Meet the Team

Sia Najafi is Executive Director of Academic and Research Computing group. Sia provides vision and leadership in developing strategic plans and policies to guide and manage research computing infrastructure and support at WPI. Sia and his team support and manage research data and high-performance computer clusters to accommodate the expanding WPI research mission and meet federal guidelines for research data management.Sia is a WPI alumnus with his BS and MS degrees in mechanical engineering.

Adriana Hera is a senior scientific applications scientist in the Academic and Research Computing Support Group. She is in charge of developing training programs on scientific/engineering software applications to support the core academic undergraduate and graduate curriculum and research activity. She currently conducts training sessions on Matlab, Mathcad, Ansys, Labview, Comsol, and CES EduPack. Her research interests include structural health monitoring, structural vibrations, system identification, stochastic dynamic systems, and wavelets. She holds a PhD in Mechanical Engineering from ...

A WPI alumnus with her BS, MS, and PhD in mechanical engineering, Erica Stults is an academic computing application scientist in the Academic and Research Computing Support Group. To support the core academic undergraduate and graduate curriculum and research activity she develops training programs on scientific/engineering software applications. She currently conducts training sessions on MatLab and Rapid Prototyping (including 3D Printing), as well as providing consultation for CAD and Rapid Prototyping. Her research interests include computer aided design, rapid prototyping, renewable ...

Dr. Ermal Toto is the Director for Scientific Data, Applications and Web Development with Academic and Research Computing. He is a WPI alumnus with a PhD in Computer Science. Prior to the current position Ermal has worked as a Data Scientist, Software Engineer, and Network Administrator. His research interests include emotion and mood detection using machine learning, AI and data mining. A list of publications can be found at https://scholar.google.com/citations?user=opJQ2k0AAAAJ&hl=en .