REGISTRATION & LIGHT BREAKFAST
Jerry Calvanese - Head of Enterprise Data Presales - Informatica
THE CHANGING FINANCIAL LANDSCAPE
Why Good AI Is Like The Perfect Slice Of Toast: Achieving Real World AI Outcomes And Escaping The Jargon Trap
Bjorn Austraat - SVP, Head of AI Acceleration - Truist
Between 60%-85% of AI projects fail to achieve the return on investment promised during the planning stage.
In this presentation we’ll discuss how good AI implementation is a lot like making the perfect piece of toast.
You will walk away with practical tools, tested in the real world, that help create solid alignment between business leaders, implementation experts and data scientists, and we’ll introduce you to a simple, jargon-free approach that helps focus all stakeholders on what really matters – outcomes for end users and verifiable business returns that live up to the promise of AI.
Bjorn Austraat brings more than two decades of diverse experience in taking complex business problems and finding pragmatic, profitable solutions to them through machine learning, AI and other technologies.
Currently, he serves as SVP and Head of AI Acceleration at Truist where he is building out the new AI & Analytics Accelerator (A3) to enable innovation and accelerate scalable solution deployment for AI and analytics across the enterprise. The Accelerator will provide a complete incubation environment including open source and commercial software, AI-optimized hardware, design thinking and Agile methodologies as well as synthetic data to support and empower teammates across Truist to grow ideas from early stage to MVP to fully deployed solutions.
Formerly, Bjorn was SVP for Agile AI at Wells Fargo, Global Cognitive Finance Leader with IBM for a top-3 International Bank and the Global Leader for Cognitive Visioning and Strategy for IBM Watson, where he provided strategic direction for marquee engagements including H&R Block and Vodafone.
Prior to joining IBM, Bjorn held a number of senior leadership roles in companies ranging from Silicon Valley startups to large, multinational consulting enterprises working with companies such as Apple, AT&T, Microsoft and Ford.
Bjorn began his career as an interpreter and translator in Vienna. Today he uses those skills to detect meaningful patterns in noisy business data and craft scalable and profitable strategies that are in tune with the competitive landscape and diverse stakeholder needs.
Bjorn holds an MBA from the Haas School of Business at UC Berkeley, an M.A. in Translation and Interpretation from the Monterey Institute of International Studies, and an M.A. in Conference Interpretation from the University of Vienna, Austria.
When he is not busy with all things technology, Bjorn spends his time with photography, international travel, nutritional science and AI ethics.
How Deep Learning is Unlocking a $362B Value Creation Opportunity in Financial Services
Sergio Rego - AI Customer Engineer - SambaNova Systems
In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.
To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.
AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:
• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks
Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.
Building AI Capabilities Successfully, from the Ground Up
Stavros Zervoudakis - Vice President of AI - Mutual of America Financial Group
Do you wish to be in the 53% of AI projects which make it from prototype to production? Why not in the select group of the 15% of AI and machine learning projects which deliver?
The key to being successful with AI can be found in replacing your reference to “A” with a different word and driving a strategy from that viewpoint. We will discuss this in detail at the AI Summit and provide examples on how to build or re-build your AI capability from the ground up and be successful in doing so. This talk provides you with insights on how to develop a vision and deliver on a strategy for Artificial Intelligence, while working with executives in business and technology, while maintaining touch with the latest advances in key areas of machine learning, deep learning, NLP/U/G, while managing stakeholder expectations and with a goal of deploying and maintaining trustworthy AI/ML solutions to production and delivering the best business value possible. Join us in this talk to learn more about what A should be and how you can set your path to be in the 15%.
Machine / Deep Learning, NLP, AutoML, MLOps, AWS, Business Analytics.
Strong technology and business acumen with resilience in decision making and demonstrated ability in engaging and communicating with stakeholders, as well as solid technical, strategic and business analysis skills with a drive for solving complex problems and delivering solutions.
