Welcome to Parham's homepage.

 

Parham Aarabi has been a professor in the department of Electrical and Computer Engineering at the University of Toronto since 2001.  He obtained his Ph.D. (Elec. Eng.) from Stanford in 2001, and his M.A.Sc. and B.A.Sc. from Univ. of Toronto in 1999 and 1998, respectively.  He has published over 150 peer-reviewed papers, won several research/teaching awards including the IEEE Mac Van Valkenburg Early Career Teaching Award, the Gordon R. Slemon Teaching of Design Award, the University of Toronto Inventor of the Year award, four ECE Professor of the Year awards, the Premier's Catalyst Award for Innovation, the Canada Research Chair, and MIT's TR35 "Top Young Innovator" award.

Parham is also the founder and CEO of ModiFace, the world's leading beauty/medical augmented reality company, and one of America's fastest growing companies (Inc. 500/5000), which was acquired by L'Oreal in 2018He is also an active angel investor having invested in various Toronto and Bay Area startups.

 
 

Former STUDENT HALL OF FAME

A current status update from past students who have gone on to do some amazing things in the world.

See the List →

 

toronto-modiface.JPG

ModiFace Invests $4M In UofT Students and Faculty

ModiFace recently committed to investing $4M over 2 years on UofT faculty research grants and student internships.

Read CBC Coverage →

 

Screen Shot 2018-05-31 at 6.54.50 AM.png

L'Oréal, The world's #1 beauty company, acquires modiface

 ModiFace becomes the first technology brand under the L'Oréal group, joining brands such as Lancome, L'Oréal Paris, and YSL.

Read CNN coverage →

Heuristically-Trained Neural Networks

Our work on HNNs has shown significant potential for combining human expertise with neural networks.

Learn More →

 

Screen Shot 2017-10-25 at 10.35.22 AM.png

Deep Learning For Video Hair Segmentation

We recently developed a novel DNN architecture for mobile-optimized live video hair segmentation and coloration.

Read NVIDIA.com Coverage  →

 

Bose_Attack.png

Adversarial training for anti-face-recognition Photo filters

Our recent work on adversarial training resulted in an anti-recognition filter that disables facial recognition systems. 

Read VentureBeat Coverage →