Venky Veeraraghavan

Rice CS Alumnus Venky Veeraraghavan leads the Microsoft Cloud-AI team.

Venky Veeraraghavan (B.A. ’95) leads the product management team for the Azure Machine Learning platform. The platform provides end-2-end APIs, tools and experiences for the Building & Training of models to Deployment, Management and Inferencing of those models in the Cloud and Edge. In addition, his team drives Open and Interoperable AI through the, andML.Net community projects.

Mitchell Koch

Rice CS Alumnus Mitchell Koch is a Manager and Sr. Data Scientist at DoorDash.

Mitchell Koch (BS ’11) is a Manager and Senior Data Scientist for Machine Learning at DoorDash. He was part of the initial team focusing on the core logistics problem including time prediction and vehicle routing and now leads machine learning initiatives including for personalized recommendations and marketing optimization.

He worked on Devika Subramanian’s research team the entire time he was at Rice, focusing on the application of Bayesian network learning methods to the T-cell signaling network and to differential equations models for validation. He wrote Bayesian network learning software using dynamic programming methods, and applied texture analysis features and supervised learning to the problem of identifying four types of mucosal patterns in Barrett’s Esophagus.

His research interest shifted at the University of Washington, where he completed his  M.S. in 2014. There, he focused on problems in natural language processing and information extraction.

Janell Straach

Janell Straach teaches machine learning and cyber security courses in Rice's online master of CS program.

Dr. Janell Straach has a diverse background including academic and industry experience. She holds a Bachelor of Science in CS degree from Angelo State University, a Master of Computer Science degree from Texas A&M University, an MBA from The University of Dallas and a Masters and PhD from UT Dallas.  Her PhD research was in intelligent systems and recent work has focused on CyberSecurity.  Prior to her current assignment with Rice University, she was a member of the faculty at UT Dallas in Richardson, TX.  Before her academic assignments, she worked in industry for IBM and a large electric/gas utility organization. She has taught at the college, university and corporate level.  Janell’s passion is recruiting and retaining females into technology careers.

Anshumali Shrivastava

Anshumali Shrivastava (Ph.D. ’15, Cornell) is an assistant professor of computer science, electrical and computer engineering and statistics at Rice University. He specializes in creating clever algorithmic strategies that enable faster, more scalable computations for both big data and machine-learning applications. Recently, Shrivastava was recognized as one of Science News Top 10, a prestigious annual list of top young scientists who are on their way to widespread acclaim for tackling the big questions facing science and society.

Shrivastava, who joined Rice in 2015, has repeatedly shown that creative approaches for handling big data can pay huge dividends in terms of time, energy and computational effort. In an analysis of six online social networks presented last month, he and Rice graduate student Chen Luo applied a 20-year-old internet search technique to show that chances of forming online friendships depend mainly on the number rather than the types of groups people join.



Amarda Shehu

Amarda Shehu, Rice CS Alumna and Associate Professor at George Mason University.

Amarda Shehu (Ph.D. ’08) is a Professor in the Department of Computer Science at George Mason University with affiliated appointments in the School of Systems Biology and the Department of Bioengineering. At Rice, she was an NIH fellow of the Nanobiology Training Program of the Gulf Coast Consortia.

My laboratory focuses on developing novel algorithms to bridge between computer science, engineering, and the life sciences. Our lab’s research emphasis is on problem solving, search, optimization, planning, and machine learning to simulate, analyze, and characterize complex dynamic systems operating in the presence of constraints. Our application domains are diverse, spanning from computational design, network science, bioinformatics and computational biology, civil engineering, and robotics.

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