EXCELLENCE IN AI & RESEARCH

SHAUNAK
MISHRA

ML Researcher

Computer Vision Engineer and ML Researcher. Specialized in medical imaging segmentation, deep learning model benchmarking, and building production fullstack systems.

8.72

SOA CGPA

250+

DSA SOLUTIONS

19

MS AI BADGES

shaunak_cognitive_brain.sh
shaunak@portfolio:~$ welcome
Initializing Shaunak Portfolio Cognitive Engine v3.1...
System: Core Loaded. Models online (ResNeXt-50, MambaOut, ViT, SwinSeg).
DSA Module: Connected (250+ solved problems verified).
MS Build AI Hackathon 2026: WorkflowOS AI Pipeline [Active].
Type 'help' to see available CLI commands.
shaunak@portfolio:~$
>
SM

Shaunak Mishra

Computer Science & Engineering Scholar • SOA University

01 / ABOUT ME

BACKGROUND & NARRATIVE

SM

Shaunak Mishra

SOA University (ITER), Bhubaneswar (CGPA: 8.72)

Pioneering adaptive algorithms in Deep Learning, Computer Vision, and modular web structures that deliver impactful diagnostics.

As a Computer Science Scholar closely tracking the rapid transition of AI from research to real-world deployment, I enjoy building at the boundary of machine learning theory and highly polished, scalable systems engineering.

My research interests focus on convolutional segmentation architectures designed for clinical brain tumor diagnosis from raw MRI. Along with theoretical exploration, I develop end-to-end web frameworks mapping agent actions, node graph operations, and audio diagnostics.

Tech Stack Focus

PythonC++TypeScriptPyTorchTensorFlowOpenCVUNetReactNext.jsTailwind CSSNode.jsExpressGitDocker

Key Statistics

250+

DSA Solutions

LeetCode & GFG algorithmic keys

14+

ML Architectures

Implemented CV & deep networks

11+

Software Projects

Fullstack platforms & products

19

MS Learn Badges

Academic cloud credentials

Education & Research Timeline

B.Tech Student (CSE)

2022 – 2027

SOA University (ITER), Bhubaneswar

CGPA: 8.72 / 10.0

In-depth specialization in Computer Vision, Advanced Data Structures, Operating Systems, and Relational Databases.

Frontend Developer

Jun 2025 – Aug 2025

CN Nexus — Coding Ninjas Club, SOA University

Improved problem discovery speed by 30% by building topic-wise categorization and memoized search filters for a DSA platform aggregating LeetCode, GFG, and Codeforces

Founding Engineer

Dec 2025 – Mar 2026

Mentomania

Designed and built complete frontend UI/UX for a government exam mentorship platform, delivering full product from design to deployment

02 / RESEARCH DISCOVERY

COMPUTER VISION IN MEDICAL IMAGING

Benchmarking 14 classification and 10 segmentation architectures on the BRISC2025 dataset (6,000 T1-weighted MRI slices, 4 classes). Published pipeline combines MambaOut classification with SwinSeg segmentation and GradCAM++ explainability.

MambaOut + SwinSeg Pipeline

01

Raw MRI Intake

Ingest high-res DICOM scans spanning T1-CE, T2, and FLAIR profiles.

02

Preprocessing

Apply pixel normalization, non-local means filtering, and spatial resizing.

03

MambaOut Classifier

State-space model classifies tumor type from preprocessed MRI slices.

04

SwinSeg Segmentation

Swin Transformer-based decoder extracts precise tumor boundaries.

05

GradCAM++ Map

Generate activation heatmaps revealing backprop weights on diagnostic regions.

Interactive Grad-CAM Simulator Lab
Live Neural Simulation
Select Patient MRI

MRI Diagnostic Sandbox Waiting

Pick a patient MRI scan from the left stack and click "Examine MRI" to boot model pipeline.

