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01 — About 02 — Skills 03 — Experience 04 — Projects 05 — Blogs 06 — Education 07 — Contact
// 17.3850° N, 78.4867° E → 42.8864° N, 78.8784° W — Buffalo, NY
NISCHITH ADAVALA
Software Engineer
MS CS @ University at Buffalo

Building distributed backend systems and full-stack microservices with Java, Spring Boot, Node.js & React. Prototyping end-to-end ML systems — speech emotion detection, biomarker imaging, deep learning pipelines.

View Projects Get in Touch GitHub ↗
3.8/4.0
MS_GPA
9.0/10
BTech_GPA
2+ yrs
Experience
10+
ML_Projects
scroll_to_explore
JavaSpring BootDistributed SystemsPythonNode.jsReactAWSKubernetesTensorFlowMongoDBDockerGenerative AI JavaSpring BootDistributed SystemsPythonNode.jsReactAWSKubernetesTensorFlowMongoDBDockerGenerative AI
// 01

About Me

Who I Am

I'm Nischith Adavala, a software engineer and MS Computer Science candidate at the University at Buffalo (GPA: 3.8/4.0), graduating December 2025.

My engineering foundation is in distributed backend systems — at Brane Enterprises I engineered a contract-evaluation engine in Java/Spring Boot achieving sub-20ms latency through AST-based rule parsing and async caching pipelines. At RADcube I built real-time WebSocket microservices across React and Angular for multi-tenant clinical systems.

Beyond the backend I've developed end-to-end ML pipelines: a speech emotion detection system with CNN+LSTM, a 90%-accurate mitosis detection pipeline on gigapixel pathology scans, and an auto annotation platform that cut dataset creation time by 90%+. I also post daily dev content on Daily.dev and Medium.

Distributed SystemsFull Stack Machine LearningGenerative AI Cloud ArchitectureMicroservices Real-time SystemsDeep Learning
Proficiency Map
Backend / Distributed Systems95%
NoviceExpert
Full Stack Development88%
NoviceExpert
Machine Learning / Deep Learning80%
NoviceExpert
Cloud & DevOps (AWS, K8s)82%
NoviceExpert
System Design & Architecture85%
NoviceExpert
2+
Years Industry Exp
10+
Languages
10+
Major Projects
40%
Recovery Time ↑
Problem SolvingSystem DesignData StructuresAlgorithmsCI/CD PipelinesREST APIsWebSocketsDeep LearningComputer VisionDaily Dev WriterMedium Author Problem SolvingSystem DesignData StructuresAlgorithmsCI/CD PipelinesREST APIsWebSocketsDeep LearningComputer VisionDaily Dev WriterMedium Author
// 02

Tech Stack

Proficient
Intermediate
Beginner
Coding Hours by Language
Skill Dimensions
Java.
// avg_score: 4.7
Expert in Java — distributed engines with Spring Boot, sub-20ms AST-based rule evaluation, and fault-tolerant microservices.
// 03

Experience

Jun 2023 — Jul 2024
Brane Enterprises
Hyderabad, India
Software Engineer Intern — Distributed Backend
JavaSpring BootAWS EC2/S3/SQSMicroservicesDockerCloudWatchCI/CD
  • Engineered a distributed contract-evaluation engine using AST-based rule parsing, deterministic execution graphs and hierarchical approver traversal — optimized to sub-20ms latency via caching layers and precompiled rule trees.
  • Delivered production-grade backend with fault-tolerant rule loading, transactional versioning, structured logging and automated integration tests; health probes improved recovery time by 40%+.
  • Led end-to-end SDLC from requirements through deployment, identifying and resolving critical bottlenecks in a high-throughput evaluation pipeline.
Dec 2022 — Jun 2023
RADcube
Hyderabad, India
Software Engineer Intern — Full Stack Cloud
TypeScriptNode.jsAWS EC2/S3/DynamoDBReactWebSocketsMongoDBCI/CD
  • Built backend microservices with sharded data-access patterns, optimized aggregation pipelines and WebSocket-based real-time event propagation for low-latency sync across distributed clinical modules.
  • Developed full-stack components in React/Angular with concurrency-safe update flows and client-state reconciliation across multi-tenant environments.
  • Built automated CI pipelines enforcing code quality gates and enabling continuous delivery, reducing deployment overhead significantly.
// 04

