ODSC Europe | June 14-15th, 2023 | In-person and Virtual
Generative AI
Join the Hype and Get Started with Generative AI
Go Beyond the Hype and Get Started with Generative AI
Generative AI is all the hype in the realm of data science and artificial intelligence right now. Tools like ChatGPT, Craiyon, DALL-E, Stable Diffusion, Whisper, and more are changing the modern landscape of AI, with even non-data science individuals becoming interested in the topic. During the ODSC Europe Generative AI conference track, you’ll gain core skills needed to become a part of this movement to both develop and implement generative AI into your organization or research.
What You'll Learn
Talks + Workshops + Special Events on these topics:
Topics
How to use generative AI in practice
The ethical use of generative AI
Generative adversarial models
Tools like Stable Diffusion, ChatGPT, DALL-E, and more
Deep learning models to develop these tools
Business models & use cases
and more…
Some of Our Confirmed Speakers
Nicole Koenigstein
Nicole is a Data Scientist & Quant and Data Engineer currently working at impactvise as Data Science and Technology Lead and at quantmate as Quant. She has over 8 years of experience leading technology projects. She additionally reviews machine learning books and online courses for Manning Publications. Her research interests include time series prediction and natural language processing. She is dedicated to showing others how to succeed in machine learning and is committed to making STEM more attractive to women.
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment(Talk)
Thomas Wiecki, PhD
Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled some of the best Bayesian modelers out there and founded PyMC Labs — the Bayesian consultancy. He did his PhD at Brown University. Website link: https://www.pymc-labs.io
Dr. Yves J. Hilpisch
Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading, and computational finance. He is also founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform.
Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance at Miami Herbert Business School.
Sonam Srivastava
Sonam Srivastava is the founder of Wright Research, an India-based Robo-advisor, where she creates data-driven portfolios out of her deep passion for quant finance. Wright Research is a wealth creator in the digital space that uses scientific data-driven methods to tactically extract opportunities across assets in the public markets to grow clients’ wealth. Wright functions as SEBI registered Robo advisor and is among the most popular advisors among millennial investors with more than 30000 clients and 125 crore+ in assets. Wright Research has delivered a 90% + outperformance over the index in the last 2.5 years. She has 10+ years of experience in investment research and portfolio management, working on systematic strategies, long-short strategies, and algorithmic trading. She started her career in the field with Mumbai-based Forefront Capital, which got acquired by Edelweiss. At Edelweiss, she worked as an algorithm designer at Edelweiss’s institutional equity broking desk. After that, she worked at HSBC Europe as a quant building factor-driven portfolio solutions. Before starting Wright Research, she also worked at Qplum, doing portfolio management at the artificial intelligence-driven Robo-advisor. She graduated from IIT Kanpur and has a master’s in financial engineering from Worldquant University. She is a globally recognized researcher and works as a visiting faculty as AI in Finance Institute New York and BSE Institute Limited.
Deep Reinforcement Learning for Asset Allocation in US Equities (Tutorial)
Isaiah Hull, PhD
Isaiah Hull is a senior economist in the research division of Sweden’s Central Bank (Sveriges Riksbank). He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, and quantum computing. He is also the instructor for DataCamp’s “Introduction to TensorFlow in Python” course and the author of “Machine Learning for Economics in Finance in TensorFlow 2.”
Machine Learning for Economics and Finance in TensorFlow 2(Tutorial)
Dr. Anand Srinivasa Rao
Dr. Anand S. Rao is the Global Artificial Intelligence Leader for PwC. He is also the leader of PwC’s AI and Emerging Technology practice. With over 35 years of industry and consulting experience, Anand leads a team of practitioners who advise C-level executives and implement advanced analytics and AI-based solutions on a variety of strategic, operational, and ethical use cases. With his PhD and research career in Artificial Intelligence and his subsequent experience in management consulting he brings business domain knowledge, software engineer expertise, and statistical expertise to generate unique insights into the practice of ‘data science’.
Prior to joining management consulting, Anand was the Chief Research Scientist at the Australian Artificial Intelligence Institute. He received his PhD from University of Sydney (with a University Postgraduate Research Award-UPRA) in 1988 and an MBA (with Award of Distinction) from Melbourne Business School in 1997. Anand has also co-edited four books on Intelligent Agents and has published over fifty papers in Computer Science and Artificial Intelligence in major journals, conferences, and workshops.
He has received widespread recognition for his extraordinary contributions in the field of consulting and Artificial Intelligence Research. He has received the Most Influential Paper Award for the Decade in 2007 from the Autonomous Agents & Multi-Agent Systems organization for his contribution on the Belief-Desire-Intention Architecture; MBA Award of Distinction from Melbourne Business School, 1997 and University Postgraduate Research Award (UPRA) from University of Sydney, 1985; Distinguished Alumnus Award from Birla Institute of Technology and Science, Pilani, India; He was recognized as one of Top 50 Data & Analytics professionals in USA and Canada by Corinium; one of Top 50 professionals in InsureTech; one of Top 25 Technology Leaders in Consulting; and has won a number of awards for his academic and business papers. Anand is an Adjunct Professor in BITS Pilani’s APPCAIR AI Center. He also serves on the Advisory Board of Oxford University’s Institute for Ethics in AI, World Economic Forum’s Global AI Council, OECD’s Network of Experts on AI (ONE), OECD’s AI Compute initiative, Advisory Board of Northwestern’s MBAi program, Responsible AI Institute, Nordic AI Institute, and International Congress for the Governance of AI. Anand Rao can be contacted on any of the following channels: Linkedin: https://www.linkedin.com/in/anandsrao/ Twitter:@AnandSRao Medium: https://anandsrao.medium.com/ Semantic Scholar: https://www.semanticscholar.org/author/Anand-Srinivasa-Rao/145946928
Suraj Subramanian
Suraj is an ML engineer and developer advocate at Meta AI. In a previous life, he was a data scientist in personal finance. After being bitten by the deep learning bug, he worked in healthcare research (predicting patient risk factors) and behavioral finance (preventing overly-risky trading). Outside of work, you can find him hiking barefoot in the Catskills or being tossed on the Aikido mat.
