2024
ODSC Europe 2024 Speakers
ODSC hosts a fantastic lineup of some of the best and brightest expert speakers and core contributors in data science
ODSC Europe Speakers
For 2023 we had some of the best and brightest minds speaking at ODSC Europe. ODSC will host more than 140 presenters in 2024! Speaker profiles will be added soon. Please check back for updates.
Previous Europe Speakers
Guglielmo Iozzia
Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner. As part of the Manufacturing IT Advanced Mathematics and Modelling Data Science Team he is currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way). He has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference.
He is also an international speaker and author of the following book: Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark
Alan Rutter
Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Riskified,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.
Marta Kwiatkowska, PhD
Marta Kwiatkowska is Professor of Computing Systems and Fellow of Trinity College, University of Oxford. She is known for fundamental contributions to the theory and practice of model checking for probabilistic systems, and is currently focusing on safety, robustness and fairness of automated decision making in Artificial Intelligence. She led the development of the PRISM model checker (www.prismmodelchecker.org), which has been adopted in diverse fields, including wireless networks, security, robotics, healthcare and DNA computing, with genuine flaws found and corrected in real-world protocols. Her research has been supported by two ERC Advanced Grants, VERIWARE and FUN2MODEL, EPSRC Programme Grant on Mobile Autonomy and EPSRC Prosperity Partnership FAIR. Kwiatkowska won the Royal Society Milner Award, the BCS Lovelace Medal and the Van Wijngaarden Award, and received an honorary doctorate from KTH Royal Institute of Technology in Stockholm. She is a Fellow of the Royal Society, Fellow of ACM and Member of Academia Europea.
Safety and Robustness for Deep Learning with Provable Guarantees(Talk)
Isaac Reyes
Isaac Reyes is a TEDx speaker, data scientist and international keynote presenter in data analytics, data visualization and data presentation. In 2018, his “Art of Data Storytelling” speaking tour visited 23 cities across 5 continents, impacting over 15,000 people with Data Storytelling skills. He is the Co-founder of StoryIQ, a data visualization training company with full-time speakers in New York City, Manila and Singapore. In previous roles, he was the Head of Data Science at Altis Consulting and lectured in statistical theory at the Australian National University. A participant experience focused trainer, he was a keynote speaker at the 2019 Open Data Science Conference in Brazil.
Thomas Wiecki, PhD
Thomas Wiecki is co-creator of PyMC, the industry-standard tool for statistical data science in Python. To help businesses solve advanced analytical problems he founded PyMC Labs (www.pymc-labs.io) consisting of world-class experts in Bayesian modeling.
Bayesian Marketing Science: Solving Marketing’s 3 Biggest Problems(Track Keynote)
Daniel Voigt Godoy
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers. He writes regularly for Towards Data Science. His blog post “Understanding PyTorch with an example: a step-by-step tutorial” reached more than 220,000 views since it was published. The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics. Daniel is also the main contributor of two python packages: HandySpark and DeepReplay. His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.
Diffusion Models 101(Workshop)
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.
Sara Khalid
Sara is a Senior Research Associate in Biomedical Data Science and University Research Lecturer at the University of Oxford, where she is the Machine Learning Lead in the Centre for Statistics in Medicine. She has 12 years of experience in machine learning, signal processing, and intelligent remote monitoring research, with applications in biomedical and planetary health informatics. Sara has served on the NASA Frontier Development Lab Artificial Intelligence Panel and the NASA Climate Challenge Big Think. She is a National Geographic Society Explorer in Tracking Plastic Pollution with Remote Monitoring and Machine Learning. Sara is also a University of Oxford Ambassador for Women in Data Science.
Me, my Health, and AI: Applications in Medical Diagnostics and Prognostics(Talk)
Henk Boelman
Henk is a Cloud Advocate specializing in Artificial intelligence and Azure with a background in application development. He is currently part of the AI cloud advocate team and based in the Netherlands. Before joining Microsoft, he was a Microsoft AI MVP and worked as a software developer and architect building lots of AI powered platforms on Azure.
He loves to share his knowledge about topics such as DevOps, Azure and Artificial Intelligence by providing training courses and he is a regular speaker at user groups and international conferences.
Build and Deploy PyTorch models with Azure Machine Learning (Keynote)
Heiko Hotz
Heiko Hotz is a Senior Solutions Architect for AI & Machine Learning at AWS with a special focus on Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI. He is also the founder of the NLP London Meetup group, bringing together NLP enthusiasts and industry experts.
Implementing Generative AI in Organisations: Challenges and Opportunities(Tutorial)
Ryan Dawson
Ryan Dawson is a technologist passionate about data. Ryan works with clients on large-scale data and AI initiatives, helping organizations get more value from data. His work includes strategies to productionize machine learning, organizing the way data is captured and shared, selecting the right data technologies and optimal team structures, as well as writing the code to make it happen. He has over 15 years of experience and, as well as many widely read articles about MLOps, software design, and delivery. is author of the Thoughtworks Guide to Evaluating MLOps Platforms.
Ed Shee
Ed Shee, Head of Developer Relations at Seldon. Having previously led a tech team at IBM, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.
Julia Lintern
Julia Lintern currently works as a Director of Data Science at Gartner. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.
Introduction to Machine Learning(Bootcamp)
Deepak Kanungo
Deepak Kanungo is the founder and CEO of Hedged Capital LLC, an AI-powered, proprietary trading and analytics firm built around probabilistic machine learning technologies. In 2005, long before machine learning was an industry buzzword, Deepak invented a probabilistic machine learning method and software system for managing the risks and returns of project portfolios. It is a unique framework that has been cited by IBM and Accenture, among others. Previously, Deepak was a financial advisor at Morgan Stanley, a Silicon Valley fintech entrepreneur, and a director in the Global Planning Department at Mastercard International. He was educated at Princeton University (astrophysics) and the London School of Economics (finance and information systems).
Probabilistic Machine Learning for Finance and Investing(Talk)
Leonidas Souliotis, PhD
Leonidas (Leo) is a Senior Data Scientist at Astrazeneca. His work is focused around machine learning in oncology, including clinical and non clinical applications. He is also enthusiastic about NLP applications in oncology and how this can be used to leverage patient treatment. He is also a workshop facilitator in the European Leadership University (ELU), NL and has also been a data science educator at DataCamp. He holds a PhD from the University of Warwick, UK. in bioinformatics and ML, an MSc in statistics from Imperial College London, UK and a BSc in Statistics and Insurance Science from the University of Piraeus, GR.
Introduction to Python for Data Analysis(Bootcamp)
Danushka Bollegala, PhD
Danushka Bollegala is a Professor in the Department of Computer Science, University of Liverpool, UK. He obtained his PhD from the University of Tokyo in 2009 and worked as an Assistant Professor before moving to the UK. He has worked on various problems related to Natural Language Processing and Machine Learning. He has received numerous awards for his research excellence such as the IEEE Young Author Award, best paper awards at GECCO and PRICAI. His research has been supported by various research council and industrial grants such as EU, DSTL, Innovate UK, JSPS, Google and MSRA. He is an Amazon Scholar.
Towards Socially Unbiased Generative Artificial Intelligence(Talk)
Kai Fricke
Kai Fricke is a senior software engineer at Anyscale. As a core maintainer of the Ray AI Runtime he is building software for distributed machine learning training and tuning. During his postdoc at Cambridge he utilized reinforcement learning to optimize large graph structures and co-authored two open source reinforcement learning libraries.
