Conference Day One: March 15th 2021

KEYNOTES & OPENING PLENARY SESSIONS

8:30 am - 8:40 am Opening remarks

Welcome and start of conference

8:45 am - 9:25 am Fireside Chat – AI, Data Science and Financial Services – What's Next?

Jeff Wecker - Chief Technology Officer, Two Sigma

After the excitement of alternative data, new AI and ML techniques and cloud elastic computing, what's actually been achieved in using these advancements in the financial services industry? A senior industry technologist shares his thoughts on the current state of play for data science and data engineering, what's been effective and what is over-hyped, and how to attract the right talent to solve these problems.

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Jeff Wecker

Chief Technology Officer
Two Sigma

·        Improving models to decrease the chances of false predictions

·        Automation technology being utilised as part of crisis solution

·        Opportunities opening up for investment strategies post-Covid 

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Rajesh Krishnamachari

Head of Data Science, Data and Innovation Group
Bank of America

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Edward Mitby

Senior AI Engineer
Vanguard

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Yevgeniy Vahlis

Head of AI Capabilities
Bank of Montreal

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Darko Matovski

Co-Founder and CEO
CausaLens

10:35 am - 11:05 am Networking break


11:15 am - 11:35 am A New Approach to AI: How a Self-Evolving Market Neural Strategy Defended Against the Pandemic and Quant Crash

Alex Bogdan - Chief Scientific Officer, Castle Ridge Asset Management, Castle Ridge Asset Management
Adrian de Valois-Franklin - CEO, Castle Ridge

·        While many firms experience massive losses as the market crashed earlier this year, a unique approach to AI allowed Castle Ridge Asset Management to continually deliver steady returns for their clients

  • Geno-Synthetic Algorithms (GSAs), a new approach to self-evolving AI that constantly adapts to changing market conditions.
  • Why a departure from traditional static ML techniques may make sense for your firm
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Alex Bogdan

Chief Scientific Officer, Castle Ridge Asset Management
Castle Ridge Asset Management

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Adrian de Valois-Franklin

CEO
Castle Ridge

11:45 am - 12:00 pm Understanding risk through the data mosaic

Peter Hafez - Chief Data Scientist, RavenPack

Uncovering future sources of risk requires an understanding of how the world is connected through data. Leveraging data mosaics allows investors to glean new insights about real-time business risk. The future of alternative data is not found off-the-shelf. Curated creation of new insights is becoming the norm. 

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Peter Hafez

Chief Data Scientist
RavenPack

12:10 pm - 1:10 pm Networking Lunch Break

Birds of a feather session: Sentiment Scoring and Event Detection Using Neural Networks

There are numerous variations in deep learning-based methodologies used to perform sentiment analysis and event detection. In this workshop, we’ll demonstrate a relatively straightforward approach that is still quite effective. After briefly reviewing the deep learning basics, we’ll create a 4-layer neural network based on Long Short-Term Memory (LSTM) networks using the TensorFlow library and its Keras interface. With the help of publicly available pre-trained word embeddings, we will fine tune the model and train it to output a sentiment score and detected events. 

Speaker: Marko Kangrga, Senior Data Scientist, RavenPack


Birds of a Feather Session: The Value and Certainty of Transaction Data During Uncertain Times

 The COVID-19 pandemic turned the economy upside down and inside-out in 2929. Investment and business models built on years of regular, expected economic data have been thrown out the window and investors are grappling with ‘what’s the new normal?’ This session will highlight how transaction data from Facteus became a key ‘source of truth’ during the pandemic and beyond to enable investors to understand the consumer economy in real-time and make strategic investments. Examples of data sourcing, compliance, normalization, and industry/merchant analysis will be shared in this session.

Speaker: Don Wood, Data Strategist, Facteus


Birds of a Feather Session: Avoid spurious correlations and optimize your portfolio with Causal AI

The current state of the art in machine learning relies on past patterns and correlations to make predictions of the future. This approach can work in static environments and for closed problems with fixed rules. However it does not work for financial time-series and other dynamic systems. In order to make consistently accurate predictions about the future, and to achieve true artificial intelligence, the development of new science that enables machines to understand cause and effect is required. This talk will present the power of Causal AI and some practical applications that are already being used in the field.

