Mission: Data, Space & Intelligent Systems
Akhil Kanukula
Data Science ▪ ML Systems ▪ Space & Climate
Data Analyst, Graduate Research Assistant, and Full-Stack Web Developer crafting ML systems for climate, space, and human behavior. From NASA-affiliated climate prediction research to LLM-driven user study platforms, my work orbits around turning complex data into cinematic, interactive experiences.
Current Orbit
M.S. Data Science @ UMBC (CGPA 4.0/4.0)
Graduate Research Assistant on NASA-funded climate projects, designing DDPM-based U-Net models for 2m temperature anomalies and ensemble uncertainty.
Primary Stack
PyTorch · Next.js · FastAPI · PostgreSQL
Coordinates
Baltimore, MD · Earth
Who I Am
I’m a Data Scientist in training and a builder at heart. My work spans climate forecasting, land-cover simulation, LLM-driven user studies, and production-grade web systems.
Previously, I developed REST APIs and microservices as a MuleSoft Developer at Tata Consultancy Services, and now I work across the full stack—Next.js, FastAPI, PostgreSQL—to design research platforms that can scale from pilot studies to real-world deployments.
I enjoy taking complex, high-dimensional problems and turning them into clean visual interfaces and robust ML pipelines—whether that’s simulating future land-cover using diffusion models or studying how humans interact with hallucinating language models.
Core Systems
Climate ML · Diffusion Models · U-Net · LLMs · Full-Stack Web
Languages & Tools
Python, C++, Java, PyTorch, Next.js, FastAPI, PostgreSQL, Power BI, MuleSoft, HPC.
System Scan
Main Tech Stack
High-performance technologies for data, ML, and full-stack delivery
Languages & Frameworks
Front-End Technologies
Databases
Cloud & DevOps
Tools
AI / ML & Scientific Computing
Experience Across Orbits
Platinum Business Services
Data AI Engineer Intern
Feb 2026 – Present
Design and develop use cases and MVPs for R&D AI applications in Health Care, Cybersecurity, and Quantum Computing, with a focus on Artificial Intelligence in Animal Testing Research.
Health Tech Alley
Data Analyst
Sept 2025 – Jan 2026
Building childcare demand/supply datasets, relational schemas, and Power BI dashboards to reveal shortages at the census block level.
UMBC · NASA
Graduate Research Assistant
Aug 2024 – Aug 2025
Developed DDPM-conditioned U-Net for monthly 2-m temperature anomaly forecasting using ERA5 GRIB/NetCDF pipelines with xarray+cfgrib.
Cyber Pack Ventures Inc.
Software Developer Intern
Jan 2025 – December 2025
Built an LLM behavioral study platform with three fixed AI personalities, hallucination logic, and full-stack logging on Next.js + FastAPI + PostgreSQL.
Tata Consultancy Services
MuleSoft Developer
Sept 2021 – May 2024
Designed RAML-based REST APIs, integrated Angular front ends with SAP Commerce Cloud, and deployed services on CloudHub with CI/CD.
Highlighted Missions
Unified Wildfire & Crop Monitoring Platform
Geospatial Machine Learning Platform for Wildfire Risk Assessment and Crop Health Monitoring
Addresses the critical convergence of environmental crises—wildfires and agricultural drought—by harmonizing data from multiple satellite constellations (VIIRS, SMAP, ERA5) and applying Deep Learning. Key capabilities include a U-Net Computer Vision model for wildfire segmentation through smoke using thermal data, and an LSTM Time-Series model for drought forecasting via soil moisture prediction.
Simulating Land Cover Changes Using Diffusion-Based Generative Models
Land Cover Simulation via Diffusion Models
Trained a UNet-based diffusion model on EuroSAT Sentinel-II imagery to simulate land cover transitions, aiding urban planning and environmental monitoring.
NSF-Funded User Study Platform
Full-Stack LLM Behavioral Web Application
Developed a Next.js + FastAPI + PostgreSQL platform analyzing how users interact with hallucinating LLMs under three fixed AI personalities and task-wise flows.
2m Temperature Anomaly Modeling
DDPM-Based Climate Forecasting
Built DDPM-conditioned U-Net models on ERA5 reanalysis data with xarray+cfgrib pipelines, achieving improved skill over deterministic baselines.