Nagur Shareef Shaik

Curriculum vitae

Research CV (PDF)Industry resume (PDF)

Nagur Shareef Shaik

Machine Learning Engineer II, AI Center of Excellence (AI CoE), UST Global Inc.

Ph.D. Candidate in Computer Science, Georgia State University

shaiknagurshareef6@gmail.com+1 (404) 203-9276Atlanta, GAshaiknagurshareef.github.ioGoogle ScholarORCID

Education

Aug 2023 – Dec 2026 (expected)

Ph.D. in Computer Science

Georgia State University, Atlanta, GA · GPA 4.23 / 4.30

Dissertation: From Perception to Clinical Reasoning: Disentangled, Evidence-Grounded Multimodal Learning for Robust Medical Image Analysis and Reporting. Proposal defended April 2026 · expected December 2026.

Aug 2023 – May 2025

M.S. in Computer Science (Thesis)

Georgia State University, Atlanta, GA · GPA 4.17 / 4.30

Thesis: Attentive Multi-modal Learning: Unifying Visual and Clinical Insights for Medical Image Classification and Report Generation.

Jun 2016 – May 2020

B.Tech. (Honours) in Computer Science & Engineering

Vignan's Foundation for Science, Technology & Research, Guntur, India · GPA 9.79 / 10 (3.92 / 4.0)

Chairman's Gold Medal; Best Outgoing Student of CSE.

Experience

Aug 2025 – Present

Graduate Research Assistant

TReNDS Center, Georgia State University, Atlanta, GA

  • Lead my doctoral research on multimodal medical AI that holds up when data is incomplete: modality-aware perception, latent disentanglement under missing modalities, and longitudinal clinical reasoning that keeps track of uncertainty.
  • Developed a probabilistic multimodal framework (HSIC-constrained latent factorization plus a Self-Critiquing Mixture-of-Experts) that set state of the art across 7 systemic and ocular conditions.
  • Developed uncertainty-aware longitudinal reasoning that treats disease progression as distributional change on statistical manifolds (Fisher-Rao), so diagnostic ambiguity survives and claims stay tied to image evidence; state of the art with +7.4% CE-F1 on Longitudinal MIMIC-CXR (Dist-RRG, STRIDE, DELTA).

Jul 2024 – Present

Machine Learning Engineer II

UST Global Inc., AI Center of Excellence, Remote · Aliso Viejo, CA

  • Lead a 21-person AI team in the AI-CoE building UST Alpha AI, UST's one-stop shop for AI workloads, and its platform-modernization tools. Led development of CodeCrafter; designed and built Orbit.
  • Run delivery on an AI-DLC approach: agents migrate legacy .NET 4→8 and COBOL monoliths to React/Java Spring Boot, generating NX monorepos from specs that land 95% functional, with delivery 70% faster at 60% of the cost. Now reimagining a NextGen Reinsurance Platform with SpecKit; the MVP already works.
  • Built PathForce, an AI-assisted anatomic-pathology LIS (rules-first eligibility checks, effort-weighted case distribution, HL7/FHIR/X12, Part 11-grade audit trail), plus RiskIQ for ICD-10 coding and risk adjustment, and PolicyIQ, which turns CMS/payer policy into SME-approved claim edits in minutes, not weeks.
  • Modernized a payer's claims adjudication by re-engineering its extracts and reporting, improving payment accuracy and audit-readiness. Architected and led Call Analytics Agents that raise First-Call Resolution, reduce repeat contacts, and flag quality and compliance issues.
  • Architected BSA DataMap (agentic profiling into BigQuery, saving 2 FTEs/$250K) and an ETL Test Agent validated with LLM-as-a-Judge. Delivered RegView, natural-language search over regulatory documents, and a RAG-powered RFI/RFP engine that cut proposal time by 60%.
  • Collaborated with Dr. Tatsunori Hashimoto (Stanford AI Lab) on AxisSQL, an inference-time-scaling Text-to-SQL framework ranked #12 on the BIRD leaderboard at submission; submitted to EACL (Industry Track).

