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I am an experienced AI Research Scientist with a Ph.D. in Computer Science, specializing in building, training, and evaluating deep learning models for computer vision tasks such as object detection and semantic segmentation. I have applied advanced architectures including CNNs, Vision Transformers, and few-shot learning models across medical, geological, and agricultural imaging domains. I enjoy preparing large-scale imaging datasets and implementing end-to-end machine learning pipelines, integrating multi-sensor data for scene understanding and tracking. My expertise spans Python, PyTorch, TensorFlow/Keras, Hugging Face Transformers, and OpenCV. I have a strong foundation in ML Ops practices and scalable deployment workflows, and I am enthusiastic about solving domain-specific challenges through collaborative innovation and interpretable AI systems. My experience includes applied machine learning research, MVP development with cross-functional teams, and clear communication of technical insights through presentations and reports.…I am an experienced AI Research Scientist with a Ph.D. in Computer Science, specializing in building, training, and evaluating deep learning models for computer vision tasks such as object detection and semantic segmentation. I have applied advanced architectures including CNNs, Vision Transformers, and few-shot learning models across medical, geological, and agricultural imaging domains. I enjoy preparing large-scale imaging datasets and implementing end-to-end machine learning pipelines, integrating multi-sensor data for scene understanding and tracking. My expertise spans Python, PyTorch, TensorFlow/Keras, Hugging Face Transformers, and OpenCV. I have a strong foundation in ML Ops practices and scalable deployment workflows, and I am enthusiastic about solving domain-specific challenges through collaborative innovation and interpretable AI systems. My experience includes applied machine learning research, MVP development with cross-functional teams, and clear communication of technical insights through presentations and reports.

S M Jaisakthi

AI Engineer, Data Scientist, Developer, +2

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I am an experienced AI Research Scientist with a Ph.D. in Computer Science, specializing in building, training, and evaluating deep learning models for computer vision tasks such as object detection and semantic segmentation. I have applied advanced architectures including CNNs, Vision Transformers, and few-shot learning models across medical, geological, and agricultural imaging domains. I enjoy preparing large-scale imaging datasets and implementing end-to-end machine learning pipelines, integrating multi-sensor data for scene understanding and tracking. My expertise spans Python, PyTorch, TensorFlow/Keras, Hugging Face Transformers, and OpenCV. I have a strong foundation in ML Ops practices and scalable deployment workflows, and I am enthusiastic about solving domain-specific challenges through collaborative innovation and interpretable AI systems. My experience includes applied machine learning research, MVP development with cross-functional teams, and clear communication of technical insights through presentations and reports.…I am an experienced AI Research Scientist with a Ph.D. in Computer Science, specializing in building, training, and evaluating deep learning models for computer vision tasks such as object detection and semantic segmentation. I have applied advanced architectures including CNNs, Vision Transformers, and few-shot learning models across medical, geological, and agricultural imaging domains. I enjoy preparing large-scale imaging datasets and implementing end-to-end machine learning pipelines, integrating multi-sensor data for scene understanding and tracking. My expertise spans Python, PyTorch, TensorFlow/Keras, Hugging Face Transformers, and OpenCV. I have a strong foundation in ML Ops practices and scalable deployment workflows, and I am enthusiastic about solving domain-specific challenges through collaborative innovation and interpretable AI systems. My experience includes applied machine learning research, MVP development with cross-functional teams, and clear communication of technical insights through presentations and reports.

Available to hire

I am an experienced AI Research Scientist with a Ph.D. in Computer Science, specializing in building, training, and evaluating deep learning models for computer vision tasks such as object detection and semantic segmentation. I have applied advanced architectures including CNNs, Vision Transformers, and few-shot learning models across medical, geological, and agricultural imaging domains. I enjoy preparing large-scale imaging datasets and implementing end-to-end machine learning pipelines, integrating multi-sensor data for scene understanding and tracking.

My expertise spans Python, PyTorch, TensorFlow/Keras, Hugging Face Transformers, and OpenCV. I have a strong foundation in ML Ops practices and scalable deployment workflows, and I am enthusiastic about solving domain-specific challenges through collaborative innovation and interpretable AI systems. My experience includes applied machine learning research, MVP development with cross-functional teams, and clear communication of technical insights through presentations and reports.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

Research Associate at University of Manitoba

February 1, 2025 - Present

Designed and implemented deep learning models for image classification, segmentation, and object detection to support plant stress detection, disease monitoring, and yield prediction in real-time field environments. Developed robust preprocessing pipelines for large-scale agricultural image data curation, augmentation, and normalization. Integrated multi-source data to enhance phenotyping accuracy and contributed to embedded-friendly image-based tracking workflows for plant growth stages. Evaluated model robustness under variable conditions, collaborated with interdisciplinary teams to translate vision outputs into interpretable agronomic insights, supported grant proposals, and explored predictive maintenance frameworks in agriculture for early intervention strategies.

