Upcoming Events

Programming Statistical Machine Learning with High-Level Knowledge

Duke Computer Science Colloquium
Speaker Name
Stephen Bach
Location
LSRC D106
Date and Time
-

Machine learning is fundamentally changing how software is developed. Rather than program behavior directly, many developers now curate training data and engineer features, but the process is slow, laborious, and expensive. In this talk I will describe two multi-year projects to study how high-level knowledge can be programmed more directly into statistical machine learning models. The resulting prototypes are used in dozens of major technology companies and research labs, and in collaboration with government agencies like the U.S.

Life on the Edge: Connecting Everyday Objects with Energy Harvesting and Fog Computing

CS/ECE Lecture Series
Speaker Name
Maria Gorlatova
Location
LSRC D106
Date and Time
-

Realizing the vision of the fully connected world — the Internet of Things (IoT) — requires advances in multiple areas. Energy harvesting and fog/edge computing can bring everyday objects to life in complementary ways: by using the environment to make the IoT nodes smaller and lighter, and by bringing advanced computing capabilities closer to the nodes to make them more adaptive and intelligent.

From Obliviousness to Privacy-Preserving Computation

Duke Computer Science Colloquium
Speaker Name
Kartik Nayak
Location
LSRC D106
Date and Time
-

Protecting sensitive user data and proprietary programs are fundamental and important challenges. For instance, when users outsource their private data to the cloud, they risk leakage of the data in the event of a data breach; encrypting their data is not a workable solution since it impedes the cloud provider’s ability to offer user-specific services. When companies execute proprietary programs on third-party cloud providers, they similarly face the risk of leaking trade secrets.

Data Management on Non-Volatile Memory

Duke Computer Science Colloquium
Speaker Name
Joy Arulraj
Location
LSRC D106
Date and Time
-

We are at an exciting point in the evolution of memory technology. Device manufacturers have created a new non-volatile memory (NVM) technology that can serve as both system memory and storage. NVM supports fast reads and writes similar to volatile memory, but all writes to it are persistent like a solid-state disk. The advent of NVM invalidates decades of design decisions that are deeply embedded in today's database management systems (DBMSs).

Efficient Recording and Analysis of Software Systems

Duke Computer Science Colloquium
Speaker Name
David Devecsery
Location
LSRC D106
Date and Time
-

Failures in medical devices, banking software, and transportation systems have led to both significant fiscal costs and even loss of life.  Researchers have developed sophisticated methods to monitor and understand many of the complex system misbehaviors behind these bugs, but their computational costs (often an order of magnitude or more) prohibit their use in production, leading to an ecosystem of critical software with little guaranteed protection, and no method of reconciling misbehaviors.

Machine learning by the people, for the people

Duke Computer Science Colloquium
Speaker Name
Nika Haghtalab
Location
LSRC D106
Date and Time
-

Typical analysis of learning algorithms considers their outcome in isolation from the effects that they may have on the process that generates the data or the entity that is interested in learning. However, current technological trends mean that people and organizations increasingly interact with learning systems, making it necessary to consider these effects, which fundamentally change the nature of learning and the challenges involved.

Scalable Learning Over Distributions

Duke Computer Science Colloquium
Speaker Name
Junier Oliva
Location
Teer 203
Date and Time
-

A great deal of attention has been applied to studying new and better ways to perform learning tasks involving static finite vectors. Indeed, over the past century the fields of statistics and machine learning have amassed a vast understanding of various learning tasks like clustering, classification, and regression using simple real valued vectors. However, we do not live in a world of simple objects.

Towards Ambient Intelligence in AI-Assisted Hospitals

Duke Computer Science Colloquium
Speaker Name
Serena Yeung
Location
Fitzpatrick Schiciano B
Date and Time
-

Artificial intelligence has begun to impact healthcare in areas including electronic health records, medical images, and genomics. But one aspect of healthcare that has been largely left behind thus far is the physical environments in which healthcare delivery takes place: hospitals and assisted living facilities, among others. In this talk I will discuss my work on endowing hospitals with ambient intelligence, using computer vision-based human activity understanding in the hospital environment to assist clinicians with complex care.

Internet of Acoustic Things (IoAT): Challenges, Opportunities, and Threats

CS/ECE Lecture Series
Speaker Name
Nirupam Roy
Location
Teer 106
Date and Time
-

The recent proliferation of acoustic devices, ranging from voice assistants to wearable health monitors, is leading to a sensing ecosystem around us – referred to as the Internet of Acoustic Things or IoAT. My research focuses on developing hardware-software building blocks that enable new capabilities for this emerging future. In this talk, I will sample some of my projects. For instance, (1) I will demonstrate carefully designed sounds that are completely inaudible to humans but recordable by all microphones.

A Multifaceted Strategy to Fight Cybercrime

Duke Computer Science Colloquium
Speaker Name
Birhanu Eshete
Location
LSRC D106
Date and Time
-

The increasingly interconnected cyber-ecosystem invites cybercriminals to advance their ill-intentioned missions by launching cyber-attacks. From high-profile data breaches with impact on billions of users to hacks into political organizations that undermine the pillars of modern democracies, from infiltration of mission-critical infrastructures to banking trojans and ransomware campaigns, cybercrime continues to find its way to our sensitive data, finances, and digital identity.

Cryptographic primitives for hardware security

Duke Computer Science Colloquium
Speaker Name
Ling Ren
Location
LSRC D106
Date and Time
-

Hardware plays a critical role in today's security landscape. Every protocol with security or privacy guarantees inevitably includes some hardware in its trusted computing base. The increasing number of vulnerability disclosures calls for a more rigorous approach to secure hardware designs. In this talk, I will present several cryptographic primitives to enhance the security of hardware.

Machine Learning for Estimating Robust Control Laws

Duke Computer Science Colloquium
Speaker Name
Aude Billard
Location
Fitzpatrick Schiciano Side B 1466
Date and Time
-

This talk will provide an overview of techniques developed in my group to enable robots to react rapidly in the face of changes in the environment when manipulating objects. Learning is guided by observing humans’ elaborate manipulatory skills. I will stress how important it is to model the various ways with which humans perform the same task. This multiplicity of solutions is the key to generate robust and flexible robotic controllers capable of adapting their strategies in the face of unexpected changes in the environment.

Privacy despite mass surveillance

Duke Computer Science Colloquium
Speaker Name
Sebastian Angel
Location
LSRC D106
Date and Time
-

In the past decade there has been a significant increase in the collection of personal information and communication metadata (with whom users communicate, when, how often) by governments, Internet providers, companies, and universities. While there are many ongoing efforts to secure users' communications, namely end-to-end encryption messaging apps and E-mail services, safeguarding metadata remains elusive.

Visual Reconstruction and Image-Based Rendering

Triangle Computer Science Distinguished Lecturer Series
Speaker Name
Richard Szeliski
Location
D106 LSRC, Duke
Date and Time
-

The reconstruction of 3D scenes and their appearance from imagery is one of the longest-standing problems in computer vision. Originally developed to support robotics and artificial intelligence applications, it has found some of its most widespread use in support of interactive 3D scene visualization. One of the keys to this success has been the melding of 3D geometric and photometric reconstruction with a heavy re-use of the original imagery, which produces more realistic rendering than a pure 3D model-driven approach.