17+ years’ hands-on project management and team leadership (small and large teams), including AI technologies, process automation, cloud computing, UX, QA/QC, agile, SDLC, with engineering mindset and delivery focus.
DECISION MAKING & AI
Using Deep Learning with Word Embeddings to Improve Customer Satisfaction
Eric Charton - Senior AI Director - National Bank of Canada
Using Deep Learning with Word Embeddings to improve Customer Satisfaction
Understanding customer satisfaction in retail banking requires exploring and comprehending multiple sources of feedback, such as emails, social networks reviews, web feedback, bot interactions, as well as speech-to-text transcripts collected from call centers. Since such a vast amount of textual data can be difficult to leverage with traditional text mining techniques, deep learning and word embeddings can be used to automatically classify and label feedback, and then deeply analyze and understand their content. In this communication we explain how we leverage all those AI techniques to get an in-depth understanding of the opinions and needs of National Bank’s retail customers. We also show how we improve the performance levels of those AI tools using in-house algorithms and data resources to improve the overall capacity of natural language understanding.
Key Takeaways: • Industrial applications • State of the art classification • Understanding of DL embedding limits
Eric Charton holds a Master in machine learning applied to voice recognition, and a Ph.D. in machine learning applied to Information extraction and natural language generation. He worked as scientist and research project coordinator in academic context in Europe (University of Avignon) and North America (CRIM, École Polytechnique de Montréal) before becoming head of search engine research and development at Yellow Pages Canada. Since March 2018, he is Senior AI Director at National Bank of Canada.
Accelerating Financial Business Decisions with NLG
Jay DeWalt - Chief Operating Officer - Arria
Arria is the global leader in Natural Language Generation - transforming data with the power of language. In this presentation, Jay will explain how Arria software replicates, through data analysis, knowledge automation, language generation and tailored information delivery, the human process of expertly analyzing and communicating data insights. Join us and hear how Arria dynamically turns data into written or spoken narrative—at machine speed and massive scale.
Prior to joining Arria, Jay held key executive positions in global companies including EMC, Documentum, Unisys, and Lockheed Martin. Jay has successfully led companies from growth phase through to post-acquisition integration.
“Arria’s key technology links applications to humans in ways never before possible. Arria is in a key leadership position with its core NLG technology supported by key patents.” - Jay DeWalt
Human + Machine: Using AI to Generate Investment Ideas
Spencer Reich - Partner - Boosted.ai
It is estimated that as much as 80% of trading in US stocks is machine-led. This is attributed to the rise of algorithmic trading and quantitative managers, many of whom use AI in their processes. How are fundamental managers and investment professionals to keep up? In this session, Spencer Reich will show the audience how to incorporate AI-driven research into equity portfolios – no coding or engineering knowledge necessary. From idea generation to position sizing to alternative data integration, Spencer will help bring AI-driven investing to the masses and show how Boosted Insights can improve results for all types of managers.
Spencer Reich has over 15 years of investing experience as a portfolio manager, research analyst, and trader across all macro products, including interest rates, currencies, commodities, and equities. Prior to joining Boosted.ai, Spencer was a senior investment analyst and portfolio manager at Tiger Management, reporting directly to Mr. Julian Robertson, Chairman of Tiger. He has ten years of hedge fund experience across all aspects of the investment process including fundamental research and idea generation, trade structuring and execution, and risk management. Spencer began his career at Goldman Sachs in the fixed income division on the interest rate products desk. He earned an MBA from The Wharton School and graduated from Duke University in 2003 with a BA in Economics, Phi Beta Kappa and Magna Cum Laude.
