February 2026

Melanoma remains the most lethal form of skin cancer, responsible for the majority of skin cancer-related fatalities despite its relatively low incidence cite{milton2018}. Melanoma can spread to different parts of the body if it is not identified and treated in its early stages. In cite{kumar2021, kumarVatsa2022} previous work on melanoma classification, the VGG16 architecture of the CNN performed better on the cancer dataset than any other popular network. % The early detection of melanoma by analyzing skin lesion […]

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Hey everyone, I’ve got access to a bunch of NVIDIA Jetson Orins through my lab and I want to do something cool and deployable. For context, I’ve previously built a small language model (SLM) from scratch and have experience in real-time ML pipelines, computer vision, anomaly detection, and explainable AI. I’ve also deployed AI models on edge devices for real-time monitoring systems. I’m looking for ideas/ research areas that could get me hired tbh, and relevant for industry […]

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OpenAI is one of the biggest AI companies to exist at this point, with rivals like Google, Anthropic, Meta, and others competing alongside it. However, OpenAI now has what it takes to scale its AI footprint and push even further toward excelling in the race for AI models. Here, we are talking about investment, and that too a massive one. As reported earlier this month, Sam Altman-led OpenAI today announced that it has closed one of the biggest […]

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In his lawsuit against OpenAI, Musk touted xAI safety compared with ChatGPT. A few months later, xAI’s Grok flooded X with nonconsensual nude images.

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Anthropic and the Pentagon are clashing over AI use in autonomous weapons and surveillance, raising high-stakes questions about national security, corporate control, and who sets the rules for military AI.

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Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning. Contemporary quantum machine learning is generally limited to toy examples or heavily restricted datasets with few elements. This is not only due to the current limitations of available quantum hardware but also due to the absence of inductive biases arising from application-agnostic designs. Current quantum solutions must resort to tricks to scale down high-resolution images, such as relying heavily on dimensionality […]

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Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with known ground-truth factors. These metrics are assumed to reflect recovery up to the equivalence class guaranteed by identifiability theory. We show that this assumption holds only under specific structural conditions: each metric implicitly encodes assumptions about both the data-generating process (DGP) and the encoder. When these assumptions are violated, metrics become misspecified and can produce systematic false positives and false […]

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