Atanasoff Today: Faculty accolades

SIMANTA MITRA was named as the co-director of the North Central North America region of the International Collegiate Programming Contest (ICPC). In this new role, he will help guide the direction of the ICPC, putting his years of experience with the organization to work.

ROBYN LUTZ won a Lifetime Service Award at the International Requirements Engineering Conference. Robyn has a long history with the RE Conference and was presented the award to thank her for her many contributions and distinguished service to the RE community.

HONGYANG GAO received a National Science Foundation (NSF) grant in the amount of $175,000 for his work with graph neural networks. Gao said the award will give him the necessary resources to conduct his research.

WEI LE was awarded a grant for rapid adjudication of static analysis alert from Carnegie Mellon University. The project aims to develop algorithms and tools for matching static warnings and vulnerabilities across software version to support rapid CI assurance.

HRIDESH RAJAN served as the General Chair for the ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity. Rajan was also selected as a Fellow of the American Association for the Advancement of Science.

ADISAK SUKUL was selected as a Google Cloud Faculty Expert for the second year in row.The title rewards Google Cloud’s top faculty advocates with professional development, recognition, networking opportunities, and access to Google developers for helping other educators explore cloud computing.

JIN TIAN joined the Simons Institute Program at the University of California, Berkeley. The program aims to integrate advances and techniques from theoretical computer science methods for casual inference and discovery.

DAVID FERNANDEZ-BACA received the LAS Dale D. Grosvenor Chair award. David’s research career spans thirty years and his work has been published in dozens of peer-reviewed journals.

QI LI received a National Science Foundation (NSF) grant for developing algorithms, systems and theories for exploiting data dependencies in crowdsourcing. The team of researchers discovers and exploit the dependencies in the data, via novel methodologies, to significantly reduce the cost and noises when providing critical knowledge for machine learning.