Fall 2020 Seminars
A complete listing
|Tue, Sept 8||11am - 12pm||Danny Y. Huang||New York University||Watching IoTs That Watch Us: Empirically Studying IoT Security & Privacy at Scale||https://nyu.zoom.us/j/98658914043||Recording Link|
|Tue, Oct 20||11am - 12pm||Jevan Hutson||University of Washington School of Law||Physiognomic AI||https://nyu.zoom.us/j/99299296540||TBD|
|Fri, Oct 23||11am - 12pm||Yury Dvorkin||New York University||Identifying and Protecting Electricity Vulnerable New Yorkers||https://nyu.zoom.us/j/92972435080||Recording Link|
|Tue, Oct 27||4pm - 5pm||Yingyan (Celine) Lin||Rice University||Efficient DNN Algorithms, Accelerators, and Automated Tools towards Green AI||https://nyu.zoom.us/j/96063948385||TBD|
|Tue, Nov 3||11am - 12pm||Michail (Mihalis) Maniatakos||NYU Abu Dhabi||Hardware-based Acceleration of Homomorphic Encryption||https://nyu.zoom.us/j/97453043207||TBD|
|Tue, Nov 10||11am - 12pm||Samah Saeed||City College-CUNY||TBD||TBD||TBD|
|Fri, Nov 13||11am - 12pm||Nicole Fern||Tortuga Logic||Protecting Systems Against Hardware Based Attacks||https://nyu.zoom.us/j/96298359766||TBD|
|Tue, Nov 17||11am - 12pm||Sangeetha Abdu||UC Irvine||TBD||TBD||TBD|
|Wed, Nov 18||11am - 12pm||Corey E. Baker||University of Kentucky||TBD||TBD||TBD|
|Tue, Nov 24||11am - 12pm||Jennie Si||Arizona State University||TBD||TBD||TBD|
|Tue, Dec 1||11am - 12pm||Iris Bahar||Brown University||TBD||TBD||TBD|
|Tue, Dec 8||11am - 12pm||Akshitha Sriraman||University of Michigan||Enabling Hyperscale Web Services||https://nyu.zoom.us/j/99555565320||TBD|
|Tue, Dec 15||11am - 12pm||Song Fang||New York University||TBD||TBD||TBD|
Speaker: Danny Y. Huang, New York University
Date: Sept 8
Abstract: Consumers today are increasingly concerned about the security and privacy risks of smart home IoT devices. However, few empirical studies have looked at these problems at scale, partly because a large variety and number of smart-home IoT devices are often closed-source and on private home networks, thus making it difficult for researchers to systematically observe the actual security and privacy issues faced by users in the wild.
In this talk, I describe two methods for researchers to empirically understand these risks to real end-users: (i) crowdsourcing network traffic from thousands of real smart home networks [IMWUT '20], and (ii) emulating user-inputs to study how thousands of smart TV apps track viewers [CCS '19]. Both methods have allowed us to conduct the largest security and privacy studies on smart TVs and other IoT devices to date. Our labeled datasets have also created new opportunities for other research areas, such as machine learning, network management, and healthcare.
About the Speaker: Danny Y. Huang is an Assistant Professor affiliated with ECE and CUSP. He officially joined NYU 10 days ago. He is broadly interested in the security and privacy of consumer technologies, such as cryptocurrency and IoT. He did a postdoc at Princeton University and obtained his PhD in Computer Science and Engineering from University of California, San Diego. For more information, visit https://mlab.engineering.nyu.edu/.
