Title of the talk: A Systems Lens on Emerging Financial Frontier
Abstract: Traditional model-centric thinking may no longer be enough, when financial systems are becoming increasingly complex and interconnected. This talk explores how applying a systems thinking lens can unlock new value across emerging areas in finance. We will try and understand the challenges such as:

What happens when supply chains are reimagined through digital tokenization?
When autonomous AI agents begin transacting on behalf of humans—and how do we model such ecosystems?

Bio: Partner and Head of NextLabs, driving innovation in Cognitive and Quantum Computing. He leads a high‑performance team of researchers and data scientists, helping customers build differentiated solutions. Under his leadership, NextLabs has received the NASSCOM Game Changer Award and has collaborated with leading institutions such as IIT Madras, IISc, and the University of Calgary.

With over 25 years of experience in technology leadership, he brings deep expertise in enterprise architecture, digital transformation, and cybersecurity. He has also led critical initiatives in mergers and acquisitions, technology portfolio management, and CXO advisory. Prior to joining Mphasis, he was a founding member of a wealth management product startup.

Title of the talk: What needs to change in DeFi for wider adoption?
Abstract: Decentralized Finance (DeFi) has introduced a new financial paradigm by enabling permissionless, programmable financial services without centralized intermediaries. While DeFi protocols demonstrate strong innovation and composability, their  broad based adoption requires some issues to be resolved.  This talks  examines key barriers to broader DeFi adoption, focusing on privacy and usability challenges that constrain both retail and institutional participation. Public transaction transparency exposes sensitive financial information, creating confidentiality and compliance concerns, while complex user experiences—spanning wallet management, transaction finality, and fragmented interfaces—raise the barrier to entry for non-technical users.

We argue that overcoming these challenges requires a shift from protocol-centric design toward user- and institution-aware architectures. Privacy-preserving techniques such as zero-knowledge proofs, selective disclosure, and confidential execution can enable secure yet compliant interactions. In parallel, improved abstraction layers and execution environments are needed to simplify user interaction with smart contracts. We conclude by outlining research directions that align cryptographic innovation with usability and real-world financial integration.

Bio: Bhaskar Balan is a Vice President, Head of Asset Management Tech, India at Fidelity Investments. He has 15 years in the industry across USA (Silicon Valley & Dallas) and India developing innovative products/solutions across domains. Recent products are in the domain of Investment Management and Healthcare . Key focus was to work in a leadership role within startups, understand the business, assist in defining the product concept and lead the engineering effort from concept to completion. Bhaskar Balan brings experience from previous roles at Fidelity Investments, McUbe Investment Technologies and Contivo. Bhaskar Balan holds a M.S. degree in System Science from the Louisiana State University.

Title of the talk: Recent trends of practical areas of research in investments and risk
Abstract: In an era dominated by the ubiquity of Smart Beta, the traditional boundary between skill-based returns and systematic factors has blurred. This plenary session explores the critical evolution of modern portfolio construction, beginning with a provocative interrogation of ""Pure Alpha"": Is it a genuine extraction of unique value, or merely a disguised beta captured by increasingly sophisticated factor models?

By decomposing returns through advanced econometric ""drill-down"" techniques, we isolate the residual drivers of performance, stripping away the noise of alternative risk premia to find what truly remains of idiosyncratic skill. However, the path to alpha is fraught with cognitive and structural traps. We will revisit the ""Seven Sins of Quantitative Investing""—ranging from backtest overfitting to the neglect of transaction costs—to understand why even the most robust models often fail upon contact with live markets.

Finally, the discussion transitions from individual security selection to risk management and sneak into latest industry trends on Total Portfolio Approach (TPA), a holistic framework designed to build portfolios.

Bio: Dr. Eshan Ahluwalia brings extensive experience of senior management role at asset management, investment banking and HFT trading firms. Currently, he works on analytics products for portfolio risk methodology, factor model delivery, and portfolio construction analytics at BlackRock, overseeing global teams. Previously, he shaped product management at Morgan Stanley Capital (MSCI) and led strategic market risk management at Nomura for trading desks. With a Doctorate in Finance, his expertise spans asset pricing, quantitative asset management, and behavioral finance, backed by publications in top-tier journals.

