2019 Summer Workshop Program

August 7-August 9: Automated Algorithm Design

Organizers:

• Nina Balcan (Carnegie Mellon University)
• Bistra Dilkina (University of Southern California)
• Carl Kingsford (Carnegie Mellon University)
• Paul Medvedev (Penn State University)

This workshop focuses on new machine learning techniques for automatically designing algorithms. Algorithms are central to modern computing, and they have lots of applications in our life. Yet, writing correct, efficient algorithms is a time-consuming and difficult task. It also often requires intuition and expertise to tailor algorithmic choices to specific instances that arise in particular applications. However, there have been a number of recent advancements that have allowed algorithms to be selected or designed from specific algorithmic families automatically, often leading to either state-of-the-art empirical performance or provable performance guarantees on observed instance distributions. In this workshop, we take a broad view of the problem and seek to bring together researchers with different viewpoints and approaches to the general challenge

Registration Form

August 12-August 14: Learning-Based Algorithms

Organizers:

• Piotr Indyk (MIT)
• Yaron Singer (Harvard)
• Ali Vakilian (MIT)
• Sergei Vassilvitskii (Google Research, NYC)

This workshop will cover recent developments in using machine learning to improve the performance of “classical” algorithms, by adapting their behavior to the properties of the input distribution. This reduces their running time, space usage or improves their accuracy, while (often) retaining worst case guarantees.

The workshop will cover general approaches to designing such algorithms, as well as specific case studies. We plan to cover learning-augmented methods for designing data structures, streaming and sketching algorithms, on-line algorithms, compressive sensing and recovery, error-correcting codes, scheduling algorithms, and combinatorial optimization. The attendees span a diverse set of areas, including theoretical computer science, machine learning, algorithmic game theory, coding theory, databases and systems.

Registration Form

September 12 - September 13: Midwest Computational Biology Workshop

Organizers:

• Michael Yu (TTIC)
• Aly Khan (TTIC)
• Jinbo Xu (TTIC)

The 2019 Midwest Computational Biology Workshop (September 12-13 @ TTIC) will explore emerging topics in the field of computational biology, covering a spectrum of algorithmic and machine learning challenges to address biological questions. The workshop will bring together a wide range of participants from different backgrounds (computer science, biology, medicine) and positions (undergrads, grad students, faculty, industry professionals). By connecting these researchers, the workshop aims to initiate new interdisciplinary interactions and collaborations. The workshop will be organized around five sessions: protein structure, cancer genomics, immunology, brain connectomics, and microbiome. Each session will include 4 invited talks about current research and open problems, as well as a discussion period to brainstorm collaborative solutions.

Registration Form

September 18 - September 20: Recent Trends in Clustering: Theory and Practice

Organizers:

• Vahab Mirrokni (Google Research, NYC)
• Suresh Venkatasubramanian (University of Utah)
• Grigory Yaroslavtsev (Indiana University, Bloomington)

Clustering and classification play a central role in machine learning systems for processing images, text, high-dimensional data, graph-based learning and various other use cases of unsupervised and supervised learning. Despite its long history, clustering and classification are still active areas of research, and in the past decade, a variety of new techniques and new models have been applied to these applications. This workshop will give an overview of new angles on this topic including scalable algorithms for high-dimensional and graph-based data, novel models and objectives (including hierarchical and overlapping), applications in semi-supervised and unsupervised learning, beyond worst-case analysis and robustness, fairness and privacy, deep learning-augmented techniques, etc.

• Schedule

• Registration Form

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