The Michigan Engineer News Center

Prof. Danai Koutra recognized as rising star with ACM SIGKDD Award

The Rising Star Award is based on an individual’s whole body of work in the first five years after the PhD.| Short Read
EnlargeProf. Danai Koutra
IMAGE:  Prof. Danai Koutra

Prof. Danai Koutra has been selected to receive the Rising Star Award from the ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). This award is based on an individual’s whole body of work in the first five years after the PhD. The award aims to promote current SIGKDD researchers as they create their career.

Prof. Koutra received her PhD in August 2015 from Carnegie Mellon University and joined the faculty at Michigan in September 2015. Her research interests are in data science, with focus on large-scale graph mining and summarization, network representation learning, and network neuroscience.

In her five years at Michigan, Koutra’s research has been focused on the development of principled, interpretable, and scalable methods for discovering and summarizing the “unknown unknowns” in the world’s data by leveraging the inherent connections within them and modeling them as graphs or networks. Her Graph Exploration and Mining at Scale (GEMS) Lab has made significant contributions to large-scale network summarization and multi-network analysis that enable scaling downstream tasks, improved decision making, and novel scientific discoveries.

Recently, Koutra earned an Army Young Investigator Award and an NSF CAREER Award in support of two projects that innovate the way we use networks to understand the world and speed up our technology. Her other ongoing projects include understanding how the functional connectivity in brain networks may change over time and its role in precision health in clinical psychiatry, devising embedding approaches for collective and single-network mining to efficiently support prediction and classification tasks in machine learning applications, enabling faster and more personalized web searching and digital assistants, and developing methods for network-level summaries that enable pattern discovery, anomaly detection, and storage savings.

Her dissertation thesis, entitled, “Exploring and Making Sense of Large Graphs,” was selected for the ACM SIGKDD Dissertation Award and received  an honorable mention for the SCS Doctoral Dissertation Award (CMU). In 2020, she was named a Morris Wellman Faculty Development Professor in the EECS department.

Koutra’s work has been funded by awards from Google, Amazon, Facebook, Adobe, and other industrial partners, as well as a recent significant award from the City of Detroit and the Knight Foundation. At Michigan, she has received funding from the Michigan Institute for Data Science (MIDAS) and from Precision Health. She has received a WSDM 2019 Outstanding PC Award and a WSDM 2020 Outstanding Senior PC Award, and several best paper awards and nominations.

Prof. Danai Koutra
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The electrons absorb laser light and set up “momentum combs” (the hills) spanning the energy valleys within the material (the red line). When the electrons have an energy allowed by the quantum mechanical structure of the material—and also touch the edge of the valley—they emit light. This is why some teeth of the combs are bright and some are dark. By measuring the emitted light and precisely locating its source, the research mapped out the energy valleys in a 2D crystal of tungsten diselenide. Credit: Markus Borsch, Quantum Science Theory Lab, University of Michigan.

Mapping quantum structures with light to unlock their capabilities

Rather than installing new “2D” semiconductors in devices to see what they can do, this new method puts them through their paces with lasers and light detectors. | Medium Read