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.
Oliver Zeigermann
Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.
MLOps: Monitoring and Managing Drift(Training)
Ori Nakar
Ori Nakar is a principal cyber-security researcher, a data engineer, and a data scientist at Imperva Threat Research group. Ori has many years of experience as a software engineer and engineering manager, focused on cloud technologies and big data infrastructure. Ori also has an AWS Data Analytics certification. In the Threat Research group, Ori is responsible for the data infrastructure and involved in analytics projects, machine learning, and innovation projects.
Botnets Detection at Scale – Lesson Learned from Clustering Billions of Web Attacks into Botnets(Talk)
Chakri Cherukuri
Chakri Cherukuri is a senior researcher in the Quantitative Research group within the CTO office at Bloomberg LP. His research interests include quantitative portfolio management, algorithmic trading strategies, applied machine learning and numerical methods. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. Before that he worked in the Silicon Valley for startups building enterprise software systems. He is a core contributor and steering council member of bqplot, a 2D plotting library for the Jupyter notebook. He has extensive experience in numerical computing and software development. He holds an undergraduate degree in mechanical engineering from Indian Institute of Technology, Madras, and an MS in computational finance from Carnegie Mellon University.
Devvret Rishi
Dev is co-founder and Chief Product Officer for Predibase, a company looking to redefine how data scientists and engineers build models with a declarative approach. Prior to Predibase, he was a ML PM at Google working across products like Firebase, Google Research and the Google Assistant as well as Vertex AI. While there, Dev was also the first product manager for Kaggle – a data science and machine learning community with over 8 million users worldwide. Dev’s academic background is in computer science and statistics, and he holds a masters in computer science from Harvard University focused on machine learning.
Meissane Chami
Meissane Chami serves ThoughtWorks, Inc. as a Senior ML Engineer, advising and developing innovative data science and machine learning solutions from proof of concept to production. She has gained expertise setting up innovation frameworks and conducting fast cycle proof of concepts. Her primary areas of expertise are in Natural Language processing, MLOps, DevOps, cloud computing, containerisation and Python. She holds a MSc degree in Machine Learning and Data Science form University College London School of Engineering.
Daniel Lenton, PhD
Daniel Lenton is the creator of Ivy, which is an open-source framework with an ambitious mission to unify all other ML frameworks. Prior to starting Ivy, Daniel was a PhD student at Imperial College London, where he published research in the areas of machine learning, robotics and computer vision.
Unifying ML With One Line of Code(Tutorial)
Jake Bengtson
Jake currently holds the position of Principal Technical Evangelist at Cloudera, where he promotes the strengths of Cloudera’s Lakehouse for delivering trusted AI. His tenure at Cloudera began as a Senior Product Marketing Manager for Cloudera Machine Learning (CML).
Before Cloudera, Jake developed his ML expertise at ExxonMobil, starting as a Data Scientist and later transitioning to a Data Science and Analytics Solution Architect role. He also contributed significantly at FarmersEdge, taking on responsibilities as a Senior Data Scientist and subsequently as a Data Science Manager.
Jake earned both his bachelor’s and master’s degrees from Brigham Young University in Information Systems Management with an emphasis in Statistics.
Outside of work, Jake is passionate about outdoor activities. He enjoys skiing, golfing, rafting, and hiking. However, spending time with his family amidst the mountains remains his most rewarding pastime.
Shawn C. Kyzer
Shawn is passionate about harnessing the power of data strategy, engineering and analytics in order to help businesses uncover new opportunities. As an innovative technologist with over 15 years experience, Shawn removes technology as a barrier, and broadens the art of the possible for business and product leaders. His holistic view of technology and emphasis on developing and motivating strong engineering talent, with a focus on delivering outcomes whilst minimising outputs, is one of the characteristics which sets him apart from the crowd.
Shawn’s deep technical knowledge includes distributed computing, cloud architecture, data science, machine learning and engineering analytics platforms. He has years of experience working as a consultant practitioner for a variety of prestigious clients ranging from secret clearance level government organizations to Fortune 500 companies.
Franz Kiraly, PhD
Franz Kiraly is the founder and a core developer of the open source framework sktime. His research is focused on software engineering for open source and data science, machine learning for structured learning tasks such as time series tasks, and robust empirical and statistical evaluation of algorithms in deployment. Franz held a faculty position at University College London 2013-2020, before he moved to industry R&D in principal data scientist roles.
sktime – Python Toolbox for Machine Learning with Time Series(Training)
Sofie Van Landeghem, PhD
Sofie is a machine learning and NLP engineer who firmly believes in the power of data to transform decision making in industry. She has a Master in Computer Science (software engineering) and a PhD in Sciences (Bioinformatics), and more than 16 years of experience in Natural Language Processing and Machine Learning, including in the pharmaceutical industry and the food industry. In 2019, she joined Explosion to work on the open-source NLP library spaCy. She is currently leading the open-source team developing and maintaining spaCy, as well as various other open-source developer tools for data scientists.
spaCy: a customizable NLP toolkit designed for developers(Talk)
David Stephenson, PhD
David Stephenson has over 20 years of experience leading analytics initiatives, including as Head of Global Business Analytics at eBay Classifieds Group. Since founding DSI Analytics in 2014, he has worked directly with dozens of companies across a wide range of industries (Adidas, Miro, Janssen Pharmaceuticals, ABN Amro, Sky Broadcasting, etc). Dr. Stephenson also serves as part time faculty at the University of Amsterdam Business School, has published two books, and has developed and delivered data science trainings for hundreds of analytics professionals around the globe.
Equipping your analytics professions with the most critical business skills (Business Talk)
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.
Philip Wauters
Philip Wauters is Customer Success Manager and Value engineer at Tangent Works working on practical applications of time series machine learning at customers from various industries such as Siemens, BASF, Borealis and Volkswagen. With a commercial background and experience with data engineering, analysis and data science his goal is to find and extract the business value in the enormous amounts of time-series data that exists at companies today.
Learn how to Efficiently Build and Operationalize Time Series Models in 2023(Workshop)
Demo Talk: The Tangent Information Modeler, time series modeling reinvented
Abstract:
Modeling time series data is difficult due to its large quantities and constantly evolving nature. Existing techniques have limitations in scalability, agility, explainability, and accuracy. Despite 50 years of research, current techniques often fall short when applied to time series data. The Tangent Information Modeler (TIM) offers a game-changing approach with efficient and effective feature engineering based on Information Geometry. This multivariate modeling co-pilot can handle a wider range of time series use cases with award-winning results and incredible performance.
During this demo session we will showcase how best-in-class and very transparent time series models can be built with just one iteration through the data. We will cover several concrete use cases for advanced time series forecasting, anomaly detection and root cause analysis.
Spiros Potamitis
Spiros Potamitis is a data scientist and a global product marketing manager of forecasting and optimization at SAS. He has extensive experience in the development and implementation of advanced analytics solutions across different industries and provides subject matter expertise in the areas of forecasting, machine learning and AI. Prior to joining SAS, Spiros worked and led advanced analytics teams in various sectors such as credit risk, customer insights and CRM.
