CMU Professor Uses Data to Connect Energy Assistance with Those in Need
<p> <img loading="lazy" src="https://www.cmu.edu/news/sites/default/files/styles/listings_desktop_1x_/public/2026-03/nock-energy-week-2000.jpg.webp?itok=PaJdKuSD" width="900" height="508" alt="Destenie Nock next to a Peoples Energy Analytics sign/Carnegie Mellon University Energy Week"> </p> Destenie Nock, assistant professor of civil and environmental engineering and engineering and public policy, leads a research group comprised of graduate students at CMU that has created ways to find people struggling with energy poverty.
Could not retrieve the full article text.
Read on Carnegie Mellon News →Carnegie Mellon News
https://www.cmu.edu/news/stories/archives/2026/march/cmu-professor-uses-data-to-connect-energy-assistance-with-those-in-needSign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
assistantpolicyresearch
DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment
arXiv:2604.01787v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF), using algorithms like Proximal Policy Optimization (PPO), aligns Large Language Models (LLMs) with human values but is costly and unstable. Alternatives have been proposed to replace PPO or integrate Supervised Fine-Tuning (SFT) and contrastive learning for direct fine-tuning and value alignment. However, these methods still require voluminous data to learn preferences and may weaken the generalization ability of LLMs. To further enhance alignment efficiency and performance while mitigating the loss of generalization ability, this paper introduces Distribution-guided Efficient Fine-Tuning (DEFT), an efficient alignment framework incorporating data filtering and distributional guidance by calc
Early View Article - Does AI Affect the Democratic Conduct of War? Analyzing US and Israeli Military AI Deployment - Global Policy Journal
Early View Article - Does AI Affect the Democratic Conduct of War? Analyzing US and Israeli Military AI Deployment Global Policy Journal
DeltaMem: Towards Agentic Memory Management via Reinforcement Learning
arXiv:2604.01560v1 Announce Type: new Abstract: Recent advances in persona-centric memory have revealed the powerful capability of multi-agent systems in managing persona memory, especially in conversational scenarios. However, these complex frameworks often suffer from information loss and are fragile across varying scenarios, resulting in suboptimal performance. In this paper, we propose DeltaMem, an agentic memory management system that formulates persona-centric memory management as an end-to-end task within a single-agent setting. To further improve the performance of our agentic memory manager, we draw inspiration from the evolution of human memory and synthesize a user-assistant dialogue dataset along with corresponding operation-level memory updating labels. Building on this, we in
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
Detecting Toxic Language: Ontology and BERT-based Approaches for Bulgarian Text
arXiv:2604.01745v1 Announce Type: new Abstract: Toxic content detection in online communication remains a significant challenge, with current solutions often inadvertently blocking valuable information, including medical terms and text related to minority groups. This paper presents a more nu-anced approach to identifying toxicity in Bulgarian text while preserving access to essential information. The research explores two distinct methodologies for detecting toxic content. The developed methodologies have po-tential applications across diverse online platforms and content moderation systems. First, we propose an ontology that models the potentially toxic words in Bulgarian language. Then, we compose a dataset that comprises 4,384 manually anno-tated sentences from Bulgarian online forums
Am I the baddie?
I am a software engineer. I work for a company that makes software for road construction. Monday last week we were under a bad crunch and we were told to start using agentic workflows. We had like 50 tickets to close by the following Tuesday. I’ve been experimenting with ai development for years now, but this was different. I had access to Opus/Sonnet 4.6, and GPT5.4—the latest models. Suddenly, they understood. I could talk about abstract concept’s and analogies, and it got them. I was soon working through tickets the first day in hours, what would have taken me days. But we still had a ton of work and not enough time. I was still bound to a single thread of work at a time. So like any problem, I hacked around it. I started with a worktree, where it basically creates a whole other copy of
Grounding AI-in-Education Development in Teachers' Voices: Findings from a National Survey in Indonesia
arXiv:2604.01630v1 Announce Type: new Abstract: Despite emerging use in Indonesian classrooms, there is limited large-scale, teacher-centred evidence on how AI is used in practice and what support teachers need, hindering the development of context-appropriate AI systems and policies. To address this gap, we conduct a nationwide survey of 349 K-12 teachers across elementary, junior high, and senior high schools. We find increasing use of AI for pedagogy, content development, and teaching media, although adoption remains uneven. Elementary teachers report more consistent use, while senior high teachers engage less; mid-career teachers assign higher importance to AI, and teachers in Eastern Indonesia perceive greater value. Across levels, teachers primarily use AI to reduce instructional pre
How to Automate Upwork Proposals with Python (Real Code Inside)
How to Automate Upwork Proposals with Python (Real Code Inside) Last month I sent 47 proposals on Upwork. I personally wrote 3 of them. The other 44 were drafted by Claude AI, filtered through a scoring algorithm I built over two weekends, and delivered to my inbox via Telegram before most freelancers even saw the job posting. My response rate on those AI-assisted proposals? 31%. Higher than my hand-written average from the previous quarter. This article shows you exactly how I built that system. The Real Problem With Upwork Proposals If you've freelanced on Upwork for more than a month, you know the grind. You refresh the job feed. You see something promising. You spend 20 minutes writing a tailored proposal. You hit submit. Nothing. Meanwhile, the client already hired someone who respond
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!