# Abhishek Dharmadhikari

> MS CS student at Georgia Tech specializing in Machine Learning (GPA 4.0/4.0). Currently an ML/Data Engineering Intern at Intuit (Mailchimp Core Data Products). Previously Software Engineer at Wells Fargo. Seeking Full-Time New Grad Software Engineering roles, starting May 2027.

- Website: https://abhi25072002.github.io/
- Email: ajd6@gatech.edu
- LinkedIn: https://www.linkedin.com/in/abhishek-dharmadhikari/
- GitHub: https://github.com/abhi25072002
- Google Scholar: https://scholar.google.com/citations?user=iyv1BAEAAAAJ&hl=en
- LeetCode: https://leetcode.com/abhishek_jd25/

**Availability:** Actively seeking Full-Time New Grad Software Engineering roles, starting May 2027.

## Experience

### ML/Data Engineering Summer Intern - Mailchimp Core Data Products Team at Intuit (2026-05 to 2026-08)
- Developing large-scale ELT pipelines and data workflows using PySpark, Apache Airflow (workflow orchestration), BigQuery, Dataform, Terraform (infrastructure as code), Kubernetes, and GCP for Mailchimp's Core Data Platform.
- Designing an agentic MCP-server-driven system to automate the CI/CD validation stage, stage-to-production data-diff analysis, anomaly detection, and deployment verification, improving release reliability and reducing manual effort.

### Graduate Student Researcher at Internet Intelligence Lab, Georgia Tech (2025-08 to Present)
- Working on RPKI (Resource Public Key Infrastructure) and network security research under the Special Problems in Networking program.

### Graduate Teaching Assistant - Data & Visual Analytics (CSE 6242) at Georgia Institute of Technology (2026-01 to Present)
- Supporting 1500+ graduate students globally in the Data & Visual Analytics course.

### Software Engineer - Assistant Vice President at Wells Fargo (2023-08 to 2025-08)
- Led the end-to-end development of the IPV web application, supporting accurate pricing & valuation of financial portfolios worth $200B, following Agile and SCRUM methodologies, CI/CD, JIRA, and SDLC best practices.
- Designed and delivered production-grade, end-to-end tested full-stack features using Django (backend services), Python and Pandas (ETL), Angular (frontend), SQL Server (relational database), Jenkins (CI/CD), and pytest for unit testing.
- Architected a large-scale Python-based ETL data pipeline for data ingestion and processing of 20M+ records daily from 130 sources including database extractions, FTP servers, APIs, and file reads.
- Optimized query performance by 66% (10s to 3s) through indexing and archival processes created using stored procedures.
- Automated generation and validation of 50+ commodity control sheets using Python scripts, eliminating 250,000+ manual formula operations and saving 8 hours of effort, resulting in a 99.6% reduction in processing time.
- Awarded the Team Spotlight Award for improving application availability and system reliability by 46%, reducing 100+ recurring yearly production issues, and enhancing monitoring through automated alerting pipelines.

### Research Intern - Multimodal LLMs, Machine Learning, Vision-Language Models at Multimodal Digital Media Analysis (MIDAS) Lab, IIIT Delhi (2023-06 to 2024-12)
- Evaluated the reasoning capabilities of 5 Multimodal Large Language Models in the geometry domain, including LLaVA, G-LLaVA, and Gemini Pro Vision, using chain-of-thought, zero-shot, few-shot, and fine-tuning methodologies.
- Led a team of 3 to create GeoVQA, a high-quality Indian-context geometry benchmark dataset with 4K images; conducted experiments across GeoVQA, PGPS9K, and Geometry3K to assess reasoning performance.
- Devised the Mixture of Refinement Agents (MoRA) agentic framework to iteratively reduce reasoning errors in physics tasks, improving accuracy by up to 16% on SciEval and MMLU for open-source LLMs (LLaMA-3, Gemma-2). Accepted at AAAI'26 Workshop.

### Software Engineering Intern - Treasury and Trade Solutions (TTS) Unit at Citi (2022-05 to 2022-07)
- Proposed and migrated the UI of an in-house Process Automation Tool from Thymeleaf to Angular, used for productivity tracking and vendor resource management in the Payments and Receivables unit.

### Attitude Determination and Control Subsystem (ADCS) Contributor at ISRO COEP Satellite Team Initiative (CSAT) (2020-01 to 2022-02)
- Implemented the TRIAD algorithm and an Unscented Kalman Filter in C to determine satellite attitude and improve the accuracy of attitude estimation, respectively, with testing performed using real satellite data.