Sep 2023 – May 2025

Graduate Research Assistant

TReNDS Center, Georgia State University, Atlanta, GA

  • Customized multimodal LLMs in PyTorch to generate reports from medical images and text: 13.4% higher BLEU-4 than VisionGPT, with inference down to 1.6 s per image.
  • Built TensorFlow/Keras image classifiers for retinopathy, schizophrenia, breast cancer and colon cancer, cutting false negatives by 5–7%.
  • Multi-modal Imaging Genomics Transformer: fused genomics with sMRI and fMRI, improving schizophrenia diagnosis accuracy by 2.12%.
  • Guided Context Gating and Spatial Sequence Attention: attention mechanisms for retinal images and structural brain MRI.
  • Medical Vision Language Transformer: an Abstractor-Adaptor framework for resource-constrained medical image captioning.

Sep 2022 – Aug 2023

Software Engineer III

Carelon Global Solutions (Elevance Health), Hyderabad, India

  • Built REST APIs for the COmpensation INcentive System, a microservices application that validates, computes and speeds up incentive payments, cutting processing time by 10%. Wrote Python/SQL clean-up scripts that stopped over $500K in overpayments.
  • Fixed critical production issues in the commissions calculation flow (agent advance computation), saving $1.5 million in historical overpayments; earned the Go Above – Impact Award.

Aug 2020 – Sep 2022

Systems Engineer, Analytics & Insights

Tata Consultancy Services, Hyderabad, India

  • Designed a log-based Bug Root Cause Prediction System with an Attention LSTM in Azure ML Studio, cutting debugging effort and saving 3 full-time equivalents a year.
  • Set up an Azure DevOps model-deployment pipeline that took deployment from 2 to 1.2 hours and raised system availability by 25%; named team Star Performer.
  • Optimized order-orchestration data pipelines, reducing fallouts by 30%, and built operational dashboards for data-driven decisions.

Publications

1.4K+ citations · h-index 14 (Google Scholar, 2026-11)

