Nicolas Pinto, M.Sc.

The PhysOrg article Researchers demonstrate a better way for computers to "see" said

Taking inspiration from genetic screening techniques, researchers from Harvard and MIT have demonstrated a way to build better artificial visual systems with the help of low-cost, high-performance gaming hardware.
 
"While studying the brain has yielded critical information about how the brain is wired, we currently don't have enough information to build a computer system that works like the brain does," adds Pinto. "Even if we take all of the clues that we have available from experimental neuroscience, there is still an enormous range of possible models for us to explore."
 
To tackle this problem, the team drew inspiration from screening techniques in molecular biology, where a multitude of candidate organisms or compounds are screened in parallel to find those that have a particular property of interest. Rather than building a single model and seeing how well it could recognize visual objects, the team constructed thousands of candidate models, and screened for those that performed best on an object recognition task.
 
The resulting models outperformed a crop of state-of-the-art computer vision systems across a range of test sets, more accurately identifying a range of objects on random natural backgrounds with variation in position, scale, and rotation.

Nicolas Pinto, M.Sc. is Ph.D. Candidate, Brain & Computer Sciences, Massachusetts Institute of Technology (MIT), USA.
 
Coming from a loving and supportive Portuguese family, Nicolas was born in France where he graduated with two M.Sc.s in computer science (Software Engineering and Artificial Intelligence from UTBM and ENSISA/UHA).
 
Attracted by multicultural experiences and highly motivated by exploring intellectual opportunities abroad, he studied in Brazil (PUC-Rio), South Korea (CBNU), and Switzerland (CERN) before coming to the United States (MIT) to complete his degrees in Jim DiCarlo's Lab.
 
He is now very excited to be joining the BCS (Brain and Computer Sciences) Ph.D. program and working in its collaborative multidisciplinary environment. He is currently a member of the DiCarlo Lab, the Sinha Lab for Vision Research at MIT, and the Cox Visual Neuroscience Group at Rowland/Harvard.
 
Nicolas coauthored PyCUDA: GPU Run-Time Code Generation for High-Performance Computing, A High-Throughput Screening Approach to Discovering Good Forms of Biologically-Inspired Visual Representation, How far can you get with a modern face recognition test set using only simple features?, Establishing Good Benchmarks and Baselines for Face Recognition, and Why is Real-World Visual Object Recognition Hard? Read the full list of his publications!
 
His current interests are in high-throughput computational neuroscience and biology-inspired silicon intelligence with emphasis on vision. He divides his spare time between sports, traveling, and managing the music association he cofounded.