Manish Pandey

Co-Founder

New Delhi, Delhi, India4 yrs experience
Most Likely To SwitchAI ML Practitioner

Key Highlights

  • Expert in Graph Machine Learning and Geometric Deep Learning.
  • Significant contributions to AI in healthcare and radiology.
  • Proven track record in developing advanced LLM models.
Stackforce AI infers this person is a Healthcare AI Specialist with expertise in advanced machine learning technologies.

Contact

manishpandey.collaborate@gmail.comLinkedIn

Skills

Core Skills

Graph Neural Networks (gnn)Large Language Models (llm)Deep Learning

Other Skills

AI AgentsAI InnovationAI LeadershipAI ProductivityAI StrategyAI for BusinessAI integrationAI-driven diagnostic solutionsAPI ManagementAlgorithmsAmazon RedshiftAmazon Web Services (AWS)AndroidArtificial Intelligence (AI)Artificial Intelligence for Business

About

I am a dedicated Machine Learning Engineer and co-founder of FreeDoctr AI, focusing strongly on leveraging advanced AI technologies to transform healthcare and radiology. My expertise spans multiple cutting-edge domains, including Graph Machine Learning, Geometric Deep Learning, 3D Computer Vision, Diffusion Models, GANs, and Generative AI. I am proficient in programming languages such as C++, Java, and Python, and skilled in frameworks like TensorFlow, Keras, PyTorch, and PyTorch Geometric. My professional journey includes significant contributions at KPMG, where I developed advanced LLM models and Graph Neural Networks for medical imaging, achieving substantial improvements in training efficiency. I have also led research projects at IITs, focusing on contactless palmprint recognition and COVID-19 detection using multi-modal frameworks. My self-initiated project on fake news detection using GNNs further highlights my ability to devise state-of-the-art solutions. Currently, I am exploring the intersection of deep learning and biomedical research, with a focus on Graph ML and Geometric Deep Learning. I am actively seeking opportunities to collaborate and contribute to groundbreaking projects in these areas. Skills : - Medical Imaging: Expertise in radiology, including X-rays, CT scans, MRIs, and PET scans. - Large Language Models (LLM): Development and inference with a focus on alignment and retrieval-augmented generation (RAG). - Graph Neural Networks (GNN): Application in medical imaging and disease diagnosis. - Reinforcement Learning from Human Feedback (RLHF): Enhancing AI models with human feedback. - Technological Integration: Proficient in Neo4j, AWS, and cloud platforms for scalable AI solutions. - Programming and Frameworks: Python, PyTorch, TensorFlow, FastAPI, and Kubeflow. - NLP and Knowledge Graphs: Leveraging NLP for enhanced biomedical insights and knowledge graph integration. Feel free to connect with me to discuss potential collaborations or explore innovative AI solutions that can drive impactful change in various industries.

Experience

4 yrs
Total Experience
1 yr 7 mos
Average Tenure
3 yrs 5 mos
Current Experience

Freedoctr

Co-Founder

Feb 2024 – Present · 2 yrs 4 mos · India · Remote

  • . Vision and Mission Alignment: At Freedoctr, our vision to transform healthcare through AI-driven diagnostic solutions mirrors the mission to integrate advanced AI technologies for precise and efficient healthcare outcomes. We have focused on developing innovative radiology-based image analysis tools and precision medicine systems, which is in line with the mission to enhance healthcare outcomes.
  • . Core Areas of Focus: Our work at Freedoctr has concentrated on radiology imaging technologies, including chest X-rays, CT scans, MRI, and PET scans. We have also explored specialized healthcare areas such as child healthcare, rare diseases, and cardiovascular treatments, leveraging AI to improve diagnostic accuracy and treatment efficiency.
  • . Technological Integration: We have employed a robust technology stack, including full-stack development, model deployment, and the use of Graph Neural Networks (GNNs) and Large Language Models (LLMs). Our integration of cloud platforms and databases like Neo4j has been pivotal in managing and analyzing complex healthcare data.
  • . Innovation and Development: At Freedoctr, we have prioritized innovation in image processing and analysis using AI, integrating LLMs to enhance diagnostic accuracy, and employing domain adaptation and fine-tuning for specific medical applications. Our focus on retrieval-augmented generation (RAG) integration has improved the relevance and accuracy of AI-generated insights, aligning with the innovation goals outlined in the business model.
Graph Neural Networks (GNN)Large Language Models (LLM)AI-driven diagnostic solutionsCloud platformsNeo4j

Kpmg india

Machine Learning Engineer

Jan 2023 – Present · 3 yrs 5 mos · India · Hybrid

  • . Staying updated on LLM Research and Development, and developed LLM model to increase Context Learning (ICL) from 32K to 1M tokens with Linear Attention, inspired by Linear Transformer’s Encoder-only Architecture.
  • . I worked on a parallel graph attention neural network for the multi-type extraction of knowledge graph embeddings. Achieved features of diverse graph types DAGs, Weighted Graphs, and Hierarchical Temporal Graphs for enhanced analysis and interpretation of complex medical imaging data.
  • . Managed Distributed Training GPUs Data Parallelism for Training LLM. Achieved 30% reduction in training time and 25% improvement in inference speed.
  • . Provided Consultation and Advice on building a Mixed-Agent Framework. Integrated Autonomous Agents with Retriever Augmented Generation Pipeline. building chat-based AI assistants through personalized solutions Fine-Tuning and Quantization.
  • . Handling the API, LLM, model deployment, evaluation frameworks, end-to-end ML pipeline, and unit testing.
  • . Experimented with a couple of open LLMs and sentence embedding models during the process like LLAMA, alpaca, Mistral-7b, DistilBERT, all-MiniLM-L6-v2, etc.
Large Language Models (LLM)Graph Attention Neural NetworkDistributed TrainingAPI ManagementModel DeploymentGraph Neural Networks (GNN)

Indian institute of technology, ropar

Research And Development Assistant

May 2022 – Aug 2022 · 3 mos · India · On-site

  • . Palmprint ROI extraction under unconstrained pose variations and implementation of a Deep Learning based system for contactless palmprint verification.
  • . Implemented segmentation over 1300+ image dataset and got approx 98% accuracy over the different metrices on different networks such as U-NET, ResNet, Attention U-NET, Seg-Net etc.
  • . Worked on different alignment techniques based on principle line or landmarks i.e. affine transformation and homography.
  • . Scaled our dataset to CompNet and further done feature extraction and matching for the final result.
Deep LearningImage SegmentationFeature Extraction

Indian institute of technology, patna

Research Intern

May 2021 – Sep 2021 · 4 mos · Patna, Bihar, India

  • Developed a project "Respiratory Scan-based COVID-19 Detection Using Multi- Modal Multi-Task Learning Framework"
  • Enhanced the quality of COVID-19 X-Rays and CT-Scans using various Image-Preprocessing Techniques and created a deep-learning novel architecture that may be configured with or without Adversarial Training.
  • Evaluated the findings and Statistical Significance Tests to check their generalizability
Deep LearningImage PreprocessingStatistical Analysis

Indian institute of technology, kharagpur

Research Intern

Dec 2020 – Feb 2021 · 2 mos

  • Developed a Deep Learning-based Covid-19 Time Series Prediction.
  • Used Deep Learning and Statistical approaches to capture the patterns and trends of varying events related to infectious
  • diseases.
  • Implemented ARIMA,HWAAS Models for exploiting linear dependencies in observations and time series forecasting for
  • univariable data.
  • Explored RNN, LSTM Neural Network to find temporal correlations in time series prediction.
Deep LearningTime Series AnalysisStatistical Modeling

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