  1. Gogineni, Akash; Shaik, Nagur Shareef; Mandava, Aasrith; Masood, Adnan; Ye, Dong Hye. C²A: Coupling Spatial Evidence with Clinical Priors via Co-occurrence Aware Class Attention for Multi-Label Chest X-Ray Classification. Proceedings of the 36th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2026), 2026. (Oral)
  2. Shaik, Nagur Shareef; Cherukuri, Teja Krishna; Masood, Adnan; Ye, Dong Hye. DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities. Proceedings of the 40th Annual Conference on Association for the Advancement of Artificial Intelligence (AAAI 2026), 2026. (Oral)
  3. Shaik, Nagur Shareef; Park, Jeongwoo; Kim, Yeong-Jin; Jung, Jaeuk; Oh, Hyunjung; Ye, Dong Hye. Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing. Proceedings of the 36th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2026), 2026. (Oral)
  4. Shaik, Nagur Shareef; Cherukuri, Teja Krishna; Ye, Dong Hye. DREAM: Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion for Expert Precision Medical Report Generation. Proceedings of the 48th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2026. (Oral)
  5. Shaik, Nagur Shareef; Cherukuri, Teja Krishna; Ye, Dong Hye. Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging. Proceedings of the 48th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2026.
  6. Cherukuri, Teja Krishna; Shaik, Nagur Shareef; Yellu, Sribhuvan Reddy; Chung, Jun-Won; Ye, Dong Hye. Dynamic Contextual Attention Network: Transforming Spatial Representations into Adaptive Insights for Endoscopic Polyp Diagnosis. 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025. (Oral)
  7. Shaik, Nagur Shareef; Naralasetti, Veeranjaneyulu; Bodapati, Jyostna Devi. Adaptive Fusion Attention for enhanced classification and interpretability in medical imaging. Machine Vision and Applications, Springer Berlin Heidelberg Berlin/Heidelberg, 2025.
  8. Cherukuri, Teja Krishna; Shaik, Nagur Shareef; Bodapati, Jyostna Devi; Ye, Dong Hye. GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning. Proceedings of the 50th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025.
  9. Shaik, Nagur Shareef; Cherukuri, Teja Krishna; Calhoun, Vince D; Ye, Dong Hye. Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification. Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging (ISBI), 2025.
  10. Shaik, Nagur Shareef; Cherukuri, Teja Krishna, Masood, Adnan; Adeli, Ehsan, Ye, Dong Hye;. Ordinal Label-Distribution Learning with Constrained Asymmetric Priors for Imbalanced Retinal Grading. 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: The Second Workshop on GenAI for Health: Potential, Trust, and Policy Compliance, 2025.
  11. Shaik, Nagur Shareef; Cherukuri, Teja Krishna; Ye, Dong Hye. M3T: Multi-Modal Medical Transformer to Bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generation. Proceedings of the IEEE International Conference on Image Processing (ICIP), 2024. (Oral)
  12. Shaik, Nagur Shareef; Cherukuri, Teja Krishna; Calhoun, Vince D; Ye, Dong Hye. Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images. Proceedings of the 21st IEEE International Symposium on Biomedical Imaging (ISBI), 2024. (Oral)
  13. Shaik, Nagur Shareef and Cherukuri, Teja Krishna. Gated contextual transformer network for multi-modal retinal image clinical description generation. Image and Vision Computing, Elsevier B.V., 2024.
  14. Cherukuri, Teja Krishna; Shaik, Nagur Shareef; Ye, Dong Hye. Guided Context Gating: Learning to Leverage Salient Lesions in Retinal Fundus Images. Proceedings of the IEEE International Conference on Image Processing (ICIP), 2024.
  15. Shaik, Nagur Shareef; Cherukuri, Teja Krishna; Naralasetti, Veeranjaneyulu; Bodapati, Jyostna Devi. Medtransnet: advanced gating transformer network for medical image classification. Machine Vision and Applications, Springer Berlin Heidelberg Berlin/Heidelberg, 2024.
  16. Shaik, Nagur Shareef and Cherukuri, Teja Krishna. Hinge attention network: A joint model for diabetic retinopathy severity grading. Applied Intelligence, Springer US New York, 2022.
  17. Shaik, Nagur Shareef and Cherukuri, Teja Krishna. Visual attention based composite dense neural network for facial expression recognition. Journal of Ambient Intelligence and Humanized Computing, Springer Berlin Heidelberg Berlin/Heidelberg, 2022.
  18. Bodapati, Jyostna Devi; Shaik, Nagur Shareef; Naralasetti, Veeranjaneyulu. Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. Journal of Ambient Intelligence and Humanized Computing, Springer Berlin Heidelberg Berlin/Heidelberg, 2021.
  19. Bodapati, Jyostna Devi; Shaik, Nagur Shareef; Naralasetti, Veeranjaneyulu. Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. Signal Image and Video Processing, Springer London, 2021.
  20. Bodapati, Jyostna Devi; Shaik, Nagur Shareef; Naralasetti, Veeranjaneyulu; Mundukur, Nirupama Bhat. Joint training of two-channel deep neural network for brain tumor classification. Signal Image and Video Processing, Springer London, 2021.
  21. Shaik, Nagur Shareef and Cherukuri, Teja Krishna. Lesion-aware attention with neural support vector machine for retinopathy diagnosis. Machine Vision and Applications, Springer Berlin Heidelberg Berlin/Heidelberg, 2021.
  22. Bodapati, Jyostna Devi; Shaik, Nagur Shareef; Naralasetti, Veeranjaneyulu; Mundukur, Nirupama Bhat. MSENet: Multi-Modal Squeeze-and-Excitation Network for Brain Tumor Severity Prediction. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), World Scientific, 2021.
  23. Shaik, Nagur Shareef and Cherukuri, Teja Krishna. Multi-level attention network: application to brain tumor classification. Signal Image and Video Processing, Springer London, 2021.
  24. Shaik, Nagur Shareef and Cherukuri, Teja Krishna. Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans. Computers in Biology and Medicine, Elsevier B.V., 2021.
  25. Bodapati, Jyostna Devi; Naralasetti, Veeranjaneyulu; Shaik, Nagur Shareef; Hakak, Saqib; Bilal, Muhammad; Maddikunta, Praveen Kumar Reddy; Jo, Ohyun. Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics, Multidisciplinary Digital Publishing Institute (MDPI), 2020.
  26. Dondeti, Venkatesulu; Bodapati, Jyostna Devi; Shaik, Nagur Shareef; Veeranjaneyulu, Naralasetti. Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification. Revue d'Intelligence Artificielle, International Information and Engineering Technology Association (IIETA), 2020.
  27. Bodapati, Jyostna Devi; Naralasetti, Veeranjaneyulu; Shaik, Nagur Shareef. Sentiment Analysis from Movie Reviews Using LSTMs.. Ingénierie des Systèmes d Inf., International Information and Engineering Technology Association (IIETA), 2019.

