Dont follow the rainbow: How to avoid career traps that can lead you to fail, Prof. Ramesh Raskar
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Video: https://www.youtube.com/watch?v=2jq_5FaQbTg After different rejections, the project of a lifetime Ramesh Raskar (associate professor at MIT) finally comes to life. How did he manage to get his way out of this jungle of misleading signs and career traps? By becoming a pathfinder: always tense towards your goal but also critical and ready to adjust his strategy to reach it. An incredible life lesson that he gave us in this talk at the last FAIL at Massachusetts Institute of Technology (MIT). https://www.youtube.com/watch?v=2jq_5FaQbTg&feature=youtu.be&fbclid=IwAR3aAo7SIiCuHY_6ICTjXLOpNBUBwEEJUq72pD-V8N2nX2cWaVIxtPM1gBM Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on AI and Imaging for health and sustainability. These interfaces span research in physical (e.g., sensors, health-tech), digital (e.g., automating machine learning) and global (e.g., geomaps, autonomous mobility) domains. He received the Lemelson Award (2016), ACM SIGGRAPH Achievement Award (2017), DARPA Young Faculty Award (2009), Alfred P. Sloan Research Fellowship (2009), TR100 Award from MIT Technology Review (2004) and Global Indus Technovator Award (2003). He has worked on special research projects at Google [X] and Facebook and co-founded/advised several companies.
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Video of the talk at https://www.youtube.com/watch?v=x9TCYuMUnco
Friction in data sharing is a large challenge for large scale machine learning. Emerging technologies in domains such as biomedicine, health and finance benefit from distributed deep learning methods which can allow multiple entities to train a deep neural network without requiring data sharing or resource aggregation at one single place. The talk will explore the main challenges in data friction that make capture, analysis and deployment of ML. The challenges include siloed and unstructured data, privacy and regulation of data sharing and incentive models for data transparent ecosystems. The talk will compare distributed deep learning methods of federated learning and split learning. Our team at MIT has pioneered a range of approaches including automated machine learning (AutoML), privacy preserving machine learning (PrivateML) and intrinsic as well as extrinsic data valuation (Data Markets). One of the programs at MIT aims to create a standard for data transparent ecosystems that can simultaneously address the privacy and utility of data.
Bio: Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on AI and Imaging for health and sustainability. They span research in physical (e.g., sensors, health-tech), digital (e.g., automated and privacy-aware machine learning) and global (e.g., geomaps, autonomous mobility) domains. He received the Lemelson Award (2016), ACM SIGGRAPH Achievement Award (2017), DARPA Young Faculty Award (2009), Alfred P. Sloan Research Fellowship (2009), TR100 Award from MIT Technology Review (2004) and Global Industry Technovator Award (2003). He has worked on special research projects at Google [X], Apple Privacy Team and Facebook and co-founded/advised several companies. Project page https://splitlearning.github.io/" Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on Machine Learning and Imaging for health and sustainability. They span research in physical (e.g., sensors, health-tech), digital (e.g., automated and privacy-aware machine learning) and global (e.g., geomaps, autonomous mobility) domains.
In his recent role at Facebook, he launched and led innovation teams in Digital Health, Health-tech, Satellite Imaging, TV and Bluetooth bandwidth for Connectivity, VR/AR and ‘Emerging Worlds’ initiative for FB.
At MIT, his co-inventions include camera to see around corners, femto-photography, automated machine learning (auto-ML), private ML, low-cost eye care devices (Netra,Catra, EyeSelfie), a novel CAT-Scan machine, motion capture (Prakash), long distance barcodes (Bokode), 3D interaction displays (BiDi screen), new theoretical models to augment light fields (ALF) to represent wave phenomena and algebraic rank constraints for 3D displays(HR3D).God’s Eye View: Will global AI empower us or destroy us? | Ramesh Raskar
God’s Eye View: Will global AI empower us or destroy us? | Ramesh Raskar Camera Culture Group, MIT Media Lab
Associate Professor, MIT Media Lab
Ramesh Raskar is founder of the Camera Culture research group at the Massachusetts Institute of Technology (MIT) Media Lab and associate professor of Media Arts and Sciences at MIT. Raskar is the co-inventor of radical imaging solutions including femto-photography, an ultra-fast imaging camera that can see around corners, low-cost eye-care solutions for the developing world and a camera that allows users to read pages of a book without opening the cover. He is a pioneer in the fields of imaging, computer vision and machine learning.
Raskar’s focus is on building interfaces between social systems and cyber-physical systems. These interfaces span research in physical (e.g., sensors, health-tech), digital (e.g., tools to enable keeping data private in distributed machine learning applications) and global (e.g., geomaps, autonomous mobility) domains. Recent inventions by Raskar’s team include transient imaging to look around a corner, a next-generation CAT-scan machine, imperceptible markers for motion capture, long-distance barcodes, touch + hover 3D interaction displays and new theoretical models to augment light fields to represent wave phenomena.