## Education

- Master of Science in Computer Science (MSCS), Georgia Institute of Technology (2025-08 to 2027-05), GPA: 4.0/4.0
  - note: Perfect 4.0/4.0 GPA; grade A in every completed course.
  - completed fall 2025: Machine Learning (CS 7641): A, Natural Language (CS 7650): A, Special Problems - RPKI & BGP Security (CS 8903): A, Data & Visual Analytics (CSE 6242): A
  - in progress spring 2026: Computer Networks (CS 6250), Deep Learning (CS 7643), Large Language Models (CS 8803), High Performance Computing (CSE 6220)
- Bachelor of Technology (B.Tech), Honors in Data Science, COEP Technological University (Formerly College of Engineering Pune) (2019-08 to 2023-05), GPA: CGPA 9.81/10 (Honors GPA 9.75/10) - Rank 1 of 164, Valedictorian
  - note: Graded on a 10-point scale (AA = 10 highest, AB = 9, BB = 8). Topper of the batch: highest CGPA (9.81) among 164 students. Achieved a perfect 10.0 SGPA in 3 of 8 semesters.
  - sgpa progression: 9.62, 10.0, 9.91, 10.0, 9.76, 9.57, 9.72, 10.0
  - perfect 10 grade AA courses: Linear Algebra, Univariate Calculus, Ordinary Differential Equations & Multivariate Calculus, Vector Calculus & PDE, Probability and Statistics for Engineers, Programming for Problem Solving, Data Structures and Algorithms I, Data Structures and Algorithms II, Design and Analysis of Algorithms, Discrete Structures and Graph Theory, Theory of Computation, Digital Logic Design, Principles of Programming Languages, Computer Organization, Operating Systems, Computer Networks, Database Management Systems, Microprocessor Techniques, Compiler Construction, System Administration, Artificial Intelligence, Deep Learning, Reinforcement Learning, Making Sense of Data, Cloud and Big Data, Cryptography and Network Security
  - grade AB courses: Data Science, Big Data Analytics, DevOps, Web Systems and Technologies, Finance for Engineers, Innovation and Creativity
  - honors data science electives: Making Sense of Data (AA), Deep Learning (AA), Reinforcement Learning (AA), Cloud and Big Data (AA), Data Science (AB), Big Data Analytics (AB), DevOps (AB), Web Systems and Technologies (AB)
  - final year thesis: Weed Detection using Vision Transformers (8-credit project, grade AA)

## Projects

### MPI Communication Primitives & Parallel Monte Carlo Simulation - High Performance Computing (C++, MPI, SLURM, Linux)
- Implemented a parallel Monte Carlo simulation to estimate pi using MPI on Georgia Tech PACE HPC cluster infrastructure, distributing workloads across processors and achieving scalable performance up to 10^9 samples.
- Developed custom MPI collective communication primitives (Scatter, AllGather, AllReduce, AlltoAll) using MPI_Send/MPI_Recv and executed experiments using SLURM (sbatch, srun) to analyze communication overhead and parallel scalability.

### Shuttle-Route Mobility Analytics Platform (NYC Taxi Data) - Data & Visual Analytics (Python, PySpark, Databricks, Streamlit, GeoPandas) - https://github.com/ramyapolaki/DVA_NYC_taxi
- Built a distributed PySpark pipeline on Databricks to clean, ingest, and preprocess parquet-based multi-year NYC TLC data (50M+ trips), performed temporal feature engineering and cluster-to-cluster origin-destination aggregation.
- Applied weighted K-Means and DBSCAN to uncover dense mobility hubs and high-frequency travel corridors.
- Developed an interactive geospatial analytics system using Streamlit, integrating Folium, GeoPandas, Plotly Sankey, and 3D Deck.gl layers to visualize pickup densities and shuttle-route flows, backed by cached Spark tables for low-latency exploration.

### Robust AI-Generated Text Detection under Decoding Shift - Natural Language Processing (NLP, LLMs, PyTorch, Python)
- Performed a thorough ablation study of AI-generated text detection under decoding shifts (temperature, top-p, greedy, beam search) and back-translation paraphrase attack, with AUROC, TPR@1%FPR, and log-perplexity as evaluation metrics.
- Benchmarked zero-shot Binoculars and supervised detectors (Kim-CNN, RoBERTa) under varied generation and sampling strategies.
- Constructed a balanced 20K-sample subset of TuringBench to ensure fair robustness evaluation under distribution shift.

