top: Simple and effective Postprocessing for Word Representations, International Conference on Learning Representations, 2018. 6. Tan, Chuanqi & Sun, Fuchun & Kong, Tao & Zhang, Wenchang & Yang, Chao & Liu, Chunfang: A Survey on Deep Transfer Learning: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4–7, 2018, Proceedings, Part III. 10.1007/978-3-030-01424-7_27. 7. Sinno Jialin Pan and Qiang Yang and Wei Fan and Sinno Jialin Pan: A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, 2010. 8. Yifan Peng and Shankai Yan and Zhiyong Lu, Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets, Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019), 2019. 9.Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain J. Marshall, Ani Nenkova, Byron C. Wallace: A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018. 10. Yanshan Wang, Naveed Afzal, Sunyang Fu, Liwei Wang, Feichen Shen, Majid Rastegar-Mojarad, Hongfang Liu: MedSTS: a resource for clinical semantic textual similarity, Language Resources and Evaluation, 2018.

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Brian Bullins will present his FPO, "Efficient Higher-Order Optimization for Machine Learning" on Tuesday, 7/23/2019 at 2pm in CS 402
by Nicki Mahler 22 Jul '19

22 Jul '19

Brian Bullins will present his FPO, "Efficient Higher-Order Optimization for Machine Learning" on Tuesday, 7/23/2019 at 2pm in CS 402. The members of his committee are: Elad Hazan (Adviser), Examiners: Mark Braverman and Sanjeev Arora; Readers: Yoram Singer and Yuxin Chen (EE). A copy of his thesis, is available upon request. Everyone is invited to attend his talk. The talk abstract follows below: In recent years, stochastic gradient descent (SGD) has taken center stage for training large-scale models in machine learning. Although some higher-order methods have improved iteration complexity in theory, the per-iteration costs render them unusable when faced with millions of parameters and training examples. In this thesis, I will present several works which enable higher-order optimization to be as scalable as first-order methods. The first method is a stochastic second-order algorithm for convex optimization, called LiSSA, which uses Hessian information to construct an unbiased Newton step in time linear in the problem dimension. To bypass the typical eciency barriers for second-order methods, we harness the ERM structure in standard machine learning tasks. While convex problems allow for global convergence, recent state-of-the-art models, such as deep neural networks, highlight the importance of developing a better understanding of non-convex guarantees. In order to handle this challenging setting, I will present FastCubic, a Hessian-based method which provably converges to first-order critical points faster than gradient descent, while additionally converging to second-order critical points. Finally, I will establish how to leverage even higher-order derivative information by means of the FastQuartic algorithm, which achieves faster convergence for both smooth and non-smooth convex optimization problems by combining ecient tensor methods with near-optimal higher-order acceleration.

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Ruth Dannenfelser will present her Pre FPO "From gene expression data to biomedical abstracts: data-driven approaches and systems to interrogate complex disease" on Tuesday, July 16, 2019 at 9am in CS 402.
by Nicki Mahler 11 Jul '19

11 Jul '19

Ruth Dannenfelser will present her Pre FPO "From gene expression data to biomedical abstracts: data-driven approaches and systems to interrogate complex disease" on Tuesday, July 16, 2019 at 9am in CS 402. The members of her committee are: Olga Troyanskaya (adviser), Mona Singh, Kai Li, Yibin Kang (Mol Bio), Wendell Lim (UCSF). Everyone is invited to attend her talk. The talk title and abstract follow below: Title: From gene expression data to biomedical abstracts: data-driven approaches and systems to interrogate complex disease Large-scale genomic studies now give more predictive power than ever, allowing us to profile the composition of tissues, study cellular functions, and understand organismal traits at an unprecedented level of detail. This is particularly important for studying heterogeneous diseases, such as cancer, where small patient-specific differences play critical roles in disease development and progression. Here we build off the wealth of publicly available data to examine the interplay between cancer and the immune system and then develop two query-based visualization systems that enable interactive data exploration for the biomedical community at large. The first part of my talk will present two perspectives on cancer and the immune system, starting with a semi-supervised approach for immune cell type quantification. Using derived immune markers we examine lymphocyte infiltration in breast cancer and find that estrogen receptor activity and genomic complexity are the key factors driving variation in lymphocytic infiltrate across individuals. In a broader scope, we leverage public gene expression data to further the development of targeted immunotherapeutics for solid tumors. Engineered T cell therapies have shown great promise for hematological cancers but have only found limited success in targeting solid tumors due to off target effects. Working closely with experimental collaborators we are developing a method to prioritize safer pairs of antigen targets that will help engineered T cells hone in on specific tumor targets minimizing damage to normal tissues. The second half will cover how we can extract unbiased signals from large collections of biomedical data in the form of scientific abstracts and large repositories of transcriptomics data. First, we show how we can obtain informative tissue-disease-gene relationships from abstracts and integrate them into a web system that presents different snapshots of curated interactions and adds tissue and disease annotations to gene lists from experimental assays (e.g. GWAS, differentially expressed genes, drug screens, etc). Secondly, we extend SEEK, a gene expression search engine that simultaneously returns coexpressed genes and relevant datasets where the genes are likely coregulated. Our extension enables significant performance improvements, expands the search space across the major model organisms, and provides a new cross-organism exploration interface to help facilitate translational research.

