Reconfigure
Open Research Group
Welcome to Reconfigure, an open research group based in India. We are a self-funded team dedicated to advancing the fields of artificial intelligence and machine learning. Our mission is to innovate and enhance how intelligent systems are developed, focusing on making them more adaptable, robust, and efficient.
Here, we explore cutting-edge techniques and tools for designing AI and ML systems that can dynamically adjust to varying conditions and requirements. Our research spans a broad spectrum of topics, including advanced algorithms, machine learning models, and AI system architecture.
We also create case studies in diverse areas such as natural language processing, computer vision, and reinforcement learning, to showcase the practical benefits and real-world applications of our groundbreaking approaches.
We actively collaborate with industry leaders and academic institutions to stay at the cutting edge of AI and ML advancements. Our goal is to push the boundaries of what is possible, driving impactful solutions and fostering strategic partnerships.
Publications
Our research group has published several papers in top-tier conferences, pre-prints and journals. Here are the list of publications:
- Security Implications and Mitigation Strategies in MPLS Networks
- D-CODE: Data Colony Optimization for Dynamic Network Efficiency
- Introducing Super RAGs in Mistral 8x7B-v1
- Applications of Optimized Distributed Systems in Healthcare
- A unified module for accelerating stable-diffusion: Lcm-lora
- Loops On Retrieval Augmented Generation (LoRAG)
- Refining Language Translator Using Indepth Machine Learning Algorithms
- Enhancing Breast Cancer Detection via Optimized Machine Learning
- Gore Diffusion LoRA Model
- Self-healing Nodes with Adaptive Data-Sharding
- Project Ore - WORD PREDICTION USING TEXT MINING ALGORITHM
- The Art of Prompting: Unleashing the Power of Large Language Models
- VR Tourism: A Comprehensive Solution with Blockchain Technology, AI-Powered Agents, and Multi-user Features
- Explainable Artificial Intelligence: A Study of Current State-of-the-Art Techniques for Making ML Models Interpretable and Transparent
- Integrating Cloud, Blockchain and AI Technologies—Challenges and Scope
- Implementation & analysis of online retail dataset using clustering algorithms
- NLP & AI speech recognition: an analytical review
- Privacy and Security Considerations in Healthcare: Navigating the Challenges of IoT and Ubiquitous Computing
- Forecasting the Future: A Survey on AI-Powered Predictive Analysis in Tourism
- An Improved Underwater Object Detection based on YOLOv8 Segmentation
Projects
Our research group is currently working on the following projects:
- Baseline - Light Speed Encryption Technology
- Lucy - Human Mimic Bot
- Content Protection Script
- Radar - Universal Resource Locator
Join Us
At Reconfigure, we're constantly seeking talented individuals who are passionate about artificial intelligence and machine learning. If you're excited about contributing to groundbreaking research and driving advancements in these dynamic fields, we'd love to hear from you.
Joining our team means participating in innovative projects that push the frontiers of technology. You'll work on cutting-edge research, from developing sophisticated machine learning models to designing advanced AI architectures. Our diverse team of experts fosters a culture of mutual support, creativity, and continuous learning.
Meet the Team
- Ayush Thakur - Founder & Lead Researcher
- Pahul Singh - COO & Researcher
- Dr. Rashmi Vashisth - Advisor & Researcher
- Dr. Sandhya Mishra - Advisor & Data Analyst
- Tannu Pandey - Software Developer
- Raghav Gupta - Software Developer
- + Apply Now
If you're enthusiastic about the opportunity to join Reconfigure, please send your resume and a brief statement of interest. In your statement, tell us about your experience, skills, and what drives your passion for AI and ML research. We look forward to exploring how you can contribute to our innovative projects and research efforts.
Follow Us on: