Researcher at Manentia AI, India
Amit Chougule is a distinguished deep learning and AI researcher with a PhD from BITS Pilani, dedicated to advancing AI through cutting-edge research, algorithm development, and impactful industry contributions. He has over 3 years of expertise in AI and Computer Vision, having worked with esteemed organizations like Philips Research, Sony Research India, and Carleton University’s TrustCAV Research. Amit is proficient in Python and skilled in utilizing various libraries for data manipulation, statistical analysis, and machine learning. His experience includes designing end-to-end AI solutions and developing models for healthcare, autonomous vehicles, and more. 🚀🤖📊
Professional Profile:
🎓 Education:
- PhD in Computer Vision & AI, BITS Pilani, Pilani (2021-2024)
- Thesis: Artificial Intelligence Enabled Vehicular Vision and Service Provisioning for Advanced Driver Assistance Systems (ADAS)
- Supervisors: Dr. Vinay Chamola, Dr. Pratik Narang & Prof. F. Richard Yu (IEEE Fellow)
- M.Tech in Big Data & IoT, PES University, Bengaluru (2018-2020)
- Specializations: IoT, Cloud Computing, Big Data & Cyber Security
- B.Tech in Computer Engineering, Shivaji University (2013-2017)
- Focus Areas: Computer Algorithm, Data Structure, Computer Networks & Operating System
💼 Experience:
- Visiting Researcher, Carleton University, Canada (Dec 2022 – June 2023)
- Developed techniques for enhancing self-driving vehicles.
- Analyzed the impact of abnormal weather on vision-based autonomous vehicle operations.
- Introduced a computer vision-based attention model for reliable autonomous driving in adverse weather.
- Computer Vision Intern (PhD), Sony Research (Oct 2022 – Nov 2022)
- Innovated a computer vision model for diagnosing diseases using biomarkers.
- Developed an advanced pipeline for predicting diseases based on anatomical biomarkers.
- Machine Learning Engineer, AIvolved Technologies (Aug 2020 – Dec 2020)
- Worked on object detection, tracking, pose estimation, and web scraping for applications like gait analysis and traffic analysis.
- Computer Vision Intern, Philips Healthcare Research (July 2019 – July 2020)
- Conducted research on ultrasound medical imaging.
- Developed an Anatomical Intelligent Fetal Heart Ultrasound Scanning technique.
- Implemented an AI model for precise estimation of fetal femur length.
🔧 Skills:
- Programming Languages: C, C++, JAVA, Python, SQL
- Machine Learning Frameworks: PyTorch, Keras, TensorFlow
- CI/CD Tools: Git
- MLOps Frameworks and Tools: Docker, Kubernetes
- Data Pipelines and Analytics Tools: Apache Spark, PySpark
- Image Processing: OpenCV, Matplotlib, Scikit-image, SciPy, Pillow
- Generative AI: Autoencoders, GANs, Diffusion Models
- Computer Vision Domains: Image Classification, Object Detection, Object Tracking, Segmentation, Pose Estimation, Computational Geometry, Image Generation
- Medical Imaging Tools: ImageJ, 3D Slicer, OsiriX, SimpleITK, ITK-SNAP, RadiAnt DICOM
- Hardware: Arduino, Raspberry Pi, Nvidia Jetson Nano
🏆 Awards and Honors:
- Generative Adversarial Networks (GANs) Specialization by Coursera
- Deep Learning with Numpy by Udemy
- Getting Started with AWS Machine Learning by Amazon (Coursera)
- Computer Vision Basics by University at Buffalo and The State University of New York (Coursera)
- Digital Image Processing with OpenCV in Python by GEO University
- Data Science with Python by Pluralsight
- Pandas Foundations by Datacamp
- Python (Advanced Level) by HackerRank
🔬 Research Focus:
Amit Chougule is dedicated to advancing artificial intelligence through cutting-edge research, innovative algorithm development, and impactful industry contributions. He focuses on:
- Deep Learning Technology: Solving complex real-world problems and driving societal impact.
- Computer Vision: Enhancing autonomous vehicle capabilities, especially in adverse weather conditions.
- Medical Imaging: Developing AI models for accurate disease diagnosis and fetal growth prediction.
- AI Solutions: Designing end-to-end AI solutions, from data pipeline architecture to model deployment.
- “A novel generative adversarial network-based super-resolution approach for face recognition”
- Authors: Chougule, A., Kolte, S., Chamola, V., Hussain, A.
- Journal: Expert Systems, 2024, 41(8), e13564
- Cited by: 0
- 📄📈
- “Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Data Sets, Methods, and Challenges”
- Authors: Chamola, V., Chougule, A., Sam, A., Hussain, A., Yu, F.R.
- Journal: IEEE Internet of Things Journal, 2024, 11(10), pp. 17911–17933
- Cited by: 0
- 🚗🧠
- “AGD-Net: Attention-Guided Dense Inception U-Net for Single-Image Dehazing”
- Authors: Chougule, A., Bhardwaj, A., Chamola, V., Narang, P.
- Journal: Cognitive Computation, 2024, 16(2), pp. 788–801
- Cited by: 0
- 🌫️🔍
- “A Novel Framework for Traffic Congestion Management at Intersections Using Federated Learning and Vertical Partitioning”
- Authors: Chougule, A., Chamola, V., Hassija, V., Gupta, P., Yu, F.R.
- Journal: IEEE Transactions on Consumer Electronics, 2024, 70(1), pp. 1725–1735
- Cited by: 2
- 🚦🧠
- “HybridSecNet: In-Vehicle Security on Controller Area Networks Through a Hybrid Two-Step LSTM-CNN Model”
- Authors: Chougule, A., Kulkarni, I., Alladi, T., Chamola, V., Yu, F.R.
- Journal: IEEE Transactions on Vehicular Technology, 2024
- Cited by: 0
- 🚗🔐
- “A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions”
- Authors: Chougule, A., Chamola, V., Sam, A., Yu, F.R., Sikdar, B.
- Journal: IEEE Open Journal of Vehicular Technology, 2024, 5, pp. 142–161
- Cited by: 4
- 🚗⚠️
- “Enabling Safe ITS: EEG-Based Microsleep Detection in VANETs”
- Authors: Chougule, A., Shah, J., Chamola, V., Kanhere, S.
- Journal: IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12), pp. 15773–15783
- Cited by: 1
- 🧠🛣️
- “Multibranch Reconstruction Error (MbRE) Intrusion Detection Architecture for Intelligent Edge-Based Policing in Vehicular Ad-Hoc Networks”
- Authors: Chougule, A., Kohli, V., Chamola, V., Yu, F.R.
- Journal: IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11), pp. 13068–13077
- Cited by: 4
- 🚓💻
- “Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG”
- Authors: Shah, J., Chougule, A., Chamola, V., Hussain, A.
- Journal: Neurocomputing, 2023, 549, 126387
- Cited by: 0
- 🧠🧬
- “SCAN-GAN: Generative Adversarial Network Based Synthetic Data Generation Technique for Controller Area Network”
- Authors: Chougule, A., Agrawal, K., Chamola, V.
- Journal: IEEE Internet of Things Magazine, 2023, 6(3), pp. 126–130
- Cited by: 3
- 🖥️🔄