LORD SEN

B-Tech CSE'27

Computer Science and Engineering

location_away
location_on Department of Computer Science and Engineering,
National Institute of Technology Rourkela,
Sundargarh, Odisha, India - 769008

"   ....And Miles to go before I sleep...!!"



school  EDUCATION QUALIFICATION


Gitanjali Public School

↠ Class 10- 99.8% (499/500)
↠ Class 12- 96.2% (481 /500)

NIT Rourkela

↠ B-Tech CSE NITR - CGPA- 10 (Till Date)

work  ACHIEVEMENTS


•Best MathWorks award by Major League Hacking and MathWorks
•Our Project Newsiee was selected as one of top 5 projects in Inno-a-thon 2024
•JEE Mains: 99.68 Percentile, AIR-3834
•WBJEE: Qualified; Rank= 305(GRL)
• JEE Advanced: Qualified
•JBNSTS Scholar
•NTSE Stage 1 Qualifier with a Rank: 176
•Also Qualified: IMO, IEO, NSO by SOF and IOM, IOE by Silver Zone

import_contacts  INTERESTS

AI

Keenly intrested in Generative AI and am learning it with great dedication to become a good AI Engineer

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ARTIFICIAL INTELLIGENCE

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  1. Exploring Generative AI: a branch of artificial intelligence focused on creating models that generate new content, such as images, text, and music, using techniques like GANs, and autoregressive models

  2. Exploring and Learning Generative Adversarial Networks (GANs): Neural network architectures comprising a generator and a discriminator, trained adversarially to generate realistic data samples, widely used in image generation, style transfer, and more

  3. AGI (Artificial General Intelligence): Theoretical AI capable of understanding, learning, and applying knowledge across diverse domains, exhibiting human-like cognitive abilities such as reasoning, planning, and problem-solving.
  4. Doing extensive studies on Chat Bots and their working by reading different research papers.
  5. I have also started using AI Tools: CHATGPT; DALLY; GEMINI; BING and many more to make the process of learning more efficient.
  6. But, I am mainly focussing on ML,DL and Quantum Computing

QC and QML

Learning QC and QML with great dedication and trying to contribute to this newly emerging field

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QUANTUM COMPUTING

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  1. QC: Types,Qubits, Superposition, Entanglement,Quantum Circuits, Quantum Fourier Transform etc Expertise in Quantum Computing, specializing in quantum principles and their practical applications. Skilled in designing and optimizing quantum circuits, implementing quantum algorithms like Grover's and Shor's, and exploring cutting-edge innovations at the forefront of quantum computing

  2. QML:Exploiting the advantages of Quantum parallelism in Machine Learning to train the model

  3. QNN:I possess a strong understanding of Quantum Neural Networks (QNNs), having thoroughly researched and studied their principles and applications. My expertise encompasses quantum computing fundamentals, variational quantum circuits, and training techniques. I am passionate about exploring the intersection of quantum computing and machine learning, with a keen interest in developing innovative solutions leveraging QNNs for real-world challenges.

  4. QDL or DQL:Proficient in Quantum Deep Learning, adept at integrating quantum computing principles with deep learning architectures. Skilled in designing quantum neural networks (QNNs), implementing variational quantum circuits, and optimizing quantum-inspired models for cutting-edge applications.

  5. Quantum Algorithms:Proficient in Quantum Algorithms including Grover's and Shor's algorithms. Skilled in leveraging these algorithms to tackle complex computational tasks, designing efficient quantum circuits, and pushing the boundaries of traditional computing paradigms.

ML

Learning Machine Learning i.e.Teaching computers to learn and improve from data

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MACHINE LEARNING

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  1. • Machine Learning (C.C.E. IIT Kanpur)- I have covered both Suprvised and Unsupervised Learning.

  2. • Deep Learning: ANN, CNN, RNN, LSTM, GRU, Transformers

  3. • Natural Language Processing: Summarization, Machine Translation, Speech Recognition, Spam Detection, Named Entity Recognition, Chatbot working

  4. • Reinforcement Learning: Proficient in Reinforcement Learning, with a strong grasp of its theoretical foundations and practical applications. Skilled in designing and implementing reinforcement learning algorithms, optimizing policies through exploration and exploitation, and leveraging deep reinforcement learning techniques for complex decision-making tasks.

  5. • KNN:Proficient in K-Nearest Neighbors (KNN), adept at utilizing this non-parametric classification algorithm for pattern recognition and regression tasks. Skilled in determining optimal values for K, evaluating model performance, and implementing KNN efficiently for various machine learning applications.

