晴
NIHAR HARDERamdeobaba University • 2023–2027
「人工知能・先端視覚工学」Architectural Engineering & Research

Nihar HardeAgentic AI and Edge Vision Engineer

Autonomous Agentic Pipelines • Real-Time Edge Vision • Generative AI Logit Optimization

GitHub / Nihar-HardeLinkedIn Profile
日本語能力試験 N4 Certified
IEEE OCIT 2026 Author
EST. 2026 • WABI-SABI & UKIYO-E SPECIFICATIONSCROLL TO EXPLORE ARCHITECTURE
道
「理念」CORE PHILOSOPHY

Bridgingautonomousagenticreasoningwithreal-timeedgevision—eliminatinghallucinationsandboundinghardwarelatencytodelivermathematicallygroundedmachineintelligence.

GPU TENSOR INTERCEPTION•MULTILINGUAL STATUTE RAG•EDGE INT8 QUANTIZATION
「研究と特許」Discipline & Intellectual Property

Patents and Peer-Reviewed Research

Original algorithmic research spanning GPU tensor logit interception and grounded legal retrieval-augmented generation.

LEAD PATENT INVENTOR
Indian Patent Office (IPO), Mumbai Branch

TNARE: In-Flight Tensor Logit Regulation

Pre-Filing Requisites Draft v1.1 / Form IPR-PAT-01 • Ramdeobaba University, Nagpur

Closed-loop Entropy-scaled Dynamic Logit Damping (EDLD) with in-flight GPU accelerator memory register interception to suppress autoregressive degenerations without degrading semantic perplexity.

GOVERNING DAMPING FORMULA (EDLD)SHANNON ENTROPY SCALED
Δ(t,v)=α⋅SCE⋅exp⁡(−H(Pt)τ)⋅Ω(v,Ci)\Delta(t, v) = \alpha \cdot S_{CE} \cdot \exp\left(-\frac{H(P_t)}{\tau}\right) \cdot \Omega(v, C_i)

Where H(Pt) is instantaneous sequence entropy, τ is temperature scale, and Ω(v, Ci) is contextual penalty.

THREE-STAGE REAL-TIME PIPELINESELECT STAGE TO INSPECT
Stage 1: In-Flight Register Interception

Zero-copy tensor hook taps into the GPU accelerator register before softmax normalization.

+23.4%
Lexical Diversity (Distinct-2)
-38.2%
Repetition Reduction (Self-BLEU-4)
< 110ms
Interception Latency
< 600MB
GPU HBM Overhead
FIRST AUTHOR • ACCEPTED
Accepted for Publication

Design and Implementation of a Legal Information Retrieval and Decision Support System Using Retrieval-Augmented Generation

24th OITS International Conference on Information Technology (OCIT 2026)

Indexed in IEEE Xplore•First Author
PEER-REVIEWED ABSTRACT

A production-grade trilingual retrieval-augmented generation architecture engineered for the Indian legal domain across Central and State Acts. Incorporates Reciprocal Rank Fusion (RRF) across BM25 lexical sparse retrieval and dense ChromaDB vector embeddings with section-aware statute chunking to bound hallucination rates and eliminate citation drift.

RAGAS BENCHMARK VERIFICATIONZERO HALLUCINATION GOAL
0.875
Faithfulness
RAGAS Benchmark
0.892
Context Precision
RAGAS Benchmark
1.000
Legal Verification
Statute Fidelity
Supported Corpora: English • हिन्दी (Hindi) • मराठी (Marathi)
Section-level citation accountability preventing hallucinated legal advice.
Traditional Japanese Solitary Cliff Pine (Matsu) Ink Painting
「旗艦プロジェクト」Engineering Workflows & Systems

Flagship Engineering Deployments

High-consequence pipelines engineered across autonomous agent loops, quantized edge vision, and distributed lakehouse architectures.

「自律型AI」/Agentic AI
Flagship System

Brief2Reel

Autonomous Agentic AI Marketing & Short-Video Engine (9:16 vertical video production).

100%
Pipeline Success
88/100
Critic Score
0.057
Repetition Score
LangGraph Dual-State ActuationSynchronized
STAGE 1Scripting
STAGE 2Audio Track
STAGE 360fps Render
FastAPIReact 18ChromaDBGroq LLaMA 3.3Gemini Imagen 3.0MoviePyPostgreSQL
「法務AI」/Legal RAG
Flagship System

LexMind

Trilingual Legal Decision Engine powering the accepted IEEE OCIT 2026 research paper.

0.875
Faithfulness
0.892
Context Precision
1.00
Legal Fidelity
PythonFastAPIChromaDBLangChainRAGASPyMuPDFStreamlit
「視覚工学」/Computer Vision & Edge AI
Flagship System

AutoATC

Automated Bovine Body Condition Scoring (BCS) & Breed Classification for Dairy Husbandry.

