Seminar

Princeton Wireless Distinguished Seminar Series

Thanks to the spectacular advances in signal processing and nano-technology, five wireless generations have been conceived over the past five decades. Indeed, near-capacity operation at an infinitesimally low error rate has become feasible and flawless multimedia communications is supported in areas of high traffic density, but how do we fill the huge coverage holes existing across the globe? As a promising system architecture, an integrated terrestrial, UAV-aided, airplane-assisted as well as satellite-based global coverage solution will be highlighted to pave the way for seamless next-generation service provision. However, these links exhibit strongly heterogeneous properties, hence requiring different enabling techniques. The joint optimization of the associated conflicting performance metrics of throughput, transmit power, latency, error probability, hand-over probability and link lifetime poses an extremely challenging problem. Explicitly, sophisticated multi-component system optimization is required for finding the Pareto-front of all optimal solutions, where none of the above-mentioned metric can be improved without degrading at least one of the others.

Bio: Lajos Hanzo is a Fellow of the Royal Academy of Engineering (FREng), FIEEE, FIET and a EURASIP Fellow, Foreign Member of the Hungarian Academy of Science. He holds honorary Doctorates from the University of Edinburgh and the Technical University of Budapest. He co-authored 19 IEEE Press – John Wiley books and 2000+ research contributions at IEEE Xplore. For further information on his research in progress and associated publications, please refer to IEEE Xplore.                

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This talk will take place over Zoom (Please Register)

Join us for the first seminar series event for Princeton's new Center for the Decentralization of Power Through Blockchain Technology, also known as DeCenter. Lunch will be available beginning at noon. 

This first event will have two parts: first, the DeCenter co-directors, Dean Andrea Goldsmith and Professor Jaswinder Singh will speak about the goals for the new center. Then, a panel will discuss some of the technology and applications aspirations around blockchains along with their implications for society.

Introduction to DeCenter:

• Andrea J. Goldsmith, Dean of the School of Engineering and Applied Science, Arthur LeGrand Doty Professor of Electrical Engineering, and co-director of DeCenter
• Jaswinder Singh, Professor of Computer Science, Technology, and Societal Change, co-director of DeCenter

Technology, Applications, and Society, a panel discussion moderated by Professor Singh, featuring:  

• Maria Apostolaki, Assistant Professor of Electrical and Computer Engineering
• Nolan McCarty, Susan Dod Brown Professor of Politics and Public Affairs and Director of the Center for Data-Driven Social Science
• Andrés Monroy-Hernández, Assistant Professor of Computer Science
• Pramod Viswanath, Forrest G. Hamrick Professor in Engineering

Members of the public (without Princeton affiliation) who wish to attend must register. PUID holders do not need to register.

And stay tuned for future DeCenter seminars!
Monthly seminars will take place fully in-person on the first Thursday of the month from 12:30 p.m. - 1:20 p.m. in Computer Science Room 105. Videos of seminars will be made available after the event. The next seminar will take place on November 3.

Attendees can view this talk in person or remotely over Zoom here.  Zoom link is available to Princeton authenticated users only.

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Over the last decade, cryo-electron microscopy (cryo-EM) has emerged as a powerful imaging technology for visualizing the 3D structure of proteins and other biomolecules at near-atomic resolution. Unlike other methods in structural biology, cryo-EM is uniquely poised to image large and dynamic protein complexes. However, this promise is limited by the computational task of 3D reconstruction, where a dataset of noisy and unlabeled 2D projection images are combined to infer the 3D structure(s) of the molecule of interest.

In this seminar, I will introduce cryoDRGN, a machine learning system for heterogeneous cryo-EM reconstruction. Underpinning the cryoDRGN method is a deep generative model parameterized by a new neural representation of 3D volumes and a learning algorithm combining exact and variational inference to optimize this model from unlabeled 2D cryo-EM images. Extended to real datasets and released as an open-source tool, cryoDRGN has been used to discover new protein structures and visualize continuous trajectories of their motion. I will discuss various extensions of the method to broaden the scope of cryo-EM to new classes of dynamic protein complexes. Finally, I will discuss how recent advances in machine learning for protein structure prediction (e.g. AlphaFold and Rosettafold) can complement methods for cryo-EM structure determination and what key algorithmic challenges remain to realize the next era of 3D visual biology.