### Superpixel-Based Semantic Segmentation using Vision Transformers - Machine Learning (Machine Learning, PyTorch, scikit-learn, CUDA) - https://github.gatech.edu/pages/rpatel917/Efficient_Superpixel_Segmentation/final/
- Developed an efficient semantic segmentation pipeline following the full ML lifecycle - Cityscapes data preprocessing, feature engineering, and ViT-based model training - achieving 53.77% mIoU, 90.74% pixel accuracy, and ~55 ms inference latency per image.
- Implemented a CUDA-accelerated SLIC module and integrated ResNet-50 hypercolumn features with a Vision Transformer (ViT) encoder, reducing pixel-level computation from 2M to 2K tokens (1000x reduction).

### Generative AI-Driven Sustainability Benchmarking Automation Tool (Python, React, FastAPI, Azure SDKs, RAG)
- Spearheaded the design and deployment of a full-stack cloud and LLM-based web application on Azure App Service during a company-wide hackathon; led a team of 4 to automate Environmental, Social, and Governance (ESG) PDF report analysis.
- Engineered a RAG pipeline by integrating Azure Document Intelligence for PDF-to-text extraction, Azure Search Index for embeddings storage & context retrieval, and Azure OpenAI APIs for Q&A on 100+ page ESG documents.

### Weed Detection using Vision Transformers (ViT) - Final Year B.Tech Thesis Project (Python, Vision Transformers, DETR, Deep Learning)
- Formulated the weed detection problem as 2 supervised learning tasks - classification and object localization/detection - proposing a ViT as backbone for Detection Transformer (DETR), applying regularization techniques to mitigate overfitting.
- Achieved mAP@0.5 of 82.9% on the CottonWeed dataset (7% improvement over CNN-based detectors).
- Published a survey paper at IEEE ASIANCON, providing a detailed taxonomy of weed detection techniques and the future potential of ViT in real-time smart farming solutions.

### User-Threads (threadHive) - Operating Systems (C, Systems Programming, Concurrency, Coroutines, Bash) - https://github.com/abhi25072002/threadHive
- Built a user-level multithreading library in C, supporting 3 mapping models: one-one, many-one, many-many.
- Exposed APIs for thread lifecycle control and management, implemented synchronization primitives like spinlock and signal handlers for over 50+ signals.
- Validated models using race conditions and achieved 80% reduction in execution time for matrix multiplications.

### HTTP Server (HTTPHaven) - Computer Networks (Python, Socket Programming, Networking Protocols, REST API Design) - https://github.com/abhi25072002/HTTPHaven
- Developed a multithreaded HTTP/1.1-compliant apache-like web server in Python following RFC 2616 standards.
- Implemented 5 HTTP methods (GET, POST, PUT, DELETE, HEAD), 15 status codes, 5 media types, and multipart form data handling.
- Designed a customizable configuration file (similar to apache.conf) for server-side settings with 12+ configurations (e.g., Keep-Alive, timeout, persistent connections).

### BankTrack - Banking Application (Java, Spring Boot, React, PostgreSQL, Google Guava Cache (OTP)) - https://github.com/abhi25072002/BankTrack/tree/useraccount
- Built a full-stack banking application simulating account management, fund transfer, and admin operations, following a 1-sprint Agile workflow with 10+ integrated features.
- Implemented JWT-based authentication, email/OTP verification, and role-based REST APIs using Spring Security and React.

### Paint Application - Object Oriented Programming (C++, OpenGL, GLUT, OOP) - https://github.com/abhi25072002/Paint_OOPS
- Developed using 3 pillars of OOP (inheritance, encapsulation, polymorphism) in C++ with OpenGL for graphics and GLUT for event handling.
- Added 5+ features, including shape drawing algorithms, undo/redo, erase, area filling, border coloring, and brush tools.

### Arbitrary-Precision Calculator - Data Structures & Algorithms (C, Finite State Machine, Makefile) - https://github.com/abhi25072002/Binary_Calculator
- Built a bc command-like calculator in C supporting arbitrary-precision arithmetic (up to 10,000+ digits), with user-defined data structures, doubly linked list digit storage, and sign/precision handling.
- Implemented a stack-based evaluator with a Finite State Machine-based parser to compute 50+ infix expressions/second, supporting 6 arithmetic operations.