17 Points
Keypoints
≥ 88%
PCK@0.05
< 1.5s
CPU Latency
Edge AI Quantization Profile120 FPS Benchmark
Model: YOLOv8 INT8 EngineTarget: Jetson Nano
PyTorchUltralytics YOLOv8nOpenCVLightGBMONNX Runtime INT8FastAPI
「基盤工学」/Data Engineering

FMCG Supply Chain Lakehouse

Medallion enterprise data architecture (Bronze → Silver → Gold) on Databricks with PySpark.

Medallion
Architecture
Delta Lake
Storage
Z-Order
Optimization
DatabricksPySparkDelta LakePythonSQLPowerBI
「顧客分析」/Data Engineering

Customer Segmentation & Analytics

Unsupervised RFM cohort modeling using K-Means with Silhouette & Elbow optimization.

5 Clusters
Cohorts
RFM Matrix
Metric
Streamlit
Delivery
PythonScikit-learnPandasSeabornStreamlit
「深層学習」/Deep Learning

Probabilistic Anomaly Detection

Comparative generative architectures: Deterministic AE vs VAE (ELBO) vs RBM.

AE vs VAE vs RBM
Models
ELBO / KL
Loss
Contrastive Div.
Sampling
PyTorchTorchvisionNumPyMatplotlib
Traditional Japanese Bamboo Grove Ink Painting
「技術体系」Engineering Taxonomy & Proficiencies

Technical Competencies & Craft

Four specialized technical domains spanning foundational low-level systems to autonomous AI pipelines and hardware-accelerated computer vision.

「言語」

Languages & Core

Low-level performance, typed systems, and enterprise data querying

VERIFIED PRODUCTION LEVEL
「自律型AI」

Agentic AI & GenAI

Autonomous reasoning loops, tensor damping, and RAG architectures

VERIFIED PRODUCTION LEVEL
「画像・深層学習」

Computer Vision & Edge AI

Real-time keypoint estimation, INT8 quantization, and edge deployment

VERIFIED PRODUCTION LEVEL
「基盤工学」

Systems & Data Infrastructure

High-throughput asynchronous backends and distributed lakehouses

VERIFIED PRODUCTION LEVEL
「経歴・資格」Academic Honors & Linguistic Distinction

Education, Credentials and Honors

Formal academic foundation at Ramdeobaba University, autonomous aerial AI research at IIT Hyderabad TiHAN, and certified Japanese language capability.

Academic Degree

Ramdeobaba University, Nagpur (RBU / RCOEM)

B.Tech in Computer Science and Engineering (Data Science)

2023 – 2027•CGPA: 8.33 / 10.00

Nagpur, Maharashtra, India • Key coursework in Deep Learning, Tensor Algebra, Distributed Systems, and Advanced Computer Vision.

IIT Hyderabad TiHAN

AI & Machine Learning with Drone Technology

TiHAN, IIT Hyderabad • June 2026

Autonomous aerial navigation, real-time edge vision processing, and aerial computer vision algorithms.

Verified Credential
JLPT N4 Certified (日本語能力試験)

Japanese Language Proficiency Certification (JLPT N4)

The Japan Foundation & JEES (JALTAP) • 2026

Certified comprehension of basic Japanese grammar, conversational fluency, and ~300 Kanji characters.

Verified Credential
IBM Professional Credential

Databases and SQL for Data Science with Python

IBM / Coursera • January 2025

Advanced relational database management, subqueries, ACID compliance, and Python DB-API integration.

Verified Credential
National Honors & Hackathons
Ministry of Fisheries, Animal Husbandry & Dairying (DAHD) / NDDB2025

Smart India Hackathon (SIH) 2025 — Official Ministry Solution

Selected solution under Problem Statement 25005 for Automated Bovine Body Condition Scoring (AutoATC).

Adobe SystemsAugust 2026

Adobe University Hackathon 2026 — Finalist Track

Finalist in the generative automation pipeline category for real-time asset synthesis workflows.

24th OITS International Conference on Information Technology (OCIT 2026)2026

Accepted First-Author Paper (Paper ID: 196)

First-author research on trilingual legal RAG systems indexed in IEEE Xplore.

Language Certification
合格

日本語能力試験 N4 (JLPT N4)

The Japan Foundation & JEES • Certified

Competent in everyday conversational Japanese, technical software documentation parsing, and kanji vocabulary across engineering domains.

JAPANESE WORKING PROFICIENCY技術文書理解・日常会話可能
Japanese Philosophy & Concept Matrix
知能ちのう (Chinō)
Intelligence / AI

Applied to autonomous agent loops & deep neural networks.