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The recent shift of focus from research and development to deployment and commercialization of 5G systems has concurrently unveiled the “what comes next?” question embodied in an overarching beyond-5G/6G vision targeting 2030 and beyond. This talk will address some questions pertinent to beyond-5G/6G technologies such as shortcomings of 5G against the grand vision of a “fully-connected world” and the consensus on a beyond-5G/6G vision that has started to shape both in industry and academia. The talk will further elaborate on the emerging pillars of beyond-5G/6G including networks, terminal architectures, and radio, along with necessary evolution and disruption anticipated in some of these pillars. Finally, we will provide an overview of most recent designs and progress on key beyond-5G enablers and challenges, including ultra-high throughput (above 100Gbps) wireless link solutions, joint communication and sensing, and AI/ML as a useful toolbox for air-interface connectivity technologies.

Bio:  Onur Sahin received his B.S. degree (hons.) in electrical and electronics engineering from Middle East Technical University, Ankara, Turkey, in 2003 and Ph.D. degree in electrical engineering from New York University, USA in 2009. He is currently a Member of Technical Staff at InterDigital Europe. His primary research and development interests are on the next generation telecommunication and wireless systems, with current emphasis on beyond 5G technologies. Dr. Sahin has held technical lead positions at multiple projects on next generation cellular and Wi-Fi systems. He is the co-author of over 45 peer-reviewed scientific articles (h-index: 23), co-inventor of 35 patents and patent applications, and co-recipient of the 2018 IEEE Signal Processing Society Best Paper Award, and 2016 Journal of Communication Networks Best Paper Award. 

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This talk will be recorded.

To request accommodations for a disability, please contact Kevin Shaw at kevinshaw@princeton.edu at least one week prior to the event.

Please register here

Advances in mobile (cellular) networks have ushered in an era of abundant connectivity. However, the stationary and expensive nature of their deployment has limited their ability to provide true "ubiquitous" connectivity under the 5G vision - especially to areas where connectivity is sparing or nonexistent (e.g. rural areas), has been compromised (e.g. disasters), or demands are extreme (e.g. venues/hotspots).

The recent advances in un-manned aerial vehicle (UAVs) technology have the potential to change the landscape of wide-area wireless connectivity by bringing a new dimension - "mobility" to the cellular network infrastructure itself. By deploying base stations on each of the UAVs, service providers can now deploy and tear-down these mobile “networks in the sky” in an on-demand and flexible manner. This allows them to supplement static mobile networks in areas where additional connectivity is needed, or provide stand-alone connectivity in areas where existing mobile networks are either absent or compromised. However, realizing this vision of deploying heavy-weight cellular networks (e.g. LTE, 5G NR) on light-weight, resource- constrained platforms such as UAVs, faces several formidable challenges both in design and deployment. This is further complicated by the complex nature of cellular networks that involve multiple interacting components - radio access network (RAN), evolved packet core (EPC) network and backhaul transport network.

In this talk, I will present our system "SkyLiTE"-- one of the first efforts to design and deploy an on-demand, end-to-end, multi-cell 5G network (on UAVs) that can self-configure itself in the sky. I will discuss how by bringing together innovations (both algorithms and systems) in wireless networking research, SkyLiTE is able to re-architect the various components (RAN, core and backhaul) of a cellular network and make it deployable on challenging UAV platforms. Beyond connectivity, I will also highlight the significant potential these “networks in the sky” offer for low latency, high bandwidth sensing services over large areas through sample applications designed for real-time tracking and 3D reconstruction.