Navigating the Complexities of Analytics in the Cloud: Enablers and Strategies for Success(Talk)
Tori Tompkins
Tori Tompkins is a Senior Data Science Consultant at Advancing Analytics. Specialising in MLOps, Tori has worked on many ML and data science projects with Azure, Databricks and graph technologies and all stages of the ML Lifecycle. She is a co-presenter of the Data & AI podcast, Totally Skewed, founder of Girls Code Too UK and regular contributor with Girls in Data.
Want End-to-End MLOps? Delta & Databricks Make This A Reality!(Workshop)
Wade Schulz, MD, PhD
Dr. Schulz is a physician scientist with a background in computational healthcare, molecular biology, and virology. Dr. Schulz has over 20 years’ experience in software development with a focus on enterprise system architecture and has a research interests in the management of large, biomedical data sets and the use of real-world data for predictive modeling. At Yale School of Medicine, he has led the deployment of the organization’s data science infrastructure which consists of a composable computing infrastructure to support the development of biomedical AI applications. Dr. Schulz is also a co-founder of Refactor Health, a digital health startup focused on the development of AI-driven digital signatures and automated healthcare DataOps.
Quantum Machine Learning and Applications in Health Care(Talk)
Alexander Billington
Alex is a data scientist at Advancing Analytics with a love for all things machine learning and MLOps. He has worked as a machine learning engineer for four years in fields ranging from wearable devices to agritech. Outside of the world of data science he is an avid fan of board games and TTRPGs, running a number of D&D campaigns and a small Lincoln based D&D community.
Want End-to-End MLOps? Delta & Databricks Make This A Reality!(Workshop)
Florian Jacta
Florian Jacta is a specialist of Taipy, a low-code open-source Python package enabling any Python developers to easily develop a production-ready AI application. Package pre-sales and after-sales functions. He is data Scientist for Groupe Les Mousquetaires (Intermarche) and ATOS. He developed several Predictive Models as part of strategic AI projects. Also, Florian got his master’s degree in Applied Mathematics from INSA, Major in Data Science and Mathematical Optimization.
How to Build Stunning Data Science Web applications in Python – Taipy Tutorial(Workshop)
Bringing AI to Retail and Fast Food with Taipy’s Applications(Track Keynote)
Demo Session Title: Turning your Data/AI algorithms into full web apps in no time with Taipy
Abstract:
In the Python open-source ecosystem, many packages are available that cater to:
– the building of great algorithms
– the visualization of data
Despite this, over 85% of Data Science Pilots remain pilots and do not make it to the production
stage.
With Taipy, a new open-source Python framework, Data Scientists/Python Developers are able to
build great pilots as well as stunning production-ready applications for end-users.
Taipy provides two independent modules: Taipy GUI and Taipy Core.
In this talk, we will demonstrate how:
1. Taipy-GUI goes way beyond the capabilities of the standard graphical stack: Gradio,
Streamlit, Dash, etc.
2. Taipy Core fills a void in the standard Python back-end stack.
Roberto Cadili
Roberto Cadili is a data scientist on the Evangelism team at KNIME. During his BSc. in Economics, he developed a genuine interest in statistics and data analysis. At the University of Konstanz, he pursued a MSc. in Social and Economic Data Science where he studied different machine learning algorithms and deep learning architectures with an emphasis on NLP and Computer Vision. As editor for Low Code for Data Science, he is helping the KNIME community shape successful data science stories, tutorials, and best practices that are worth sharing.
A Walkthrough of Low-Code Deep Learning with KNIME(Tutorial)
Laura Skylaki, PhD
Laura Skylaki is a Manager of Applied Research in Thomson Reuters Labs, where she leads advanced machine learning projects in the domain of Legal and Tax AI.With a career spanning more than a decade at the intersection of research and practical application, she has contributed technical expertise in diverse fields such as bioinformatics and stem cell biology, image processing and natural language processing. She holds a doctorate in stem cell bioinformatics from the University of Edinburgh, UK, and has been publishing on machine learning applications in leading academic journals since 2012.
NLP Fundamentals(Training)
Emilio Silvestri
Emilio Silvestri is a Junior Data Scientist on the Evangelism Team at KNIME. He has a Master’s Degree in Computer Science at the University of Konstanz, with a special focus on Data Science and Artificial Intelligence. He is a certified KNIME Trainer and works for the KNIME Education Team to onboard and upskill people in their data science journey with courses and webinars.
A Walkthrough of Low-Code Deep Learning with KNIME(Tutorial)
Clara Higuera Cabañes, PhD
Clara is senior data scientist at BBVA AI Factory. She has worked in the data science field for many years applying NLP techniques to different sectors such as media or banking. At the BBC in London she worked building recommender systems for BBC News and developed several tools to help editors understand audience feedback. At the banking sector in BBVA she has worked on building data products to help financial advisors better manage customers queries. She currently leads the collections data science team at BBVA AI factory. Prior to her industry experience she carried out her PhD in artificial intelligence and bioinformatics and holds a degree in computer science. Clara advocates for a responsible use of technology and is actively involved in activities which encourage women and girls to pursue a career in technology and science to help bridge the gender gap in these disciplines.
Brent Mittelstadt, PhD
Professor Brent Mittelstadt is an Associate Professor, Senior Research Fellow, and Director of Research at the Oxford Internet Institute, University of Oxford. He leads the Governance of Emerging Technologies (GET) research programme which works across ethics, law, and emerging information technologies. He is a prominent data ethicist and philosopher specializing in AI ethics, algorithmic fairness and explainability, and technology law and policy. Prof. Mittelstadt is the author of foundational works addressing the ethics of algorithms, AI, and Big Data; fairness, accountability, and transparency in machine learning; data protection and non-discrimination law; group privacy; ethical auditing of automated systems; and digital epidemiology and public health ethics. His contributions in these areas are widely cited and have been implemented by researchers, policy-makers, and companies internationally, featuring in policy proposals and guidelines from the UK government, Information Commissioner’s Office, and European Commission, as well as products from Google, Amazon, and Microsoft.
The Unfairness of Fair Machine Learning: Levelling Down and Strict Egalitarianism by Default(Talk)
Matthias Seeger, PhD
Matthias W. Seeger is a principal applied scientist at Amazon. He received a Ph.D. from the School of Informatics, Edinburgh university, UK, in 2003 (advisor Christopher Williams). He was a research fellow with Michael Jordan and Peter Bartlett, University of California at Berkeley, from 2003, and with Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems, Tuebingen, Germany, from 2005. He led a research group at the University of Saarbruecken, Germany, from 2008, and was assistant professor at the Ecole Polytechnique Federale de Lausanne from fall 2010. He joined Amazon as machine learning scientist in 2014. He received the ICML Test of Time Award in 2020.
His interests center around Bayesian learning and decision making with probabilistic models, from gaining understanding to making it work in large scale practice. He has been working on theory and practice of Gaussian processes and Bayesian optimization, scalable variational approximate inference algorithms, Bayesian compressed sensing, and active learning for medical imaging. More recently, he worked on demand forecasting, hyperparameter tuning (Bayesian optimization) applied to deep learning (NLP), and AutoML.
