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Aneeq Zia
Manager, Machine Learning Engineering and MLOps
Aneeq Zia is currently the Manager of Machine Learning Engineering and MLOps at Intuitive, located in Peachtree Corners, Georgia. He has a strong academic background, holding a PhD in Electrical and Computer Engineering from the Georgia Institute of Technology. His professional journey includes various roles that highlight his expertise in both research and practical applications within technology and engineering.
Professional Experience
- Intuitive: Manager, Machine Learning Engineering and MLOps.
- Graduate Research Assistant: Georgia Institute of Technology (August 2015 - December 2018).
- Medical Research Intern: Intuitive Surgical (May 2016 - August 2016).
- Entrepreneurial Lead: NSF I-Corps (April 2016 - May 2016).
- Graduate Trainee Engineer: Engro Polymer & Chemicals Ltd (June 2012 - June 2013).
- Various research internships at institutions such as Koç University and Lahore University of Management Sciences.
Skills and Languages
Aneeq is proficient in both English and Urdu, which enhances his ability to communicate in diverse environments.
His LinkedIn profile showcases a commitment to advancing machine learning applications in healthcare, particularly focusing on surgical technologies.
Highlights
Excited to announce the launch of the Surgical Visual Understanding (SurgVU) challenge that our group in #Intuitive is organizing at #miccai this year (as part of EndoVis 2024). This challenge will focus on two key #machinelearning problems within surgical visual understanding - tool detection and step recognition. The participating teams will get a chance to work on one of the largest surgical video datasets available consisting of 280 long surgical videos (18M+ images!), and will be eligible to win some exciting prizes (upto $8000 per team!). Check out the challenge website link below for more details!
Anthony Jarc, Max Berniker, Conor Perreault, Rogerio Nespolo, Kiran Bhattacharyya, Xi Liu, Ziheng Wang, Ph.D, Dan Stoyanov, Stefanie Speidel
Excited to share that our group at Intuitive is organizing a #machinelearning challenge at #miccai this year as part of EndoVis 2023. This challenge will be a repeat from last year's challenge and will focus on #surgical tool localization using weak training labels. The participating teams will get to work on one of the largest #surgicalrobotics video dataset and we also have some exciting prizes for the winning teams as well (up to $6000!). Check out the link below for more details.
Moreover, we have also publicly released the training data used in SurgToolLoc 2022/2023 challenges (consisting of ~25000 30s video clips) with the publication of our 2022 challenge paper on arxiv. Check it out at https://lnkd.in/dC6aWrzK Anthony Jarc, Max Berniker, Kiran Bhattacharyya, Xi Liu, Conor Perreault, Ziheng Wang, Ph.D, Dan Stoyanov, Stefanie Speidel,
