CMMRS will include lectures from ca. 7 professors from the University of Maryland, Cornell University, and the Max Planck Institutes in Computer Science. The full list of lectures and topics is yet to be finalized. Lectures will cover a diverse range of topics.
Tapomayukh Bhattacharjee, Cornell University
(Title and abstract coming soon)
Jordan Boyd-Graber, University of Maryland
The questions that computers still cannot answer: how to find them, why they’re fun, and what they mean for AI
You’ve probably heard of ChatGPT and other large language models. They can do some neat stuff! But they still have some weaknesses. After briefly reviewing how transformer language models work, we will go into the weaknesses of models in 2023: recognizing unconventional language, numerical and logical reasoning, multihop questions, cross-cultural understanding, and incorporating new information. Next, we’ll describe how to elicit questions that challenge today’s computer models using adversarial human-in-the-loop authoring and how computers and humans compare at their ability to answer those questions.
Laxman Dhulipala, University of Maryland
Building Scalable and Practical Batch-Dynamic Graph Algorithms
In this talk, I will give an overview of recent work on parallel dynamic graph algorithms and the closely related graph-streaming setting where a continuous stream of graph updates is mixed with graph queries. The study of these settings are motivated by the need to answer time-sensitive queries over rapidly changing data, e.g., detecting fraud in financial networks, identifying emerging trends, or providing real-time recommendations. To cope with the rapid rate of change in these settings, parallel algorithms that can process batches of updates in parallel are necessary. I will present our work on (1) parallel batch-dynamic data structures for representing dynamic graphs, (2) ongoing work on parallelizing several important dynamic graph problems, and (3) end by describing open questions and directions for future work in this area.
Moritz Hardt, Max Planck Institute for Intelligent Systems (MPI-IS)
(Title and abstract coming soon)
Owolabi Legunsen, Cornell University
(Title and abstract coming soon)
Murphy Yuezhen Niu, University of Maryland
(Title and abstract coming soon)
Yiting Xia, Max Planck Institute for Informatics (MPI-INF)
Towards High-Performance and Power-Efficient Network Infrastructure for Cloud Computing
The data center network is a key infrastructure for cloud computing. Emerging cloud applications such as machine learning, big data, and Internet of Things are driving the demand for highly available and highly performing data center networks, which brings cost and carbon footprint into play. Optical data center networks show promise to serve as the next-generation cloud infrastructure with their performance and power benefits. In this talk, the speaker will introduce optical data center networks, summarize the challenges for practical deployment, and propose a solution towards full adoption of the technology.
Yixin Zou, Max Planck Institute for Security and Privacy (MPI-SP)
(Title and abstract coming soon)