Distributed Hyperparameter Tuning: Finding the Right Model can be Fast and Fun(Tutorial)
Dr. Gözde Gül Şahin
Dr. Gözde Gül Şahin is an Assistant Prof. at Koç University and a KUIS AI Fellow since February 2022. Previously, she was a postdoctoral researcher in the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University of Darmstadt, Germany. Her research spans the fields of linguistics and machine learning, in particular semantics, multilingual representations and large language models. She completed her PhD studies in Istanbul Technical University (İTÜ) Computer Engineering department in 2018. She was a visiting researcher at the Institute for Language, Cognition and Computation (ILCC) of the University of Edinburgh in 2017. Before her Ph.D., she received her Masters and Bachelor degrees from Sabancı University in 2011 and İTÜ in 2009, respectively. She regularly serves as a PC member for *ACL conferences and is a co-organizer for the Workshop on Multilingual Representation Learning (MRL). Her research on NLP has been funded by Tübitak 2232, and 2236 grant programs that are granted to outstanding young principal investigators.
Semantic Analysis and Procedural Language Understanding in the Era of Large Language Models(Talk)
Peter Schwendner, PhD
Peter Schwendner leads the Institute of Wealth & Asset Management at Zurich University of Applied Sciences, School of Management and Law, Switzerland. His interests are financial markets, asset management and machine learning applications. With the European Stability Mechanism (ESM), he has been developing analytics for primary and secondary bond markets and tools for optimizing the issuance process. Currently, he is working on the BRIDGE Discovery project “Spatial sustainable finance: Satellite-based ratings of company footprints in biodiversity and water”. Within the European COST Action «Fintech and AI in Finance», he leads the working group «Transparency into Investment Product Performance for Clients».
ML Applications in Asset Allocation and Portfolio Management(Talk)
Piotr Mirowski, PhD
Dr. Piotr Mirowski is a Staff Research Scientist at DeepMind. His research on artificial intelligence covers the subjects of reinforcement learning, navigation, weather and climate forecasting, as well as a socio-technical systems approach to human-machine collaboration and to computational creativity. He is the author of over 60 papers that have been published in Nature, Genome Biology, Clinical Neurophysiology or at ICLR, AAAI and NeurIPS. Piotr studied computer science in France at ENSEEIHT Toulouse and obtained his PhD in computer science in 2011 at New York University, with a thesis supervised by Prof. Yann LeCun (Outstanding Dissertation Award, 2011). A trained actor himself, Piotr founded and directs Improbotics, a theatre company where human actors and robots improvise live comedy performances and investigate the use of AI for artistic human and machine-based co-creation. https://piotrmirowski.com
Alexander Denev
Alexander is a Quant & Data Scientist with 20 years of accumulated experience both in specialist and leadership positions in global financial institutions.. Mastering the main AI/ML techniques, he is also strictly specialized and personally contributed to the field of Probabilistic Graphical Models, Causal AI and Alternative Data. Alexander has authored/co-authored 10+ papers and 3 books on these topics. He holds a degree in Mathematical Finance from University of Oxford where he is a Visiting Lecturer on Bayesian Risk Management and Alternative Data. Currently he is CEO of Turnleaf Analytics.
Macroeconomic Predictions – a Machine Learning Approach(Talk)
Carles Sierra, PhD
Carles Sierra is Director of the Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council (CSIC) located in Barcelona. He is the President of EurAI, the European Association of Artificial Intelligence. He has been contributing to Artificial Intelligence research since 1985 in the areas of Knowledge Representation, Auctions, Electronic Institutions, Autonomous Agents, Multiagent Systems and Agreement Technologies. He is or has been a member of several editorial boards of journals, including AIJ and JAIR, two of the most prestigious generalist journals, and was the editor in chief of the JAAMAS journal, specialized in autonomous agents. He organized IJCAI, the most important international artificial intelligence conference in 2011 in Barcelona and was the President of the IJCAI Program Committee in 2017 in Melbourne. He is a Fellow of the European Association of AI, EurAI, and recipient of the ACM/SIGAI Autonomous Agents Research Award 2019.
Elisa Fromont
Elisa Fromont is a full professor at Université de Rennes France, since 2017 and a Junior member of the Institut Universitaire de France (IUF). She works at IRISA research institute in the INRIA LACODAM (“Large Scale Collaborative Data Mining”) team. From 2008 until 2017, she was associate professor at Université Jean Monnet in Saint-Etienne, France. She worked at the Hubert Curien research institute in the Data Intelligence team. Elisa received her Research Habilitation (HDR) in December 2015 from the University of Saint-Etienne. Her research interests lie in (explainable) machine learning, data mining and, in particular, time series analysis.
Explainable Time Series Classification (Tutorial)
Valerio Piccioni
Valerio Piccioni is an AI Engineer at LARUS who primarily focuses on Graph Neural Networks, but also likes to have a go with other deep learning fields like NLP and Computer Vision. He is also interested in MLOps as building machine learning models that can arrive into production is harder than it seems. Currently he is working on a project regarding fraud detection with graphs.
Exploiting GNNs for Business Recommendation on Yelp Data(Tutorial)
Timo Möller
Timo Möller is Co-Founder of deepset and Head of Solution Engineering. He works closely together with deepset’s clients to bring modern NLP into production. He is an open-source fan and a passionate NLP engineer. Currently, he works on retrieval augmented generation, auto-generating training data, and ways to detect hallucinations.
Gustavo Sato dos Santos, PhD
Gustavo is the esteemed Vice President of Research at Vortexa Ltd., where he has focused on applying statistical modelling and Machine Learning to the energy and freight markets. His research interests span computational neuroscience, medical imaging, and the development of innovative solutions for the energy sector.
Prior to his tenure at Vortexa, Gustavo amassed a wealth of experience in both the academic and professional realms. He has published his research in prestigious international journals and presented his findings at scientific conferences across the globe. Gustavo’s dedication to finding optimal solutions for complex business problems is evident in his work.
Gustavo holds an SB and MEng in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT) and a PhD from the University of Tokyo. As an expert in his field, Gustavo brings a depth of knowledge and experience to ODSC, where attendees can expect to learn from his invaluable insights.
Tim Santos
Tim is leading Graphcore’s Cloud Solutions product to help AI & ML software development teams build AI products and deploy ML capabilities in production. Tim has worn many hats in his career, from being a research engineer, data scientist and leading MLOps teams. Along the way, he’s gained experience across all stages of the development lifecycle, taking AI applications from experimentation to deployment.
Generative AI in Practice: How to build your own Stable Diffusion API(Workshop)
María Hernandez Rubio
María is Senior Data Scientist and Data Product Owner at BBVA AI Factory, with ten years of experience in the Data Science field, she was one of the first Data Scientists in BBVA, taking part in the Big Data ecosystems set up in the bank. Graduated in Mathematics and Computer Engineering, she holds a MSc in Computational Intelligence from Universidad Autónoma de Madrid (UAM), specialized in Aspect-based sentiment Analysis and Item Recommendation.
She has worked in several analytical domains, ranging from Retail and Urban Analysis to Customer Intelligence. Now, she is trying to enhance the customers’ relationship with the bank through Natural Language Processing and Text Analytics. María focuses on understanding business challenges and developing the best analytical solution for each problem.
Damian Bogunowicz
Damian is engineer, roboticist, software developer, and problem solver. Previous experience in autonomous driving (Argo AI), AI in industrial robotics (Arrival), and building machines that build machines (Tesla). Currently working in Neural Magic, focusing on the sparse future of AI computation. Works towards unlocking creative and economic potential with intelligent robotics while avoiding the uprising of sentient machines.
:https://dtransposed.github.io
Dillon Bostwick
Dillon Bostwick is a Solutions Architect at Databricks, where he’s spent the last five years advising customers ranging from startups to Fortune 500 enterprises. He currently helps lead a team of field ambassadors for streaming products and is interested in improving industry awareness of effective streaming patterns for data integration and production machine learning. He previously worked as a product engineer in infrastructure automation.
