Amit Chougule | Cognitive Neuroscience | Best Researcher Award

Mr Amit Chougule | Cognitive Neuroscience | Best Researcher Award

 

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:

  1. Generative Adversarial Networks (GANs) Specialization by Coursera
  2. Deep Learning with Numpy by Udemy
  3. Getting Started with AWS Machine Learning by Amazon (Coursera)
  4. Computer Vision Basics by University at Buffalo and The State University of New York (Coursera)
  5. Digital Image Processing with OpenCV in Python by GEO University
  6. Data Science with Python by Pluralsight
  7. Pandas Foundations by Datacamp
  8. 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.

Publications: 

Mark Myers | Neuroscience | Excellence in Research

Dr Mark Myers | Neuroscience | Excellence in Research

Professor  at University of Tennessee Health Science Center, United States

Mark H. Myers, Ph.D. is a distinguished professional in the fields of computer science, biomedical engineering, and computational neurodynamics. He is currently an Assistant Professor in the Department of Anatomy and Neurobiology at the University of Tennessee Health Science Center (UTHSC), a position he has held since 2014, and serves as an Adjunct Professor in the Mathematics/Computer Science Department at Christian Brothers University since 2020.

Profile:

scopus

Education:

🎓 Ph.D. in Computer Science – University of Memphis (2005-2011) 🧠 Dissertation: Simulation of Abnormal/Normal Brain States Using the KIV Model 🎓 M.Sc. in Biomedical Engineering – University of Memphis 🧩 Thesis: Vagus Nerve Implant Simulator for Seizure Prediction and Treatment 🎓 M.Sc. in Computer Science – University of Memphis (2004-2005) 🧪 Thesis: Optimization of EEG Analysis for Cognitive Phase Transitions 🎓 B.Sc. in Physics, Minor in Math – University of Alabama

Professional Experience:

🔬 Assistant Professor – University of Tennessee Health Science Center, College of Medicine (2014-present) 🖥️ Adjunct Professor – Christian Brothers University, Mathematics/Computer Science Department (2020-present) 📊 Sr. Data Science Architect Consultant – Cognosante, LLC for Veteran’s Administration (2020-2021) 💼 CTO/CEO – NeuroDyne, Inc (2017-present) 🔬 Director, Center for Biomedical Informatics – UTHSC, College of Medicine (2015-2016) 🔬 Assistant Professor, Ophthalmology – UTHSC, College of Medicine (2012-2015) 👨‍🏫 Adjunct Professor, Computer Science – University of Memphis (2012-2015) 🔬 Research Assistant – Computational Neurodynamics Lab, University of Memphis (2004-2012) ⚓ Sr. R&D Software Engineer Consultant – U.S. Navy-Department of Defense Contractor (2009-2011) 📦 Senior Enterprise Architect – FedEx Express (2006-2009)

Associations: 🧠 Guest Editor and Topic Editor – Frontiers in Neuroscience, Brain Sciences 🧠 Review Editor – Frontiers in Computational Neuroscience 🧬 Member – Society for Neuroscience, International Neural Network Society, The Association of Research in Vision and Ophthalmology

Teaching Experience:

👨‍🏫 Adjunct Professor – Christian Brothers University Courses: Neural Networks, Operating Systems, C Programming, Introduction to AI 👨‍🏫 Assistant Professor – UTHSC Courses: Neuroanatomy Lab 👨‍🏫 Adjunct Professor – University of Memphis Courses: Java Network Programming, Control Theory/Systems Neuroscience 👨‍🏫 Adjunct Professor – Rhodes College Courses: Physics I, Physics II

Research  Focus   :

Dr. Mark H. Myers’ neuroscience research is centered around several key areas:

Seizure Prediction and Detection:Development of Algorithms: Dr. Myers has developed advanced algorithms for the prediction and detection of seizures, with a focus on non-invasive techniques using EEG data.Ambulatory Monitoring: His patented work on ambulatory seizure monitoring systems aims to provide real-time detection and management of seizures, improving patient outcomes.

Brain-State Simulation:KIV Model: His Ph.D. dissertation involved the simulation of abnormal and normal brain states using the KIV model, contributing to the understanding of various neurological conditions.

EEG Analysis and Cognitive Phase Transitions:EEG Optimization: Dr. Myers’ research includes optimizing EEG analysis to detect cognitive phase transitions, enhancing the ability to monitor and interpret brain activity during different cognitive states.

Neurodynamic Modeling:Mesoscopic Modeling: He has worked on modeling neuron population dynamics to study brain disorders such as epilepsy, providing insights into the neural mechanisms underlying these conditions.

Neural Interfaces and Vagus Nerve Stimulation:Vagus Nerve Research: His master’s thesis on a vagus nerve implant simulator aimed at predicting and treating seizures showcases his interest in neural interfaces and their therapeutic potential.

Traumatic Brain Injury (TBI):Visual Evoked Potentials: Recent research by Dr. Myers investigates the effects of mild traumatic brain injuries on visual pathways, providing valuable data on the impact of TBI on cognitive functions.

Multisensory Integration:Audiovisual Processing: His studies on multisensory integration, particularly in patients with inherited retinal dystrophies, explore how the brain processes and integrates information from different sensory modalities.

Collaborative Research:NSF and DOD Grants: Dr. Myers has led and contributed to numerous research projects funded by the National Science Foundation and the Department of Defense, focusing on brain-computer interfaces, cognitive modeling, and autonomous systems.

Citations:

📚Citations: 179

📄 Documents: 173

📊h-index: 8

Publication Top Notes:

  1. Auditory and olfactory findings in patients with USH2A-related retinal degeneration—Findings at baseline from the rate of progression in USH2A-related retinal degeneration natural history study (RUSH2A)
    Authors: Iannaccone, A., Brewer, C.C., Cheng, P., … Stingl, K., Zein, W.M.
    Journal: American Journal of Medical Genetics, Part A, 2021, 185(12), pp. 3717–3727
  2. Automatic detection of a student’s affective states for intelligent teaching systems
    Author: Myers, M.H.
    Journal: Brain Sciences, 2021, 11(3), pp. 1–15, 331
  3. Seizure localization using EEG analytical signals
    Authors: Myers, M.H., Padmanabha, A., Bidelman, G.M., Wheless, J.W.
    Journal: Clinical Neurophysiology, 2020, 131(9), pp. 2131–2139
  4. Frontal cortex selectively overrides auditory processing to bias perception for looming sonic motion
    Authors: Bidelman, G.M., Myers, M.H.
    Journal: Brain Research, 2020, 1726, 146507
  5. Mesoscopic neuron population modeling of normal/epileptic brain dynamics
    Authors: Myers, M.H., Kozma, R.
    Journal: Cognitive Neurodynamics, 2018, 12(2), pp. 211–223
  6. A pilot investigation of audiovisual processing and multisensory integration in patients with inherited retinal dystrophies
    Authors: Myers, M.H., Iannaccone, A., Bidelman, G.M.
    Journal: BMC Ophthalmology, 2017, 17(1), pp. 240