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Maciej Halber will present his FPO, "RGBD pipeline for Indoor Scene Reconstruction and Understanding" on Monday, July 15th, 2019 at 1pm in CS 402.
by Nicki Mahler 08 Jul '19

08 Jul '19

Maciej Halber will present his FPO,"RGBD pipeline for Indoor Scene Reconstruction and Understanding" on Monday, July 15th, 2019 at 1pm in CS 402. The members of his committee are Thomas Funkhouser (adviser); Readers: Szymon Rusinkiewicz and Matthias Niessner (The Technical University of Munich); Examiners: Thomas Funkhouser, Olga Russakovsky, and Adam Finkelstein. A copy of his thesis is available upon request. Everyone is invited to attend his talk. The talk abstract follows below: In this work, we consider the problem of reconstructing a 3D model from a sequence of color and depth frames. Generating such a model has many important applications, ranging from the entertainment industry to real estate. However, transforming the RGBD frames into high-quality 3D models is a challenging problem, especially if additional semantic information is required. In this document, we introduce three projects, which implement various stages of a robust RGBD processing pipeline. First, we consider the challenges arising during the RGBD data capture process. While the depth cameras are providing dense, per-pixel depth measurements, there is a non-trivial error associated with the resulting data. We discuss the depth generation problem and propose an error reduction technique based on estimating an image-space undistortion field. We describe the capture process of the data required for the generation of such an undistortion field. We showcase how correcting the depth measurements improves the reconstruction quality. Second, we address the problem of registering RGBD frames over a long video sequence into a globally consistent 3D model. We propose a “fine-to-coarse” global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of geometrical constraints, modeled as planar structures, at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. We find that our fine-to-coarse algorithm registers long RGBD sequences better than previous methods. Last, we show how repeated scans of the same space can be used to establish associations between the different observations. Specifically, we consider a situation where 3D scans are acquired repeatedly at sparse time intervals. We develop an algorithm that analyzes these “rescans” and builds a temporal model of a scene with semantic instance information. The proposed algorithm operates inductively by using a temporal model resulting from past observations to infer instance segmentation of a new scan. The temporal model is continuously updated to reflect the changes that occur in the scene over time, providing object associations across time. The algorithm outperforms alternate approaches based on state-of-the-art networks for semantic instance segmentation.

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Elena Balashova will present her FPO, "Shape Synthesis Using Structure-Aware Reasoning" on Monday, July 8, 2019 at 1pm in CS 105
by Nicki Mahler 08 Jul '19

08 Jul '19

Elena Balashova will present her FPO, "Shape Synthesis Using Structure-Aware Reasoning" on Monday, July 8, 2019 at 1pm in CS 105. The members of her committee are as follows: Thomas Funkhouser (adviser), Readers: Szymon Rusinkiewicz and Vivek Singh (Siemens Healthcare); Examiners: Olga Russakovsky, Adam Finkelstein, and Thomas Funkhouser (adviser). A copy of her thesis is available upon request. Everyone is invited to attend her talk. The talk abstract follows below. Shape synthesis is an important area of computer vision and graphics that concerns creation of new shapes and reconstruction from partial data. Its goal is to learn a model that can generate shapes within an object category suitable for novel shape creation, interpolation, completion, editing, and other geometric modeling applications. Existing tools learn shape properties from large collections of shapes. Although these methods have been very successful at learning how to synthesize the coarse shapes of objects in categories with highly diverse shapes, they have not always produced examples that reconstruct important structural elements of a shape. In this thesis, I describe how structure can be incorporated into the synthesis process, and how it can be used to improve generative models. First, I introduce a template-defined skeleton structure for learning a part-aware generative model in typography, where the shapes have a known structure and can be explained by a small number of templates. Next, I present a scenario of noisy archaeological wall painting (fresco) reconstruction from eroded fragments, where there is no well-defined structure and exponentially many arrangement possibilities in this case, I present a cluster evaluation function that guides the assembly process and encourages selection of good clusters. Finally, I describe a semantic landmark-based structure and how it can be used to improve a generative model of examples with extremely varied topology by means of a geometric shape-structure consistency loss. Through exploration of each type of structure, I show how reasoning with proposed structures helps synthesize more accurate and realistic shapes. I also propose a fully automatic framework for font completion. Finally, I design a genetic algorithm for wall painting reconstruction and propose an iterative outlier detection technique based on the eigenvector method.

