Microsoft Azure Credit Awardees

Congratulations to the following faculty who received Microsoft Azure Cloud Computing grants:


Tanzeem Choudhury
Ann S. Bowers College of Computing and Information Science, Professor of Information Science

Developing machine learning modeling methods to create mental health digital biomarkers that are reliable across populations, time, and data types. 



Will Cong
SC Johnson College of Business, Associate Professor of Finance and the Rudd Family Professor of Management 

Developing AI models to help with high dimensional managerial decisions and learning large corporations’ management objectives. 


Greeshma Gadikota 
College of Engineering, Croll Sesquicentennial Fellow and Assistant Professor of Civil and Environmental Engineering 

The aim of the project is to develop molecular-scale predictive controls on engineered accelerated weathering of silicate minerals for capturing CO2 from air. This project is motivated by the need to develop negative emissions technologies in response to a rapidly changing climate. Molecular scale insights into the solvation behavior of CO2 and metals in silicate minerals inform the design of scalable engineered processes for carbon removal. 


Kevin Tang
College of Engineering, Professor of Electrical and Computer Engineering 

Determining the right data to pass around in Edge Cloud assisted IoT (Internet of Things) applications with the goal of automatically generating task-specific inexpensive representations of environment sensory data that can enhance the performance of downstream controllers. 


Co PI’s:    Robert Van Renesse,  Lorenzo Alvisi


Robbert Van Renesse
Ann S. Bowers College of Computing and Information Science, Professor of Computer Science  


Lorenzo Alvisi
Ann S. Bowers College of Computing and Information Science, Tisch University Professor of Computer Science 

Ziplog is a new approach to building fault tolerant totally ordered logs.  Using RDMA and sharding, Ziplog achieves essentially unlimited throughput at tail latencies below 100 microseconds.


Lars Vilhuber
The College of Arts and Sciences, Department of Economics, Director of Labor Dynamics Institute and Data Editor, American Economic Association 

Developing a cloud-based human-mediated scalable workflow to streamline the computational reproducibility checks of 100s of social science articles when data cannot be machine-acquired.

Matthew Wilkens
Ann S. Bowers College of Computing and Information Science, Associate Professor of Information Science 

Transfer Learning for Textual Geography. This project uses large neural language models to extract geographic references from hundreds of thousands of novels in support of library-scale literary historical analyses. 


Qian Yang
Ann S. Bowers College of Computing and Information Science, Assistant Professor of Information Science 

Developing novel methods and tools for interactive Natural Language Generation (NLG) application design.


Fengqi You
College of Engineering, Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering 

We will use the granted resources to develop a novel framework that uses quantum computing-based techniques for molecular property estimation and computer-aided molecular design (CAMD) for agrochemical and/or pharmaceutical applications 



Zhiru Zang
College of Engineering, Associate Professor of Electrical and Computer Engineering 

This project investigates efficient machine learning techniques to improve the accuracy of genotype imputation, a key technique for genome-wide association studies, which has recently been used to identify potential risk loci contributing to the COVID-19 mortality. 

Madeleine Udell
College of Engineering, Assistant Professor of Operations Research and Information Engineering


Co-PI’s:  Julio Giordano,  Ken Birman

Julio Giordano
College of Agriculture and Life Sciences, Professor of Animal Science  

Ken Birman
Ann S. Bowers College of Computing and Information Science, N. Rama Rao Professor of Computer Science