Bio: Karthikeyan (Karthik) Sundaresan is a Professor in the School of ECE, Georgia Tech. Prior to that he spent fifteen years in wireless and telecom research at NEC Labs America, Princeton. His research interests are broadly in wireless networking and mobile computing, and span both algorithm design as well as system prototyping. He is the recipient of ACM Sigmobile’s Rockstar award (2016) for early career contributions to mobile computing and wireless networking, as well as several best paper awards at prestigious ACM and IEEE conferences. He holds over fifty patents, and received business contribution awards for bringing research technology to commercialization at NEC. He also led the spin-out effort of an innovative, lab- grown research technology (TrackIO) for infrastructure-free tracking of first responders in GPS- denied environments. He has participated in various organizational and editorial roles for IEEE and ACM conferences and journals, and served as the PC co-chair for ACM MobiCom’16. He is a Fellow of the IEEE and an ACM distinguished scientist.

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This talk will be recorded.

To request accommodations for a disability, please contact Kevin Shaw at kevinshaw@princeton.edu at least one week prior to the event.

Please register here

Living organisms can perform incredible feats. Plants like dandelions can disperse their seeds over a kilometer in the wind, and small insects like bumblebees can see, smell, communicate, and fly around the world, despite their tiny size. Enabling some of these capabilities for the Internet of things (IoT) and cyber-physical systems would be transformative for applications ranging from large-scale sensor deployments to micro-drones, biological tracking, and robotic implants. In this talk, I will explain how by taking an interdisciplinary approach spanning wireless communication, sensing, and biology, we can create programmable devices for the internet of biological and bio-inspired things. I will present the first battery-free wireless sensors, inspired by dandelion seeds, that can be dispersed by the wind to automate deployment of large-scale sensor networks. I will then discuss how integrating programmable wireless sensors with live animals like bumblebees can enable mobility for IoT devices, and how this technique has been used for real-world applications like tracking invasive "murder" hornets. Finally, I will present an energy-efficient insect-scale steerable vision system inspired by animal head motion that can ride on the back of a live beetle and enable tiny terrestrial robots to see.

Bio:  Shyam Gollakota is a Torode Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He works across multiple disciplines at the University including Computer Science, Electrical Engineering, Mechanical Engineering, biology and the School of Medicine.  His work has led to three startups, Jeeva Wireless, Sound Life Sciences and Wavely Diagnostics, has been licensed by ResMed Inc and is in use by millions of users. His lab also worked closely with the Washington Department of Agriculture to wireless track the invasive "murder" hornets, which resulted in the destruction of the first nest in the United States. He is the recipient of a  National Science Foundation Career Award, an Alfred P. Sloan Fellowship, the SIGMOBILE Rockstar award, ACM Grace Murray Hopper Award in 2020 and recently named as a Moore Inventor Fellow in 2021. He was also named in MIT Technology Review’s 35 Innovators Under 35, Popular Science ‘brilliant 10’ and twice to the Forbes’ 30 Under 30 list. His group’s research has earned Best Paper awards at MOBICOM, SIGCOMM, UbiComp, SenSys, NSDI and CHI, appeared in interdisciplinary journals like Science Translational Medicine, Science Robotics and Nature Digital Medicine as well as named as a MIT Technology Review Breakthrough technology of 2016 as well as Popular Science top innovations in 2015. He is an alumni of MIT (Ph.D., 2013, winner of ACM doctoral dissertation award) and IIT Madras.

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This talk will be recorded.

To request accommodations for a disability, please contact Kevin Shaw at kevinshaw@princeton.edu at least one week prior to the event.

Please register here

This talk will present an overview of recent research in FUNLAB around the use of vehicular radar for advanced driver assistance systems (en route to a future vision of autonomous driving). Wideband (typically FMCW or chirp) radars are being increasingly deployed onboard vehicles as key high-resolution sensor for environmental mapping/imaging and various safety features. The talk will focus on the evolving role of radar `cognition’ in complex operating environments to address two important future challenges:

1.  Mitigating multi-access interference among Radars (e.g. dense traffic scenario)
The talk will first illustrate the impact of mutual interference on detection performance in Chirp/FMCW radars and then highlight some multi-access protocol design approaches for effective resource sharing among multiple radars.