Manuscripts

  1. Shaik, Nagur Shareef et al.. Claims You Can See: Differential-Evidence Learning of Temporal Assertions for Longitudinal Radiology Report Generation. Under review (CVPR 2027).
  2. Shaik, Nagur Shareef et al.. Dist-RRG: Distributional Change Modeling on Statistical Manifolds for Longitudinal Radiology Report Generation. Under review (ICLR 2027).
  3. Shaik, Nagur Shareef et al.. Evidence That Can Contradict: What Report-Auditing Metrics Actually Measure in Medical Vision Language Models. Under review (ICLR 2027).
  4. Shaik, Nagur Shareef; Park, J.; Kim, Y.-J.; Jo, Y.; Ye, Dong Hye. Oculo-MoE: Retina-Anchored, Interaction-Gated Mixture-of-Experts for Bilateral Multimodal Oculomics. Submitted to the IEEE International Symposium on Biomedical Imaging (ISBI) 2027.
  5. Shaik, Nagur Shareef; Park, J.; Kim, Y.-J.; Jung, J.; Oh, H.; Ye, Dong Hye. SC-CoMoE: Self-Critiquing Cooperative Mixture-of-Experts for Multi-Modal Mobile Oculomics. Submitted to the IEEE International Symposium on Biomedical Imaging (ISBI) 2027.
  6. Shaik, Nagur Shareef et al.. STRIDE: Stochastic Trajectory Reasoning via Iterative Distributional Evolution in Longitudinal Radiology Report Generation. Under review (AAAI 2027).

Honors and awards

  • Graduate Research Award, Department of Computer Science, Georgia State University2026
  • STELLER, NOVA Star and Super Star Awards, UST Global Inc.2026
  • 100% graduate scholarship, Georgia State University2023–2026
  • Winner, D3CODE Hackathon (US Region & Global), UST Global Inc.2025
  • NeurIPS 2025 GenAI4Health Travel Grant, NeurIPS 2025 GenAI4Health Workshop2025
  • IEEE SPS Travel Grant (ICIP 2024), IEEE Signal Processing Society2024
  • Go Above – Impact Award, Elevance Health (Carelon Global Solutions)2023
  • Star Performer Award, Tata Consultancy Services2022
  • Chairman's Gold Medal, VFSTR University2020
  • Best Outgoing Student of CSE, VFSTR University2020
  • Academic Excellence Awards (all four years), VFSTR University2016–2020

Service

Reviewer for 25+ journals and major conferences (AAAI, ICLR, MICCAI, NeurIPS, EACL, ISBI), with 60+ papers reviewed across AI/ML, biomedical imaging and computer vision.

Conferences: AAAI 2027, ICLR 2027, EACL 2027, NeurIPS 2026 GenAI4Health Workshop, IEEE ISBI 2026, AAAI 2026, IEEE ISBI 2025, MICCAI 2025. Journals: Applied Sciences, Artificial Intelligence Review, BMC Medical Imaging, BMC Cancer, Computers in Biology and Medicine, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Cognitive Computation, Diagnostics, Evolutionary Intelligence, IEEE Access, IEEE Transactions on Medical Imaging, IEEE Transactions on Circuits and Systems for Video Technology, International Journal of Data Science and Analytics, International Journal of Information Management Data Insights, International Journal of Machine Learning and Cybernetics, Journal of Big Data, Journal of Ambient Intelligence and Humanized Computing, Knowledge and Information Systems, Machine Vision and Applications, Neural Processing Letters, Pattern Analysis and Applications, Scientific Reports, Sensors, Signal, Image and Video Processing.

Skills

Languages
Python, Java, SQL
ML & research
PyTorch, torchtune, TensorFlow, Keras, scikit-learn, OpenCV, NLTK, NumPy, Pandas, Matplotlib
Agents & LLM apps
LangGraph, LangChain, Google ADK, RAG, FastAPI, Flask, Streamlit
Agentic tools
Claude Code, Codex, Cursor, Windsurf, GitHub Copilot
Cloud & MLOps
AWS (EC2, Lambda, SageMaker, Bedrock AgentCore), Azure (AI Services, DevOps), GCP (Agentspace, Gemini Enterprise), MLflow, Docker, CI/CD, Git
Engineering
Spring Boot, REST APIs, Microservices, Tableau
Research interests
Deep learning, Multimodal learning, Attention networks, Large language models, Agentic AI