Christoph Studer received his M.Sc. and Ph.D. degrees in Information Technology and Electrical Engineering from ETH Zurich in 2005 and 2009, respectively. In 2005, he was a Visiting Researcher with the Smart Antennas Research Group at Stanford University. From 2006 to 2009, he was a Research Assistant in both the Integrated Systems Laboratory and the Communication Technology Laboratory at ETH Zurich. From 2009 to 2012, Dr. Studer was a Postdoctoral Researcher at CTL, ETH Zurich, and the Digital Signal Processing Group at Rice University. In 2013, he has held the position of Research Scientist at Rice University. Since 2014, Dr. Studer is an Assistant Professor at Cornell University and an adjunct Assistant Professor at Rice University, TX.
Dr. Studer’s research spans digital signal processing (DSP) and the design of digital very-large-scale integration (VLSI) circuits. The current research focus is on the design of low-precision multi-antenna wireless systems, analog-to-feature (A2F) conversion for low-power signal classification and inference, nonlinear signal processing, as well as manifold learning in the realm of wireless systems.
Dr. Studer received ETH Medals for his M.S. and Ph.D. theses in 2006 and 2009, respectively. He received a two-year Swiss National Science Foundation fellowship for Advanced Researchers in 2011 and a US National Science Foundation CAREER Award in 2017. Dr. Studer won a Michael Tien '72 Excellence in Teaching Award from the College of Engineering, Cornell University, in 2016. He shared the Swisscom/ICTnet Innovations Award in both 2010 and 2013. Dr. Studer was the winner of the Student Paper Contest of the 2007 Asilomar Conf. on Signals, Systems, and Computers, received a Best Student Paper Award of the 2008 IEEE Int. Symp. on Circuits and Systems (ISCAS), and shared the best Live Demonstration Award at the IEEE ISCAS in 2013. His paper with Ph.D. student Igor Labutov on “Calibrated Self-Assessment” received the Best Student Paper award at the 2016 International Conference on Educational Data Mining (EDM). The paper “Towards a Deeper Understanding of Training Quantized Neural Networks” with H. Li, S. De, Z. Xu, H. Samet, and T. Goldstein from University of Maryland received the Google Best Student Paper Award at the ICML Workshop on Principled Approaches to Deep Learning.