Stefanie Molin
Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
Allan Stevenson
Allan’s background covers a broad technology stack in infrastructure and cloud, working across a variety of roles in large enterprises before moving into Data Science and ML in recent years. His last role was working on time series forecasting at a fintech scale-up before joining Weights and Biases as the first member of the Customer Success team in EMEA.
Best Practices of Effective ML Teams(Demo Talk)
Nollie Maoto
Nollie is a savvy business intelligence and business analytics leader with over 13 years of diverse financial services background including performance-driven results in strategic planning, action plan execution, leadership, project/programme management, business intelligence and analytics, operational efficiency, business and target operating models and process engineering and re-engineering. As a dynamic leader, has consistently developed high performing teams through effective coaching, team building, motivation, strength driven performance, and 360-degree performance focus. Nollie is a highly motivated, innovative and performance-driven Head of Business Intelligence and Analytics offering comprehensive achievements and experience in Retail and Corporate Investment Banking, Wealth Management, Insurance, Financial Services, and Management Consulting particularly in the contact center environment, operations, mobile payments, operating models, electronic delivery channels, digitization, cash management, and financial regulatory compliance in South Africa and Rest of Africa. Nollie holds a Master’s in Business Administration (MBA) from the Gordon Institute of Business Science (GIBS) with a focus in Strategy and Digital Innovation. She recently completed her Data Science and Business Analytics post-graduate program with the University of Texas at Austin McCombs School of Business. The focus of this program was advanced statistics, machine learning, and predictive modelling.
The Gender Gap in Data Science and What You Can Do About It!(Women in Ignite)
Dr. Andre Franca
Andre joined causaLens from Goldman Sachs, where he was an executive director in the Model Risk Management group in Hong Kong and Frankfurt. Today he is working with industry leading, global organisations to apply cutting edge Causal AI research in production level solutions that empower individuals and teams to make better decisions. Andre received his PhD in theoretical physics from the University of Munich, where he studied the interplay between quantum mechanics and general relativity in black-holes.
From Correlation to Causality in AI(Tutorial)
Christos Hadjinikolis, PhD
Christos has a PhD in Computing and has worked for many years as an ML consultant for many companies covering different domains (telcom, finance, gaming). For the last 3 years, he has been focussing on ML-Ops, defining and curating the ML-Development Lifecycle for the companies that hire him. He has recently embarked on a new adventure with Vortexa Ltd, working as a Lead ML Engineer and helping the company scale technically as it grows.
Dynamicio (a pandas I/O wrapper); Why you Should Start your ML-Ops Journey with Wrapping your I/O(Talk)
Chandini Jain
Chandini Jain is the CEO/founder of Auquan – a london based fintech using NLP and AI to distill relevant and impactful information from unstructured text. Prior to Auquan, she worked as a derivatives trader at Optiver in Chicago/Amsterdam and Deutsche Bank. At Auquan, she oversee the development of our machine learning strategies.
Aspect-Based Sentiment Analysis: Predict Market Impact of Financial Documents and other Use Cases(Tutorial)
Hadrien Jean, PhD
Hadrien Jean is a machine learning scientist working at My Medical Assistent where he is developing deep learning models in the medical domain. He wrote the book Essential Math for Data Science (https://www.essentialmathfordatascience.com/) aimed at helping people to get the math needed in data science from a coding perspective. He previously worked at Ava on speech diarization. He also worked on a bird detection project using deep learning. He completed his Ph.D. in cognitive science at the École Normale Supérieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https://hadrienj.github.io/posts/).
Introduction to Linear Algebra for Data Science and Machine Learning With Python(Bootcamp)
Leonardo De Marchi
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks. He now works in Thomson Reuters as VP of Labs, and also provides consultancy and training for small and large companies. His previous experience includes being Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through acquisition and an IPO.
Creative AI(Training)
NLP Fundamentals(Training)
Why Attend
Immerse yourself in talks and workshops on Generative AI
With numerous introductory level workshops, get hands-on experience to quickly build up your skills
Post-conference, get access to recorded talks online and learn from over 100+ high-quality recording sessions that let you review content at your own pace
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science
Learn directly from world-class instructors who are the authors of and contributors to many of the tools and frameworks used in quant finance today
Meet hiring companies ranging from hot startups to Fortune 500s looking to hire professionals with data science skills at all levels
Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms
Get access to other focus area content, including ML/DL, Data Visualization Big Data, and Open Data Science
More Reasons To Attend?
Download the why attend guideWho should attend
Data scientists looking to use generative AI in their work
AI researchers who want to understand more about the field
Students who can benefit from getting a head start on an emerging topic
Decision makers who can implement generative AI into their business
Programmers who can use AI to supplement and improve their work
Professionals from all verticals to see how they can use generative AI to make their lives easier
Educators so they can prepare for a changing landscape
Anyone else interested in the latest trend in AI!
ODSC EUROPE Hybrid Conference 2023 | June 14-15th
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