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Name: Teja Krishna Cherukuri Phone: 470-662-7146 Address: Atlanta, GA (Open to Relocation) Email: [email protected] Linkedin Url: https://linkedin.com/in/tejacherukuri Github Url: https://github.com/tejacherukuri Portfolio Url: https://tejacherukuri.github.io Google Scholar Url: https://scholar.google.com/citations?user=6S9WmqwAAAAJ&hl=en Education College: Georgia State University Location: Atlanta, GA Degree: Master of Science in Computer Science, GPA: 4.21/4.3 Duration: Aug 2023 β May 2025 Coursework: Deep Learning, Advanced Machine Learning, Computer Vision, Natural Language Processing, Digital Image Processing, Computational Intelligence, Data Science Technical Skills Languages: Python, Java, SQL Frameworks: PyTorch, TensorFlow, Keras, Flask, FastAPI, LangChain, Streamlit Libraries: NumPy, Pandas, Scikit-Learn, Matplotlib, OpenCV, NLTK Cloud & DevOps Tools: Git, Docker, Azure ML Studio, Azure AI Services Work Experience Role: Graduate Research Assistant Company: Georgia State University (TReNDS Lab) Duration: Sep 2023 β Present Location: Atlanta, GA Responsibilities or duties: β’ Published 5 IEEE papers showcasing the impact of our research methods for advancing AI in Medicine. β’ Developed and fine-tuned Multi-modal LLMs for medical image captioning using PyTorch, integrating images with diagnostic text, achieving a 13.4% higher BLEU4 over VisionGPT, with just 440M parameters, and reducing inference time to 1.6 sec per image. β’ Designed various medical image classification models for diagnosing chronic diseases such as schizophrenia, diabetic retinopathy, breast cancer, and colon cancer using specific imaging modalities with 5%β7% lower false negatives. β’ Enabled high-performance computing for training deep learning models through Slurm job scheduling, optimizing resource allocation and accelerating processing times. ---Below are more details about the work I did, in the GRA role, can be used more in the context of research positions if needed--- More details starts here Note: The below is just a background info of my works, you can only use this when there is absolute need of drafting to detail and stuff. β’ Medical Vision Language Transformer: Pioneered a novel approach for resource-constrained environments, integrating Abstractor & Adaptor to enhance feature focus and fusion, achieving expert-level precision in medical image captioning. β’ Multi-Modal Medical Transformer: Devised a vision-language model integrating retinal image features & clinical keywords, achieving a 13.5% improvement in BLEU-4 over GPT-2 for accurate diagnostic report generation and improving explainability by visualizing attention to diseased regions. β’ Guided Context Gating: Innovated a novel attention model to improve context learning in retinal images, boosting accuracy by 2.63% over advanced attention methods & 6.53% over Vision Transformer, enhancing retinopathy diagnosis. β’ Spatial Sequence Attention Network: Formulated a unique attention mechanism to identify Schizophrenia specific regions in brain sMRI, improving diagnosis accuracy by 6.52% and clinical interpretability with neuroanatomical insights. β’ Multi-Modal Imaging Genomics Transformer: Designed a fusion model combining genomics with sMRI & fMRI, bettering Schizophrenia diagnosis accuracy by 2.12% and revealing associated genetic markers. More details ends here Role: Data Scientist Company: Tata Consultancy Services Limited Duration: Nov 2020 β Aug 2023 Location: Hyderabad, TS Responsibilities or duties: β’ Built a customer attrition system based on ensemble of SVM, Random Forest, and AdaBoost in Python using scikit-learn, improving 42 basis points in annual customer retention. β’ Led a POC for a dynamic risk-based pricing model, aligning interest rates with borrower risk profiles and market conditions, which reduced underpriced loans by 18%, and generated $3M in annual revenue growth. β’ Implemented REST APIs using FastAPI to surface machine learning models for loan approval and fraud detection, reducing workflows processing time by 30% and preventing potential fraud losses of $25M, annually. β’ Developed and deployed a chatbot using Azure AI Bot Service for handling customer queries in collaboration with the Customer Experience & Personalization team, achieving a 96% CSI and a 3.5 FTE reduction. β’ Achieved sub-100ms response times for high-volume inference requests by containerizing models with Docker and deploying them on GPU-enabled Azure Container Instances. Research Experience and Accomplishments β’ Published 7 research papers in journals, with 280+ citations and 5 H-index, pioneering Attention models, Multi-modal learning, Transformers and Large Vision Language Models. β’ Presented 5 works at reputed conferences, including ISBI 2024, ICIP 2024, ISBI 2025, and ICASSP 2025. Projects Name: RetinAI Doctor Link: https://github.com/TejaCherukuri/Guided-Context-Gating Demo: https://huggingface.co/spaces/tejacherukuri/Guided-Context-Gating Technologies used: Python, Streamlit, TensorFlow, OpenCV, Git Duration: Feb 2024 - Jun 2024 β’ Built an AI tool to process retinal scans, predict diabetic retinopathy (DR) severity, and achieved 90.13% accuracy and recall using a novel Guided Context Gating (GCG) attention mechanism. β’ Enhanced interpretability by generating attention maps that highlight areas of focus, empowering ophthalmologists with insights for early and reliable DR diagnosis. Additional details about my resume: Job Interests: Data Scientist, Applied Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Deep Learning Engineer, Research Engineer Open to any location to work within the U.S (Comfortable with all modes of working - hybrid, onsite and remote) I can only apply to jobs with (university grad roles, early career postings, associate level postings, 3 years of experienced roles) I have end-to-end machine learning project experience, from requirement gathering, model development, model optimisation, model evaluation and model deployment. Additional Skills: Machine Learning, Linear Regression, Logistic Regression, Classification, PCA, Ensembling. Deep Learning: Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, LLMs, MLLMs, Vision Language Models, Generative AI, VAE. Certification: Deep Learning Specialisation from DeepLearning.AI, Python for Everybody certification from University of Michigan |