“Anurag is one of the most dedicated and hard working guys that I've worked with. He has always been a strong mathematician, but when he joined Delhivery, he didn't have a lot of experience with ML. He worked really hard in this area and as a result he has become an extremely good data scientist. His programming skills are better than most developers and his documentation is always detailed, updated and easy to understand. He genuinely seems to be quite interested in the design / architecture of the entire application and tries to be part of any such discussions. During my discussions with him, he has always tried to understand the business and the technical problems as well as the "on-ground" problems and tried to come up with a solution that works best for all cases (wherever possible). I think this is quite a rare (and very desirable) attribute. During his time at Delhivery he has mentored several newcomers (both developers and data scientists) and all of them were quite happy to work with him. I think Anurag has the determination and the ability to succeed in whatever area he chooses. I wish him all the best for the future.”
About
Experienced Machine Learning Engineer with a demonstrated history of working in the…
Activity
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You'll be surprised at how many FAANG middle managers make $500,000+ per year and add 0 value to their engineering teams. From my 4 years at Big…
You'll be surprised at how many FAANG middle managers make $500,000+ per year and add 0 value to their engineering teams. From my 4 years at Big…
Liked by Anurag Paul
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2024 has been a monumental year for Plus, another banner year that we’ve all been working long and hard for since starting the company in 2016. As…
2024 has been a monumental year for Plus, another banner year that we’ve all been working long and hard for since starting the company in 2016. As…
Liked by Anurag Paul
Experience
Education
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Activities and Societies: Information Management Group (IMG), Literary Section (Debating)
Received Silver Medal from IIT Roorkee for scoring highest CGPA in the Production and Industrial Engineering Batch of 2012.
Leadership Roles:
• Executive Member, Students Affairs Council, IIT Roorkee
• Coordinator, Information Management Group, IIT Roorkee –
• Manager(Web), Cognizance 2011 – the technical festival of IIT Roorkee
• Joint Secretary, Literary and Debating Society, IIT Roorkee
Licenses & Certifications
Publications
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Efficient Transformer Attention for GenAI
DZone AI/ML
Explore this overview of recent research focused on building efficient transformer models by reducing the computational complexity of self-attention.
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Unboxing the Black Box
DZone AI/ML
Explore explainable AI by unpacking heat maps and Structured Attention Graphs for explaining model decisions in computer vision applications.
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Practitioner’s Guide to Deep Learning
DZone AI/ML
An overview of best practices, recommendations, and fundamentals for managing a deep learning project to ensure a robust AI product.
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Characterizing Joint Attention Behavior during Real World Interactions using Automated Object and Gaze Detection
ACM ETRA 2019
Joint attention is an essential part of the development process of children, and impairments in joint attention are considered as one of the first symptoms of autism. In this paper, we develop a novel technique to characterize joint attention in real time, by studying the interaction of two human subjects with each other and with multiple objects present in the room. This is done by capturing the subjects' gaze through eye-tracking glasses and detecting their looks on predefined indicator…
Joint attention is an essential part of the development process of children, and impairments in joint attention are considered as one of the first symptoms of autism. In this paper, we develop a novel technique to characterize joint attention in real time, by studying the interaction of two human subjects with each other and with multiple objects present in the room. This is done by capturing the subjects' gaze through eye-tracking glasses and detecting their looks on predefined indicator objects. A deep learning network is trained and deployed to detect the objects in the field of vision of the subject by processing the video feed of the world view camera mounted on the eye-tracking glasses. The looking patterns of the subjects are determined and a real-time audio response is provided when a joint attention is detected, i.e., when their looks coincide. Our findings suggest a trade-off between the accuracy measure (Look Positive Predictive Value) and the latency of joint look detection for various system parameters. For more accurate joint look detection, the system has higher latency, and for faster detection, the detection accuracy goes down.
Patents
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Turn and brake action prediction using vehicle light detection
Issued US 12,033,399
This application is directed to predicting vehicle turn and brake actions for at least partially autonomous vehicle driving. A first vehicle obtains a plurality of images along a road. For each image of the plurality of images, the first vehicle detects, from the image, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle. The first vehicle determines, for each image region, a probability that a…
This application is directed to predicting vehicle turn and brake actions for at least partially autonomous vehicle driving. A first vehicle obtains a plurality of images along a road. For each image of the plurality of images, the first vehicle detects, from the image, a plurality of image regions each corresponding to a respective vehicle light of a second vehicle positioned on the road near the first vehicle. The first vehicle determines, for each image region, a probability that a respective vehicle light of the second vehicle changed its state. The first vehicle predicts a vehicle action of the second vehicle based on the probability for each image region. The first vehicle at least partially autonomously driving the first vehicle based on the predicted vehicle action of the second vehicle.
Other inventorsSee patent -
Systems and methods for characterizing joint attention during real world interaction
Filed US WO2020264101A1
Courses
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Advanced Statistical NLP
CSE 291 E
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Deep Learning for Sequences
CSE 291G
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Linear Algebra
ECE 269
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Machine Learning for Image Processing
ECE 285
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Probability and Statistics for Data Science in Python
ECE 225A
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Statistical Learning 1
ECE 271A
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Statistical learning 2
ECE 271B
Projects
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Domain Adaptation using Generative Adversarial Networks
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Developed a system to generate realistic images from synthetic dataset to create more training data for self-driving cars. For this, we developed and compared the performance of Cycle GANs and Dual GANs in adapting images from GTA V to Cityscapes dataset.
Other creatorsSee project
Honors & Awards
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Kaizen
Delhivery
For exceptional work in developing a facility location planner.
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Star Novitiate
iRunway India Pvt. Ltd.
For outstanding performance and lasting contribution
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Silver Medal
Indian Institute of Technology Roorkee
For scoring the highest CGPA among the graduating students of my batch
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DAAD Scholarship
German Academic Exchange Service
Research Scholarship for summer internship in Germany
Recommendations received
2 people have recommended Anurag
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Plus is a 2024 Best in Business Honoree. 🎉 We’re thrilled to announce that Inc. Magazine has named Plus to its #BestInBusiness Awards in the AI &…
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SuperDrive™ was recently named an Innovation Winner at the 2024 Reuters Automotive D.R.I.V.E Honours. 🥇 The judging panel awarded us this…
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My mission at SpaceX has come to an end. In 2017, at the age of 50, I quit an Executive Vice President (OFK) job at BMW to manage a small shop…
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Plus is at the forefront of autonomy and AI. We are hiring for multiple roles…
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I wrote an article covering the basics of model compression, a key component for edge deployment of deep learning models. https://lnkd.in/g8iNQ6aP
I wrote an article covering the basics of model compression, a key component for edge deployment of deep learning models. https://lnkd.in/g8iNQ6aP
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