About me | Sudarshan Anand

SUDARSHAN ANAND

AI/ML Research Scientist | Product Developer | Data Scientist

MS Computational Science & Engineering @ Georgia Institute of Technology (Aug 2024 - May 2026)

asudarshan14@gmail.com|Atlanta, GA

Hey, I'm Sudarshan Anand

I am an NSF-EMBS-Google NextGen Scholar and AI researcher who focuses on building ML systems that are as interpretable as they are powerful.

With dual Master's degrees in computational fields and hands-on experience in building scalable ML solutions, I bridge the gap between mathematical rigor and production-ready software.

Actively seeking ML Research, Engineering, and Data Science roles focused on scalable, safety-aware AI.

Core strengths

Core Strengths & Current Work

  • Explainable AI (XAI)

    Architecting multimodal Concept Bottleneck Models (CBMs) to enhance interpretability and transparency in AI decision-making.

  • Medical Imaging AI

    Have experience working with multiple imaging modalities (MRI, CT, X-ray) and building production-grade vision models for healthcare (Qure.ai). Worked on projects focused on early-detection of chronic diseases (Parkinson's Disease, Lung Cancer, Pneumonia, etc).

  • Full-Stack AI Products

    Designed end-to-end, user-centric AI-based applications, such as SearchIQ (RAG-based search) for employees to chat with company policy documents and knowledge bases. Integrated conversational memory and personalized search suggestions to enhance user experience and engagement.

  • Open-Source Tooling

    Developed accessible, developer-focused repositories, such as Samay (open-source Python library), which unifies training and inference across 10+ time-series foundation models, making it easier for research & developers to adopt and experiment with cutting-edge techniques in time-series analysis.

  • High-Performance Scaling

    Orchestrating distributed PyTorch workflows across HPC clusters to accelerate model training and data processing.

  • Graphs and Networks

    Developed GPLAN , a python-based application for designing 2D-floor plans that optimize for user and design requirements using only graph algorithms and optimization. Led the research and development team involved in adding corridors to floorplans with the power of graph algorithms.

Recognition

Honors & Awards

  • IEEE BHI 2025 Data Challenge Competition Champion

    Oct 2025
    Developed an AI/ML-driven depression risk prediction model, winning the IEEE-sponsored competition (backed by NSF & Google)

  • NSF-EMBS-Google Young ProfessionalNextGen Scholar

    Sept 2025
    Recognized for high-impact Biomedical AI research, including an invitation to present at the IEEE BHI 2025 conference