Title of the talk: ML for Pattern Discovery in Equities
Abstract: This talk provides a practical overview of how machine learning techniques can be applied to discover patterns and predictive signals in equity markets. After introducing core ML paradigms—supervised, unsupervised, and reinforcement learning—we walk through the end-to-end data-science pipeline used in quantitative trading: data preparation, feature engineering, model selection, and robust evaluation. Special emphasis is placed on challenges unique to financial data, including noise, non-stationarity, and overfitting, along with methods such as regularization, cross-validation, and ensemble approaches to address them. The session concludes with real-world use cases—returns prediction, regime modeling, peer discovery, anomaly detection—and research directions involving deep learning, graph networks, and reinforcement learning, for strategy design. 

Bio: Hemang Mandalia is a Senior Vice President, and Quant Equities Portfolio Manager, at AlphaGrep. In his 16 years of experience in the industry, he has researched and traded medium and low frequency quant strategies for global equities, with systematic and rigorous approach to machine learning driven algo strategy discovery pipeline, and risk management. He has also set up from scratch and mentored industry-leading collaborative quant teams. Prior to this role, he was Quantitative Research Director at Millennium, and prior to that at Qube Research & Technologies (QRT) where he worked for almost 12 years (including group's time at Credit Suisse (CS)). His initial years work experience at CS was in tech, developing algo trading and back-testing systems. Hemang obtained an M.Tech in Computer Science & Engineering from the Indian Institute of Technology Madras.

Title of the talk: A new approach for the pricing of share buyback contracts
Abstract: In this talk, we will review a recent methodology for pricing share buyback contracts that replaces traditional control-based approaches with optimized heuristic strategies designed to maximize contract value. The valuation framework builds on classical techniques used for pricing path-dependent Bermudan options, enabling efficient numerical implementation. An additional feature of the approach is that it naturally leads to a corresponding hedging strategy. The material presented in this talk is based on recent work by Bastien Baldacci and co-authors.

Bio: After a BSc, MSc and PhD in Theoretical Physics from the Chennai Mathematical Institute, Himalaya has been working in the finance industry for the last four years, initially as a Core Quant Strat at Goldman Sachs and now as a Equity Derivative Analyst at HSBC. Furthermore, he is also a Visiting Faculty at CMI where he teaches a course on Mathematical Finance..

Title of the talk: New Rails, New Instruments, New Participants: What Happens When They Share an Execution Layer
Abstract: Stablecoins have matured into programmable settlement rails that are fast, global, and increasingly used at scale. Tokenization is producing instruments that carry their own compliance, transfer, and settlement logic natively, making them composable in ways traditional instruments aren't. And a new category of participant is arriving: autonomous AI agents that can hold assets and transact independently. Right now, they have no identity, no reputation, and no way to pay each other without going through existing accounts and rails that weren't built for them. ERC-8004 gives agents portable identity and reputation on-chain. x402 lets them settle payments in a single HTTP request at near-zero cost.

Separately, these are well-understood developments. Together, on shared infrastructure, they start to compound. An agent can settle against a tokenized instrument, accumulate reputation from the interaction, and use that reputation to access better terms on the next one, all within a single composable environment, with no intermediary in the loop. The dynamics that emerge, new forms of counterparty assessment, continuous settlement, machine-scale liquidity demands, programmable compliance that travels with the asset rather than being enforced externally, don't arise when these layers operate apart. How these protocols connect at the execution layer, what assumptions they encode, and what they make possible when they interact is where the most interesting and least examined design space sits today.

Bio: Isha Sangani works in App Relations and Research at the Ethereum Foundation, and is part of the ERC-8004 team focused on standards for onchain identity, reputation, and validation of autonomous agents. Her work engages closely with applications, protocols, and emerging systems across the Ethereum ecosystem.

Previously, she worked across ecosystem development and growth in zero-knowledge proof scaling and verifiable computation infrastructure.

Title of the talk: What needs to change in DeFi for wider adoption?
Abstract: Decentralized Finance (DeFi) has introduced a new financial paradigm by enabling permissionless, programmable financial services without centralized intermediaries. While DeFi protocols demonstrate strong innovation and composability, their  broad based adoption requires some issues to be resolved.  This talks  examines key barriers to broader DeFi adoption, focusing on privacy and usability challenges that constrain both retail and institutional participation. Public transaction transparency exposes sensitive financial information, creating confidentiality and compliance concerns, while complex user experiences—spanning wallet management, transaction finality, and fragmented interfaces—raise the barrier to entry for non-technical users.

