Aniruddha Mukherjee

SPARKS Pre-Doctoral fellow (https://www.csa.iisc.ac.in/sparks-programme/) at CSA under the guidance of Dr Gugan Thoppe and Dr Yogesh Simmhan. Working to optimize transaction throughputs in the enterprise use case of permissioned blockchains (HLF [https://hyperledger-fabric.readthedocs.io/en/release-2.5/])

Working with people to innovate and disrupt @ the IITM-BS degree.

Working on creating AI-Dermatology models for this New Delhi based Startup

- co-created a face-detection attendance system. used a pre-trained model with frozen weights, to generate an encoding for each face. sent the attendance data to the lab's server via the lab's api - created a voiced obstacle detection system. used the YOLO architecture to determine the object and pyttsx for the audio output

Worked on Deepfake detection models for image and video quality assessment. Surveyed literature. Performed a comparitive analysis to detect differences between real images and synthetic images by testing them using various techniques that included FFTs, Colour Histograms and other Natural Scene statistics. Created a dataset utilising Stable Diffusion to estimate the quality/“naturalness” of AI generated images. This will used in a future publication by the Lab Advisor: Dr Alan Bovik, Asvin Kumar

Developed a meta model to dynamically ensemble multiple models predicting 10candle returns in a near realtime Intraday trading scenario by leveraging the Weighted Majority Average algorithm Advisor: Dr Gautam Shroff Paper: http://doi.acm.org/?doi=3677052.3698656 Venue: 5th ACM International Conference on AI in Finance

Worked on creating a framework for anomaly detection using surveillance footage. Injected Pennylane layers into Sultani et al.’s architecture and observed better performance. Besides the boost in the classifier's performance, we fine-tuned GPT-2 with a linear head to output a score for how urgent a particular anomaly is, thereby enabling efficient prioritisation of crimes. Advisors: Dr Vinay Chamola, Dr Vikas Hassija Paper: https://ieeexplore.ieee.org/document/10630662 Venue: IEEE Transactions on Consumer Electronics

Relevant Coursework: Foundational Level • Programming in Python (Grade: S) • Mathematics for Data Science I (Grade: S) • Mathematics for Data Science II (Grade: B) • Statistics for Data Science I (Grade: A) • Statistics for Data Science II (Grade: S) • Computational Thinking (Grade: S) • English I (Grade: S) • English II (Grade: S) Diploma Level Courses • Programming Concepts using Java (Grade: S) • Database Management Systems (Grade: A) • Programming, Data Structures and Algorithms using Python (Grade: A) • Business Data Management (Grade: A) • System Commands (Grade: S) • Modern Application Development I (Grade: A)

7th Semester / 8 Semesters Graduating 2026 2nd Rank in the Batch

- Assistant Prefect - Lead role in the annual school play - Member of the school football team for 4 years - Lead a team of 3 to build a retractable cloth hanger using Arduino