CSE 2025 Spring Departmental Demo Day

(CSE 611: Master's Capstone Project)

Gaitless Feet Height Control Quadrupedal Robot

• Aman Arora

The project aims to train a policy which enables a GO2 quadrupedal operator to control feet height without any designed gaits. If possible we will enable it to climb stairs with a blind policy.

Kaleida Health App 4.0

• M V N S H Praneeth
• Neellohit Somayajula
• Muhammad Waseem Thameem Ansari
• Sri Bhargava Bhamidi
• Mayush Timmapuram

Kaleida Health App 4.0 primarily focuses on improving communication between patients and family, helping patients and their families follow their medical journey, having physician teams provide customized information on diagnosis for patients, and most importantly, decreasing patient anxiety about the hospitalization process.

(Blockchain Think Lab)

Blind Match

• rohanshr
• mohantej

We propose a library in Golang to for parallelized homomorphic cosine similarity operations. To demonstrate our implementation we also present Blind Match. A Fuzzy Private set intersection protocol to efficiently compare encrypted entity data using TF-IDF vectors while preserving privacy. To reduce the computational cost of homomorphic encryption, we exploit the sparsity of n-gram TF-IDF vectors and apply com- pression techniques that maintain cosine similarity, such as Fourier and Orthonormal Wavelet Transforms. Our project leverages the CKKS encryption scheme for secure dot product computations and is developed as an open-source Go Implementation

(Independent Study / Research)

DriverSight: Dynamic Attention for Distraction Detection

• Krishnakant Sonji
• Shweta Bankar

DriverSight is a real-time system that leverages dynamic attention networks to detect driver distractions by analyzing in-vehicle sensor data. It identifies distraction patterns quickly, enabling timely alerts and promoting safer driving.

SAMO:A Lightweight Sharpness-Aware approach for Multi-Task optimization with Joint Global-Local Perturbation

• Gokul Ram Subramani

This project focuses on integrating Sharpness-Aware Minimization into Multi-task learning. We propose a lightweight Sharpness-Aware Multi-task Optimization (SAMO) approach, which combines the benefits of Global Information (G-SAM) and Local Information(L-SAM) to mitigate task conflicts while keeping the computational cost manageable. The effectiveness of our approach was then validated with extensive experiments on different datasets (NYUv2, Cityscapes, QM9, Office-Home).

One Data Share

• Vamshi Krishna Kyatham

One Data Share is a web application used for cloning or transferring your data from one cloud service to another cloud service in a very quick and an efficient way.

Biomarker-Driven Modeling for Early Alzheimer's Trajectory Prediction

• rahuldil

AI and Pharmacy leveraged research project focused on unraveling the early progression of Alzheimer’s Disease by modeling causal relationships between key biomarkers and genetic risk factors like APOE4. Using a nonlinear mixed-effects (NLME) modeling framework inspired by physiologically-based pharmacokinetic (PBPK) models, the project captures individual biomarker trajectories (e.g., ABETA, TAU, PTAU) over time. By leveraging the ADNI dataset and integrating longitudinal data in long format, the model simulates disease progression and identifies early indicators of cognitive decline. This approach enables a deeper understanding of disease onset mechanisms and has potential applications in personalized early diagnosis and treatment planning.

ActDiffNet: Affective State Recognition using Biological Signals

• Jatin Chhabria

We present ActDiffNet, a novel active learning framework for affective state recognition using multisensor biosignals. The model addresses key challenges in biomedical machine learning, such as data scarcity and class imbalance, by synthesizing hard-to-learn minority patterns using conditional diffusion models. ActDiffNet starts with minimal labeled data and iteratively improves through targeted synthetic signal generation. Our method achieves state-of-the-art accuracy on benchmark datasets, outperforming existing supervised and self-supervised models. This research is accepted for publication at the IEEE CHASE 2025 conference.