Sreenath Kyathanahally

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Senior AI Engineer

I'm Sreenath Kyathanahally, a Senior AI Engineer with expertise in deep learning and computer vision. I hold a Ph.D. in Biomedical Engineering from the University of Bern and have worked on diverse projects, including medical image analysis. My passion lies in solving real-world challenges using AI techniques. Currently, I'm at b-rayZ, assembling complex medical datasets and designing AI algorithms for image segmentation and classification. I enjoy staying at the forefront of AI research and have a strong track record of publications in top conferences and journals. Let's innovate and make a positive impact with AI!

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Skills

  • Deep Learning: TensorFlow, PyTorch, Keras
  • Computer Vision: OpenCV, Vision Transformer
  • Web Development: Flask
  • Machine Learning: Scikit-learn, CNN, Transformer, Unet
  • Data Manipulation and Analysis: NumPy, Pandas
  • Cloud Platforms: Amazon AWS, Microsoft Azure
  • Database Management: SQL, MongoDB
  • Programming Languages: Python, MATLAB
  • Model Tracking: Weights and Biases
  • Docker Icon Version Control: Git
  • Docker Icon Containerization: Docker
  • Medical Imaging Icon Medical Imaging: MRI, CT, PET

Experience

Senior AI Engineer

Zurich, Switzerland
June 2023 - Present
  • Assemble large, complex medical datasets for AI models.
  • Implement process improvements for automation and scalability.
  • Build infrastructure for data extraction and loading using Mongo, SQL, and Cloud.
  • Develop AI algorithms for medical image segmentation and classification using TensorFlow and PyTorch.
  • Implement YOLO-v8 based object detection and segmentation for medical images.
  • Conduct data exploration and analysis to derive insights for model development.
  • Maintain pipelines for continuous integration and deployment.
  • Ensure compliance with medical device standards.

Senior Deep Learning Engineer

Zurich, Switzerland
Feb 2022 - May 2023
  • Develop deep learning algorithms for medical imaging.
  • Utilize heart atlas based training for cardiac image segmentation.
  • Annotate and segment CT images to improve AI model accuracy.
  • Adhere to Quality Management System (QMS) requirements.
  • Support cloud-based AI algorithms for real-time analysis.
  • Foster collaboration and knowledge sharing within the team.

Machine Learning Researcher - Scientist FS8

Zurich, Switzerland
Sep 2020 - Present
  • Develop deployable deep learning algorithms, including GANs, for plankton classification.
  • Use transfer learning and data augmentation techniques, characterizing noise, to improve model generalization.
  • Collaborate with domain experts to understand and address ecological implications of plankton classifications.
  • Implement vision transformers for efficient and scalable image processing.
  • Publish research findings in top-tier machine learning and ecology venues.

Computer Vision Scientist

Zurich, Switzerland
Dec 2019 - Aug 2020
  • Developed state-of-the-art machine learning algorithms for computer vision problems, such as classifying Soybeans and Corn based on certain features.
  • Maintained modular, scalable, and sustainable code following Scrum Agile methodologies.

Postdoctoral Researcher

Zurich, Switzerland
Nov 2017 - Nov 2019
  • Develop deep learning algorithms for segmenting spinal cord lesions using medical images.
  • Investigated neuro-imaging biomarkers responsible for pain in spinal cord injury patients.
  • Conducted longitudinal studies to analyze structural and microstructural changes in the brain of spinal cord injury patients.
  • Predicted clinical outcomes of spinal cord injury patients based on tissue bridges using deep learning algorithms.

Research Scholar

Montreal, Canada
Aug 2018 - Sept 2018
  • Implement a machine learning pipeline for automatic lesion segmentation in spinal cord images using Python and TensorFlow.
  • Utilize transfer learning techniques to improve the performance of lesion segmentation models on limited annotated data.

Early Stage Researcher - Marie-Curie ITN

University of Bern, Switzerland
Sept 2013 - Aug 2017
  • Develop machine learning tools and classifiers to analyze brain tumor spectra and assist radiologists in interpreting data.
  • Implement deep learning networks, including CNNs and autoencoders, to remove artifacts and improve the quality of MR spectra.
  • Research and develop methodologies to predict and assess the quality of clinical MR spectra data.
  • Develop a JAVA plugin for the jMRUI tool, enhancing its functionality and usability for medical image analysis.

Early Stage Researcher Secondment

Barcelona, Spain
Dec 2014 - Feb 2015
  • Acquire and apply basic machine learning skills to classify brain tumor spectra using RUSBOOST, SVM, and Random Forest algorithms.

Graduate Research Assistant

Auburn, AL, USA
Sept 2011 - Aug 2013
  • Apply signal processing techniques (denoising, ICA, PCA) to remove noise and motion artifacts from fMRI and EEG data.
  • Integrate fMRI and EEG data to enhance spatial and temporal resolution for more precise brain activity analysis.
  • Conduct statistical analysis and generate visualizations for research papers and conference presentations.

Bachelors Thesis: Real-Time Industrial Production Counter

Bangalore, India
January 2011 - June 2011
  • Develop an improved counter using Arduino microcontroller board for efficient assembly line production monitoring.
  • Design and implement control algorithms to synchronize the counter with the assembly line system.

Internship

Bangalore, India
July 2010 - Oct 2010
  • Design an LCD Timer handheld device using PIC microcontroller assembly language programming.
  • Develop firmware for the device to accurately measure and display time.

Education

Doctor of Philosophy: Biomedical Sciences/Engineering

University of Bern

Sept 2013 - Aug 2017

Thesis: Quality Aspects of Clinical Magnetic Resonance Spectroscopy: Quantification Issues, Quality Prediction, and Quality Assessment by Machine Learning

Master of Science: Electrical and Computer Engineering

Auburn University

Aug 2011 - Aug 2013

Thesis: Blind Source Separation Methods for Analysis and Fusion of Multimodal Brain Imaging Data

Bachelor of Engineering: Electronics and Communication Engineering

Visvesvaraya Technological University

Sept 2007 - June 2011

Thesis: Real-Time Industrial Production Counter using Arduino Microcontroller

Awards & Honors

IRP Research grant, Zurich, Switzerland

Co-applicant, 150k CHF, 2020-23

Magnetic Resonance in Medicine journal

Top downloaded author, 2018-19

Ecole Polytechnic, Montréal, Canada

Research travel grant, 4k CHF, 2018

ISMRM

Educational stipend award, 2013, 2014, 2015

ISMRM

Magna Cum Laude merit award, 2014-2015

ISMRM

Summa Cum Laude merit award, 2013

Auburn University, USA

Graduate Assistantship Award, ~45k USD, 2011-13

Auburn University, USA

Graduate Fellowship Award, ~20k USD, 2011-13

University of Bern, Switzerland

2nd prize in SLAM competition, 2016

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