2.  Contributions to radar vision via new radar hardware (MIMO radar) + associated advanced signal processing (Synthetic Aperture principles) as well as Convolutional Neural Network (`Radar Net’) based machine learning approach for enhanced object detection/classification in challenging circumstances.

Bio: Sumit Roy received the B. Tech. degree from the Indian Institute of Technology (Kanpur) in 1983, and the M. S. and Ph. D. degrees from the University of California (Santa Barbara), all in Electrical Engineering in 1985 and 1988 respectively, as well as an M. A. in Statistics and Applied Probability in 1988. He is currently Professor, Electrical & Comp. Engineering, appointed to a term Distinguished Professorship for Integrated Systems (2014-19); since Sep. 2020, he serves as a rotator for US Dept. of Defense’s Program Lead for Innovate Beyond 5G https://5g-to-xg.org. His research interests include fundamental design and evaluation of wireless communication and sensor network systems spanning a diversity of technologies and application areas: next-gen (5G & beyond) wireless LANs and cellular networks, heterogeneous network coexistence, spectrum sharing, software defined radio platforms, vehicular and sensor networking.  He spent 2001-03 on academic leave at Intel Wireless Technology Lab as a Senior Researcher engaged in systems architecture and standards development for ultra-wideband systems (Wireless PANs) and next generation high-speed wireless LANs. During Jan-July 2008, he was Science Foundation of Ireland’s E.T.S.   Walton   Awardee for a sabbatical at University College, Dublin and also recipient of a Royal Acad. Engineering (UK) Distinguished Visiting Fellowship during summer 2011.  He has served as IEEE Communications Society (ComSoc) Distinguished Lecturer and as Associate Editor for all the major ComSoc journals. He served 2 terms as (elected) member of Executive Committee, National Spectrum Consortium dedicated to efficient spectrum sharing between Federal and commercial networks and is the co-author of IEEE TAES 2016 Best paper award for work on Radar-Comm coexistence.  He was elevated to IEEE Fellow by Communications Society in 2007 for ``contributions to multi-user communications theory and cross-layer design of wireless networking standards”.

Please register here

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Today’s AI is too big. Deep neural networks demand extraordinary levels of data and computation, and therefore power, for training and inference. This severely limits the practical deployment of AI in edge devices. We aim to improve the efficiency of neural network design. First, I’ll present MCUNet [1] that brings deep learning to IoT devices. MCUNet is a framework that jointly designs the efficient neural architecture (TinyNAS) and the light-weight inference engine (TinyEngine), enabling ImageNet-scale inference on micro-controllers that have only 1MB of Flash. Next I will introduce Once-for-All Network [2], an efficient neural architecture search approach, that can elastically grow and shrink the model capacity according to the target hardware resource and latency constraints. From inference to training, I’ll present TinyTL [3] that enables tiny transfer learning on-device, reducing the memory footprint by 7-13x.  Finally, I will describe data-efficient GAN training techniques [4] that can generate photo-realistic images using only 100 images, which used to require tens of thousands of images. We hope such TinyML techniques can make AI greener, faster, more efficient and more sustainable.

[1] MCUNet: Tiny Deep Learning on IoT Devices, (NeurIPS’20 spotlight)
[2] Once-for-All: Train One Network and Specialize it for Efficient Deployment (ICLR’19)
[3] Tiny Transfer Learning: Reduce Memory, not Parameters for Efficient On-Device Learning (NeurIPS’20)
[4] Differentiable Augmentation for Data-Efficient GAN Training (NeurIPS’20)

