Professional AssociationsProfessional Associations: ACM | Society for Industrial and Applied Mathematics
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Cristiano Malossi is Principal Research Staff Member (PRSM) and Manager of the AI Automation group at the IBM Research laboratory in Zurich. The group focuses on creating solutions for scalable AI model development and deployment on Cloud and High-Performance on-prem systems. In 2018 Cristiano’s team released on the IBM Cloud the first IBM engine for automation of neural network synthesis (NeuNetS). In 2019, this work is being further extended and refined into a complete framework for accelerating the full data science experience.
Since 2017 Cristiano is coordinator of the FET-H2020 Open transPREcision COMPuting (OPRECOMP) project, with focus on low-power/low-energy computing paradigms based on approximation and transprecision. Cristiano is a recipient of the 2016 IPDPS Best Paper Award and the 2015 ACM Gordon Bell Prize. Since 2015 he is also member of ACM and SIAM societies, and he is part of Technical Program committee of top conferences, including SC, ISC, IPDPS, and DATE.
Before IBM, Cristiano graduated from the Swiss Federal Institute of Technology in Lausanne (EPFL) in Lausanne with a PhD in applied mathematics. In 2013, his thesis on parallel algorithms and mathematical methods for the numerical simulation of cardiovascular problems granted him the IBM Research Prize for Scientific Computing. Cristiano has also a B.Sc. in Aerospace Engineering and a M.Sc. in Aeronautical Engineering from the Politecnico di Milano (Italy).
Cristiano research interests include: acceleration and new computing paradigms for machine learning and deep learning methods, AI lifecycle automation for enterprise data, AI systems design and user experience, high performance computing, transprecision & energy-aware computing, CFD and FEM, Aircraft design.
Honors and Awards:
2019 - ACM Distinguished Speaker
2016 - IEEE/ACM IPDPS Best Paper Award
2015 - IBM Pat Goldberg Memorial Best Paper Award
2015 - ACM Gordon Bell Prize
2013 - IBM Research Prize for Computational Science (for the PhD thesis)
Research in the News:
- Swiss-EU Success Story: OPRECOMP (SwissCore - December 2020)
- Why Smarter Roads, Bridges, and Tunnels are good for Economies and Societies (Youtube - October 2020)
- OPRECOMP, Transprecision computing for energy efficiency (Open Access Government - October 2020)
- Artificial intelligence, drones and sensors set to save our crumbling infrastructure (Medium.com - December 2019)
- Mit KI und Drohnen auf der Suche nach Brückenschäden (Computerworld - December 2019)
- AI for AI: in the middle of the future (Migros Magazin Cover, 3-millions printed copies - May 2019)
- Radical computing rethink to save time and energy (EC Research and Innovation Success Stories - February 2019)
- NeuNetS: Automating Neural Network Model Synthesis for Broader Adoption of AI (IBM Blog - December 2018)
- TAPAS: Frugally Predicting the Accuracy of a Neural Network Prior to Training (IBM Blog - December 2018)
- Restoring Balance in Machine Learning Datasets (IBM Blog - October 2018)
- Come funzionano le reti neurali (MaddMaths! - October 2017)
- The future belongs to cognitive systems (SIX Connect - May 2017)
- Gordon Bell Prize Winners Simulate Earth's Mantle (IBM Systems Magazine - November 2016)
- Data Centric Systems, la frontiera del supercalcolo (01net. - 6 May 2016)
- Trade talk: Serial solver (Nature Careers Q&A - 14 April 2016)
- Finding job satisfaction in high performance computing (Naturejobs blog - 13 April 2016)
- SC15 Gordon Bell Prize Winners (PR Newswire; IBM Blog; HPCWire - 20 November 2015)
- Meet an IBM Researcher (IBM Blog - 6 November 2015)
- IBM Research Prize for Computational Science (EPFL News - 10 October 2013)
- F. Scheidegger, L. Benini, C. Bekas, A. C. I. Malossi. Constrained deep neural network architecture search for IoT devices accounting hardware calibration. NeurIPS - Thirty-third Conference on Neural Information Processing Systems, 2019. (Acceptance rate 21.2% over 6743 reviewed submissions)
- R. Istrate, F. Scheidegger, G. Mariani, D. S. Nikolopoulos, C. Bekas, A. C. I. Malossi. TAPAS: Train-less Accuracy Predictor for Architecture Search. AAAI, 2019. (Acceptance rate 16.2% over 7095 reviewed submissions)
- P. W. J. Staar, P. K. Barkoutsos, R. Istrate, A. C. I. Malossi, I. Tavernelli, N. Moll, H. Giefers, C. Hagleitner, C. Bekas, A. Curioni. Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance. IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 812-821, 2016. (Best Paper Winner)
- J. Rudi, A. C. I. Malossi, T. Isaac, G. Stadler, M. Gurnis, P. W. J. Staar, Y. Ineichen, C. Bekas, A. Curioni, O. Ghattas.An Extreme-scale Implicit Solver for Complex PDEs: Highly Heterogeneous Flow in Earth's Mantle. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-12, ACM, 2015. (Winner of the ACM Gordon Bell Price at SC15)
- A. C. I. Malossi, P. J. Blanco, P. Crosetto, S. Deparis, A. Quarteroni. Implicit coupling of one-dimensional and three-dimensional blood flow models with compliant vessels. Multiscale Modeling & Simulation 11(2), 474-506, SIAM, 2013.
- A. C. I. Malossi, P. J. Blanco, S. Deparis. A two-level time step technique for the partitioned solution of one-dimensional arterial networks. Computer Methods in Applied Mechanics and Engineering 237-240, 212-226, Elsevier, 2012.
Code and tools:
- NeuNetS: Neural Network Synthesizer (IBM Cloud)
- OPRECOMP: EU Project on Transprecision Computing (GitHub)
- BAGAN: Keras implementation of BAlancing GAN (IBM GitHub)
- IBM Optimized High Performance Conjugate Gradient (IBM GitHub)
- LifeV: Library for the numerical solution of PDEs with FEM (BitBucket)