Avinash Sooriyarachchi
Avinash Sooriyarachchi is a Senior Solutions Architect at Databricks. His current work involves working with large Retail and Consumer Packaged Goods organizations across the United States and enabling them to build Machine Learning based systems. His specific interests include streaming machine learning systems and building applications leveraging foundation models. Avi holds a Master’s degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania.
Dr.-Ing. Thomas Albin
Thomas is a Senior Machine Learning engineer, working in the automotive industry since 2019. Before joining the Research & Development department of a large manufacturer he was conducting research activities in space science. In parallel to his studies in Astro- and Geo-Physics and later PhD program, he participated in 2 major missions: ESA’s comet mission Rosetta/Philae and NASA’s & ESA’s Saturn spacecraft Cassini/Huygens; always with a special focus on cosmic dust. Additionally, he applies Machine Learning algorithms to analyse astronomy- and space-related data to derive new scientific insights or to create new methods for calibrating instruments. Besides his industry work, Thomas is a guest scientist at the Free University of Berlin, where he continues working on the Cassini-related datasets using Deep Learning. On his active YouTube channel Astroniz he shares his Python + Space Science + Machine Learning knowledge with a small community.
Space Science with Python – Enabling Citizen Scientists(Workshop)
Sophia Ananiadou
Sophia Ananiadou is Professor in Computer Science, Department of Computer Science, the University of Manchester. She is also Director of the National Centre for Text Mining (NaCTeM)); Deputy Director of the University’s Institute of Data Science and AI (IDSAI); Distinguished Research Fellow at the AI Research Centre of the National Institute of Advanced Industrial Science and Technology, Japan; Alan Turing Institute Fellow; Honorary Professor, University of the Aegean and Member of European Laboratory for Learning and Intelligent Systems Society. Her research interests evolved from abstract work on fragments of linguistic theory and logic to exploration of how AI systems could acquire and exploit knowledge of language, particularly in specialised domains (biomedicine, chemistry, exposome, law, public health). Research contributions include neural information extraction, text summarisation and simplification, emotion detection, terminology, development of resources (lexica, terminologies and labelled data), annotation tools and interoperable platforms for NLP workflows. She has developed tools such as the RobotAnalyst to improve evidence-based decisions, cut costs and improve efficiency and robustness of key policy decisions in public health.
Felipe de Pontes Adachi
Felipe is a Data Scientist at WhyLabs. He is a core contributor to whylogs, an open-source data logging library, and focuses on writing technical content and expanding the whylogs library in order to make AI more accessible, robust, and responsible. Previously, Felipe was an AI Researcher at WEG, where he researched and deployed Natural Language Processing approaches to extract knowledge from textual information about electric machinery. He is also a Master in Electronic Systems Engineering from UFSC (Universidade Federal de Santa Catarina), with research focused on developing and deploying fault detection strategies based on machine learning for unmanned underwater vehicles. Felipe has published a series of blog articles about MLOps, Monitoring, and Natural Language Processing in publications such as Towards Data Science, Analytics Vidhya, and Google Cloud Community.
Data Validation at Scale – Detecting and Responding to Data Misbehavior(Workshop)
Mark Needham
Mark Needham is an Apache Pinot advocate and developer relations engineer at StarTree. As a developer relations engineer, Mark helps users learn how to use Apache Pinot to build their real-time user-facing analytics applications. He also does developer experience, simplifying the getting started experience by making product tweaks and improvements to the documentation. Mark writes about his experiences working with Pinot at markhneedham.com. He tweets at @markhneedham.
Building a Real-time Analytics Application for a Pizza Delivery Service(Workshop)
Ian Ozsvald
Ian is a Chief Data Scientist, has helped co-organise the annual PyDataLondon conference raising $100k+ annually for the open source movement along with the associated 12,000+ member monthly meetup. Using data science he’s helped clients find $2M in recoverable fraud, created the core IP which opened funding rounds for automated recruitment start-ups and diagnosed how major media companies can better supply recommendations to viewers. He gives conference talks internationally often as keynote speaker and is the author of the bestselling O’Reilly book High Performance Python (2nd edition). He has over 25 years of experience as a senior data science leader, trainer and team coach. For fun he’s walked by his high-energy Springer Spaniel, surfs the Cornish coast and drinks fine coffee. Past talks and articles can be found at:
https://notanumber.email/
https://github.com/ianozsvald/
Tweets by ianozsvald
https://fosstodon.org/@ianozsvald
https://www.linkedin.com/in/ianozsvald/
Pandas 2, Dask or Polars? Quickly Tackling Larger Data on a Single Machine(Talk)
Christian Ramirez
Christian is Machine Learning Technical Leader at Mercado Libre, the largest e-commerce/fintech company in Latin America, where he dedicates his efforts to creating tools for monitoring and quality of learning models. He is a Computer Engineer and Master in Science with a major in Astronomy from UNAM (Universidad Nacional Autonoma de Mexico). He is a “Xoogler” and has more than 15 years of experience in the field of machine learning. He has lectured in almost a dozen countries.
Introduction to Topological Data Analysis Workshop(Tutorial)
Jutta Treviranus
Jutta Treviranus is the Director of the Inclusive Design Research Centre (IDRC) and professor in the faculty of Design at OCAD University in Toronto (http://idrc.ocadu.ca ). Jutta established the IDRC in 1993 as the nexus of a growing global community that proactively works to ensure that our digitally transformed and globally connected society is designed inclusively. Dr. Treviranus also founded an innovative graduate program in inclusive design at OCAD University. Jutta is credited with developing an inclusive design methodology that has been adopted by large enterprise companies such as Microsoft, as well as public sector organizations internationally. In 2022 Jutta was recognized for her work in AI by Women in AI with the AI for Good – DEI AI Leader of the Year award.
Dr. Phil Winder
Dr. Phil Winder is a multidisciplinary engineer and data scientist. As the CEO of Winder.AI, an AI consultancy, he provides AI, ML, Data Science, and MLOps development and consulting services to businesses of all sizes. Previous clients include the likes of Google, Microsoft, Shell, Nestle, the UK Government and many more. More information is available on the website: https://Winder.AI.
Phil is also the author of the book “Reinforcement Learning: Industrial Applications of Intelligent Agents” published by O’Reilly (https://rl-book.com) and was an early champion of MLOps. Over the past decade, he has also trained thousands of data scientists and is a celebrated global speaker on AI topics.
Phil holds a Ph.D. and M.Eng. in electronic engineering from the University of Hull and lives in Yorkshire, U.K., with his brewing equipment and family.
Robert Crowe
A data scientist and ML enthusiast, Robert has a passion for helping developers quickly learn what they need to be productive. Robert is currently the Senior Product Manager for TensorFlow Open-Source and MLOps at Google and helps ML teams meet the challenges of creating products and services with ML. Previously Robert led software engineering teams for both large and small companies, always focusing on moving fast to implement clean, elegant solutions to well-defined needs. You can find him on LinkedIn at robert-crowe.
MLOps v LMOps – What’s Different?(Talk)
Alexandre Sajus
Alexandre worked in Amazon Business Intelligence.He developed a graph-based interactive Python editor: Pyflow (1.2k stars!). He is skilled in MLOps, Data Engineering, and Python. He has studied Master of Engineering – CentraleSupélec from University of Paris-Saclay.