Bio:  Song Han is an assistant professor at MIT’s EECS. He received his PhD degree from Stanford University. His research focuses on efficient deep learning computing. He proposed “deep compression” technique that can reduce neural network size by an order of magnitude without losing accuracy, and the hardware implementation “efficient inference engine” that first exploited pruning and weight sparsity in deep learning accelerators. His team’s work on hardware-aware neural architecture search that bring deep learning to IoT devices was highlighted by MIT News, Wired, Qualcomm News, VentureBeat, IEEE Spectrum, integrated in PyTorch and AutoGluon, and received many low-power computer vision contest awards in flagship AI conferences (CVPR’19, ICCV’19 and NeurIPS’19). Song received Best Paper awards at ICLR’16 and FPGA’17, Amazon Machine Learning Research Award, SONY Faculty Award, Facebook Faculty Award, NVIDIA Academic Partnership Award. Song was named “35 Innovators Under 35” by MIT Technology Review for his contribution on “deep compression” technique that “lets powerful artificial intelligence (AI) programs run more efficiently on low-power mobile devices.” Song received the NSF CAREER Award for “efficient algorithms and hardware for accelerated machine learning” and the IEEE “AIs 10 to Watch: The Future of AI” award.

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To request accommodations for a disability please contact Lori Bailey at lbailey@princeton.edu at least one week prior to the event.

Zoom link: https://princeton.zoom.us/j/906163778 

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Deep Neural Networks (DNNs) enable computers to excel across many different applications such as image classification, speech recognition and robotics control. To accelerate DNN training and serving, parallel computing is widely adopted. System efficiency is a big issue when scaling out. In this talk, I will make three arguments towards better system efficiency in distributed DNN training and serving.

First, Ring All-Reduce for model synchronization is not optimal, but Blink is. By packing spanning trees rather than forming rings, Blink achieves higher flexibility in arbitrary networking environments and provides near-optimal network throughput. Blink is filed as a US patent and is being used by Microsoft. Blink gains lots of attention from industry, such as Facebook (distributed PyTorch team), ByteDance (parent company of TikTok app). Blink was also featured on Nvidia GTC China 2019 and news from Baidu, Tencent, etc. 

Second, communication can be eliminated via sensAI's class parallelism. sensAI decouples a multi-task model into disconnected subnets, each is responsible for decision making of a single task. sensAI's attribute of low-latency, real-time model serving attracts several Venture Capitals in the Bay Area.

Third, Wavelet is more efficient than gang-scheduling. By intentionally adding task launching latency, Wavelet interleaves peak memory usage across different waves of training tasks on the accelerators, and thus it improves both computation and on-device memory utilization.

Bio: Guanhua Wang is a final year CS PhD in the RISELab at UC Berkeley, advised by Prof. Ion Stoica. His research lies primarily in the ML+System area including fast collective communication schemes for model synchronization, efficient in-parallel model training and real-time model serving.

Please register using this link.

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In this seminar, we will examine the underinvested deep learning personalization and recommendation systems in the overall research community. The training of state-of-the-art industry-scale personalized and recommendation models consumes the highest number of compute cycles among all deep learning use cases. For AI inference, personalization and recommendation consumes even higher compute cycles of 80%. What does state-of-the-art industry-scale neural personalization and recommendation models look like?

I will present recent advancement on the development of deep learning recommender systems, the implications on system and architectural design and parallelism opportunities across the machine learning system stack over a variety of platforms [HPCA-2020, ISCA-2020, IISWC-2020]. I will conclude the talk with future directions on multi-scale system design and optimization to advance the field of AI [HPCA-2019, MICRO-2020].
 
Bio:
Carole-Jean Wu is a Research Scientist at Facebook AI Research. Her research focus lies in the domain of computer system architecture with particular emphasis on energy- and memory-efficient systems. Her recent research has pivoted into designing systems for machine learning execution at-scale, such as for personalized recommender systems and mobile deployment. Carole-Jean chairs the MLPerf Recommendation Benchmark Advisory Board and co-chairs MLPerf Inference.

Carole-Jean holds tenure from ASU. She received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell. She is the recipient of the NSF CAREER Award, Facebook AI Infrastructure Mentorship Award, the IEEE Young Engineer of the Year Award, the Science Foundation Arizona Bisgrove Early Career Scholarship, and the Intel PhD Fellowship, among a number of Best Paper awards. She is a senior member of both ACM and IEEE.

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This seminar is supported by Computer Science and Electrical Engineering Korhammer Lecture Series Funds.

To request accommodations for a disability please contact Emily Lawrence, emilyl@cs.princeton.edu at least one week prior to the event.