Raskar has dedicated his career to linking the best of the academic and entrepreneurial worlds with young engineers, igniting a passion for impact inventing. Raskar seeks to catalyze change on a massive scale by launching platforms that empower inventors to create solutions to improve lives globally.
Raskar has received the Lemelson Award, ACM SIGGRAPH Achievement Award, DARPA Young Faculty Award, Alfred P. Sloan Research Fellowship, TR100 Award from MIT Technology Review and Global Indus Technovator Award. He has worked on special research projects at Google [X] and Facebook and co-founded and advised several companies. He holds more than 80 US patents.
Making the Invisible Visible: Within Our Bodies, the World Around Us, and BeyondMaking Invisible Visible, Ramesh Raskar Keynote at Embedded Vision 2019
Making Invisible Visible, Ramesh Raskar Keynote at Embedded Vision 2019Camera Culture Group, MIT Media Lab
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Video of the talk at https://www.youtube.com/watch?v=x9TCYuMUnco
Friction in data sharing is a large challenge for large scale machine learning. Emerging technologies in domains such as biomedicine, health and finance benefit from distributed deep learning methods which can allow multiple entities to train a deep neural network without requiring data sharing or resource aggregation at one single place. The talk will explore the main challenges in data friction that make capture, analysis and deployment of ML. The challenges include siloed and unstructured data, privacy and regulation of data sharing and incentive models for data transparent ecosystems. The talk will compare distributed deep learning methods of federated learning and split learning. Our team at MIT has pioneered a range of approaches including automated machine learning (AutoML), privacy preserving machine learning (PrivateML) and intrinsic as well as extrinsic data valuation (Data Markets). One of the programs at MIT aims to create a standard for data transparent ecosystems that can simultaneously address the privacy and utility of data.
Bio: Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on AI and Imaging for health and sustainability. They span research in physical (e.g., sensors, health-tech), digital (e.g., automated and privacy-aware machine learning) and global (e.g., geomaps, autonomous mobility) domains. He received the Lemelson Award (2016), ACM SIGGRAPH Achievement Award (2017), DARPA Young Faculty Award (2009), Alfred P. Sloan Research Fellowship (2009), TR100 Award from MIT Technology Review (2004) and Global Industry Technovator Award (2003). He has worked on special research projects at Google [X], Apple Privacy Team and Facebook and co-founded/advised several companies. Project page https://splitlearning.github.io/" Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on Machine Learning and Imaging for health and sustainability. They span research in physical (e.g., sensors, health-tech), digital (e.g., automated and privacy-aware machine learning) and global (e.g., geomaps, autonomous mobility) domains.
In his recent role at Facebook, he launched and led innovation teams in Digital Health, Health-tech, Satellite Imaging, TV and Bluetooth bandwidth for Connectivity, VR/AR and ‘Emerging Worlds’ initiative for FB.
At MIT, his co-inventions include camera to see around corners, femto-photography, automated machine learning (auto-ML), private ML, low-cost eye care devices (Netra,Catra, EyeSelfie), a novel CAT-Scan machine, motion capture (Prakash), long distance barcodes (Bokode), 3D interaction displays (BiDi screen), new theoretical models to augment light fields (ALF) to represent wave phenomena and algebraic rank constraints for 3D displays(HR3D).God’s Eye View: Will global AI empower us or destroy us? | Ramesh Raskar
God’s Eye View: Will global AI empower us or destroy us? | Ramesh Raskar Camera Culture Group, MIT Media Lab
Associate Professor, MIT Media Lab
Ramesh Raskar is founder of the Camera Culture research group at the Massachusetts Institute of Technology (MIT) Media Lab and associate professor of Media Arts and Sciences at MIT. Raskar is the co-inventor of radical imaging solutions including femto-photography, an ultra-fast imaging camera that can see around corners, low-cost eye-care solutions for the developing world and a camera that allows users to read pages of a book without opening the cover. He is a pioneer in the fields of imaging, computer vision and machine learning.
Raskar’s focus is on building interfaces between social systems and cyber-physical systems. These interfaces span research in physical (e.g., sensors, health-tech), digital (e.g., tools to enable keeping data private in distributed machine learning applications) and global (e.g., geomaps, autonomous mobility) domains. Recent inventions by Raskar’s team include transient imaging to look around a corner, a next-generation CAT-scan machine, imperceptible markers for motion capture, long-distance barcodes, touch + hover 3D interaction displays and new theoretical models to augment light fields to represent wave phenomena.
Raskar has dedicated his career to linking the best of the academic and entrepreneurial worlds with young engineers, igniting a passion for impact inventing. Raskar seeks to catalyze change on a massive scale by launching platforms that empower inventors to create solutions to improve lives globally.
Raskar has received the Lemelson Award, ACM SIGGRAPH Achievement Award, DARPA Young Faculty Award, Alfred P. Sloan Research Fellowship, TR100 Award from MIT Technology Review and Global Indus Technovator Award. He has worked on special research projects at Google [X] and Facebook and co-founded and advised several companies. He holds more than 80 US patents.