Projects

// 01
Generative AI · RAG · Full Stack
AI PDF Chatbot & Agent — LangChain + LangGraph

Full-stack RAG chatbot that ingests PDF documents, stores vector embeddings in Supabase, and answers user queries using Groq-powered Llama models (70B & 8B). Built with a LangGraph state-machine architecture — separate Ingestion and Retrieval graphs handle document parsing and question-answering with real-time SSE streaming. Features multi-chat sessions, model switching, pinned chats, ⌘K search, and chat export. Deployed live on Railway with a CI/CD GitHub Actions pipeline.

LangChainLangGraphGroq / LlamaSupabaseNext.js 14TypeScriptRAGSSE Streaming
Live on Railway · MIT License
⌨ View on GitHub →
// 02
Computer Vision · Automation
Auto Image Annotation Platform

Scalable object detection workflow using SSD MobileNet + TensorFlow to auto-annotate image datasets and generate COCO-format bounding box metadata. Reduced manual annotation time by 90%+. Supports batch processing, configurable confidence thresholds, and multi-class filtering — cutting ML iteration cycles dramatically.

SSD MobileNetTensorFlowCOCO FormatPythonBatch ProcessingOpenCV
90%+ Time Saved
⌨ View on GitHub →
// 03
ML · Sports Analytics
T20 Cricket Score Predictor

XGBoost + Random Forest ensemble predicting T20 cricket innings scores from live match state — team, venue, overs, wickets, current run rate, and last-5-over momentum. Trained on historical IPL + T20I data. Deployed as an interactive Streamlit dashboard with live confidence intervals per over.

XGBoostRandom ForestStreamlitPythonIPL DataFeature Engineering
IPL / T20I Dataset
⌨ View on GitHub →
// 04
ML · Audio Processing
Distributed Speech Emotion Detection System

End-to-end distributed ML system combining MFCC, Chroma and Mel-Spectrogram feature extraction with CNN + LSTM architectures for temporal modeling of speech signals. Scalable preprocessing pipeline merging 4 benchmark datasets across accents and noise environments. Exposed via AWS Lambda inference API with a live React interface for real-time audio classification at sub-100ms latency.

CNN + LSTMPythonTensorFlowlibrosaReactMFCCMel-SpectrogramAWS Lambda
Jan 2025 — Apr 2025
⌨ View on GitHub →
// 05
OS · Kernel · Systems
Pintos Operating System Kernel

Unix-like OS kernel in C on x86 — implemented process execution (fork/exec/wait), argument parsing, stack setup, parent–child synchronization via semaphores, and a per-process file descriptor table (0–127). Secure system calls with kernel-level pointer validation. Refactored 5K+ lines; 90%+ test pass rate with GDB and Makefiles.

Cx86 KernelSystem Callsfork/exec/waitSemaphoresGDBMemory Protection
Jan 2024 — May 2024
⌨ View on GitHub →
// 06
Deep Learning · Medical Imaging
Mitosis Biomarker Detection + Nottingham Scoring

Research-grade DL pipeline achieving 90% detection accuracy on gigapixel pathology TIFFs using multi-scale tiling strategies. Built an interactive high-resolution OpenSeadragon tile viewer with model prediction overlays — enabling fast visualization of mitotic hotspots with minimal memory footprint for clinical pathologist workflows.

Deep LearningOpenSeadragonPythonTIF TilingReactResearch
Jan 2023 — Apr 2024
⌨ View on GitHub →
// 07
Computer Vision · Sports Analytics
YOLO on the Pitch — Football Tracker

Real-time football player and ball detection using YOLOv8 on live match footage. Tracks players, referees, and ball with team classification via K-Means color clustering on jersey pixels. Estimates player speed and distance covered using optical flow and perspective transformation into real-world coordinates.

