Lead Product Owner at IWS India and AI/ML PhD Scholar at Woxsen University. Alumnus of UBC Sauder (PG Diploma in Accounting), IIM Ahmedabad (MBA), and Osmania University (B.E. in ECE).
As the Lead Product Owner at IWS, Sai is responsible for shaping solutions that bridge technology and business impact.
Directed the successful delivery of key features in alignment with strategic objectives.
Responsible for defining and owning the product vision, strategy, and roadmap for the HR Suite SaaS offering at Mantra Group.
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Experience with Perl, Java, C, C++, CSS, JavaScript, HTML, Python, MySQL, Rust, and R, with a strong eagerness to continuously improve and learn new technologies.
Highly skilled in UI design with a strong understanding of design principles, user experience, and industry-standard tools such as Figma, Whimsical, Balsamiq, and Visily.ai. Proficient in creating visually appealing, user-friendly interfaces for web and mobile applications.
Proficient in English with strong speaking, writing, and comprehension abilities. Able to effectively communicate ideas and understand complex information in both personal and professional settings. Demonstrates strong grammar skills and a broad vocabulary.
Successfully completed the Contentful Certified Content Manager certification, achieving a score of 96%.
Reference: https://www.credly.com/badges/b4eb7fbb-d7fa-414a-93e0-cf48f11b8917/public_url
Secured 1st place among 1,550 students across North America in the Hedge Fund Trading Competition, the “Fortress Challenge,” and won prize money of US $4,000 in 2015.
Reference: https://ucptl.com/2015/04/
Worked as an Assistant Professor in the Department of Business Administration, teaching technology and finance subjects.
We introduce an Automated Market Maker (AMM)-based lending mechanism that applies a Reverse Kelly criterion to establish loan premiums based on model-estimated default probabilities and collateral ratios. We embed this mechanism in a multi-agent system that completes the financial loop using on-chain reputation and enforcement. This specific combination of Kelly-optimal credit pricing with multi-agent orchestration for under-collateralized assets constitutes the core novelty of our research. Unlike traditional AMMs designed for token exchange, we adapt Kelly’s growth-optimal allocation principle to the credit market, thereby establishing a dynamic pricing surface that explicitly links premiums to probabilistic risk. The AI-Blockchain Reverse Kelly AMM (rkAMM) framework integrates three types of autonomous agents: (i) AI-based Risk Assessment Agents (RAA) that estimate borrower default probability (PD); (ii) AMM Pricing Agents (PA) that use the Reverse Kelly criterion to determine loan premiums and an optimal capital allocation fraction; and (iii) Smart Contract Enforcement Agents (SEA) that guarantee transparent execution and update an immutable on-chain reputation registry.
We provide empirical validation of the framework through extensive simulation. First, a head-to-head ablation study on an identical 10,000-loan stream demonstrates that the Reverse Kelly strategy achieves a 14.3% annualized growth rate, which surpasses proportional-premium (10.8%) and fixed-premium (7.6%) models. We report critical risk metrics, including max drawdown (18.2% vs. 25.4%) and loss ratio (8.1% vs. 12.7%), confirming superior risk-adjusted returns. Second, a stress test utilizing fat-tailed PD shocks verifies that the Kelly-based allocation rule automatically clips exposure, which maintains pool stability. Finally, a minimal Layer 2 (L2) testnet deployment validates the proposed on-chain logic. Our results offer robust and reproducible evidence for this novel, capital-efficient, and resilient architecture intended for decentralized under-collateralized lending.
The global financial architecture is undergoing a paradigmatic shift from account-based legacy systems to token-based distributed ledger technologies (DLT). While the internet has democratized the exchange of information, the movement of economic value remains constrained by pre-digital, siloed infrastructures that impose significant latency and cost. This paper presents a comprehensive architectural proposal for the IFSCA Tokenization Network (ITN), a regulated, permissioned DLT infrastructure designed specifically for the Gujarat International Finance Tec-City (GIFT City). Addressing the "blockchain trilemma" of decentralization, security, and scalability within a sovereign regulatory framework, this proposal synthesizes the architectural principles of the Centre Consortium’s open protocols with the statutory requirements of the International Financial Services Centres Authority (IFSCA). We introduce and formalize the "Twin-Rupee" liquidity model, utilizing Asset Reserve Certificates (ARC) backed 1:1 by sovereign debt to solve the currency convertibility challenges inherent to the IFSC. Furthermore, we provide rigorous technical specifications for identity-gated token standards (ERC-3643), high-throughput state channels for institutional settlement, and Hashed TimeLock Contracts (HTLC) for atomic cross-chain interoperability. We conclude with a detailed analysis of the system’s compliance with the IFSCA Payment Services Regulations 2024, demonstrating how programmable value can coexist with strict anti-money laundering (AML) and counter-terrorist financing (CFT) mandates.