Event Archive

From Narrow Robots to General Robots

Duke Computer Science Colloquium
Speaker Name
Boyuan Chen
Location
LSRC D106
Date and Time
-

Despite the accelerating progress in robotics, robots today remain relatively narrow in their capabilities. To have robots that can work seamlessly with humans, I will advocate building “generalist robots” that are good at multiple tasks, in various complex environments. My research studies how to build generalist robots by learning to model the world.

Calling in the Nice White Ladies of the Tech World & Beyond

Identity & Computing Lecture Series
Speaker Name
Dr. Jessie Daniels
Location
Virtual, registration is required
Date and Time
-

Sponsored by The Alliance for Identity-Inclusive Computing Education (AiiCE), the 2022-2023 Identity & Computing Lecture Series begins September 20 with Dr. Jessie Daniels.

Graph limits and graph homomorphism density inequalities

Duke Computer Science Colloquium
Speaker Name
Fan Wei
Location
LSRC D106
Date and Time
-

Graph limits is a recently developed powerful theory in studying large (weighted) graphs from a continuous and analytical perspective. It is particular useful when studying subgraph homomorphism density, which is closely related to graph property testing, graph parameter estimation, and many central questions in extremal combinatorics. In this talk, we will show how the perspective of graph limits helps with graph homomorphism inequalities and how to make advances in a common theme in extremal combinatorics: when is the random construction close to optimal?

Demystifying (Deep) Reinforcement Learning with Optimism and Pessimism

Biostatistics and Bioinformatics Seminar Series
Speaker Name
Zhaoran Wang
Location
CRTP #214, Hock Plaza and Zoom https://bit.ly/3wYRJ5R
Date and Time
-

Coupled with powerful function approximators such as deep neural networks, reinforcement learning (RL) achieves tremendous empirical successes. However, its theoretical understandings lag behind. In particular, it remains unclear how to provably attain the optimal policy with a finite regret or sample complexity. In this talk, we will present the two sides of the same coin, which demonstrates an intriguing duality between optimism and pessimism.

Towards Programmable Genome, Proteome, and Cell Engineering

Duke Computer Science Colloquium
Speaker Name
Pranam Chatterjee
Location
LSRC D106
Date and Time
-

In this faculty seminar, we will explore the integration of state-of-the-art computational and experimental methodologies to generate programmable protein-based platforms for applications in genome, proteome, and cellular engineering.

Scalability in Low-power Wide Area Networks

Duke Computer Science/Electrical Computer Engineering Colloquium
Speaker Name
Bhuvana Krishnaswamy
Location
Hybrid: LSRC D106 or Zoom: register @ https://duke.zoom.us/meeting/register/tJMtceGtpz0vGdJBnz4rHeNYhzOW1-Lt_7fE
Date and Time
-

Wireless data delivery over long distance is power consuming and challenging for large-scale deployments. Low-power wide area networks (LPWAN) are increasingly in need to develop wireless solutions that satisfy the following requirements (1) Increased battery life, (2) Longer communication range, (3) Large-Scale, and (4) Low-cost. Existing strategies for addressing low-power and long-range do not efficiently address all of these in a large-scale network. In this talk, the fundamental challenges in meeting the above needs of LPWANs will be identified.

CS+ Undergraduate Summer Projects Poster Fair

Special Event
Location
Gross Hall, Duke University
Date and Time
-

Join us for an in-person poster fair to celebrate the results of over 100 talented Duke undergraduate students who participated in computer science, data science, and software development research projects this summer in CS+, Data+, Code+ and Climate+. Nearly 50 posters will be on display, and more than 2/3 of the participating students are studying CS. 

Lower Bounds for Shortcut Sets and Additive Spanners

Algorithms Seminar
Speaker Name
Nicole Wein
Location
The talk will be virtual on Zoom.
Date and Time
-

There are many graph problems of the following form: Given a graph G, construct a graph H that preserves some information about G, while optimizing some property of H. Some examples include spanners, distance preservers, reachability preservers, shortcut sets, and hopsets. I will focus on two of these:

- A spanner is a subgraph H of G that approximately preserves distances while being as sparse as possible.

Improving and extending "One of the only really fundamental data structures that came out in the last 25 years"

Miscellaneous Talk
Speaker Name
Gianfranco Ciardo
Location
Wilkinson Auditorium 021 AND Zoom
Date and Time
-

In his 2008 lecture "Fun With Binary Decision Diagrams", Donald Knuth called BDDs "one of the only really fundamental data structures that came out in the last 25 years". It is indeed impossible to overstate the impact of BDDs on the verification of industrial hardware circuits and software protocols. A BDD is a graph-based data structure to encode a boolean function of boolean variables. In the first half of this talk, we introduce a new variant, RexBDDs, that provably improves the efficiency of BDDs and can seamlessly replace ordinary BDDs in practical implementations.