ROUNDTABLE: How Deep Learning Models Help Infuse AI to Assess, Automate and Accelerate Core Financial Functions
Bill Cox - Senior Director, North America Sales - SambaNova Systems
ROUNDTABLE: How Deep Learning Models Help Infuse AI to Assess, Automate and Accelerate Core Financial Functions
Finance has long been seen as visionary; applying machine learning algorithms to automate and accelerate key business processes from capital markets to compliance, risk, and enhancing the customer experience. Today, many of the legacy models are losing their effectiveness while the velocity of data has grown exponentially with an increasing variety of data types and formats. Quite often, the teams that build these older models are long gone. The convergence of these factors presents both a dilemma and an opportunity. In this roundtable session you will learn how NLP models can uncover non-linear patterns between disparate & unstructured datsets impacting risk, compliance, capital markets and customer engagement technology.
Bill Cox is the Senior Director Sales North America at SambaNova Systems driving deep learning implementations for both Public Sector and Enterprise accounts. Bill has extensive experience leading Global Sales teams in HPC compute and storage companies.
ML/AI and Alternative Data in Income Verification
Jessica Xu - VP, Head of Data Science - Octane
• Working with customers who tend not to have substantial footprint with traditional credit bureaus, Octane supplements traditional underwriting with forward-looking cashflow analysis in which income is a key data element. • Validating an applicant’s trustworthiness via an innovative ML/AI solution to detect outliers who have mis-stated their income on application. • Using a smart workflow to automate income verification by triangulating the application income with various alternative data (payroll data, historical spend and payment patterns, earnings on previous employment) to stay true to the company’s mission of automated, smooth, frictionless application experience, with optimization to achieve the right ROI on the automation.
Experienced data scientist with over a decade's experience in leveraging various Machine Learning and analytical techniques to synthesize large amounts of data into evidence-based models and strategies to improve business efficiency and profitability.
Proven people leader with a track record of building, developing, and inspiring teams of data scientists of various sizes across geographic locations.
Accoladed collaborator who can bring people together across functional teams and across levels to drive enterprise level initiatives from inception to successful completion.
Substantial expertise in credit data and alternative data for consumers, merchants and businesses in the U.S.
Closed Loop Decision Augmentation with AutoML and Automations
Elif Tutuk - VP of Innovation and Design - Qlik
In this session, Elif Tutuk, Vice President of Innovation and Design will cover the shift from linear decision support to closed loop decision automation systems. Generating insights from data with AI in a linear fashion only goes so far in addressing how we can respond in today’s complex and uncertain world. Data Analytics pipelines supporting closed-loop decision automation need to be more action focused and more outcome driven while ensuring trust is present at each step of the pipeline. In this session, Elif will cover how Qlik’s Active Intelligence platform augments the user with near real time predictive insights and compels trusted actions with application automation.
In her role as Vice President of Innovation and Design at Qlik, Elif is responsible for managing a global team of engineers and designers in planning and executing design, UX and innovation strategies for Qlik’s end-to-end cloud data integration & analytics products. She is an experienced product person in analytics area with diversified background; product management, product design, development and research. She has 15 years of experience in Business Intelligence and Analytics. She is passionate about analytics, innovating with data and augmenting human intelligence with the power of analytics. She is the winner of The Business Intelligence Group’s Artificial Intelligence Excellence Awards program. This award recognizes her work in BI and AI, leading the charge to blend AI into analytics to further the Artificial Intelligence (AI)/human interaction with data.
Machine Learning in Quantitative Investment and Wealth Management: Hype versus Reality
Cristian Homescu - Director, Portfolio Analytics - Bank of America Merrill Lynch
This presentation delves into successes, opportunities, challenges of ML applications for QWIM: • classification and pattern recognition • network analysis and clustering • time series forecasting • reinforcement learning • synthetic financial data generation • testing investment strategies and portfolios • factor-based investment strategies • nowcasting • incorporating market states and regimes into investment portfolios It also presents practical challenges for ML within context of QWIM: • lack of sufficient data • need to satisfy privacy, fairness and regulatory requirements • model overfitting • causality • explainability and interpretability • hyperparameter tuning
Cristian is part of the Portfolio Analytics team within Chief Investment Office, Global Wealth and Investment Management division Bank of America Merrill Lynch. He is developing and investigating quantitative solutions in areas such as investment strategies, goals-based wealth management, asset allocation, machine learning and big data analysis, factor-based investing and risk factor models, portfolio risk and attribution, stress testing and scenario construction. He is very interested in application of state-of-the-art algorithms and numerical methods in wealth and investment management, and in high-performance computing. Prior to joining Bank of America Merrill Lynch, Cristian was a front office quant for Wachovia and Wells Fargo. After supporting interest rate trading desk, he was the lead quant for FX and Commodities trading desks. He has a PhD from Florida State University in computational and applied mathematics, and MSc degrees from University of Paris XI and University of Craiova.
