Aug 2025 – Present · 7 mos · Guwahati, Assam, India · On-site

Sep 2024 – Aug 2025 · 11 mos · Guwahati, Assam, India · On-site

• As the GDG on Campus Co-lead for App Development and Flutter at Gauhati University, I actively lead initiatives to foster a vibrant tech community, guiding students in exploring mobile app development through Flutter. I collaborate with team members to organize workshops, hackathons, and meetups aimed at enhancing skills in cross-platform development. My role also involves mentoring peers, sharing insights on best practices, and contributing to hands-on projects that drive innovation within the community. Through this position, I aim to inspire others while continuously expanding my expertise in app development and leadership.
• Activities:
• Mentored a team of 5 students to make them complete their Google Cloud Study Jams Program
• Mentoring 3 teams of 4 members each for completing their projects for GDG Solution Challenge
• Mentored 2 teams of 5 members each for a hackathon hosted by our campus

Aug 2025 – Jan 2026 · 5 mos · Guwahati, Assam, India

Jun 2025 – Present · 9 mos · Guwahati, Assam, India · On-site

• I recently completed a two-month summer internship on “Automated Gas Burner” at IIIT Guwahati:
• Worked under the esteemed guidance of Dr. Sudip Biswas (Associate Professor, Dept. of ECE) and mentored by Mr. Sayantan Das on practical IoT systems and real-time integration.
• Collaborated with Ankita Rabha, Bhargab Saikia, Mallena Vardhan, Nahid Tanvir Alam and Prachurjya Ray in researching and developing the solutions for the "Automated Gas Burner".
• Contributed to the design and development of a smart, sensor-based gas burner to improve kitchen safety, energy efficiency, and ease of use.
• Integrated Arduino, ESP, and Raspberry Pi with gas leakage and temperature sensors, plus solenoid valves for flame control and autonomous emergency shutdown.
• Built a web-connected dashboard for real-time monitoring and remote browser-based control.
• Implemented WiFi and voice control, enabling flame adjustments, burner on/off, and real-time voice feedback like a kitchen Alexa.
• Developed a voice-assisted chatbot for hands-free operation, safety alerts, and step-by-step cooking or shutdown guidance.
• Helped create a social media-inspired app with chat, comment, and streak features, using real-time backend services, notifications, and user interactions.
• Worked on sensor calibration, embedded programming, and circuit debugging to enhance reliability.
• Prioritized safety and sustainability via fail-safes, gas leak alerts, and low-power design.
• Gained hands-on experience in hardware-software interfacing, IoT automation, voice interaction systems, embedded deployment, plus exposure to ML integration, cloud connectivity, and user-centric design thinking.
• This internship solidified my practical understanding of IoT, real world embedded systems, and smart device development, with exposure to ML integration, cloud connectivity, and user centric design thinking.

Jun 2025 – Present · 9 mos · Guwahati, Assam, India · On-site

Jun 2025 – Jul 2025 · 1 mo · North Guwahati, Assam, India · On-site

• I recently completed a five week summer internship on UAV Systems and Embedded Control at IIT Guwahati:
• Worked under the esteemed guidance of Dr. Pratiksha Sarma ma’am, Post-Doctoral Fellow, IIT Guwahati, whose mentorship deepened our understanding of embedded systems and UAV design
• Collaborated with Ankita Rabha, Denim Das and Nahid Tanvir Alam in researching and developing the solutions for the "Mini Quadcopter Flight Controller with PID Control (Basic Hover)".
• Contributed to the project “Mini Quadcopter Flight Controller with PID Control (Basic Hover)” involving the design and testing of custom built quadcopters.
• Participated in developing two quadcopter prototypes using ESP32 microcontrollers, custom PCB design, and 3D printed frames created with Fusion 360 and Bambu Lab AMS printers
• Implemented PWM based motor control, ESC calibration, and firmware development in Arduino IDE to enable partial flight and stability testing
• Collaborated on hardware integration, manual PCB fabrication using Voltera V One, and signal routing for motors and sensors
• Focused on real time system integration, ensuring power efficiency, thrust balance, and modular architecture for future enhancements
• Played a key role in safety testing, firmware debugging, and the development of a custom control interface using ESP32 WiFi capabilities
• This internship equipped me with hands on experience in drone architecture, embedded programming, and rapid prototyping, paving the way for future work in autonomous UAVs and IoT integrated flight systems.