We argue that overcoming these challenges requires a shift from protocol-centric design toward user- and institution-aware architectures. Privacy-preserving techniques such as zero-knowledge proofs, selective disclosure, and confidential execution can enable secure yet compliant interactions. In parallel, improved abstraction layers and execution environments are needed to simplify user interaction with smart contracts. We conclude by outlining research directions that align cryptographic innovation with usability and real-world financial integration.

Bio: Mridul Mishra leads tokenization and staking at Fidelity Digital Asset Management. In 14 years at Fidelity, he’s wandered (productively) through computational finance, NLP, and enterprise tech—collecting scars, lessons, and a healthy skepticism along the way.

Title of the talk: Static Factors to Adaptive Signals: The Evolution of Quantitative Investing
Abstract: Quantitative investing has evolved rapidly with advances in data availability, modelling techniques, and computational scale. This talk discusses how classical investment factors—such as value, quality, and sentiment—are being augmented through machine-learning-based signal extraction, alternative data sources, and event-driven indicators. Drawing on practical examples from global equity and fixed-income markets, the session highlights the limitations of static factor and smart-beta approaches, particularly in emerging markets, and motivates the shift toward dynamic factor construction. The talk also explores the growing role of systematic fixed-income strategies enabled by improved market transparency and electronic trading, and reflects on the research infrastructure required to support scalable, adaptive quantitative investment frameworks.

Bio: Lokesh Mrig, CFA, FRM, is a Managing Director, Head of Quantitative Investments at State Street Investment Management, India, where he leads research, strategy, and data-driven investment solutions across global equities, fixed income, and multi-asset portfolios. With over 15 years of experience in quantitative research, portfolio management, and systematic investment design, Lokesh has worked at leading institutions including MSCI and Motilal Oswal Financial Services. He is a recognised expert in factor investing, machine-learning-enhanced signal design, and the integration of alternative data into investment frameworks. Lokesh holds the CFA and FRM designations and frequently speaks at industry forums on quantitative finance and investment innovation.

Title of the talk: Market Microstructure in Decentralized Finance : Design, Latency, and Strategies
Abstract: Decentralized Finance (DeFi) has progressed from simple automated market makers to on-chain markets with increasingly sophisticated microstructure. This talk examines how foundational ideas from quantitative finance—market design, price discovery, latency, and risk management—are being reimplemented in blockchain-based systems, and the implications for systematic and high-frequency trading. We analyze the dominant DeFi market architectures, contrasting pool-based designs built on automated market makers and concentrated liquidity with central limit order book (CLOB)–based exchanges. The discussion highlights how pricing functions, liquidity placement, and protocol constraints shape the feasible strategy space for quantitative traders. Particular attention is given to recent CLOB-based systems that demonstrate low-latency, on-chain execution through custom blockchain design and Byzantine fault-tolerant consensus. The talk further situates decentralized markets relative to centralized exchanges, emphasizing the trade-offs between transparency, latency, and custodial trust. Finally, we explore the emerging role of network- and hardware-level optimization, arguing that the next phase of quantitative finance in DeFi will be driven not only by financial modeling, but also by advances in distributed systems, networking, and hardware-aware market design.

Bio: Pavan is a Senior Quantitative Researcher, currently co-leading BitQCode Capital’s Multi-Strategy All-Weather pod and managing multiple trading desks across HFT and MFT strategies in global FX, commodities, and cryptocurrencies. He primarily serves as a Designated Market Maker on leading crypto exchanges.

Earlier, Pavan founded golddust.fi, a crypto market-making protocol on Uniswap, during his dorm years. The platform scaled to become one of the top protocols on Ethereum, was recognized among the top student-led startups in India by AWS and CampusFund VC, raised approximately $800K in convertible notes, and was successfully exited.

He began his career as an intern at Samsung. Pavan holds a Bachelor’s in Electronics and Communication Engineering from Vellore Institute of Technology and is currently completing Education in Finance and Risk Management at IIM Ahmedabad.

Title of the talk: Toward Intelligent Financial Decision-Making: Decision-Centric AI, Interactive Optimization, and Quantum Computing
Abstract: Quantitative finance is undergoing a structural shift from prediction-driven workflows toward intelligent systems that directly operationalize financial decision-making. This talk presents three research thrusts advancing this transition.