How to Build Stunning Data Science Web Applications in Python – Taipy Tutorial
Demo Session Title: Turning your Data/AI algorithms into full web apps in no time with Taipy
Abstract:
In the Python open-source ecosystem, many packages are available that cater to:
– the building of great algorithms
– the visualization of data
Despite this, over 85% of Data Science Pilots remain pilots and do not make it to the production
stage.
With Taipy, a new open-source Python framework, Data Scientists/Python Developers are able to
build great pilots as well as stunning production-ready applications for end-users.
Taipy provides two independent modules: Taipy GUI and Taipy Core.
In this talk, we will demonstrate how:
1. Taipy-GUI goes way beyond the capabilities of the standard graphical stack: Gradio,
Streamlit, Dash, etc.
2. Taipy Core fills a void in the standard Python back-end stack.
Luc De Raedt, PhD
Prof. Dr. Luc De Raedt is currently Director of Leuven.AI, the KU Leuven Institute for AI, full professor of Computer Science at KU Leuven, and guestprofessor at Örebro University (Sweden) at the Center for Applied Autonomous Sensor Systems in the Wallenberg AI, Autonomous Systems and Software Program.
Luc De Raedt obtained his PhD in Computer Science from the KU Leuven (1991), was post-doctoral researcher of the Fund for Scientific Research, Flanders (FWO) (1991-99) and part-time assistant/associate professor (1993-1999) KU Leuven; full professor (C4) and Chair of the Machine Learning and Natural Language Processing Lab at the Albert-Ludwigs-University Freiburg, Germany (1999-2006); head of the Lab for Declarative Languages and Artificial intelligence at KU Leuven from (2015-2019).
Luc De Raedt’s research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is well known for his contributions in the areas of learning and reasoning, in particular, for his contributions to statistical relational learning, probabilistic and inductive programming. Today he is working on the next generation of programming languages, which can automatically learn from data, on combining probabilistic and logical reasoning and learning, on the automation of (data) science, and on verifying learning artificial intelligence systems and robotics. He is also now also focusing on integrating the probabilistic logics with neural networks and wants to apply these to reinforcement learning as well as program induction.
Anna-Maria Wykes
Anna is a veteran software & data engineer and a Microsoft Data Platform MVP, with over 17 years of experience. Anna has tackled projects from real-time analytics with Scala & Kafka, building out Data Lakes with spark and applying engineering to Data Science. She is a senior consultant with Advancing Analytics, helping shape & evolve their data engineering practice. Anna has a real passion for data and strives to bring the worlds of Software Development and Data Science closer together. Other areas of interest include UX, Agile methodologies, and helping to organize/run local Code Clubs.
Demo Session Title: DeltaLake – Enabling Open Source Lakehouses
Abstract: Once upon a time we had the Data Warehouse, life was good but it had its limitations, particularly around loading/storing complex data types. As data grew larger and more varied, the warehouse became too rigid and opinionated.
So we dove headfirst into Data Lakes to store our data. Again, things were good, but missed some of the good times that the Data Warehouse had given us. The lake had become too flexible, we needed stability in our life. In particular, we needed A.C.I.D (Atomicity, Consistency, Isolation, and Durability) Transactions.
Delta Lake, hosted by the Linux Foundation, is an open-source file layout protocol for giving us back those good times, whilst retaining all of the flexibility of the lake. Delta has gone from strength to strength, and in 2022 Databricks finally open-sourced the entire code-base, including lots of advanced features that were previously Databricks-only.
This session will take you from the absolute basics of using Delta within a Lake, through to some of those advancing engineering features, including:
• Handling Schema Drift
• Applying Constraints
• Time-Travel & Management
• Optimize & Performance Tuning
Hossam Amer, PhD
Hossam Amer joined Microsoft as a scientist in 2021. His research interests are Image/Video Compression, Computer Vision, and most recently Natural Language Processing. Hossam is contributing to many products including Microsoft Translator and Microsoft SwiftKey. Prior to joining Microsoft, Hossam was a Postdoctoral-Fellow at the Multimedia Communications Lab at the University of Waterloo (UW), where he mentored several MSc and PhD students. He obtained his PhD from the same lab, where he received the prestigious annual UW teaching award based on students’ and instructors’ nominations as well as published papers in top venues. Hossam also acts as a reviewer in several IEEE conferences and journals and supervises students in research and teaching. In addition, Hossam was the Chair of the ECE Graduate Student Association at UW. Hossam is a strong believer in constantly transferring his knowledge in order to make a difference.
Deep Learning and Comparisons between Large Language Models(Talk)
Avik Sengupta
Avik Sengupta is the head of product development and software engineering at Julia Computing, contributor to open source Julia and maintainer of several Julia packages. Avik is the author of Julia High Performance, co-founder of two artificial intelligence start-ups in the financial services sector and creator of large complex trading systems for the world’s leading investment banks.
Emanuele Fabbiani, PhD
Emanuele is Engineer by education, Data Scientist by choice, researcher and lecturer by passion. During his PhD in ML, he got invited to EPFL Lausanne for a 6-month visit and published 9 papers in top journals.He is the co-founder of xtream, an AI boutique applying academic research to business. Contributing to the community is part of their mission: He was a speaker and track organizer at eRum, AMLD, and PyCon and he lectured at Italian, Swiss, and Polish universities.
Should You Trust Your Copilot? Limitations and Merits of AI Coding Assistants(Talk)
Kai Waehner
Kai Waehner is Field CTO at Confluent. He works with customers and partners across the globe and with internal teams like engineering and marketing. Kai’s main area of expertise lies within the fields of Data Streaming, Analytics, Hybrid Cloud Architectures and Internet of Things. Kai is a regular speaker at international conferences, writes articles for professional journals, and shares his experiences with industry use cases and new technologies on his blog: www.kai-waehner.de. Contact: kai.waehner@confluent.io / @KaiWaehner / linkedin.com/in/kaiwaehner.
Apache Kafka for Real-Time Machine Learning Without a Data Lake(Talk)
Sandra Wachter, PhD
Professor Sandra Wachter is Professor of Technology and Regulation at the Oxford Internet Institute at the University of Oxford where she researches the legal and ethical implications of AI, Big Data, and robotics as well as Internet and platform regulation. At the OII, Professor Sandra Wachter leads and coordinates the Governance of Emerging Technologies (GET) Research Programme that investigates legal, ethical, and technical aspects of AI, machine learning, and other emerging technologies.
Professor Wachter is also an affiliate and member at numerous institutions, such as the Berkman Klein Center for Internet & Society at Harvard University, World Economic Forum’s Global Futures Council on Values, Ethics and Innovation, the European Commission’s Expert Group on Autonomous Cars, the Law Committee of the IEEE, the World Bank’s Task Force on Access to Justice and Technology, the United Kingdom Police Ethics Guidance Group, the British Standards Institution, the Bonavero Institute of Human Rights at Oxford’s Law Faculty and the Oxford Martin School. Professor Wachter also serves as a policy advisor for governments, companies, and NGO’s around the world on regulatory and ethical questions concerning emerging technologies.