May 2025 – Present · 10 mos · Remote

Jan 2025 – Present · 1 yr 2 mos · Remote

• Top 1% Creator on Topmate.io
• Mentored Over 25+ students
• Featured among Topmate’s Fresh Faces in Emerging Experienced Coders, recognized alongside 52 top IT professionals. Ranked #7 around the world
• Featured on a billboard 2 times at Times Square, NYC for my contributions.

May 2025 – Present · 10 mos · Indore, Madhya Pradesh, India · Hybrid

• I recently completed a three month summer internship on “Malware Detection” at IIT Indore:
• Worked under the esteemed supervision of Dr. Aruna Tiwari (Associate Professor, IIT Indore) and Dr. Sharmila S.P. (Post Doctorate Fellow, IIT Indore) on advanced machine learning and deep learning applications in cybersecurity.
• Researched and implemented binary and multiclass malware classification using the VolMemDataset, covering diverse malware families.
• Applied classical ML models such as Random Forest, XGBoost, and Gradient Boosting to establish baseline performance.
• Designed and trained deep learning architectures including DNN, CNN, RNN, GRU, LSTM, Autoencoders, and Kolmogorov–Arnold Networks (KAN) for high-dimensional malware detection.
• Conducted extensive performance evaluation using Accuracy, Precision, Recall, F1-score, MCC, Balanced Accuracy, Specificity, Sensitivity, TPR, TNR, FPR, FNR, and Cohen’s Kappa.
• Analyzed confusion matrices to identify error distributions, class imbalance challenges, and misclassification trends.
• Explored scalability by extending experiments from binary detection to 3-class, 4-class, and 16-class classification setups.
• Benchmarked models to reveal CNN as the most consistent performer, while KAN demonstrated strong potential in handling complex, high-class classification problems.
• Documented results in Google Sheets with visualizations (confusion matrices, bar charts) for comparative study.
• Gained hands-on experience in cybersecurity, supervised learning, neural architectures, adversarial robustness, and explainable AI concepts.
• This internship strengthened my understanding of AI-driven cybersecurity, supervised learning, and neural networks, while enhancing my ability to design, implement, and critically evaluate robust malware detection systems.

May 2025 – Aug 2025 · 3 mos · Jaipur, Rajasthan, India · Remote

• I recently completed a two-month summer internship on “Deep Learning-based Channel Estimation in RIS-Assisted MIMO Systems” at The LNM Institute of Information Technology (LNMIIT), Jaipur under the LUSIP Summer Internship Programme:
• Worked under the esteemed guidance of Mr. Purnendu Karmakar, Assistant Professor, Department of ECE, LNMIIT Jaipur.
• Collaborated with Ankita Rabha in researching and developing AI-driven solutions for channel estimation in Reconfigurable Intelligent Surface (RIS)-assisted MIMO systems.
• Conducted an extensive literature review on RIS technology, conventional estimation methods (LS, MMSE), and deep learning-based techniques (DNN, CNN, Autoencoder).
• Simulated RIS-assisted MIMO channels using the SimRIS platform under varying SNR levels, spatial correlations, and pilot configurations.
• Developed and trained DNN, CNN, and Autoencoder architectures for robust channel estimation with reduced pilot overhead and low SNR resilience.
• Implemented data preprocessing, normalization, and augmentation techniques to enhance model robustness against hardware imperfections and environmental variability.
• Achieved significant performance improvements—CNN and Autoencoder models outperforming LS and MMSE estimators by up to 6 dB NMSE in challenging conditions.
• Evaluated models using NMSE, MSE, and BER metrics, and conducted comparative analysis to balance accuracy, computational complexity, and deployment feasibility.
• Deployed a Streamlit-based web application for interactive demonstration of RIS channel estimation results.
• Gained hands-on experience in wireless communication simulations, AI/ML model development, performance benchmarking, and real-time web deployment.
• This internship solidified my expertise in deep learning for communication systems, RIS channel modeling, and intelligent wireless network design, with exposure to 6G-enabling technologies, edge deployment strategies, and AI-driven optimization.

