5 Search Results for "Krishnamachari, Bhaskar"

Document
DOI: 10.4230/OASIcs.NINeS.2026.5
OrbitalBrain: A Distributed Framework for Training ML Models in Space

Authors: Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Earth observation nanosatellites capture high-resolution photos of the Earth in near real-time. These images increasingly support ML applications that are critical for safety and response, such as forest fire and flood detection. However, the downlink bandwidth is limited, resulting in days or weeks of delay from image capture to training. In this work, we propose OrbitalBrain, an efficient in-space distributed ML training framework that leverages limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training. Our evaluations demonstrate that OrbitalBrain achieves 1.52×-12.4× speedup in time-to-accuracy while always reaching a higher final model accuracy compared to state-of-the-art ground-based or federated learning baselines. Furthermore, our approach is complementary to satellite imagery capturing and downloading, enhancing the overall efficiency of satellite-based applications.
Cite as

Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra. OrbitalBrain: A Distributed Framework for Training ML Models in Space. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 5:1-5:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chabra_et_al:OASIcs.NINeS.2026.5,
  author =	{Chabra, Om and Li, Chenning and Hsieh, Kevin and Segarra, Santiago and Arzani, Behnaz and Olsen, Peder and Chandra, Ranveer},
  title =	{{OrbitalBrain: A Distributed Framework for Training ML Models in Space}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{5:1--5:32},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.5},
  URN =		{urn:nbn:de:0030-drops-255907},
  doi =		{10.4230/OASIcs.NINeS.2026.5},
  annote =	{Keywords: Satellite networks, Distributed machine learning, Federated learning, Earth observation, In-orbit computing}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.35
On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation

Authors: Abdulrahman Alhaidari, Balaji Palanisamy, and Prashant Krishnamurthy

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that execute within the same block. We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded, low-latency inference inside smart contracts. A novel Proof-of-Improvement (PoIm) protocol governs the training process and verifies each decentralized micro update as a self-verifying training transaction. Updates are accepted by PoIm only if they demonstrably improve at least one core metric (e.g., accuracy, F1-score, precision, or recall) on a public benchmark without degrading any of the other core metrics, while adversarial proposals get financially penalized through an adaptable test set for evolving threats. We develop quantization and loop-unrolling techniques that enable inference for logistic regression, SVM, MLPs, CNNs, and gated RNNs (with support for formally verified decision tree inference) within the Ethereum block gas limit, while remaining bit-exact to their off-chain counterparts, formally proven in Z3. We curate 298 unique real-world exploits (2020 - 2025) with 402 exploit transactions across eight EVM chains, collectively responsible for $3.74 B in losses. We demonstrate that on-chain ML governed by PoIm detects previously unseen attacks with over 97% attack detection accuracy and 82.0% F1. A single inference, such as one made via an external call, typically incurs zero cost. Fully on-chain inference consumes 57,603 gas (≈ $0.18) for linear models, 143,647 gas (≈ $0.49) for CNN(F2, K1), and 506,397 gas (≈ $1.77) for CNN(F8, K4) on L1 (e.g., Ethereum). Our results show that practical and continually evolving DeFi defenses can be embedded directly in protocol logic without trusted guardians, and our solution achieves highly cost-effective protection while filling a critical gap between vulnerability scanners and real-time transaction screening.
Cite as

Abdulrahman Alhaidari, Balaji Palanisamy, and Prashant Krishnamurthy. On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 35:1-35:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{alhaidari_et_al:LIPIcs.AFT.2025.35,
  author =	{Alhaidari, Abdulrahman and Palanisamy, Balaji and Krishnamurthy, Prashant},
  title =	{{On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{35:1--35:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.35},
  URN =		{urn:nbn:de:0030-drops-247548},
  doi =		{10.4230/LIPIcs.AFT.2025.35},
  annote =	{Keywords: DeFi attacks, on-chain machine learning, decentralized learning, real-time defense}
}
Document
DOI: 10.4230/OASIcs.FAB.2021.3
DAISIM: A Computational Simulator for the MakerDAO Stablecoin

Authors: Shreyas Bhat, Ayten Betul Kahya, Bhaskar Krishnamachari, and Rohit Kumar

Published in: OASIcs, Volume 92, 4th International Symposium on Foundations and Applications of Blockchain 2021 (FAB 2021)

Abstract
We present a computational simulation of the single-collateral DAI stablecoin launched by the MakerDAO project in 2017. At the core of the simulation is a model of cryptocurrency investors acting as rational Markowitz mean-variance portfolio optimizers, with heterogeneous risk tolerance. The simulator, called DAISIM, incorporates automated order matching and price update mechanisms to determine the DAI price. We use the simulator to evaluate how the single-collateral DAI price, as well as portfolio allocations, vary for a given population of investors as a function of exogenous parameters such as the price of ETH and various system parameters including stability rate and transaction fee. DAISIM is being made available as open-source and may be useful in evaluating other similar projects.
Cite as

