CS department welcomes three new faculty members
CS department welcomes three new faculty members Heading link
UIC computer science welcomes three new faculty members: Clinical Assistant Professor Adam Koehler, Assistant Professor Peihan Miao, and Assistant Professor Fabio Miranda.
Adam Koehler most recently was a teaching assistant in the department of computer science and engineering at the University of California Riverside. He spent nearly a decade there, also serving as a research assistant and associate instructor while earning his doctorate in computer science. He received his BS and MS in computer science from Marquette University. Koehler’s focus is computer science education and includes work on automated tools and techniques for improving its delivery. He grew up outside Chicago and is happy to return to the area. Koehler will teach all sections of CS 109, Programming for Engineers with MatLab.
Piehan Miao worked as a research scientist in the cryptography group of Visa Research before joining UIC. She received her PhD from University of California, Berkeley, and her BS degree from ACM Honors Class at Shanghai Jiao Tong University, China. Her research interests are in cryptography and security, including secure computation, applied cryptography, secure authentication, and blockchain. Miao develops cryptographic tools to build practical systems with provable security and privacy guarantees, aiming to bridge the gap between theory and practice. She is a member of the Theory Group at UIC and will teach CS 494, Special Topics in Computer Science.
Fabio Miranda will join the department in October. Most recently, he was a postdoctoral research associate at the Visualization and Data Analytics Center and the Center for Urban Science and Progress at New York University, where he earned his PhD. During his doctoral studies, he completed internships at Argonne National Laboratory, IBM Research, AT&T Labs Research, and Sandia National Laboratories. Miranda’s research focuses on proposing new methods and systems that allow for the interactive visual analysis of large data of different types, such as time-series, spatio-temporal, geometry, and image data. By combining visualization, machine learning, data management, and computer graphics, his work tackles fundamental challenges in data science, enabling effective analysis of large data to untangle real-world problems.