Mar 2025 – Apr 2025 · 1 mo · Missouri, United States · Remote

• Led a team in the Saint Louis University's Data Visualization Virtual Internship, working on a project for an employer partner company.
• Analyzed social media analytics from Facebook ad campaigns for an event, helping the Marketing team identify cost-saving opportunities.
• Developed data visualizations to assess ad campaign performance, leading to the recommendation of at least one campaign for discontinuation.
• Refined visualization design to enhance clarity, ensuring insights were effectively communicated to stakeholders.
• Designed a compelling presentation for the Finance team, explaining marketing data, analysis, and recommendations in an accessible manner.
• Gained hands-on experience in data analysis, visualization best practices, and presentation design, improving decision-making through data-driven insights.
• This internship provided valuable exposure to marketing analytics, strategic decision-making, and cross-functional communication, reinforcing the importance of data storytelling in business environments.

Mar 2025 – Apr 2025 · 1 mo · Rochester, New York, United States · Remote

• I recently completed a Research Internship organized by Rochester Institute of Technology, where I was given the role of QA Specialist:
• Worked under the guidance of Cheng Zhi Lai and Kolawole Oparinde, gaining valuable insights into prompt engineering frameworks and AI integration strategies.
• Collaborated with a cross-functional team including team leads, project managers, and prompt optimization specialists to evaluate and implement AI tools for real-world applications.
• Contributed to the quality assurance and effectiveness assessment of tools like ChatGPT, Claude AI, Jasper, EasyGen, and Notion AI across education, marketing, and internship management domains.
• Conducted meticulous testing and prompt response analysis to validate AI outputs for clarity, relevance, and accuracy.
• Ensured consistency, usability, and technical soundness in integrated tools across platforms like Moodle, Engage, and LinkedIn.
• Played a key role in Week 3 implementation of gamified learning through Kahoot! and post-event content automation using EasyGen.
• Assisted in comparative analysis of tool performance, scalability, customization, and prompt adaptability in different domains.
• Documented feedback, test cases, and tool outcomes to guide future AI adoption strategies and boost intern productivity

Feb 2025 – Mar 2025 · 1 mo · Bengaluru, Karnataka, India · Remote

• Worked on a project titled "Computer Vision Solution for Hearing-Impaired."
• Developed a deep learning model for sign language detection and identification, enabling real-time translation of hand gestures into textual representations.
• Designed a lip-reading system utilizing computer vision and speech recognition to extract intended information from speech and facial expressions, improving communication accessibility.
• Addressed challenges related to data collection for the Indian demographic, ensuring the model was trained on culturally and linguistically relevant datasets to enhance accuracy.
• Created a scalable and modular framework, maintaining a public GitHub repository with comprehensive documentation to facilitate further research and development.
• Gained hands-on experience in deep computer vision, natural language processing, and model optimization techniques for real-world healthcare applications.
• This project provided valuable experience in leveraging AI and computer vision to bridge communication barriers for the hearing-impaired, fostering inclusivity in everyday interactions.

Jan 2025 – Jan 2026 · 1 yr · Guwahati, Assam, India · On-site

Jan 2025 – Jan 2026 · 1 yr · Guwahati, Assam, India · On-site

• Hosting monthly webinars with our alumnas and other tech professionals
• Preparing in depth minutes of the events

Jan 2025 – Jan 2026 · 1 yr · Guwahati, Assam, India · On-site

Dec 2024 – Jan 2025 · 1 mo · Guwahati, Assam, India · Remote

• I recently completed a Research Analyst Internship under Web3Assam, contributing to a research paper titled “Understanding Climate Data X Web3: Reshaping the Future of Data Protocols”:
• Worked under the guidance of Sumit Mazumder, the project coordinator, who provided valuable insights throughout the research process.
• Collaborated with Migangko Rajkonwar, Wasim Choudhury, and Pallabi Das as co-authors to explore innovative solutions at the intersection of climate data analysis and Web3 technologies.
• Conducted in-depth research on blockchain consensus algorithms and decentralized frameworks for transparent and reliable climate data management.
• Analyzed challenges in existing climate data protocols, including issues of data accessibility, traceability, and trustworthiness.
• Proposed solutions leveraging smart contracts, IPFS, and blockchain ecosystems, emphasizing security, scalability, and sustainability.
• Explored DeFi-based incentives to encourage stakeholder participation in climate data sharing.
• Developed a proof-of-concept model demonstrating the integration of Web3 technologies for climate data management.
• Drafted technical documentation and created engaging visual presentations to summarize findings and methodologies.

