Alexander Heinecke is an Intel Fellow at Intel’s Parallel Computing Lab. His core research field is hardware/software co-design in scientific computing and deep learning. Applications under investigation are complexly structured, normally adaptive and/or numerical methods which are difficult to parallelize. Special focus is hereby given to deep learning primitives such as CNN, RNN/LSTM, Transformers & MLPs and as well to their use in applications ranging from various ResNets to GPTs in GenAI.
Alexander studied Computer Science and Finance and Information Management at Technical University of Munich, Germany. In 2010 and 2012, he completed internships in the High Performance and Throughput Computing team at Intel, Munich, Germany and at Intel Labs Santa Clara, CA, USA, working on the Intel MIC architecture. In 2013 he finished his Ph.D. studies at Technical University of Munich, Germany. He joined Intel's Parallel Computing Lab in Santa Clara, CA, USA in 2014.