Applying ML to Data in the Cryptospace to Generate Signals
Gurraj Singh Sangha - Chief Quantitative Investment Officer - Token Metrics
Mr. Sangha is the Chief Quantitative Investment Officer at Token Metrics Ventures, a cryptocurrency hedge fund, where he leads a team of quantitative analysts and data scientists in deploying quantitative alpha-generating strategies in the blockchain arena across a wide variety of digital assets. Previously, he served as the Global Head of Data Science , Risk, and Market Intelligence at State Street Verus where he helped lead a risk and investment strategy team in developing an artificial intelligence platform that integrated machine learning, natural language processing, portfolio and risk management, and human experiences to explore connections and extract relevant insights between market-moving events and multi-asset class portfolios. Mr. Sangha is an accomplished global macro portfolio manager, strategist, and risk manager, with over 20 years’ experience. He has advised firms on developing quantamental approaches to trading --- strategies at the intersection of structural, statistical, and fundamental trading, utilizing data science, machine learning on both structured and unstructured data, and behavioral analytics. Further, he has led several investment strategy and risk management teams and held a number of senior positions, including Chief Investment Strategist at a $6 billion global macro volatility hedge fund, and Senior Global Macro Trader at a $400 million hedge fund. Mr. Sangha began his career at Goldman, Sachs & Co.,in the Global Investment Research Division. He received honors at the International Mathematics Olympiad, served on the Canadian Mathematics Olympic Team, and is a Magna Cum Laude graduate of Brown University.
PANEL: The ROI of AI in Finance
Olga Kane - Managing Director - Synthesis
Rajeev Sambyal - Director, AI & Machine Learning, Blockchain & Digital Assets - BNY Mellon
Sr. Technology Leader with a track record of building high performing teams and successfully driving business transformation & digital innovation using new and emerging technologies. An AI and Machine Learning practitioner with passion for solving business problems using technology.
Accelerate effective use of advanced technologies- Blockchain, AI, and Machine Learning to support core digitization efforts, and new products and services. Lead a multi-geography, multi-disciplinary team of data scientists, and engineers in the execution of AI/ML and Digital Assets strategy.
Partner with fintech’s and other vendors to evaluate new and emerging technologies, and accelerate their adoption in the enterprise.
Edward Tong - Executive Director, Applied AI & Machine Learning, CIB Operations - JPMorgan Chase
Edward Tong is Executive Director in Applied AI & Machine Learning at JPMorgan Corporate & Investment Bank. He leads teams developing scalable machine learning solutions across businesses to drive efficiency and automation. He has broad modeling experience covering Custody, Securities / Markets Operations, Wholesale, Corporate and Consumer Credit with industry experience spanning US, UK and Australia. Edward has prior model development and validation experience at Bank of America, AIG and Royal Bank of Scotland. He has published in several journals including European Journal of Operational Research and International Journal of Forecasting. He holds a PhD in Operations Research from University of Southampton, UK and an MSc in Statistics from University of Queensland, Australia.
John Ashley - General Manager, Financial Services and Technology - NVIDIA
John leads the global Financial Services and Technology business at NVIDIA. Their teams work with customers around the globe to bring accelerated computing solutions to bear on their most challenging problems.