Design Justice: Community-Led Practices to Build the Worlds We Need

Identity & Computing Lecture Series
Speaker Name
Dr. Sasha Constanza-Chock
Location
Virtual, registration is required
Date and Time
-

This fifth lecture in the Identity & Computing Lecture Series: Understanding Racism and Bias in Computing welcomes Dr. Sasha Constanza-Chock, who works to support community-led processes that build shared power, dismantle the matrix of domination, and advance ecological survival.

Bridging the Gap Between Deep Learning and Probabilistic Modeling

Duke Computer Science Colloquium
Speaker Name
Geoff Pleiss
Location
The talk will be virtual on Zoom.
Date and Time
-

Deep learning excels with large-scale unstructured data - common across many modern application domains - while probabilistic modeling offers the ability to encode prior knowledge and quantify uncertainty - necessary for safety-critical applications and downstream decision-making tasks. I will discuss examples from my research that bridge the gap between these two learning paradigms. The first half will show that insights from deep learning can improve the practicality of probabilistic models.

NetHint: White-Box Networking for Multi-Tenant Data Centers

Systems and Networking Seminar
Speaker Name
Jingrong Chen
Location
The talk will be virtual on Zoom.
Date and Time
-

A cloud provider today provides its network resources to its tenants as a black box, such that cloud tenants have little knowledge of the underlying network characteristics. Meanwhile, data-intensive applications have increasingly migrated to the cloud, and these applications have both the ability and the incentive to adapt their data transfer schedules based on the cloud network characteristics. We find that the black-box networking abstraction and the adaptiveness of data-intensive applications together create a mismatch, leading to sub-optimal application performance.

Towards a Foundation for Reinforcement Learning

Duke Computer Science Colloquium
Speaker Name
Ruosong Wang
Location
The talk will be virtual on Zoom.
Date and Time
-

In recent years, reinforcement learning algorithms have achieved strong empirical success on a wide variety of real-world problems. However, these algorithms usually require a huge number of samples even just for solving simple tasks. It is unclear if there are fundamental statistical limits on such methods, or such sample complexity burden can be alleviated by a better algorithm. In this talk, I will give an overview of my research efforts towards bridging the gap between the theory and the practice of reinforcement learning.

Racism and Bias in Computing

Identity & Computing Lecture Series
Speaker Name
Dr. Ebony McGee
Location
Virtual, registration is required
Date and Time
-

This fourth lecture in the Identity & Computing Lecture Series: Understanding Racism and Bias in Computing welcomes Dr. Ebony McGee, who  investigates what it means to be racially marginalized while minoritized in the context of learning and achieving in STEM higher education and in the STEM professions.

Register HERE

How to handle Biased Data and Multiple Agents in Machine Learning?

Duke Computer Science Colloquium
Speaker Name
Manolis Zampetakis
Location
The talk will be virtual on Zoom.
Date and Time
-

Modern machine learning (ML) methods commonly postulate strong assumptions such as: (1) access to data that adequately captures the application environment, (2) the goal is to optimize the objective function of a single agent, assuming that the application environment is isolated and is not affected by the outcome chosen by the ML system. In this talk I will present methods with theoretical guarantees that are applicable in the absence of (1) and (2) as well as corresponding fundamental lower bounds.

Empowering People to Have Secure and Private Interactions with Digital Technologies

Duke Computer Science Colloquium
Speaker Name
Pardis Emami-Naeini
Location
The talk will be virtual on Zoom.
Date and Time
-

Advanced digital technologies rely on collecting and processing various types of sensitive data from their users. These data practices could expose users to a wide array of security and privacy risks. My research at the intersection of security, privacy, and human-computer interaction aims to help all people have safer interactions with digital technologies. In this talk, I will share quantitative and qualitative results on people’s security and privacy preferences and attitudes toward technologies such as smart devices and remote communication tools.

Characterizing Physical-Layer Transmission Errors in Cable Broadband Networks

Systems and Networking Seminar
Speaker Name
Jiyao Hu
Location
The talk will be virtual on Zoom.
Date and Time
-

Packet loss rate in a broadband network is an important quality of service metric. Previous work that characterizes broadband performance does not separate packet loss caused by physical layer transmission errors from that caused by congestion. In this work, we investigate the physical layer transmission errors using data provided by a regional cable ISP. The data were collected from 77K+ devices that spread across 394 hybrid-fiber-coaxial (HFC) network segments during a 16-month period. We present a number of findings that are relevant to network operations and network research.

Information leakage in ML deployments: How, when, and why?

Duke Computer Science Colloquium
Speaker Name
Varun Chandrasekaran
Location
The talk will be virtual on Zoom.
Date and Time
-

Machine learning (ML) is widely used today, ranging from applications in medicine to those in autonomous driving. Across all these applications, various forms of sensitive information is shared with the ML model, such as private medical records, or a user’s location. In this talk, I will explain what forms of private information can be learnt through interacting with the ML model. In particular, I will discuss when ML model parameters in cloud deployments are not confidential, and how this can be remediated.