8 citations · 17 across the 21 of their papers we have counts for
26 papers
Teaching Vision-Language Models to Decline: Support Completion Against Mirage
Alfaj Uddin Ahmed, Md. Shaown Miah, Md Arid Hasan +2
Vision-language models answer questions about images they were never shown, a failure called mirage. Such an answer can be correct without being justified by the evidence, and the…
Vmem-: Low-Compute Out-of-Distribution Detection in Spiking Neural Networks from Membrane-Potential Statistics
Arul Rana, Agrim Tripathi, Shoaib Ahmed Dipu +3
Spiking Neural Networks (SNNs) offer an energy-efficient approach to processing event-camera data, yet out-of-distribution (OOD) detection remains challenging in this setting. Exis…
Stealth Is a Relation, Not a Property: How Event Representations Create Blind Spots for Timing Attacks in Event-Based Perception
Shoaib Ahmed Dipu, Md. Shaown Miah, Kamrul Hasan +1
An event camera produces an asynchronous stream, but what is visible in that stream depends on how a downstream consumer, such as a model or detector, processes time. The same time…
Neural Succession: A Mesoscopic Theory of Invasion, Coexistence, and Stabilization in Continual Learning
Shoaib Ahmed Dipu, Md Salman Shamil, Sayeed Shafayet Chowdhury
Continual learning is usually studied through mechanisms that preserve old knowledge. We develop Successional Learning Theory (SLT), a mesoscopic account in which the current repre…
On The Robustness-Resolution Tradeoff In Temporal Quantization Of Event Streams
Sayeed Shafayet Chowdhury, Ruhi Sharmin, Syed Ishtiaque Ahmed
Event pipelines often discretize asynchronous timestamps before learning. This step looks harmless, but its stability depends directly on temporal resolution. We study this depende…
The Ceiling Is in the Channel: Auditing Learner Gaps and Measurement Frontiers in Clinical Prediction
Sayeed Shafayet Chowdhury, Nusrat Jahan, Snehasis Mukhopadhyay +2
Clinical prediction can saturate for two different reasons: a fitted learner may fail to extract available information, or the recorded variables may impose a population frontier.…
Active Spiking Perception: The Membrane Potential as a Belief State for Anytime 3D Point Cloud Recognition
Akarsh Jain, Arya Pawa, Ayush Debnath +2
Spiking point cloud networks usually scan space in a fixed, input-agnostic order, which leaves the most distinctive resource of spiking computation, the temporal evolution of the m…
Detect Before You Leap: Mirage Detection in Vision-Language Models
Sayeed Shafayet Chowdhury, Md. Shaown Miah, S. M. Taiabul Haque +1
Vision-language models (VLMs) can produce confident answers without relevant visual evidence, a failure mode known as mirage (Asadi et al., 2026). We study pre-release mirage detec…
BAT-RM: A Boundary-Aware Transformer with Region-Aware Multi-Directional Mamba for Clinically Deployed Cervical Cancer Radiotherapy Auto-Contouring
Istiak Ahmed, Kazi Shahriar Sanjid, Galib Ahmed +16
We present a clinically deployed end-to-end auto-contouring system for cervical cancer radiotherapy planning, anchored by the Boundary-Aware Transformer with Region-Aware Mamba (BA…
Who Benefits From Sinus Surgery? Comparing Generative AI and Supervised Machine Learning for Predicting Surgical Outcomes in Chronic Rhinosinusitis
Sayeed Shafayet Chowdhury, Snehasis Mukhopadhyay, Shiaofen Fang +1
Artificial intelligence has reshaped medical imaging, yet the use of AI on clinical data for prospective decision support remains limited. We study pre-operative prediction of clin…
2D-ThermAl: Physics-Informed Framework for Thermal Analysis of Circuits using Generative AI
Soumyadeep Chandra, Sayeed Shafayet Chowdhury, Kaushik Roy
Thermal analysis is increasingly critical in modern integrated circuits, where non-uniform power dissipation and high transistor densities can cause rapid temperature spikes and re…
Roadmap to Neuromorphic Computing with Emerging Technologies
Adnan Mehonic, Daniele Ielmini, Kaushik Roy +50
The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently util…
ViTALS: Vision Transformer for Action Localization in Surgical Nephrectomy
Soumyadeep Chandra, Sayeed Shafayet Chowdhury, Courtney Yong +2
Surgical action localization is a challenging computer vision problem. While it has promising applications including automated training of surgery procedures, surgical workflow opt…
Towards Visual Syntactical Understanding
Sayeed Shafayet Chowdhury, Soumyadeep Chandra, Kaushik Roy
Syntax is usually studied in the realm of linguistics and refers to the arrangement of words in a sentence. Similarly, an image can be considered as a visual 'sentence', with the s…
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
Yinghan Long, Sayeed Shafayet Chowdhury, Kaushik Roy
Transformers have shown dominant performance across a range of domains including language and vision. However, their computational cost grows quadratically with the sequence length…
Implementation of MPPT Technique of Solar Module with Supervised Machine Learning
Ruhi Sharmin, Sayeed Shafayet Chowdhury, Farihal Abedin +1
In this paper, we proposed a method using supervised ML in solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulate…
One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
Sayeed Shafayet Chowdhury, Nitin Rathi, Kaushik Roy
Spiking Neural Networks (SNNs) are energy efficient alternatives to commonly used deep neural networks (DNNs). Through event-driven information processing, SNNs can reduce the expe…
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks
Sayeed Shafayet Chowdhury, Isha Garg, Kaushik Roy
Spiking Neural Networks (SNNs) are a promising alternative to traditional deep learning methods since they perform event-driven information processing. However, a major drawback of…
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks
Isha Garg, Sayeed Shafayet Chowdhury, Kaushik Roy
Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning frameworks, since they provide higher computational efficiency due to event-driven informa…
Numerical Analysis of a Highly Sensitive Surface Plasmon Resonance Sensor for SARS-CoV-2 Detection
Syed Mohammad Ashab Uddin, Sayeed Shafayet Chowdhury, Ehsan Kabir
In this paper, we propose a surface plasmon resonance (SPR) structure based on Kretschmann configuration incorporating layers of Silicon and BaTiO on top of Ag for real-time de…
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense
Sayeed Shafayet Chowdhury, Kaji Mejbaul Islam, Rouhan Noor
Autonomous aerial surveillance using drone feed is an interesting and challenging research domain. To ensure safety from intruders and potential objects posing threats to the zone…
Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems
Sayeed Shafayet Chowdhury, Kazi Mejbaul Islam, Rouhan Noor
Anomaly detection (AD) in a surveillance scenario is an emerging and challenging field of research. For autonomous vehicles like drones or cars, it is immensely important to distin…
Optimization of Surface Plasmon Resonance Biosensor for Analysis of Lipid Molecules
Ehsan Kabir, Syed Mohammad Ashab Uddin, Sayeed Shafayet Chowdhury
Surface Plasmon Resonance (SPR) is an important bio-sensing technique for real-time label-free detection. However, it is pivotal to optimize various parameters of the sensor config…
Towards Understanding the Effect of Leak in Spiking Neural Networks
Sayeed Shafayet Chowdhury, Chankyu Lee, Kaushik Roy
Spiking Neural Networks (SNNs) are being explored to emulate the astounding capabilities of human brain that can learn and compute functions robustly and efficiently with noisy spi…