NVIDIA's Financial Services and Technology team includes quants, data scientists, and IT leaders who worked for banks and trading firms. The local team is the tip of the global spear; backed by the full spectrum of NVIDIA resources and technologies – always brought to bear through an industry lens. They bring unique value to their strategic financial services customers at every level of the organization, making your AI strategy and partnerships better.
Sandeep Kumar - Managing Director- FinLabs/Accelerator Programs - Synechron
Sandeep Kumar is a proven business operations strategist and technology expert with over 30 years’ experience in capital markets consulting. He has worked with some of the world’s largest buy- and sell-side firms to design and implement transformative, global technology solutions that enhance operating models, streamline data capabilities, and increase revenue. Sandeep has been instrumental in the conception of Synechron’s Blockchain-led accelerators which streamline operations, simplify complex data synchronization, and consolidate fragmented business function, as well as in Synechron’s Artificial Intelligence-driven accelerators helping clients to understand how they can derive benefits from techniques like NLP (natural language processing), RPA (robotic process automation), and machine learning into their businesses. Sandeep is currently spearheading Synechron’s Financial Innovation Labs (‘FinLabs’) Accelerator Programs, all focused on solving some of the complex business challenges of the Financial Services industry. Under his leadership, Synechron has launched industry-leading solutions for the Buy-side industry – InvestTech Accelerators – as well as solutions for the Payments industry – PayTech Accelerators. Sandeep is an alumni of the prestigious Birla Institute of Technology and Science, Pilani, India.
END OF DAY 1
Jerry Calvanese - Head of Enterprise Data Presales - Informatica
FRAUD & RISK MANAGEMENT
From Legacy Models to Data Science and AI
Jake Katz - Head of Non-Agency RMBS Research and Data Science - Yield Book, London Stock Exchange Group
Data Science and ML techniques are now ubiquitous. That doesn’t mean the path from data-driven model discovery to industry adoption is straightforward. Join Jake Katz the Head of Non-Agency RMBS Research and Data Science at Yield Book, the leading mortgage analytics provider, as he discusses the insights and challenges around enhancing models all while maintaining best in class quantitative analytics for 350 institutional clients worldwide.
Jake is a residential mortgage performance modeling expert with 15 years of experience. He leads the Non-Agency RMBS research and modeling effort for Yield Book. Jake’s responsibilities include collateral performance models covering Subprime, Alt-A, Legacy Jumbo, Re-performing, CRT, MI-CRT, Jumbo near Prime, and Non-QM sectors. In addition, Jake leads data science research for securitized products. Prior to joining Yield Book, he was the Head of Analytics at Laurel Road where he directed collateral performance modeling for ABS loans and quantitative investment strategy for the firm. Jake has worked modeling and trading roles in Non-Agency RMBS and other fixed income products at AIG, Brevan Howard US Asset Management, and Lehman Brothers. Jake holds a Master’s degree in Statistics from Yale University and a BS (Hons) in Statistics from the University of Chicago.
Empower Your Data Analysts With Self-Service Application Deployment Using Panel
Philipp Rudiger - Sr Software Engineer - Anaconda
Empower Your Data Analysts With Self-Service Application Deployment Using Panel
Rapid development and deployment of ML and data science applications empowers individual analysts and data scientists to put applications directly into production, generating tremendous value for an organization. We will briefly cover the process of rapidly developing an application in Python using the open-source Panel and Lumen libraries from Anaconda’s HoloViz group. Next we will discuss the infrastructure required to make self-service deployment possible, using one of the largest financial institutions as a case study and looking at a number of deployed applications. Lastly we will demonstrate how analysts can exploit many of the features included in Panel, including out of the box database integrations, authentication using external or internal providers like Okta, persisting user state, deep linking, and much more.
A long-term veteran at Anaconda Inc., Philipp Rudiger is a Senior Software Engineer developing open-source and client-specific solutions for data management, visualization and analysis. He is the author of the open source dashboarding and visualization libraries Panel, hvPlot, and GeoViews and one of the core developers of Bokeh, Datashader and HoloViews. Before making the switch to software development he completed a PhD and Masters in Computational Neuroscience at the University of Edinburgh working on biologically inspired, deep and recurrent neural network models of the visual system.