Architecture Benchmark (BRISC2025)
MambaOut (Classification)99.50% Acc
Params: 42.3MTest Accuracy
maxViT (Classification)99.50% Acc
Params: 39.6MTest Accuracy
ViT (Classification)99.40% Acc
Params: 45.7MTest Accuracy
ResNeXt-50 (Classification)99.30% Acc
Params: 25.0MTest Accuracy
SwinSeg (Segmentation)0.8765 Dice
Params: 62.4MDice Score
DeepLabV3+ (Segmentation)0.8568 Dice
Params: 43.5MDice Score
UNet++ (Segmentation)0.8178 Dice
Params: 36.7MDice Score
UNet (Segmentation)0.8080 Dice
Params: 31.0MDice Score

PAPER IN PREPARATION

Comprehensive Benchmarking of Deep Learning Architectures for Brain Tumor Classification and Segmentation on BRISC2025

Systematic evaluation of 14 classification models and 10 segmentation architectures on 6,000 T1-weighted MRI slices. The proposed MambaOut + SwinSeg pipeline with GradCAM++ explainability achieves state-of-the-art results under faculty supervision at SOA University.

03 / CREATIVE IMPLEMENTATIONS

ADVANCED ENGINEERING WORK

11

Active Projects

1

OS Contributions

5

Featured Projects

FULLSTACK
Featured

WorkflowOS

FastAPI

AI agent swarm that converts meeting transcripts into execution plans. Microsoft Build AI Hackathon 2026.

FastAPIAzure OpenAINext.jsCosmos DB+3
Explore Architecture
ML-AI
Featured

Brain Tumor Detection

Python

Medical image classification & segmentation research. 14 classification + 10 segmentation architectures benchmarked on BRISC dataset (6,000 MRI scans).

PythonPyTorchTensorFlowtimm+6
Explore Architecture
FULLSTACK
Featured

MentorHub / MentoMania

Next.js

Mentorship platform connecting students with exam toppers (IIT-JEE, NEET, CAT, UPSC).

Next.jsReactTypeScriptTailwind CSS+3
Explore Architecture
FULLSTACK
Featured

AI Vehicle Health Diagnostics

Next.js

AI/ML-based vehicle health diagnostics, predictive maintenance, and driving simulation.

Next.jsFirebaseGenkitTailwind CSS+1
Explore Architecture
ML-AI

Metal Defect Detector

Python

Surface defect detection on metal surfaces using deep learning.

PythonFlaskDeep LearningOpenCV
Explore Architecture
ML-AI

AI Resume Analyzer

React Router

AI-powered resume parsing and scoring with Docker deployment.

React RouterTypeScriptTailwind CSSDocker
Explore Architecture
FULLSTACK

Civic Issue System

React

Platform for civic issue reporting and tracking.

ReactTypeScriptViteTailwind CSS
Explore Architecture
OPEN SOURCE
Featured

openfoodfacts-nodejs

TypeScript

PR #646 merged into v2.0.0-alpha.8. Added getProductImageFolder() to production SDK.

TypeScriptJestNode.jsSDK Development
Explore Architecture
TOOLS

leetsync-organizer

Python

Python tool that auto-categorizes LeetCode solutions into difficulty folders via GitHub Actions.

PythonGitHub ActionsGit
Explore Architecture
TOOLS

DSA-Solve

C++

250+ DSA problems solved in C++ covering Arrays, DP, Graphs, Trees, Binary Search.

C++
Explore Architecture
FULLSTACK

Nexus-E1

TypeScript

DSA platform aggregating LeetCode, GFG, and Codeforces problems.

TypeScriptReactREST APIs
Explore Architecture
04 / SKILLS MIND-MAP

COMPETENCY CONSTELLATION

Core Mind Nodes

Active Learn Domain

Multi-Modal Vision Transformers & Mamba Architectures

Studying self-attention maps in transformers under spatial limits to identify multi-task medical annotations.

CONSTELLATION READOUT: MACHINE LEARNING & CV

Advanced Deep learning networks, image segmentation pipelines, object detection models, diagnostic heatmapping.

PyTorch & Deep Stack95% Competency

Applied: Researched BRISC architectures on glioma patient MRI brain scans.

Segmentation U-Net92% Competency

Applied: Isolate tumor contours on raw glioma patient MRI brain scans.

ResNeXt / ViT90% Competency

Applied: Implemented residual and transformer pipelines within video diagnostics.

Explainable AI GradCAM++90% Competency

Applied: Map backpropagated dense weights to display activation heatmaps.