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EE Seminar - June 13, 2019, 11:00 am, E-Quad B205 - Prof. Zhenkai Liang
by Emily Lawrence 10 Jun '19

10 Jun '19

Department of Electrical Engineering Seminar Speaker: Prof. Zhenkai Liang, National University of Singapore Title: Scaling up Binary Analysis via Knowledge-oriented Techniques Date: Thursday, June 13, 2019 Time: 11:00 am Room: E-Quad, Room B205 Host: Prof. Ruby Lee Abstract: Binary analysis is a fundamental technique in software and system security. It has a wide range of applications, such as vulnerability discovery, attack response, malware analysis, and software testing and debugging. Due to the lack of high-level semantics and complex program behaviors, it is challenging for binary analysis solutions to scale up to large real-world binaries in practice. Existing solutions are often task-driven and bounded by a practical time limit, which hinders comprehensively understanding of binaries. Furthermore, it is difficult to integrate the knowledge generated from different solutions. In this talk, we discuss our research in scaling up binary analysis in a knowledge-oriented manner. We believe knowledge abstraction is the key to scale up binary analysis, where binary analysis solutions generate understandings that can be shared and reused in other solution. Our investigation includes techniques for knowledge extraction, tools for knowledge integration, and platforms for knowledge accumulations and sharing. The accumulated knowledge not only allows broader and deeper analysis into binaries. It also enables emerging data-driven and learning techniques to be effectively adopted in binary analysis solutions. In this talk, I will also share our experience and reflection in system security education. Bio: Zhenkai Liang is an Associate Professor of the School of Computing, National University of Singapore. His main research interests are in system and software security, web security, mobile security, and program analysis. He is also the Co-Lead PI of National Cybersecurity R&D Lab in Singapore. He has served as the technical program committee members of many system security conferences, including the ACM Conference on Computer and Communications Security (CCS), USENIX Security Symposium and the Network and Distributed System Security Symposium (NDSS), as well as a member of NDSS Steering Group. As a co-author, he received the Best Paper Award in ICECCS 2014, the Best Paper Award in W2SP 2014, the ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009, the Best Paper Award at USENIX Security Symposium 2007, and the Outstanding Paper Award at ACSAC 2003. He also won the Annual Teaching Excellence Award of National University of Singapore in 2014 and 2015. He received his Ph.D. degree in Computer Science from Stony Brook University in 2006, and B.S. degrees in Computer Science and Economics from Peking University in 1999.

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Yiying Zhang TODAY at 1:30pm
by Emily C. Lawrence 21 May '19

