Nobel Chowdary Mandepudi

It’s disheartening to see how the review process in some IEEE Transactions has declined in quality. Having served as an Associate Editor for several journals, I’ve always valued fairness and rigor in peer review — but my recent experience has been troubling. Our team’s paper, focused on optimization and control of power systems (specifically DCOPF/ACOPF solution conversion and AGC), underwent two rounds of revision before being rejected. Rejection is part of research — I accept that. But the concern arises when the process itself becomes arbitrary. One reviewer repeatedly raised irrelevant or mistaken questions — for instance, asking about DC/AC inverter hardware design for a purely optimization-based study, or confusing “AGC” with “ACG” throughout the review. Such experiences have broader implications. They can discourage PhD students who see their hard work dismissed unfairly, and may distort their understanding of what academic integrity and constructive reviewing should look like. In the long run, this undermines both the peer review system and the research community as a whole. We need to reflect — as authors, reviewers, and editors — on how to restore quality, professionalism, and accountability in peer review. Constructive criticism should build science up, not tear researchers down.

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NVIDIA joined a $2 billion India Deep Tech Alliance as a founding member to mentor and train emerging startups. https://lnkd.in/gb2hDGib

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As someone who grew up in an IBM family I’m proud to be a part of the next generation of truly world changing computing power. The transition from the fastest 1960s IBM computer to a modern exascale system represents a computing power increase of roughly one trillion-fold to two trillion-fold, that blows my mind. Two years before I was born in 1961, the IBM 7030 was the world's fastest computer, capable of approximately 0.5 to 1.2 megaFLOPS (millions of floating-point operations per second). Now we are operating at 8 EXAFLOPs and building beyond that. #IamCerebras #ml #ai

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Left Qualcomm, no customers for two years, built an 11,800 CR company. 1. After completing his master's and PhD in Electrical Engineering from Stanford University, Avneesh Agrawal joined Qualcomm. He led the semiconductor and mobile communications space there for a decade and got the big news. 👇🏻 2. He became the President of Qualcomm India and South Asia. Over four years, he was granted over 200 US patents for his work. During this time, he noticed something. 🤔 3. India faced 4,64,674 road accidents and 1.5 lakh people lost their lives. He believed that computer vision and AI could change driver safety by making roads safer through real-time intelligence. After 15 years, He left his top job. ✅ 4. He started a company with his colleague, Julian, to solve the road safety problem in Bangalore. In August 2015, Netradyne was born. 🚀 5. The idea was simple ⏩ Build a camera-first, vision-driven fleet safety solution with on-device (edge) AI. The flagship product - Driver. i, a rugged multi-lens dashcam that analysed 100% of drive time to identify risky and positive driving behaviours. He thought he had it, but he did not. 👇🏻 6. Low latency for in-cab coaching, building training data pipelines was a challenge. Moreover, convincing fleet operators to try a camera that watched drivers was another. He had to find answers or else Netradyne would not lift off. 🤔 7. He focused on anonymised analytics and Driver training programs to ensure in-cab positive reinforcement for good driving. Moreover, he split the team between the US and India to speed model training and hardware design. By 2016, it raised 108 CR led by Reliance. 💰 8. On 24th March 2017, Netradyne finally had its first commercial customer. It worked with Load One to make their drivers the safest and best-trained drivers on the road. And then customers kept coming - Stewart Transport, General Motors and Bryan Truck Line. By 2018, it raised 148 CR from Microsoft and Reliance. 💸 9. With funds, Avneesh focused on dynamic 3D/HD mapping and packaged underwriting data for insurance companies. By 2021, it had grown to support over 1000 fleets, including Titan Freight Systems and Ace Endico and crossed 1 billion driving miles. In 2021, it raised 1120 CR from Softbank. 📈 10. As it launched Driver.i one, a professional-grade video telematics device in 2023 and scaled to 18 billion minutes with 3000 customers across US, Canada, Mexico, Germany, UK, Australia, New Zealand, India, the big moment came on 16th January 2025.👇🏻 11. Netradyne raised 778 CR led by Point 72 Ventures at a valuation of 8328 CR. It became the first Indian unicorn of 2025. 🦄 11. Today, Netradyne has clocked over 25 billion minutes of driving data and safeguarded over 125 million drivers with an impeccable 99% accuracy of alerts. 💪 ➡️Avneesh Agarwal left his crores in salary at Qualcomm with no customers for two years to build an 11,800 CR company today. He is a man of true inspiration! 🙏 #startups #india

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Adaptive test models are known to give disproportionate weight to early responses. A momentary lapse of concentration, a misread question, or even a technical glitch can push a student onto a permanently different trajectory. In a traditional exam, one wrong answer costs one mark. In an adaptive system, it can silently reshape the entire test. #jeeadvanced #iit https://lnkd.in/dJp4y7md

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💥💥💥 Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision Dulhan Jayalath, Shashwat Goel, Thomas Foster, Parag Jain, Suchin Gururangan, Cheng Zhang, Anirudh Goyal, Alan Schelten Abstract Where do learning signals come from when there is no ground truth in post-training? We propose turning exploration into supervision through Compute as Teacher (CaT), which converts the model's own exploration at inference-time into reference-free supervision by synthesizing a single reference from a group of parallel rollouts and then optimizing toward it. Concretely, the current policy produces a group of rollouts; a frozen anchor (the initial policy) reconciles omissions and contradictions to estimate a reference, turning extra inference-time compute into a teacher signal. We turn this into rewards in two regimes: (i) verifiable tasks use programmatic equivalence on final answers; (ii) non-verifiable tasks use self-proposed rubrics-binary, auditable criteria scored by an independent LLM judge, with reward given by the fraction satisfied. Unlike selection methods (best-of-N, majority, perplexity, or judge scores), synthesis may disagree with the majority and be correct even when all rollouts are wrong; performance scales with the number of rollouts. As a test-time procedure, CaT improves Gemma 3 4B, Qwen 3 4B, and Llama 3.1 8B (up to +27% on MATH-500; +12% on HealthBench). With reinforcement learning (CaT-RL), we obtain further gains (up to +33% and +30%), with the trained policy surpassing the initial teacher signal. 👉 https://lnkd.in/dTxA7uDP #machinelearning

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