Professional AssociationsProfessional Associations: ACM | ACM Distinguished Speaker
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Cristiano Malossi is Principal Research Scientist and Manager at the IBM Research Laboratory in Zurich. Since 2021, Cristiano is global research lead for Visual Inspection. Cristiano’s team owns the design and development of IBM One Click Learning (OCL), a research-industry platform that leverages deep learning and advanced computer vision methods to accelerate and improve accuracy in critical inspection tasks. IBM OCL is the result of many years of research in AI Automation, adapted to the needs of enterprise inspectors. The platform is available as a managed web service, running on IBM Cloud. A live demo can be requested by interested Clients.
Between 2017 and 2019 Cristiano led a global research team around neural network automation. In 2018, Cristiano’s team released on the IBM Cloud the first IBM engine for automation of neural network synthesis (NeuNetS) applicable to image and text classification. In the earlier stages of his career at IBM, Cristiano was responsible of the development of energy-aware computing algorithms, as part of the Exa2Green project. Later, between 2017 and 2020, Cristiano was coordinator of the Open Transprecision Computing (OPRECOMP) project. The project focused on low-power/low-energy computing paradigms based on transprecision computing - a new computing paradigm ideated by the project that combines approximation and automation. As part of his research and network, Cristiano contributed to many European research-innovation projects, including VIMMP, IM-SAFE, APROPOS, ROMEO, and more recently SustainML.
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 from 2019 he is ACM Distinguished Speaker. Cristiano has served in the technical program committee of top HPC and AI conferences, including SC, ISC, IPDPS, AAAI, NeurIPS, ICML, 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’s research interests include computer vision, acceleration and new computing paradigms for machine learning and deep learning, scalable AI services, datacentric AI, lifecycle automation, user experience, high performance computing, transprecision & energy-aware computing, and - from his former studies - CFD, FEM, and 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)