APPLIED AI IN FINANCE
ML Techniques for Forecasting Equity Movements
David Mascio - Managing Founder and Principal - Della Parolla Capital
This presentation introduces machine learning techniques to investigate whether popular macroeconomic or sentiment factors are better at predicting stock market returns. We find that although either macroeconomic or sentiment variables alone fail to improve the Sharpe ratio of the stock market, combining the factors improves the Sharpe ratio from 0.48 to 0.62 and reduces the investment drawdowns by roughly 30% from 53 percentage points to 36 percentage points. This improvement is significant in both economic and statistical terms. We further evaluate the performance of strategies across business cycle and find that macroeconomic variables tend to outperform sentiment variables during market expansions and underperform during recessions. The combined performance of the macroeconomic and sentiment variables is particularly strong during the late stage of recessions when the stock market is close to its bottom. Our finding is robust to the choice of machine learning technique and indicates that sentiment and macroeconomic information is complementary and, therefore, should be considered jointly by investors.
David Mascio, PhD, is the Founder & Chief Executive Officer of Della Parola Capital Management. He is also an Endowed Chair and the Roland and Sarah George Professor of Applied Finance at Stetson University. Over the past 20 years, Dr. Mascio has served as a university professor, a hedge fund manager, keynote speaker on economic forecasting and the chief investment officer of a billion-dollar trust bank. He has also been published in top academic journals in the area of machine learning in economic and investment forecasting.
Dr. Mascio earned a B.A. in Economics and Business Management from the University of New Mexico, he also earned an MBA from the University of Liverpool (United Kingdom) and a PhD in Finance at EDHEC Business School (Nice, France). He is an accredited Asset Management Specialist (AAMS), and a member of the CFA Institute and CFA society of Orlando.
Common Data Issues in Implementing Machine Learning Platforms
Niraj Mehta - Machine Learning Engineering Manager - Barclays
Visionary technologist with strong strategic and leadership experience in building & managing next generation systems for Securities Lending, Collateral Management, and Asset Management businesses. Experience working in fast paced environment with Front Office traders to build low latency, highly available and scalable applications supporting trade life cycle, collateral management and post trading. Extensive experience in building and leading small, medium and large sized teams.
Specialties: * Designed, developed and managed Enterprise wide solutions using Microservices and Cloud based Architecture ,Web Architecture, Service Oriented Architecture, Data Warehousing and Business Intelligence * Complete end to end execution of Strategic Projects and Initiatives from concept, design, team building, defining milestones, detailing the roadmap to delivery. * Demonstrated success in managing deliverables within aggressive time-frames. * Proficient in working cross functionally to help solve problems fast. * Managed relationships with internal and external stakeholders as well as third part vendors * Managed Business Continuity and Resiliency programs
Read Less, Learn More: Using AI and NLP to Extract Key News Themes
Shaun Waters - Product Manager, News Search and Analytics - Bloomberg
Millions of news articles from hundreds of thousands of sources around the globe appear in news aggregators every day. The Bloomberg Terminal alone ingests and makes available more than 1.5 million news and research headlines from about 170,000 sources every day. Consuming such a volume of news presents an almost insurmountable challenge. For example, a reader searching on Bloomberg's system for news about the U.K. would find 10,000 articles on a typical day. Apple, the world's most journalistically covered company, garners around 1,800 news articles a day. We realized that a new kind of summarization engine was needed, one that would condense large volumes of news into short, easy to absorb points. Bloomberg's solution, Key News Themes, uses state-of-the-art semantic clustering techniques and novel summarization methods to produce comprehensive, yet concise, summaries to simplify the news consumption process.