  6. • KAN(Kolmogorov- Arnold Networks): This is an alternative to MLP(Recently published on 2nd May 2024) and others

WEB DEV

Learning Web Development: Creating, designing, and building interactive websites and applications

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WEB DEVELOPMENT:

Frontend Development:

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  1. Html:Proficient in HTML, with expertise in creating structured and semantically meaningful web content. Skilled in using HTML5 features, optimizing for accessibility and SEO, and integrating HTML with CSS and JavaScript to build responsive and interactive web experiences

  2. CSS: Proficient in CSS, with a strong understanding of styling and layout techniques for web development. Skilled in CSS3 features, including flexbox and grid layouts, responsive design principles, and CSS preprocessors like Sass, to create visually appealing and dynamic web interfaces.

  3. Js: Studying JavaScript for Frontend Development, focusing on DOM manipulation, event handling, and asynchronous programming. Exploring modern frameworks like React.js to build responsive and interactive user interfaces for web applications.

  4. Bootstrap:Exploring Bootstrap for frontend design, mastering its grid system, responsive utilities, and component library. Learning to streamline UI development and enhance user experience with Bootstrap's pre-designed elements and responsive features.


DSA

Learning DSA as they are essential tools for efficient problem-solving.

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DATA STRUCTURES AND ALGORITHMS:

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  1. Arrays:Studying Arrays in-depth, exploring their fundamental operations, such as insertion, deletion, and traversal. Focusing on array manipulation techniques, optimizing algorithms for searching and sorting, and understanding their applications in data structures and algorithms.

  2. Recursion: Delving into Recursion, mastering its concepts and applications in problem-solving. Exploring recursive algorithms, understanding base cases, and analyzing the stack frame to efficiently solve complex computational tasks in programming and computer science.

  3. Linked List:Diving deep into Linked Lists, understanding their structure, traversal, and manipulation techniques. Exploring singly linked lists, doubly linked lists, and circular linked lists, and mastering their applications in data structures and algorithms.

  4. Trees:Studying Trees: hierarchical data structures with nodes and edges,traversal algo etc used for organizing data efficiently in computer science.

  5. Stack, Queue and Heap:"Understanding Stack, Queue, and Heap: fundamental data structures with specific access patterns, used for efficient storage and retrieval of elements in computer science."

  6. Graphs:Exploring Graphs: versatile data structures consisting of nodes and edges, used to model relationships between objects and solve complex problems in various domains of computer science and beyond.

menu_book  RELEVANT COURSES



•C-Programing Language(Practical and Theory)
•CAD Graphics( AUTO-CAD)
•Electromagnetics and Quantum Mechanics
•Maths(Sequence,Series,Calculus,D.E.,Integral Equations,Laplace Transform,Linear Algebra)

psychology  RESEARCH

ML-DL

Being an AI-ML enthusiast, I am keenly intrested in doinig research on ML and DL.

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QC

I am very intrested in this new field and want to contribute to the same.

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QUANTUM COMPUTING

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Reading resarch papers on Quantum Computing especially on the advantage due to Quantum Parallelism, trying to improve the pre-existing algorithms.

QML

This field exploits Quantum advantage to train ML models.

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QUANTUM MACHINE LEARNING

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I am trying to find new procedure and algorihms to train ML models usin Quantum Parallelism by designing quantum circuits and others.

social_leaderboard  PROJECTS



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NEWSIEE

Newsie: A News Summarization app and website

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NEWSIEE:

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  1. 1. Newsie: A News Summarization app and website(Our projrct was selected as one of the top 5 projects in NES-Inno-a-thon 2024 and I was also the spokes-person from our side to represent the model)

  2. • News is fetched from an API, then the ML model summarizes the news to about 60 words summary which is displayed in the website

  3. • Audio feature is added by using ML. Hence, the user can listen to news and relate using the relatable pictures.

  4. • Newsie Community where people can share news and discuss.

  5. • Debating site where debating enthusiasts can initiate APD/BPD and debate on current news.

  6. • Quiz section on current news for UPSE aspirants with Leader board.

SUSTAINABILITY BITES

: An umbrella place that ensures surplus food management, air pollution predictor, smart energy management.

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SUATAINABILITY BITES:

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  1. We got the Best MathWorks award by MathWorks amd MLH(Major League Hacking) and I was also the spokes-person from our side to represent the model.

  2. SUATAINABILITY BITES: An umbrella place that ensures the following:

  3. Gateway between restaurants and food donors to manage surplus food and a model to predict the amount of surplus food.

  4. Model to help the city in charge predict the AQI value and suggest necessary measures.

  5. Model to predict the future energy usage of a country and the resource planning of the country.