Dec 2024 – Jan 2025 · 1 mo · Hyderabad, Telangana, India

• I worked as a Data Analyst intern, focusing on a project titled “Advanced Vehicle Data Analysis.”
• Leveraged Python and industry-standard tools to extract, analyze, and visualize insights from the dataset provided by them as part of a comprehensive program.
• Conducted in depth data exploration to assess key characteristics, including the dataset’s structure, summary statistics, and percentile interpretations, enabling data driven decision making processes.
• Designed a robust analytical framework using Anaconda and advanced Python libraries such as Pandas, NumPy, and Matplotlib to deliver precise insights.
• Created and presented compelling visualizations to identify patterns and trends in the automotive industry, enhancing the comprehensibility of technical data for non-technical stakeholders.
• Maintained a rigorous documentation process, ensuring transparency and reproducibility in data analysis workflows.
• Worked collaboratively on three key assignments that simulated real-world analytics scenarios, ranging from statistical interpretation to graphical representation of data.
• Gained hands on experience in data preprocessing, exploratory data analysis (EDA), and effective communication of findings through professional-grade presentations.
• This role provided me with valuable insights into the importance of data analytics in solving complex, real-world problems, equipping me with critical skills in Python programming, statistical analysis, and data visualization to create meaningful solutions.

Sep 2024 – Oct 2024 · 1 mo · Noida, Uttar Pradesh, India · Remote

• I worked as a Computer Vision intern at Physics Wallah, focusing on a project titled “Crop Yield Prediction.”
• Developed a deep learning framework using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to accurately predict crop yields based on environmental data and management practices.
• Addressed challenges related to genotype-environment interaction and nonlinear effects of weather components, aiming to improve prediction accuracy across different geographical locations and time periods.
• Collected and analyzed datasets from the USDA - National Agricultural Statistics Service, designing a scalable solution that generalizes well to untested environments.
• Ensured modularity and maintainability of code, while maintaining a public GitHub repository with detailed documentation of the project.
• Gained hands-on experience in deep learning, cloud platforms for deployment, and system optimization techniques.
• This role has provided me with valuable experience in applying computer vision and machine learning technologies to real-world agricultural problems, enhancing my ability to create impactful solutions.

Jul 2024 – Jul 2024 · 0 mo · Guwahati, Assam, India · On-site

• I recently completed a week-long summer internship on Robotics and IoT at Tech-Booster Guwahati.
• Learned under the guidance of Manoj Dutta and Ankur Sir, who provided a thorough and detailed examination of robotics and IoT concepts.
• Explored key topics necessary for understanding and applying robotics and IoT principles, both from academic and career perspectives.
• Acquired hands-on experience through projects like controlling an LED using Bluetooth, integrating an IR sensor with a servo motor, creating a radar system, and controlling an LED light using the internet.
• Benefited from a well-structured course that was carefully organized, with each topic building on the previous one, making complex ideas accessible to students with varying levels of background in robotics and IoT.
• Gained the foundational knowledge and skills needed to analyze, implement, and innovate in the fields of robotics and IoT.

Jun 2024 – Jul 2024 · 1 mo · Guwahati, Assam, India · On-site

• I recently completed a week-long summer internship on Machine Learning at NIELIT Guwahati:
• Learned under the guidance of Dr. Mayuri Sarma and Dr. Pankaj Shukla, who made the learning process engaging and insightful.
• Worked on the project “Image Classification Dataset Analysis using Machine Learning.”
• Utilized machine learning algorithms, including regression and supervised learning models, to classify fruit and flower datasets.
• Employed Python libraries such as Scikit-Learn, TensorFlow, and Keras to develop and train models.
• Conducted data visualization and analysis using Matplotlib and Seaborn to explore dataset features and patterns.
• Integrated image processing techniques and deep learning frameworks to improve classification accuracy.