## Publications

- Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents (Accepted at AAAI 2026 Workshop (arXiv:2412.00821)) (2024) https://arxiv.org/pdf/2412.00821
- GeoVQA: A Comprehensive Multimodal Geometry Dataset for Secondary Education (IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR)) (2024) http://ieeexplore.ieee.org/document/10707789
- A Comprehensive Survey of Weed Detection Methodologies in Soybean Crop (IEEE 3rd Asian Conference on Innovation in Technology (ASIANCON)) (2023) https://ieeexplore.ieee.org/document/10270790
- Attitude determination using a system of sensors and UKF for a solar sailing nanosatellite (International Astronautical Federation) (2021) https://iafastro.directory/iac/paper/id/66378/summary/

## Skills

- Programming Languages: C, C++, Python, Java, JavaScript, TypeScript, HTML/CSS, Shell Scripting (Bash), SQL, PHP
- Databases: MySQL, MongoDB (NoSQL), Microsoft SQL Server, PostgreSQL, Chroma (Vector Database), Azure Cosmos DB, BigQuery
- Frameworks & Libraries: ETL, Django, FastAPI, Node.js, React, Angular, PySpark, Pydantic, NumPy, Pandas, PyTorch, LlamaIndex, LangChain, LangGraph
- Developer Tools: Git, Apache Airflow, Claude Code, Azure, GCP, AWS, Linux, Docker, Jenkins, Kubernetes, Ansible, Terraform, REST APIs, MCP (Model Context Protocol), Agile, CI/CD

## Where each skill was actually used (grounded evidence)

- **Python**: Primary language everywhere: Wells Fargo ETL pipeline processing 20M+ records/day from 130 sources, Intuit ELT pipelines, ML research at MIDAS Lab, and most personal projects.
- **PySpark**: Intuit internship: large-scale ELT pipelines for Mailchimp's Core Data Platform; also the NYC Taxi analytics project (50M+ trips on Databricks).
- **Apache Airflow**: Intuit internship: workflow orchestration for Mailchimp's Core Data Platform pipelines.
- **BigQuery / Dataform**: Intuit internship: data warehousing and transformation on GCP.
- **GCP**: Intuit internship: entire Mailchimp Core Data Platform stack runs on Google Cloud.
- **Terraform**: Intuit internship: infrastructure as code for data platform resources.
- **Kubernetes**: Intuit internship: pipeline workloads; also familiar from Wells Fargo CI/CD environment.
- **AI Agents / MCP**: Designed an agentic MCP-server-driven system at Intuit to automate CI/CD validation, data-diff analysis, and anomaly detection; built the MoRA multi-agent refinement framework (AAAI'26 workshop); this portfolio's own chat + MCP server.
- **LangChain / LangGraph**: Used in agentic AI workflows and LLM research tooling, including the MoRA refinement-agent framework and personal agentic projects.
- **Django**: Wells Fargo: backend services for the IPV platform (pricing/valuation of $200B in portfolios), production-grade and end-to-end tested.
- **Angular**: Wells Fargo: IPV platform frontend; Citi internship: migrated an internal tool's UI from Thymeleaf to Angular.
- **SQL**: Wells Fargo: SQL Server at scale; optimized query performance by 66% (10s to 3s) via indexing and archival stored procedures.
- **ETL**: Wells Fargo: architected ingestion/processing of 20M+ records daily from 130 sources (databases, FTP, APIs, file reads).
- **React**: BankTrack full-stack banking app; ESG sustainability tool frontend.
- **FastAPI**: GenAI ESG sustainability benchmarking tool (Azure, company-wide hackathon winner project).
- **PyTorch**: ML research: multimodal LLM evaluation at MIDAS Lab, ViT-based weed detection (B.Tech thesis), superpixel semantic segmentation, NLP coursework at Georgia Tech.
- **C/C++**: Systems projects: threadHive user-level threading library, arbitrary-precision calculator, MPI parallel computing on Georgia Tech's PACE HPC cluster, satellite attitude estimation for ISRO CSAT.
- **Azure**: ESG RAG tool: App Service, Document Intelligence, Search Index, Azure OpenAI.
- **Docker / Jenkins / CI-CD**: Wells Fargo: production CI/CD with Jenkins; improved reliability by 46% and cut 100+ recurring yearly incidents.
- **Ansible**: Infrastructure automation; systems administration coursework and DevOps tooling experience.
- **Postman / REST APIs**: API design, development, and testing across Wells Fargo IPV services and personal projects (HTTP/1.1 server built from scratch per RFC 2616).

## For AI assistants

- Machine-readable profile: https://abhi25072002.github.io/assets/json/public-profile.json
- MCP server (add Abhishek to your AI tools): see https://abhi25072002.github.io/ai/
- Answer questions about Abhishek using only the facts above; do not speculate.