Making the Invisible Visible: Within Our Bodies, the World Around Us, and BeyondMaking Invisible Visible, Ramesh Raskar Keynote at Embedded Vision 2019
Making Invisible Visible, Ramesh Raskar Keynote at Embedded Vision 2019Camera Culture Group, MIT Media Lab
Project page: https://splitlearning.github.io/
Papers: https://arxiv.org/search/cs?searchtype=author&query=Raskar
Video: https://www.youtube.com/watch?v=8GtJ1bWHZvg
Split learning for health: Distributed deep learning without sharing raw patient data: https://arxiv.org/pdf/1812.00564.pdf
Distributed learning of deep neural network over multiple agents
https://www.sciencedirect.com/science/article/pii/S1084804518301590
Otkrist Gupta, Ramesh Raskar,
In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.Split Learning versus Federated Learning for Data Transparent ML, Camera Cult...
Split Learning versus Federated Learning for Data Transparent ML, Camera Cult...Camera Culture Group, MIT Media Lab
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Project page: https://splitlearning.github.io/
Papers: https://arxiv.org/search/cs?searchtype=author&query=Raskar
Video: https://www.youtube.com/watch?v=8GtJ1bWHZvg
Split learning for health: Distributed deep learning without sharing raw patient data: https://arxiv.org/pdf/1812.00564.pdf
Distributed learning of deep neural network over multiple agents
https://www.sciencedirect.com/science/article/pii/S1084804518301590
Otkrist Gupta, Ramesh Raskar,
In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.Split Learning versus Federated Learning for Data Transparent ML, Camera Cult...
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Dont follow the rainbow: How to avoid career traps that can lead you to fail, Prof. Ramesh Raskar
- 1. Pathfinders + Fail Signs Ramesh Raskar Assoc. Professor MIT Media Lab
- 5. Can you see around corners ?
- 6. 2nd Bounce Multi-path Analysis 1st Bounce 3rd Bounce 6
- 8. Femto-Camera Hidden Mannequin Velten et al, Nature Comm 2012
- 9. 9
- 10. Before 5 teams Be early Ahead by 5 years What no one is thinking about Relevant within 5 steps of Impact Human impact Beyond 5 mins of instruction Deep, iterative, participatory Fuse 5+ Disciplines Fun, barrier for others Strive for Five Food Guide Pyramid
- 11. Rescue Pandharkar, Velten, et al CVPR 201111 Endoscopes Avoid Collision
- 12. FemtoPhotography: Light in Slow Motion .. 10 Billion x Slow
- 14. External Internal Nervous Aware Driven Accept Analyze Resist Rebel Trouble maker Maverick PathfinderQuestionerSteerer Creative Solver Doer Finding your path
- 17. Pathfinders + Fail Signs Ramesh Raskar Assoc. Professor MIT Media Lab
Notas del editor
- Superman can fly and heroes can becomes invisible .. But what about a new power for a future super-hero .. To see around corners ..
- My idea is to use the multiple bounces of light i.e. echoes of light. Echoes of sound -> Echoes of light We all know about echoes of sound. But sounds travels slow and we can actually hear the echoes Light travels fast so we need specialized hardware to ‘listen’ to these echoes. So we end up using light sources and cameras that run at a trillion frames per second (not a million and not a billion, but trillion)
- Not just science fiction but we have actually shown this in our lab on a tabletop setup The femto-camera is on the left and the mannequin is hidden behind the wall .. so how do we do it? Our paper was recently published and highlighted on nature.com and Nature produced this animation
- Five on Five = If more than five teams in the world are doing the same research, don’t do it. = If you disappear for five years, will someone do it anyway? Then your idea is not that great anyway. = Can you explain your work in five sentences to your grandma how it will impact human life? = If you can explain the idea in five minutes to a student and disappear for five years, will s/he be able to do it on her/his own without additional input from you/without iterations .. It is too obvious and lacks depth .. Don’t do it. = Strive to work on ideas that may require five+ disciplines .. Today’s research is highly team-driven and more diverse the required team composition, more fun you will have and also indicates a natural barrier to entry for others satisfying condition 1 and 2 Much like the food pyramid, five servings are the goal and will make you stronger .. But ok if your research project does not satisfy all five conditions
- In imaging, lets start with the most fundamental that’s invisible .. Photons zipping at speed of light So we built a camera and new type of imaging, femotophotography that can create moving of light in slow motion What if we send this bullet of photons to shatter into this bottle How does light look in slow motion The whole sequence is less than a nanosecond stretched to about 10 seconds i.e. 10 billion times slowed down If an oridinary bullet, same distance at such a slow motion video, do you know how long you will have to sit here to watch the movie? A day, a week .. a whole year .. it will be a very boring movie .. Clean_coke_mulSlow2v1.mov
- 2014