TensorFlow & Keras85% Competency

Applied: Trained spectrogram diagnostic classifications for sound models.

05 / ALGORITHMIC RIGOR

DATA STRUCTURES & COMPETITIVE CODE

/**
 * @file dijkstra.cpp
 * @brief Verified sub-15ms Dijkstra Shortest Path Solution
 */
#include <vector>
#include <queue>

using namespace std;

class ShortestPathSolver {
public:
    vector<int> matchPath(int N, vector<vector<pair<int, int>>>& adj, int src) {
        vector<int> dist(N, 1e9);
        priority_queue<pair<int, int>, vector<pair<int, int>>, greater<pair<int, int>>> pq;
        
        dist[src] = 0;
        pq.push({0, src});
        
        while(!pq.empty()) {
            int d = pq.top().first;
            int u = pq.top().second;
            pq.pop();
            
            if(d > dist[u]) continue;
            
            for(auto& edge : adj[u]) {
                int v = edge.first;
                int weight = edge.second;
                if(dist[u] + weight < dist[v]) {
                    dist[v] = dist[u] + weight;
                    pq.push({dist[v], v});
                }
            }
        }
        return dist; // Time: O(E log V) matched
    }
};
C++17 Compiler • GCC 11.2
Lines: 32
LeetCode Diagnostic Center
Interactive Widget
1+Solved
Easy Level
95 solved
Medium Level
128 solved
Hard Level
30 solved
Algorithmic Subtopic Distribution
Dynamic Programming: 45 solvedGraph Algorithms: 38 solvedBacktracking / DFS: 32 solvedTree Node Traversals: 54 solvedTwo Pointers / Sliding: 44 solvedGreedy Heuristics: 40 solved

DSA-Solve Hub Repository

Holds production implementations of graph traversal, dynamic programming structures, trees maps. Underpinned by speed analysis test units.

06 / MILESTONES & BADGES

JOURNEY & CREDENTIALS

Chronological Journey Timeline

2024

Introduction to Software Engineering

IBM via Coursera

Examine Certificate Details
2024 & 2025Leadership

Runner-Up, SOA University Chess Tournament

SOA University

Secured runner-up position in university-level chess tournaments two consecutive years.

Examine Certificate Details
Dec 2025Certificate ID: 25231210471

Mastering AI and Data Science — Design and Build Intelligent Solutions

IIT (ISM) Dhanbad & TCS iON

Completed intensive course organized by IIT Dhanbad in collaboration with TCS iON. Sep–Nov 2025. Certificate ID: 25231210471.

Examine Certificate Details
2025University Level

Smart India Hackathon 2025

Ministry of Education, India

Problem Statement #95 — Participated in SIH 2025 at university level (SOA University).

Examine Certificate Details
2026

Microsoft Build AI Hackathon 2026

Microsoft

Built WorkflowOS — AI agent swarm converting meeting transcripts to execution plans. Track: Agent Swarms.

Examine Certificate Details
Microsoft Learn Badge Wall
15 Credentials

Verified Microsoft Learn credentials spanning AI, security, compliance, and infrastructure.

Learning PathJun 2026

Design a dream destination with AI

Microsoft Learn Verified
Learning PathJun 2026

Introduction to Microsoft security solutions

Microsoft Learn Verified
Learning PathJun 2025

Introduction to security, compliance, and identity concepts

Microsoft Learn Verified
Learning PathMay 2026

Describe the concepts of cybersecurity

Microsoft Learn Verified
ModuleJun 2026

Search and investigate with Microsoft Purview Audit

Microsoft Learn Verified
ModuleJun 2026

Design a dream destination using Microsoft Copilot

Microsoft Learn Verified
ModuleJun 2026

Describe threat protection with Microsoft Defender XDR

Microsoft Learn Verified
ModuleJun 2026

Describe security management capabilities in Azure

Microsoft Learn Verified
07 / CONNECT WITH AGENT

SECURE AN APPOINTMENT

Geographic Target Grid

UTM ZONE: 45Q

LAT: 20.2961° N | LON: 85.8245° E

Bhubaneswar, Odisha, India

Siksha 'O' Anusandhan University Campus Area, Bhubaneswar.