Cem C Tutum
Research Scientist
I am a Research Scientist at the Department of Computer Science, UT-Austin.

In June 2018, I started working on two projects in DARPA’s Lifelong Learning Machines (L2M) program:
  1. Context-dependent reconfiguration of an intelligent neural system,
  2. STELLAR (Super Turing Evolving Lifelong Learning ARchitecture).

I am a mechanical engineer by training (B.Sc. in System Dynamics and Control, M.Sc. in Solid Mechanics and Ph.D. in Manufacturing Engineering), but optimization and its applications in various disciplines have always been in the center of my research interests:
  • Evolutionary computation for
    • single, multi and many-objective & constrained optimization problems,
    • training Neural Networks,
  • Surrogate-based efficient blackbox optimization for compute-heavy problems,
  • 3D Printing,
  • Deep learning for sequence modeling and generative design,
  • Multi-physics computer simulations of manufacturing processes.
Functional Generative Design: An Evolutionary Approach to 3D-Printing 2018
Cem C. Tutum, Supawit Chockchowwat, Etienne Vouga and Risto Miikkulainen, To Appear In Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2018), pp. 8, Kyoto, Japan, July 2018.
Efficient Sampling for Design Optimization of an SLS Product 2017
Nancy Xu, Cem C. Tutum, In Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium, pp. 12, Austin, TX, August 2017.
Evolutionary Decomposition for 3D Printing 2017
Eric A. Yu, Jin Yeom, Cem C. Tutum, Etienne Vouga, Risto Miikkulainen, To Appear In Proceedings of The Genetic and Evolutionary Computation Conference (GECCO 2017) (Best Paper Award), pp. 8 pages, Berlin, Germany, July 2017.
Surrogate-based Evolutionary Optimization for Friction Stir Welding 2016
Cem C Tutum, Shaayaan Sayed and Risto Miikkulainen, In Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2016), pp. 8 pages, Vancouver, BC, Canada, July 2016.
Currently affiliated with Neural Networks