Dr. Carlos R.P. Hartmann, 75, of Syracuse, died at University Hospital in Syracuse, NY on April 21, 2015. He is survived by two daughters, Silvia Hartmann and Cristina Hartmann. Carlos, a Professor at Syracuse University, was a true scholar, enthusiastic teacher, and a dedicated administrator.

Carlos received his bachelor’s and master’s degrees from the Instituto Technologico de Aeronautica in Sao Paulo, Brazil, and a Ph.D. from the University of Illinois at Urbana-Champaign under Dr. Robert T. Chien. He joined the faculty of Syracuse University in 1970, where he remained until his death. He became the Director of the former School of Computer and Information Science (CIS) in 1992, and oversaw the merger of the School of CIS with the former Department of Electrical and Computer Engineering in 1996. He then served as department chair of the newly formed Department of Electrical Engineering and Computer Science until 2011. He was a Fellow of the IEEE and served as Associate Editor of the IEEE Transactions on Information Theory.

Carlos was known for his innovative research in information and coding theory. In 1972, he and Kenneth K. Tzeng discovered a generalization of the BCH bound that came to be called the Hartmann-Tzeng bound. In 1975, he and Luther D. Rudolph proposed a new optimal symbol-by-symbol decoding algorithm for linear block codes that remains to this day one of the best symbol-by-symbol decoding algorithms. In 1982, he and Pramod K. Varshney published an information theoretic approach for the design of decision trees that had a great impact on pattern recognition applications. In 1984, Carlos and Lev B. Levitin presented a new minimum distance decoding algorithm for linear block codes, thus addressing a very difficult problem. The now-famous algorithm is known as the zero-neighbors algorithm. In 1993, he and his Ph. D. student, Yunghsiang S. Han, developed a sequential-type algorithm based on Algorithm A* from artificial intelligence. At the time, this algorithm drew a lot of attention since it was the most efficient maximum-likelihood decoding algorithm for binary linear block codes.