Sheamus McGovern
Sheamus McGovern is the founder of ODSC (The Open Data Science Conference). He is also a software architect, data engineer, and AI expert. He started his career in finance by building stock and bond trading systems and risk assessment platforms and has worked for numerous financial institutions and quant hedge funds. Over the last decade, Sheamus has consulted with dozens of companies and startups to build leading-edge data-driven applications in finance, healthcare, eCommerce, and venture capital. He holds degrees from Northeastern University, Boston University, Harvard University, and a CQF in Quantitative Finance.
Seth Juarez
My name is Seth Juarez. I currently live near Redmond, Washington and work for Microsoft.
I received my Bachelors Degree in Computer Science at UNLV with a Minor in Mathematics. I also completed a Masters Degree at the University of Utah in the field of Computer Science. I currently am interested in Artificial Intelligence specifically in the realm of Machine Learning. I currently work as a Program Manager in the Azure Artificial Intelligence Product Group.
I’ve been married now for 21 years to a fabulously talented woman and have two beautiful daughters, and two feisty sons.
Session Title: Ask the Experts! ML Pros Deep-Dive into Machine Learning Techniques and MLOps
Abstract: Experienced machine learning engineers and data scientists care about ways to easily get their models up and running quickly and share ML assets across teams for collaboration. Collaborate and streamline the management of thousands of models across teams with new, innovative features in Azure Machine Learning. Come and join us in this interactive session with our product experts and get your questions answered on the latest capabilities in Azure Machine Learning!
Alberto De Lazzari
Alberto De Lazzari is Chief Scientist at LARUS, he takes care of research and development in the world of artificial intelligence and network science, following collaborations with various Italian universities. In the last 10 years he has worked in different areas: from automotive and management sectors to banking and insurance. He has experience in IT process internalization and digital transformation projects.
Demo Session Title: Building Next-gen Recommendation Systems with Galileo.XAI
Abstract:
Graph AI can achieve the state of the art on many machine learning tasks regarding relational data. One of them is recommendations which can be found in many services such as content streaming, shopping or social media. Discrete data approaches are limited by definition while analysing interconnections is fundamental to understanding complex interactions and behaviours. Our customer-centric approach lets you create a holistic view of the customer from different perspectives. With our solution, a Graph AI platform with explainability at the core, you can build a recommendation engine powered by connected data to provide better recommendations. We will show, step by step, how the user can interact with the platform to get new insights and better understand customer behaviour and preferences that are the basis for recommending better content to them. The platform also provides the explainability of the recommendation which is fundamental to building better and more trustworthy models.
Chris Butcher
Chris is a specialist in business analysis, system infrastructure, management information, business Intelligence, hardware and software implementation and project management. As a Business Analyst, he has worked with blue chip and global organisations such as Imperial Collage Hospital, Nottingham University Teaching Hospital, Smith & Nephew, St Andrews University, Chelsea FC and The Super League.
Today, Chris is a lead specialist at TimeXtender showing businesses a better way to work with data building modern data estates for analytics and AI applications.
Demo Session Title: Build a Modern Data Estate in 15 Minutes
Abstract:
TimeXtender provides all the features you need to build a future-proof infrastructure for ingesting, transforming, modelling, and delivering clean, reliable data in the fastest, most efficient way possible. You can’t optimize for everything all at once. That’s why we take a holistic approach to data integration that optimizes for agility, not fragmentation. By unifying each layer of the data stack, TimeXtender empowers you to build data solutions 10x faster, while reducing your costs by 70%-80%.
In this session, we shill show you how to:
Gather data in raw form and structure for use in advanced analytics and AI
Create a modern data warehouse with access to improved data
Build semantic models for self-service analytics
Document your analytics data for compliance
Marine Gosselin
Marine has 5+ years of experience as Data Scientist. She is skilled in Machine Learning techniques, Python, Rule-based models & AI. She has strong experience in Predictive and Descriptive Analytics, Fraud detection. She has done her Master’s Degree, Msc Big Data Analytics for Business from IÉSEG School of Management. Accounting & Finance from McGill University, Hong Kong University of Science and Technology and Europe Business School.
Bringing AI to Retail and Fast Food with Taipy’s Applications(Track Keynote)
Marc Rovira, PhD
Marc Rovira is a data scientist at Electrolux Group in Stockholm, with a strong focus on forecasting and time series analysis. He actively contributes to the sktime community as a council member and user representative. Prior to his industry experience, Marc completed a Ph.D. that explored the intersection of computational fluid mechanics, chemical engineering, and machine learning, with the aim of mitigating air pollution. His educational background also includes a master’s degree in aerospace engineering.
sktime – Python Toolbox for Machine Learning with Time Series(Training)
Moez Ali
Innovator, Technologist, and a Data Scientist turned Product Manager with proven track record of building and scaling data products, platforms, and communities. Experienced in building and leading teams of data scientists, data engineers, and product managers. Strongly opinionated tech visionary and a thought partner to C-level leadership.
Moez Ali is an inventor and creator of PyCaret. PyCaret is an open-source, low-code, machine learning software. Ranked in top 1%, 8M+ downloads, 7K+ GitHub stars, 100+ contributors, and 1000+ citations.
Globally recognized personality for open-source work on PyCaret. Keynote speaker and top ten most-read writer in the field of artificial intelligence. Teaching AI and ML courses at Cornell, NY and Queens University, CA. Currently building world’s first hyper-focused Data and ML Platform.
Automate Machine Learning Workflows with PyCaret 3.0(Workshop)
Joon-Pil (JP) Hwang
JP finds joy in technology and learning, as well as empowering others by helping to distill complex technologies into relatable concepts. He works at Weaviate as the Technical Curriculum Coordinator, facilitating education for vector databases and data science topics. When he’s not working, JP enjoys immersing himself in the worlds of games and sports. You might spot him working on his serve on the tennis court, or engaging in spirited board game sessions.
An Introduction to Vector Database: The Power of LLMs, with your Data (Python and Weaviate) – Part I(Workshop)
Julien Simon
Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.
Hyper-productive NLP with Hugging Face Transformers(Workshop)
Leanne Fitzpatrick
Leanne is Director of Data Science at the Financial Times and is a passionate, experienced data leader having built and developed empowered data science and analytics teams for a variety of businesses; from startups to large organisations. Leanne is in her element when developing and implementing strategic, technical and cultural solutions to getting data & analytical capabilities into the operational ecosystem. She is an active part of the data and technology community, sharing innovation and insights to encourage best practice, from Manchester, UK to Austin, TX and is an Advisory Panel Board Member. Outside of all things data you can ask Leanne about her golf swing (it’s not good – yet), her passion for American Football (specifically the Cincinnati Bengals), her latest sewing project, and her love for good music, food and whisky.
Andras Zsom, PhD
Andras Zsom is an Assistant Professor of the Practice and Director of Graduate Studies at the Data Science Initiative at Brown University, Providence, RI. He is teaching two mandatory courses in the data science master’s program, and helps the students navigate through their studies and curriculum. He also supervises interns on various research projects related to missing data, interpretability, and developing machine learning pipelines.
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.
Causal AI: from Data to Action(Workshop)
Philip Tracton
Phil Tracton is an IC design engineer at Medtronic and an instructor at UCLA Extension. He has worked at Medtronic for over 20 years and has experience in implementing firmware, FPGAs, and custom ASICs. Many thousands of people have his work implanted in them. Most of these devices are focused on Neuromodulation. He has recently joined an internal team focused on long term research for implantable devices.
At UCLA he teaches multiple Python based courses including Learning Python and Python on the Raspberry Pi.