We first examine decision-focused learning with deep neural architectures that generate optimized decision variables, challenging the traditional predict-then-optimize paradigm. Next, we introduce cognitive agent architectures that leverage multi-agent large language model systems for financial analysis, personalized portfolio construction, and interactive refinement. Finally, we explore quantum and quantum-inspired optimization for asset allocation and portfolio optimization, investigating their quantum potential for improved sampling efficiency and scalable approximation.

Collectively, these directions signal a move beyond passive modeling toward adaptive, decision-centric intelligence in quantitative finance.

Bio: Sai Barath Sundar is a Senior Manager at Mphasis NEXT Labs leading a team of AI Engineers in applied R&D. Over seven years at the labs, he has specialized in natural language processing, information mining from images, multi-modal architectures, process mining, optimization, knowledge management through ontologies, and agentic language model training. He is responsible for building and scaling these technologies into solutions for AI-ready customers. His work has covered applications in customs clearance processing, insurance claims decisioning, logistics forecasting, product label compliance, and conversational systems. He holds an MBA in Industrial and Management Engineering from the Indian Institute of Technology, Kanpur.

Title of the talk: Counterparty Credit Risk of Traded Derivatives
Abstract: Counterparty Credit Risk (CCR) refers to the risk of financial loss arising from a counterparty’s potential default before the maturity of a derivative contract. Unlike traditional credit risk, CCR is bilateral and driven by the stochastic evolution of market variables such as interest rates, foreign exchange rates, and equity prices, leading to time-varying and path-dependent exposures. Effective CCR measurement relies on exposure metrics including Expected Exposure (EE), Potential Future Exposure (PFE), and valuation adjustments such as Credit Valuation Adjustment (CVA). Modern regulatory frameworks emphasize accurate CCR modelling, particularly incorporating dependencies between exposure and credit quality, including wrong-way risk. Collateralization, netting, and central clearing play key roles in mitigating CCR, while advanced numerical methods such as Monte Carlo simulation are widely used for valuation and risk management.

More recently, Artificial Intelligence and Machine Learning techniques are gaining significant attention in CCR modelling, offering potential improvements in computational efficiency, exposure approximation, scenario generation, and real-time risk analytics, thereby complementing traditional quantitative approaches.

Bio: Sumit Patel holds a PhD in Aerodynamics from IISc, where his research focused on high resolution numerical solution of Navier–Stokes equations using Large Eddy Simulation (LES) to understand supersonic jet noise. He got the masters from the same institute where  the research concentrated on developing mean preserving polynomial reconstruction techniques within the finite volume framework to improve numerical consistency and accuracy. Prior to transitioning into finance, he has worked with ISRO, contributing to research and development efforts related to scramjet engine technology.

He currently works as a Quantitative Model developer at HSBC, specializing in Counterparty Credit Risk (CCR). His professional interests include quantitative modelling, risk analytics, and the application of advanced computational and machine learning techniques in traded risk domain.

Title of the talk: Toward Intelligent Financial Decision-Making: Decision-Centric AI, Interactive Optimization, and Quantum Computing
Abstract: Financial systems are moving past prediction-driven workflows towards intelligent systems that operationalize decision-making. This talk presents three research thrusts advancing this transition. 

We first examine decision-focused learning with deep neural architectures that generate optimized decision variables, challenging the traditional predict-then-optimize paradigm. Next, we introduce cognitive agent architectures that leverage multi-agent large language model systems for financial analysis, personalized portfolio construction, and interactive refinement. Finally, we explore quantum and quantum-inspired optimization for asset allocation and portfolio optimization, investigating their quantum potential for improved sampling efficiency and scalable approximation. 

Collectively, these directions signal a move beyond passive modeling toward adaptive, decision-centric intelligence in quantitative finance.

Bio: Dr. Udayaadithya Avadhanam is Vice President & Principal at Mphasis NEXTLabs, working at the intersection of cognitive AI architectures, decision intelligence, and quantum computing. He earned his PhD from the Indian Institute of Science (IISc) in computational sociology and has over 15 years of experience in industrial R&D, translating advances in artificial intelligence into real-world intelligent decision systems. His research interests include interactive intelligent systems, agent-based modeling, and complex adaptive systems for understanding and supporting real-world decision-making.