Speaker: Darko Matovski, CEO and Co-Founder, CausalLens

AFTERNOON STREAMS

STREAM A

1:15 pm - 1:20 pm Stream A: Machine Learning Case Studies

STREAM A

1:20 pm - 1:40 pm Reinforcement Learning for optimal execution of future hedging in a limit order book
Matteo Rolle - Head of Cross Market Trading, Banca Sella Holding

·      Building a model that can optimally hedge an option while reducing risk

·      Examining the feasibility of using RL to hedge basis risk in a realistic setting

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Matteo Rolle

Head of Cross Market Trading
Banca Sella Holding

·        How to personalize, maintain and monitor your models

·        Using AI for complex and dynamic decision-making

·        Building models to optimize and predict investment outcomes 

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Benoit Mondoloni

DIrector
Bank of America Merrill Lynch

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Alejandra Litterio

Co-founder & Chief Research Officer
Eye Capital

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Stuart Kozola

Global Head of Finance
MathWorks

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Simon Chao

Head of Asset Management Emerging Technologies,
Fidelity Investments

STREAM A

2:50 pm - 3:10 pm Modelling uncertainty with Bayesian ML
Brandon Da Silva - Associate Portfolio Manager, OPTrust

·        Bayesian interpretation of Q-learning

·        Applications of Baysian Q-Learning to Finance

·        Broader applications of Bayesian Deep Learning 

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Brandon Da Silva

Associate Portfolio Manager
OPTrust

STREAM B

1:15 pm - 1:20 pm Stream B: Data quality, infrastructure and engineering

STREAM B

1:35 pm - 1:55 pm Building data teams and data engineering

·        Mapping the recruitment landscape and how to ensure you remain competitive in hiring and retaining data professionals

·        Where to find the best talent in the industry

·  Outlining the infrastructural data strategy and a leading fund

·        How to reliably build a data strategy from scratch

·        Defining your data strategy and investing in the infrastructural elements to support it

·        Building models to optimize and predict investment outcomes 

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Christos Koutsoyannis

CEO
Atlas Ridge Capital

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Ravi Trivedi

Assistant VP, Innovation and Decision Sciences
Nuveen

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Joshua Packwood

Senior Analyst
Balyasny Asset Management

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Bill Dague

Head of Alternative Data Research
Quandl


STREAM B

2:50 pm - 3:10 pm Commoditization of data engineering
Tom Taylor - Head of Alpha Technology, Man Numeric

- The future for the commoditization of data engineering

- Utilising cloud providers and open source tools to streamline your optimise your investment workflow 

- Defining key aspects from whole investment workflow 

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Tom Taylor

Head of Alpha Technology
Man Numeric

3:15 pm - 3:45 pm Networking break

STREAM A

3:50 pm - 3:55 pm Stream A: Machine Learning Case Studies

·        Identifying at what point human intervention is necessary

·        Avoiding building models on spurious relationships

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Peng Cheng

Head of Machine Learning Strategies
JP Morgan

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Brandon Da Silva

Associate Portfolio Manager
OPTrust

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Stan Yakoff

Head of Americas Supervision
Citadel

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Melissa Lin

AI Investing Data Scientist & Quantitative Strategist
NN Investment Partners

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Maximillian Stroh

Co-Head of Forecasts
Quoniam Asset Management

STREAM A

4:50 pm - 5:10 pm Utilizing ML for prediction in private markets
Francesco Filia - CEO, Fasanara

·        Overcoming structural challenges and training models differently

·        Driving value through cost reduction

·        How to organize institutional data to scale 

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Francesco Filia

CEO
Fasanara

STREAM B

3:50 pm - 3:55 pm Stream B: Data quality, infrastructure and engineering

·        Assessing the best cloud deployment models for your fund

·        Challenges in switching to cloud computing

·        Leveraging cloud services in a scalable, streamlined way

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Christopher Thomas

Vice President, Director, Content and Technology Solutions, Delivery Platform Strategy
FactSet

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Sameer Gupta

Head of Data, Fusion
Point72

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Vijay Devadiga

Director
Credit Suisse

STREAM B

4:50 pm - 5:10 pm The strategy for accumulating knowledge in ML and Data
Sylvain Champonnois - Quantitative Researcher, CFM

·        Articulating a new strategy – testing new alternative datasets, new open-source techniques and machine learning processes

·        Importance of organisations to allow for the development of creativity and innovation 

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Sylvain Champonnois

Quantitative Researcher
CFM

5:45 pm - 6:45 pm Virtual drinks and networking