21 May '19

Yiying Zhang from Purdue University Tuesday, May 21, 2019 Computer Science - Room 105 at 1:30pm Host: Amit Levy [ https://www.cs.princeton.edu/events/25862 | https://www.cs.princeton.edu/events/25862 ] Farewell to Servers: Software and Hardware Approaches towards Datacenter Resource Disaggregation Datacenters have been using the "monolithic" server model for decades, where each server hosts a set of hardware devices like CPU and DRAM on a motherboard and runs an OS on top to manage the hardware resources. This monolithic server model fundamentally restricts datacenters from achieving efficient resource packing, hardware rightsizing, and great heterogeneity. Recent hardware and application trends such as serverless computing further call for a rethinking of the long-standing server-centric model. My answer is to "disaggregate" monolithic servers into network-attached hardware components that host different hardware resources and offer different functionalities (e.g., a processor component for computation, a memory component for fast data accesses). I believe that after evolving from physical (DC-1.0) to virtual (DC-2.0), datacenters should evolve further into a disaggregated one (DC-3.0), where hardware resources can be allocated and scaled to the exact amount that applications use and can be individually managed and customized for different application needs. By not having servers, DC-3.0 disrupts designs and technologies in almost every layer in today's datacenters, from hardware and networking to OS and applications. My lab undertook pioneering efforts in building an end-to-end solution for DC-3.0 with a new OS, a new hardware platform, and a new network system. This talk will focus on two systems that are central to the design of DC-3.0: 1) LegoOS , a new distributed operating system designed for managing disaggregated resources. LegoOS splits OS functionalities into different units, each running at a hardware component and managing the component's hardware resources. LegoOS enables the disaggregation and customization of OS functionalities, a significant step towards building DC-3.0's software infrastructure. 2) LegoFPGA , a new approach of using FPGA to efficiently manage and virtualize hardware resources. LegoFPGA offers a solution to co-design application, OS, and hardware functionalities and customize them for different hardware resources and application domains, an important step towards building DC-3.0's hardware infrastructure. With LegoOS and LegoFPGA , we demonstrate that separating core OS and hardware functionalities is not only feasible but can largely improve performance per dollar over the current datacenter monolithic server model. Bio: Yiying Zhang is an assistant professor in the School of Electrical and Computer Engineering at Purdue University. Her research interests span operating systems, distributed systems, computer architecture, and datacenter networking. She also works on the intersection of systems and programming language, security, and AI/ML. She won an OSDI best paper award in 2018 and an NSF CAREER award in 2019. Yiying’s lab is among the few groups in the world now that build new OSes and full-stack, cross-layer systems. Yiying received her Ph.D. from the Department of Computer Sciences at the University of Wisconsin-Madison under the supervision of Andrea and Remzi Arpaci-Dusseau and worked as a postdoctoral scholar at the University of California, San Diego before joining Purdue.

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Reminder: Yiying Zhang tomorrow at 1:30pm
by Emily C. Lawrence 20 May '19

20 May '19

Yiying Zhang from Purdue University Tuesday, May 21, 2019 Computer Science - Room 105 at 1:30pm Host: Amit Levy [ https://www.cs.princeton.edu/events/25862 | https://www.cs.princeton.edu/events/25862 ] Farewell to Servers: Software and Hardware Approaches towards Datacenter Resource Disaggregation Datacenters have been using the "monolithic" server model for decades, where each server hosts a set of hardware devices like CPU and DRAM on a motherboard and runs an OS on top to manage the hardware resources. This monolithic server model fundamentally restricts datacenters from achieving efficient resource packing, hardware rightsizing, and great heterogeneity. Recent hardware and application trends such as serverless computing further call for a rethinking of the long-standing server-centric model. My answer is to "disaggregate" monolithic servers into network-attached hardware components that host different hardware resources and offer different functionalities (e.g., a processor component for computation, a memory component for fast data accesses). I believe that after evolving from physical (DC-1.0) to virtual (DC-2.0), datacenters should evolve further into a disaggregated one (DC-3.0), where hardware resources can be allocated and scaled to the exact amount that applications use and can be individually managed and customized for different application needs. By not having servers, DC-3.0 disrupts designs and technologies in almost every layer in today's datacenters, from hardware and networking to OS and applications. My lab undertook pioneering efforts in building an end-to-end solution for DC-3.0 with a new OS, a new hardware platform, and a new network system. This talk will focus on two systems that are central to the design of DC-3.0: 1) LegoOS , a new distributed operating system designed for managing disaggregated resources. LegoOS splits OS functionalities into different units, each running at a hardware component and managing the component's hardware resources. LegoOS enables the disaggregation and customization of OS functionalities, a significant step towards building DC-3.0's software infrastructure. 2) LegoFPGA , a new approach of using FPGA to efficiently manage and virtualize hardware resources. LegoFPGA offers a solution to co-design application, OS, and hardware functionalities and customize them for different hardware resources and application domains, an important step towards building DC-3.0's hardware infrastructure. With LegoOS and LegoFPGA , we demonstrate that separating core OS and hardware functionalities is not only feasible but can largely improve performance per dollar over the current datacenter monolithic server model. Bio: Yiying Zhang is an assistant professor in the School of Electrical and Computer Engineering at Purdue University. Her research interests span operating systems, distributed systems, computer architecture, and datacenter networking. She also works on the intersection of systems and programming language, security, and AI/ML. She won an OSDI best paper award in 2018 and an NSF CAREER award in 2019. Yiying’s lab is among the few groups in the world now that build new OSes and full-stack, cross-layer systems. Yiying received her Ph.D. from the Department of Computer Sciences at the University of Wisconsin-Madison under the supervision of Andrea and Remzi Arpaci-Dusseau and worked as a postdoctoral scholar at the University of California, San Diego before joining Purdue.

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