Shreyas Bhat, Ayten Betul Kahya, Bhaskar Krishnamachari, and Rohit Kumar. DAISIM: A Computational Simulator for the MakerDAO Stablecoin. In 4th International Symposium on Foundations and Applications of Blockchain 2021 (FAB 2021). Open Access Series in Informatics (OASIcs), Volume 92, pp. 3:1-3:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{bhat_et_al:OASIcs.FAB.2021.3,
  author =	{Bhat, Shreyas and Kahya, Ayten Betul and Krishnamachari, Bhaskar and Kumar, Rohit},
  title =	{{DAISIM: A Computational Simulator for the MakerDAO Stablecoin}},
  booktitle =	{4th International Symposium on Foundations and Applications of Blockchain 2021 (FAB 2021)},
  pages =	{3:1--3:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-196-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{92},
  editor =	{Gramoli, Vincent and Sadoghi, Mohammad},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.FAB.2021.3},
  URN =		{urn:nbn:de:0030-drops-139899},
  doi =		{10.4230/OASIcs.FAB.2021.3},
  annote =	{Keywords: Stablecoin, Simulator, MakerDAO}
}
Document
DOI: 10.4230/OASIcs.FAB.2021.5
Dynamic Curves for Decentralized Autonomous Cryptocurrency Exchanges

Authors: Bhaskar Krishnamachari, Qi Feng, and Eugenio Grippo

Published in: OASIcs, Volume 92, 4th International Symposium on Foundations and Applications of Blockchain 2021 (FAB 2021)

Abstract
One of the exciting recent developments in decentralized finance (DeFi) has been the development of decentralized cryptocurrency exchanges that can autonomously handle conversion between different cryptocurrencies. Decentralized exchange protocols such as Uniswap, Curve and other types of Automated Market Makers (AMMs) maintain a liquidity pool (LP) of two or more assets constrained to maintain at all times a mathematical relation to each other, defined by a given function or curve. Examples of such functions are the constant-sum and constant-product AMMs. Existing systems however suffer from several challenges. They require external arbitrageurs to restore the price of tokens in the pool to match the market price. Such activities can potentially drain resources from the liquidity pool. In particular dramatic market price changes can result in low liquidity with respect to one or more of the assets and reduce the total value of the LP. We propose in this work a new approach to constructing the AMM by proposing the idea of dynamic curves. It utilizes input from a market price oracle to modify the mathematical relationship between the assets so that the pool price continuously and automatically adjusts to be identical to the market price. This approach eliminates arbitrage opportunities and, as we show through simulations, maintains liquidity in the LP for all assets and the total value of the LP over a wide range of market prices.
Cite as

Bhaskar Krishnamachari, Qi Feng, and Eugenio Grippo. Dynamic Curves for Decentralized Autonomous Cryptocurrency Exchanges. In 4th International Symposium on Foundations and Applications of Blockchain 2021 (FAB 2021). Open Access Series in Informatics (OASIcs), Volume 92, pp. 5:1-5:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{krishnamachari_et_al:OASIcs.FAB.2021.5,
  author =	{Krishnamachari, Bhaskar and Feng, Qi and Grippo, Eugenio},
  title =	{{Dynamic Curves for Decentralized Autonomous Cryptocurrency Exchanges}},
  booktitle =	{4th International Symposium on Foundations and Applications of Blockchain 2021 (FAB 2021)},
  pages =	{5:1--5:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-196-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{92},
  editor =	{Gramoli, Vincent and Sadoghi, Mohammad},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.FAB.2021.5},
  URN =		{urn:nbn:de:0030-drops-139911},
  doi =		{10.4230/OASIcs.FAB.2021.5},
  annote =	{Keywords: Decentralized Exchange, Automated Market Maker, Decentralized Finance, Dynamic Curves}
}
Document
DOI: 10.4230/DagRep.7.7.1
Foundations of Wireless Networking (Dagstuhl Seminar 17271)

Authors: Christina Fragouli, Magnús M. Halldórson, Kyle Jamieson, and Bhaskar Krishnamachari

Published in: Dagstuhl Reports, Volume 7, Issue 7 (2018)

Abstract
This report documents the talks and discussions of Dagstuhl Seminar 17271 "Foundations of Wireless Networking". The presented talks represent a wide spectrum of work on wireless networks.
Cite as

Christina Fragouli, Magnús M. Halldórson, Kyle Jamieson, and Bhaskar Krishnamachari. Foundations of Wireless Networking (Dagstuhl Seminar 17271). In Dagstuhl Reports, Volume 7, Issue 7, pp. 1-21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{fragouli_et_al:DagRep.7.7.1,
  author =	{Fragouli, Christina and Halld\'{o}rson, Magn\'{u}s M. and Jamieson, Kyle and Krishnamachari, Bhaskar},
  title =	{{Foundations of Wireless Networking (Dagstuhl Seminar 17271)}},
  pages =	{1--21},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{7},
  editor =	{Fragouli, Christina and Halld\'{o}rson, Magn\'{u}s M. and Jamieson, Kyle and Krishnamachari, Bhaskar},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.7.1},
  URN =		{urn:nbn:de:0030-drops-84209},
  doi =		{10.4230/DagRep.7.7.1},
  annote =	{Keywords: wireless networks}
}
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