YOLOv8OpenCVPythonK-Means ClusteringOptical FlowPerspective Transform
Computer Vision
⌨ View on GitHub →
// 08
Computer Vision · Pose Estimation
AI Body Language Detector

Real-time body language classification from webcam using MediaPipe Holistic — extracting 543 facial and body pose landmarks per frame. Exported landmark coordinates as structured feature vectors, trained scikit-learn classifiers (Random Forest, SVM) for gesture and emotion posture recognition at 30 FPS.

MediaPipeScikit-learnPythonOpenCVRandom ForestReal-time
30 FPS · Real-time
⌨ View on GitHub →
// 09
ML · Full Stack Web App
Digit Recognition Web App

Full-stack handwritten digit recognizer with a live HTML5 canvas draw interface. CNN trained on MNIST (98.7% accuracy) served via Flask REST API — returns top-5 class probabilities rendered as animated confidence bars. Preprocessing pipeline handles canvas-to-28×28 normalization in real time.

CNNMNISTTensorFlowFlaskJavaScriptCanvas API
98.7% Accuracy
⌨ View on GitHub →
// 10
ML · Health Analytics
Calories Burnt Prediction

Regression pipeline predicting calories burned from biometric and workout features — age, BMI, exercise duration, heart rate, body temperature. XGBoost achieves low MAE (<5 kcal). Includes Seaborn correlation heatmaps, feature importance ranking, and an interactive prediction widget.

XGBoostRegressionPythonScikit-learnSeabornHealth Data
<5 kcal MAE
⌨ View on GitHub →
// 11
ML · Recommendation Systems
Music Recommendation System

Hybrid recommendation engine combining content-based filtering on Spotify audio features (danceability, energy, valence, tempo, key) with collaborative SVD matrix factorization. Cosine similarity and KNN for item-to-item lookup. Generates personalized playlists from listening history with cold-start handling.

SVDCosine SimilarityKNNPythonPandasScikit-learnSpotify Data
Hybrid Filtering
⌨ View on GitHub →
// 12
Deep Learning · Agriculture
Potato Disease Classification

CNN image classifier for potato leaf disease detection (Early Blight, Late Blight, Healthy) trained on the PlantVillage dataset — achieving 97%+ accuracy. Full-stack deployment: React drag-and-drop frontend uploads leaf photos, FastAPI backend returns diagnosis with confidence scores and treatment recommendations.

CNNTensorFlowFastAPIReactPlantVillage97% Accuracy
97%+ Accuracy
⌨ View on GitHub →
// 13
Blockchain · Healthcare
Blockchain for Secure EHR Sharing

Decentralized Electronic Health Records platform on Ethereum smart contracts — patient-controlled access permissions, immutable on-chain audit trails, and AES-encrypted medical records stored on IPFS. Ensures HIPAA-compliant data integrity across mobile cloud e-health systems with role-based key management.

EthereumSolidityIPFSSmart ContractsWeb3.jsAES Encryption
HIPAA Compliant Design
⌨ View on GitHub →
// 14
Blockchain · DeFi
Blockchain Bank Smart Contract

Solidity DeFi banking contract on Ethereum with deposit, withdraw, transfer, and interest accrual. Reentrancy guard, access control via OpenZeppelin, and on-chain event emission for audits. Deployed on Goerli testnet with ethers.js + React frontend for MetaMask wallet interaction and transaction history.

SolidityEthereumethers.jsReactHardhatOpenZeppelinMetaMask
Goerli Testnet Deploy
⌨ View on GitHub →
// 15
Deep Learning · Medical Imaging
Melanoma Detection — Skin Cancer Classifier

End-to-end deep learning system for classifying skin lesions into 9 diagnostic categories using CNNs and Transfer Learning (VGG16). Trained on the ISIC Kaggle dataset (~2,239 images, balanced to ~5,850 via Augmentor), with an 80/10/10 train-val-test split. Multi-GPU training via TensorFlow Mirrored Strategy. Deployed as a Streamlit web app where users upload dermoscopic images and receive real-time class predictions with confidence scores.