Shaun Waters is a Product Manager for News Search and Analytics for the Bloomberg Terminal. His responsibilities include News Search and Text Analytics machine learning and natural language strategy, development, and execution. Areas of product specialization include search ranking, classification, and sentiment for news and social media.
Hired by Bloomberg L.P. in 1999 from The College of the Holy Cross, Shaun has had prior roles managing a global Execution and Order Management businesses and heading Enterprise Products Software Risk Management.
Creating a Collaborative and Scalable System of Record for Machine Learning Projects
Jack Bailin - Solutions Engineering Lead - Weights & Biases
Delivering business value from a machine learning project requires practitioners to iterate on vast amounts of data as well as execute many different experiments. By the time a given project is producing production-ready results, the relationship between data used and computation executed can become incredibly difficult to keep organized.
At the end of a given project, how does a machine learning team effectively communicate their findings to other parts of the organization? How would a team lead easily understand if the current project efforts are trending in the right direction, when the work is dispersed across a number of team members (or separate teams)? How would an executive ensure their organization’s machine learning efforts are documented in a centralized fashion for auditability and risk management?
In this talk we’ll explore three principles of an ideal machine learning workflow: traceability, reproducibility, and collaboration, and see how the Weights & Biases platform can easily enable organizations to achieve these at scale.
Jack Bailin is the solutions engineering lead at Weights & Biases. His focus is on helping enterprise AI machine learning teams adopt best practices for scalable workflows, from code instrumentation with MLOps platforms/tooling all the way through effective dissemination of information across the organization.
Jack got his start in machine learning during his undergraduate research studies in Physics, when he implemented a deep learning classifier for identification of basal cell carcinoma skin tissue based on spectral data input. He then spent time as the lead machine learning engineer at Foyer AI, where he built and productionalized deep learning models for classification and semantic segmentation of real estate photography.
Overview of Conversational AI in Finance
Hanoz Bhathena - Applied Machine Learning Scientist Lead - JPMorgan Chase & Co.
Conversational combines multiple sub-disciplines in AI/ML and NLP, including but not limited to intent classification, entity extraction and linking, state tracking, language generation, question answering (including open domain QA) etc. The field often combines the state-of-the-art in research and industry with new approaches released and quickly being integrated in real world dialogue systems we use in our daily lives. In this talk I will go over some of the key components that go into designing and developing a dialogue system including different taxonomies of conversational systems, key challenges and mitigations, evaluation approaches and then focus specifically on task-oriented dialogue systems which are generally more common in commercial settings.
Hanoz Bhathena is an Applied Machine Learning Scientist Lead at the Machine Learning Center of Excellence at JPMorgan Chase & Co. He has experience executing and leading several data science projects particularly in deep learning, natural language understanding, information extraction and information retrieval. Currently, his focus includes areas like question answering, dialogue systems and semantic search. Previously he worked as a Machine Learning Data Scientist within the Evidence Lab Innovations division at UBS, where he was responsible for developing machine learning models that uncovered insights from unstructured data relevant to investment research. Here, one of his key achievements was the Deep Theme Explorer, an application to conveniently find complex and emerging topics present in large corpora and attribute fine grained sentiment to help predict a company’s exposure to an investment theme. He holds a Master’s degree in Operations Research from Columbia University and a Bachelor’s degree in Electrical Engineering from the University of Mumbai, VJTI. He has also completed the Artificial Intelligence Graduate Certificate program from Stanford University.
Combining the Direct and Inverse Reinforcement Learning for Asset Allocation Decisions
Igor Halperin - Research Professor of Financial Machine Learning/ AI Asset Management - NYU/ Fidelity Investments
We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.