Speaker: Jevan Hutson, University of Washington School of Law
Date: Oct 20
Abstract: Artificial intelligence (AI) techniques in computer vision and related fields enabled by machine learning (ML) are ushering in a new era of computational physiognomy and phrenology. These scientifically baseless, racist, and socially discredited fields purporting to determine a person character, capability, or future social outcomes based on their facial features, expressions, or other physical or biometric characteristics should be anathema to any researcher or product developer working in computer science today, yet physiognomic and phrenological claims now appear regularly in research papers, at top AI conferences, and in the sales pitches of some digital technology companies. The reanimation of physiognomy and phrenology at scale through computer vision and machine learning is a matter of urgent concern. Physiognomic AI, this talk contends, is the practice of using computer software to infer an individual’s character, natural capabilities, and future social outcomes based on their physical or behavioral characteristics. This talk, which represents an ongoing collaboration with Dr. Luke Stark and contributes to the intersection of critical data studies, consumer protection law, biometric privacy law, and civil rights law, endeavors to conceptualize and problematize physiognomic AI and offer policy recommendations for state and federal lawmakers to forestall its proliferation.
About the Speaker: Jevan Hutson is a lawyer, data justice and privacy advocate, and human-computer interaction researcher, who proposed restrictions on facial recognition technology and AI-enabled profiling in the Washington State Legislature. He recently completed his law degree at the University of Washington School of Law, where he led Facial Recognition & AI Policy at the Technology Law & Public Policy Clinic and served on the External Biometrics Advisory Board of the Port of Seattle. He also holds an M.P.S. information Science and B.A. in History of Art & Visual Studies from Cornell University.
Speaker: Yury Dvorkin, New York University
Date: Oct 23
Abstract: Electricity vulnerability is a design factor that has been commonly overlooked in electricity planning, thus putting thousands in danger of serious health implications in case of even short electric power outages. Naturally, the ongoing COVID-19 outbreak has only exacerbated this danger as electricity demand has shifted to residential areas, thus causing unexpected stress, and response times of repair teams are expected to increase due to COVID-19 restrictions. This presentation will describe our ongoing project that collects power outage data in real-time and use social computing and open-source socio-demographic and environmental data to evaluate the severity of each outage for electricity vulnerable population groups, and prioritize outage repairs in the areas with vulnerable population groups.
About the Speaker: Yury is an Assistant Professor and Goddard Junior Faculty Fellow in the Department of Electrical and Computer Engineering at New York University’s Tandon School of Engineering with an affiliated appointment at NYU’s Center for Urban Science and Progress.
Speaker: Yingyan (Celine) Lin, Rice University
Date: Oct 27
Abstract: While machine learning powered intelligence promises to revolutionize the way we live and work by enhancing our ability to recognize, analyze, and classify the world around us, this revolution has yet to be unleashed. First, powerful machine learning algorithms require prohibitive energy consumption (e.g., hundreds of layers and tens of millions of parameters), whereas many daily life devices, such as smartphones, smart sensors, and drones, have limited energy and computation resources since they are battery-powered and have a small form factor. Second, the excellent performance of modern deep neural networks (DNNs) comes at an often exorbitant training cost due to the required vast volume of training data and model parameters, e.g., training a single DNN can cost over $10K US dollars and emit as much carbon as five cars in their lifetimes, raising various environmental concerns. To address the aforementioned gap and challenge, the Efficient and Intelligent Computing (EIC) Lab at Rice University has been developing efficient DNN algorithms, accelerators, and automated tools. In this talk, I will share some promising techniques we recently developed and exciting projects that we are working on.
About the Speaker: Yingyan (Celine) Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at Rice University. She leads the Efficient and Intelligent Computing (EIC) Lab at Rice, which focuses on embedded machine learning and aims to develop techniques towards green AI and ubiquitous machine learning powered intelligence. She received a Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017, a Best Student Paper Award at the 2016 IEEE International Workshop on Signal Processing Systems (SiPS 2016), and the 2016 Robert T. Chien Memorial Award for Excellence in Research at UIUC. She was selected as a Rising Star in EECS by the 2017 Academic Career Workshop for Women at Stanford University. Dr. Lin is currently the lead PI on multiple multi-university projects and her group has been funded by NSF, NIH, ONR, Qualcomm, and Intel.