TensorFlowVGG16CNNTransfer LearningAugmentorStreamlitISIC Dataset
9 Lesion Classes · Multi-GPU
⌨ View on GitHub →
// 16
Deep Learning · Vision · NLP
Lip Reading — Silent Speech Recognition

End-to-end audioless speech recognition system that reads lip movements from video to transcribe spoken words. A spatiotemporal CNN processes individual frames for visual features while a Bi-LSTM captures temporal dependencies across the sequence. CTC (Connectionist Temporal Classification) loss enables alignment-free training on variable-length utterances — bridging computer vision and NLP without any audio input.

CNN + Bi-LSTMCTC LossPythonTensorFlowOpenCVVideo ProcessingNLP
Audioless · CTC Training
⌨ View on GitHub →
// 17
Deep Learning · Animal Behaviour
EfficientPet — Pet Emotion Decoder

Automated pet emotion recognition system that classifies facial expressions of cats and dogs into Angry, Happy, Sad, and Other using an EfficientNet CNN backbone. Trained on the Kaggle Pets Facial Expression Dataset (~250 images/class) with an 80/10/10 split and ImageDataGenerator augmentation. Deployed via Streamlit — upload a pet photo and get real-time emotion prediction with confidence score.

EfficientNetTensorFlowKerasStreamlitImageDataGeneratorOpenCVPIL
4 Emotion Classes · Real-time
⌨ View on GitHub →
// 18
Data Analytics · Business Intelligence
All-India CPI Analysis — IBM Cognos Dashboard

End-to-end data analytics project analyzing the All India Consumer Price Index (Urban & Rural, base year 2010) to uncover inflation trends, regional variations, and consumer purchasing power shifts. Data loaded into IBM DB2 and processed in IBM Cognos Analytics — building interactive dashboards, multi-scene data stories, and crosstab reports. Embedded into a Flask web application via Cognos iframe integration for public access.

IBM CognosIBM DB2FlaskData AnalyticsPythonHTML/CSSCPI Dataset
Urban vs Rural · Inflation Insights
⌨ View on GitHub →
Showing 6 of 18 projects
// 05

Blogs & Writing

Daily.dev
@nischithadavala

Posting daily developer content — tech insights, engineering deep dives, and curated reads on distributed systems, ML, and modern backend architectures. Part of a community of 1M+ developers.

Daily
Posting Freq
1M+
Community Size
Dev
Content Focus
Read on Daily.dev →
Medium
@nischithadavala

Writing in-depth technical articles on distributed systems, backend engineering patterns, machine learning pipelines, and cloud architecture — bridging theory with real production experience.

Tech
Content Type
Deep
Article Depth
ML/BE
Focus Area
Read on Medium →
// Follow along for daily content on distributed systems, ML engineering, and backend architecture
// Latest Articles
View all on Medium →
// 06

Education

🎓 Graduate
Master of Science
Computer Science
University at Buffalo — SUNY
📅 Aug 2024 – Dec 2025📍 Buffalo, New York
★ GPA: 3.8 / 4.0
Key Coursework
Distributed SystemsArtificial IntelligenceAlgorithmsData Intensive Computing
🏛️ Undergraduate
Bachelor of Technology
Computer Science & Engineering
Jawaharlal Nehru Technological University
📅 Dec 2020 – May 2024📍 Hyderabad, India
★ GPA: 9.0 / 10.0
Key Coursework
OOPDatabase SystemsOSComputer NetworksSoftware Engineering
🏆
157.22/175
// competitive_programming_score
GeeksforGeeks Job-A-Thon 13

Scored 157.22 out of 175 in the Job-A-Thon 13 Hiring Contest by GeeksforGeeks — demonstrating strong competitive programming and algorithmic problem-solving against thousands of candidates nationally in a timed, high-stakes environment.

// Contest field: Competitive Programming & Problem Solving
// 07 — Let's Connect

Have an
Opportunity?
Let's Talk.

Software engineer with a strong background in distributed systems, full-stack development and ML engineering. Let's build something great together.