Hedge Fund in a Box: Automating Investment Decisions with AI
Milind Sharma - CEO - QuantZ/QMIT
Milind’s 24+ yrs of market experience span running prop desks at RBC (GAT entity) & Deutsche Bank (Saba entity) as well as hedge funds (QuantZ) & mutual funds (MLIM). His funds have won many awards over the years including those from Morningstar, Lipper, WSJ, Battle of the Quants & BattleFin. He was a co-founder of Quant Strategies at MLIM (now BlackRock) where his investment role spanned a dozen quantitatively managed funds & separate accounts with approx $30 Billion in AUM (under MLIM President & CIO as Senior PM). He was also a Manager of the Risk Analytics and Research Group at Ernst & Young where he was co-architect of Raven TM. He co-authored a model for pricing cross-currency puttable Bermudan swaptions & created the AIRAP risk-adjusted measure amongst other publications.
Amongst the first to ever receive a degree in Financial Engineering from the pioneering & #1 ranked MSCF program at Tepper (Carnegie Mellon), he also has an MS in Applied Math from CMU where he was in the Logic/ AI PhD program. Other education includes Oxford, Vassar, Princeton (ORFE audit courses) & Wharton. He was the recipient of many academic scholarships financing the entirety of his education including that at the best boarding school in Canada.
QuantZ's Quark EMN is a Stat Arb fund which was a winner at BattleFin 2014, Hedge Fund Awards 2015, AI Awards 2015, Battle of the Quants 2012 & 2015. QuantZ's QMIT affiliate is a signal provider which leverages ML/ AI towards turnkey HF alphas & Enhanced Smart Betas.
Publications have appeared in the Journal of Investment Management, Risk, Wiley, HedgeQuest, World Scientific, Elsevier etc. He is a frequent speaker at conferences - Battle of the Quants, Risk, GARP, Institutional Investor, Terrapinn, NYU, Georgia Tech, Carnegie Mellon, QuantInvest etc. Media coverage includes CNCB, BloombergTV, WSJ, FT, Bloomberg, Hedge Alert, AR magazine, HFMWeek etc.
PANEL: Successful Institutions of the Future - Finding the Right Balance
Supreet Kaur - Assistant Vice President - Morgan Stanley
MLOps/Trusted AI Summit: Closing General Session: Complexity vs Simplicity in ML and AI Projects
Women in AI Reception: Pivoting into AI
Supreet is an AVP at Morgan Stanley. Prior to Morgan Stanley, she was a management consultant at ZS Associates where she automated different workflows and built data driven solutions for fortune 500 clients. She is extremely passionate about technology and AI and hence started her own community called DataBuzz where she engages the audience by sharing the latest AI and Tech trends and also mentors people who want to pivot in this field.
Jatindeep Singh - Senior Associate, Applied AI & ML Scientist -
Jatindeep Singh graduated from Columbia University with a Masters in Financial Engineering. He has been working as an Applied AI Professional in the Financial Service Industry for the past three years. Prior to this, he worked as an AI Engineer in the Life Science and Healthcare Industry.
He has worked on developing and deploying NLP, Deep Learning and Ensemble based AI/ML Techniques for various Industry use cases.
Cheryl Chiodi - Director of Solution Marketing - ABBYY
Cheryl Chiodi is Director of Solution Marketing at ABBYY. She is responsible for helping enterprises gain more context and insight from their content and processes so they can drive significant impact where it matters most: customer experience, regulatory compliance, operational excellence, and competitive advantage. Informed by engagement with customers, partners, and analysts, she is focused on enabling businesses to achieve true business transformation by leveraging the latest AI, machine learning, and other intelligent automation solutions. Cheryl is an experienced author and speaker on financial services industry trends, delivering keynotes at the Wall Street Technology Association and The Taiwan Academy of Banking and Finance, and recently presented at Transform Finance, speaking on the topic of fraud, Digital Transformation for Banking, Financial Services & Insurance (BFSI), American Banker Digital Banking Virtual Summit, and ABA/ABA Financial Crimes Enforcement Conference. Before joining ABBYY, Cheryl led Industry Marketing for Financial Services at Appian and prior to that, held a number of positions across the business at large organizations such as Red Hat, Akamai, Pegasystems, BAE Systems — Applied Intelligence, and Monitor-Deloitte.
END OF SUMMIT