Speaker: Mihalis Maniatakos, NYU-AD
Date: Nov 13
Abstract: The rapid expansion and increased popularity of cloud computing comes with no shortage of privacy concerns about outsourcing computation to semi-trusted parties. While cryptography has been successfully used to solve data-in-transit (e.g., HTTPS) and data-at-rest (e.g., AES encrypted hard disks) concerns, data-in-use protection remains unsolved. Homomorphic encryption, the ability to meaningfully manipulate data while data remains encrypted, has emerged as a prominent solution. The performance degradation compared to non-private computation, however, limits its practicality. In this talk, we will discuss our ongoing efforts towards accelerating homomorphic encryption at the hardware level. We will present the first ASIC implementation of a partially homomorphic encrypted co-processor, as well as discuss the prototype of a fully homomorphic encryption accelerator. The talk will also introduce E3, our framework for compiling C++ programs to their homomorphically encrypted counterparts, as well as E3X, our architectural extensions for accelerating computation on encrypted data demonstrated on an OpenRISC architecture.
About the Speaker: Mihalis Maniatakos is an Associate Professor of Electrical and Computer Engineering at New York University Abu Dhabi, UAE, and a Global Network University Associate Professor at the NYU Tandon School of Engineering, USA. He is the Director of the MoMA Laboratory (nyuad.nyu.edu/momalab). He received his Ph.D. in Electrical Engineering, as well as M.Sc., M.Phil. degrees from Yale University, New Haven, CT, USA. He also received the B.Sc. and M.Sc. degrees in Computer Science and Embedded Systems, respectively, from the University of Piraeus, Greece. His research interests, funded by industrial partners, the US government, and the UAE government, include privacy-preserving computation, industrial control systems security, and machine learning security. Prof. Maniatakos has authored several publications in IEEE transactions and conferences, holds patents on privacy-preserving data processing and serves in the technical program committee for various international conferences. His cybersecurity work has also been extensively covered by Reuters and BBC.
Speaker: Nicole Fern, Tortuga Logic
Date: Nov 13
Abstract: Hardware is integral to system security, however hardware-focused attacks such as Meltdown and Spectre, Starbleed, and Rowhammer are on the rise. There are many challenges chip vendors face when trying to implement a security strategy on top of aggressive time-to-market schedules and increasing demands for better performance and more features. I will speak to these challenges and emerging solutions from the perspective of a security engineer working for Tortuga Logic, a hardware security startup, and as an academic whose PhD research focus was on pre-silicon security verification. This presentation will provide an overview of “what can go wrong” in hardware, and touch on topics such as security verification versus functional verification and implementing a secure development lifecycle for hardware by leveraging information flow tracking.
About the Speaker: Nicole Fern is a Senior Hardware Security Engineer at Tortuga Logic whose primary role is providing security expertise and defining future features and applications for the product line. Before joining Tortuga Logic she was a postdoc at UC Santa Barbara. Her research focused on the topics of hardware verification and security.
Speaker: Akshitha Sriraman, University of Michigan
Date: Dec 8
Abstract: Modern hyperscale web service systems introduce trade-offs between performance and numerous features essential for cost- and energy-efficient operation of data centers (e.g., high server utilization, continuous power management, and use of commodity hardware and software). In this talk, I will present two solutions to bridge the performance vs. cost and energy efficiency gap in hyperscale web services (1) a software system that auto-tunes threading models during system run-time to minimize web service tail latency (OSDI 2018) and (2) a system that exploits coarse-grained OS and hardware configuration knobs to tune cost-efficient commodity server processors, to better support their assigned service (ISCA 2019).
About the Speaker: Akshitha Sriraman is a PhD candidate in Computer Science and Engineering at the University of Michigan. Her dissertation research is on the topic of enabling hyperscale web services. Specifically, her work bridges computer architecture and software systems and demonstrates the importance of that bridge by realizing efficient web services from models on paper to deployment at hyperscale. Sriraman has influenced the design of server architectures both via hardware analysis of production data centers and her subsequent software designs that use data center hardware more efficiently. Sriraman has been recognized with a Facebook Fellowship (Distributed Systems), a Rackham Merit Ph.D. Fellowship, and was selected for the Rising Stars in EECS workshop. She hopes to enter academia after her PhD program, and will be on the academic job market (for tenure-track faculty positions) this upcoming cycle.