Data Analyst/Biostatistician at Canadian Grain Commission

January 31, 2025 - July 21, 2025

Conducted statistical modeling and predictive analytics on agricultural datasets using Python, R, and SAS. Built ML models to forecast crop quality and assess disease risks under varying climate conditions. Managed large-scale datasets involving preprocessing, cleansing, and exploratory analysis. Presented analytical results in dashboards and reports for research and policy teams, collaborated with domain experts to translate insights into practical recommendations, and developed early warning systems for crop failure using anomaly detection techniques on environmental sensor data.

Research Scientist at Centre for Advanced Data Science, VIT University

March 31, 2024 - July 21, 2025

Led research and development of deep learning models including CNNs and Vision Transformers for scientific image analysis in medical, agricultural, and satellite imaging domains. Designed AI pipelines integrating multi-sensor data, and built tracking and segmentation architectures for dynamic scenes and high-resolution imagery. Conducted preprocessing and calibration of multimodal datasets to enhance model robustness. Developed lightweight, embedded-friendly models with pruning and quantization for efficient inference. Gained practical experience with medical image formats and collaborated with interdisciplinary teams in disaster management, agronomy, and healthcare. Delivered research presentations and mentored junior researchers to support skill growth and publication writing.

Senior Research Fellow at Anna University

April 30, 2016 - July 21, 2025

Engineered simulators and algorithms optimizing banking network stability and clustering. Designed decision-tree-based classifiers to analyze complex datasets, bridging classical ML techniques and modern applications. Developed and automated ML algorithms to enhance data preprocessing, model training, and deployment efficiency using Python and MLFlow. Published research findings in peer-reviewed journals and conferences.

Research Associate at University of Manitoba

February 1, 2025 - Present

Designed and implemented deep learning models using MobileNetV3, VGG, Inception CNNs, and Vision Transformers for image classification, segmentation, and object detection to monitor plant stress, disease symptoms, and yield-related traits. Developed scalable preprocessing and inference pipelines using Python, PyTorch/TensorFlow, and OpenCV to process large agricultural image datasets. Integrated multi-source data including agronomic, environmental, and visual data to enhance phenotyping accuracy, with a future goal of fusing LiDAR and multispectral sources. Architected real-time tracking and classification pipelines optimized for resource-constrained environments for embedded robotics applications. Evaluated model robustness under variable conditions such as dust, occlusion, and crop diversity, improving generalization. Collaborated with interdisciplinary teams and contributed to grant proposals. Explored predictive maintenance frameworks by analyzing temporal patterns in plant growth

Data Analyst/Biostatistician at Canadian Grain Commission

January 31, 2025 - August 26, 2025

Conducted statistical modeling and predictive analytics on agricultural datasets using Python, R, and SAS. Built machine learning models to forecast crop quality and assess disease risks under varying climate conditions. Managed and preprocessed large-scale datasets, performed exploratory data analysis, and presented analytical results in dashboards and reports for research and policy teams. Collaborated with domain experts to translate insights into practical recommendations. Developed early warning systems for crop failure using anomaly detection and predictive maintenance on environmental sensor data.

Research Scientist (Professor) at Centre for Advanced Data Science, VIT University

March 31, 2024 - August 26, 2025

Led R&D of deep learning models (CNNs, Vision Transformers) for scientific image analysis across medical, agricultural, and satellite imaging domains. Designed AI pipelines integrating multi-sensor data including RGB cameras and satellite multispectral imagery, paving way for LiDAR/radar fusion. Built tracking and segmentation architectures for dynamic scenes and high-resolution images. Conducted multimodal data preprocessing and calibration to enhance model robustness. Developed lightweight, embedded-friendly models leveraging quantization, pruning, and knowledge distillation for optimizing inference on constrained platforms. Hands-on experience with medical imaging modalities like MRI and CT, delivering preprocessing workflows. Collaborated with interdisciplinary teams and contributed research presentations and technical reports supporting prototype translation to operations. Mentored students and junior researchers, supported team skill growth, and contributed to funded projects dep