He is interested in low power AI on edge devices.
He will be running the Fundamentals of Python training class. This is his second time teaching at an ODSC event.
Python Fundamentals(Bootcamp)
Dr Paul A. Bilokon
Bio Coming Soon!
Iterated and Exponentially Weighted Moving Principal Component Analysis(Talk)
Sam Blake, PhD
Sam leads the supply chain machine learning team at Ocado Technology, responsible for the demand forecasting and replenishment optimisation algorithms used by Ocado’s international partners. Sam holds a DPhil in Condensed Matter Physics from the University of Oxford and volunteers as an ambassador for DataKindUK. Prior to joining Ocado he spent a number of years working in AI startups in the Netherlands.
Using Deep Learning to Forecast Demand for Thousands of Grocery Items(Business Talk)
Casper Rutjes, PhD
Dr. Casper Rutjes is Chief Technology Officer (CTO) at ADC (Amsterdam Data Collective), a Data & AI Consultancy in Europe. Rutjes is responsible for R&D, (Tech) Partnerships, consulting quality & standardization and IT. He leads global teams of consulting specialists in the areas of strategy & innovation, data engineering and data science across our key industries, mainly healthcare/life science, public and finance. At clients he is a trusted advisor and senior project lead for challenges on the interface of regulation, IT and business.
Tanvir Ahmed Shaikh
Tanvir Ahmed Shaikh is a highly entrepreneurial and visionary data strategist with a passion for driving business growth through innovative data-driven solutions. With a track record of success in data science and digital transformation, Tanvir has been instrumental in developing and implementing strategies that improve efficiency, quality, and compliance. He possesses strong collaboration skills and effectively communicates technical concepts to non-technical stakeholders.
Currently serving as a Data Strategist (Director) at Genentech Inc., Tanvir leads the digital roadmap for the Global Pharma Manufacturing Quality organization. His expertise in prioritizing digital initiatives, building consensus, and driving change management has resulted in significant positive impacts on the organization.
Tanvir’s leadership abilities are exemplified through his role as the Founder and Digital Strategy Lead of the Roche Intrapreneur Network, a global network of over 350+ Roche technologists focused on executive capabilities and experiential learning. Through this network, he fosters a culture of entrepreneurship, product management, and storytelling, encouraging innovation and empowering individuals to think like CEOs of their products.
In his previous role as a Principal Data Scientist, Tanvir spearheaded cross-functional projects, driving operational excellence in forecasting, automation, and AI education. His contributions have led to substantial cost savings and increased efficiency within the organization. Tanvir’s passion for education and continuous learning is evident in his role as an Adjunct Professor at Carnegie Mellon University. He teaches courses on Time Series Forecasting in Python, AI Product Management, and Storytelling with Data, inspiring students to think holistically and take an end-to-end view of problem-solving. He actively promotes a culture of continuous learning, inclusive community building, and inspirational storytelling. Beyond his professional pursuits, Tanvir embraces a diverse range of interests. He finds joy in the culinary arts, experimenting with new recipes and creating culinary delights. Music also holds a special place in his heart, and he enjoys singing and playing the ukulele in his free time. Tanvir’s curiosity extends to the financial world, where he actively researches stocks and shares his knowledge, promoting personal finance education. Additionally, he stays active through the sport of tennis, both in competitive settings and for leisure. Tanvir’s dedication to data-driven strategies, love for storytelling, and commitment to personal growth and education make him a versatile and accomplished professional. He embodies the values of continuous learning, community building, and innovative thinking, making a significant impact in the field of data science and beyond.
Time Series Forecasting for Managers – All forecasts are wrong but some are useful (Talk)
Yetunde Dada
Yetunde Dada is the Director of Product Management at QuantumBlack, an AI-focused branch of McKinsey. She is instrumental in building products for Data Engineers and Data Scientists, including a notable Python library known as Kedro. Kedro is a distinguished product, marking the first open-source offering from McKinsey and QuantumBlack.
She holds an MBA from the Said Business School at the University of Oxford, earned in the 2017/2018 academic year. Her professional background is diverse and includes roles such as Data Engineer and Data Product Manager at Absa (formerly known as Barclays Africa Group Limited), Innovation Consultant at Engineers Without Borders South Africa, and a Mechanical Engineer.
Creating Maintainable ML Code: Lessons from Software Engineering(Talk)
Colin Priest
Colin is a seasoned data scientist who has worked in the finance, healthcare, security, oil and gas, government, telecommunications, and marketing industries. He has a keen interest in exploring the relationship between humans and AI and has contributed to projects on AI ethics, governance, and the future of work. His work has gained global recognition from the World Economic Forum, and he has contributed to several important initiatives, including the Singapore government’s official AI strategy, PDPC AI Governance and Ethics Guidelines, and the Monetary Authority of Singapore Veritas Initiative. In addition to his professional work, Colin is a dedicated healthcare advocate who volunteers for cancer research.
Feature Engineering With Signal Types(Workshop)
Mike Kraus
Bio Coming Soon!
From Notebook to Production: Building Mollie’s ML Platform(Business Talk)
Drazen Dodik
Bio Coming Soon!
Session Title: Driving AI Forward: Continental Tire’s Journey to MLOps Excellence
Abstract:
In this session, we will hear from Continental Tire about their journey towards implementing MLOps since 2015. We will explore how they enable data scientists from diverse backgrounds to easily build models with the languages, frameworks, and tools they are comfortable with.
The session will delve into the challenges faced by Continental Tire’s data science teams, and the strategies they have used to address them. Additionally, the session will cover important considerations for those starting on their MLOps journey, including what to keep in mind when building infrastructure and workflows for data science projects.
The session will conclude with a demo and overview of the Valohai platform, which has been used by Continental Tire to streamline their MLOps workflows.
Pavel Klushin
Pavel Klushin is a seasoned solution architecture expert who currently leads the function at Qwak. With years of experience in the technology industry, he is known for his exceptional ability to design and deliver innovative solutions that meet the specific needs of his clients. Pavel previously led the solution architecture team at Spot (Aquired by NetApp).
Session Title: End to End Machine Learning Pipeline Management
Abstract: Join this demo to find how to centralize your ML pipeline and cut down operational complexities at each stage along the way. Qwak’s platform supports multiple use cases across any business vertical and allows data teams to productionize their models more efficiently and without depending on engineering resources. Join us to watch how <presenter name> uses Qwak to create features from data and build, train and deploy models into production. All under a single platform and with unprecedented simplicity.
Andy Petrella
Andy Petrella is the CPO and founder of Kensu, a data observability solution that helps data teams trust what they deliver and create more value from data.
Andy is an entrepreneur with a background in data mining, data engineering, and data science. He is known as an early evangelist of Apache Spark and the Spark Notebook creator in the data community.
Since 2015, Andy has been an O’Reilly instructor and author, including the first O’Reilly book about Data Observability: “Fundamentals of Data Observability”
Two Methods to Automate Data Observability at a Larger Scale: Agents and Collectors(Talk)
Jay Alammar
Jay Alammar, Through his popular machine learning blog, Jay has helped millions of engineers visually understand machine learning tools and concepts from the basic (ending up in NumPy, pandas docs) to the cutting-edge (The Illustrated Transformer, BERT, GPT-3).
Yuval Fernbach
Yuval Fernbach is the Co-founder & CTO of Qwak, where he is focused on building next-generation ML Infrastructure for ML teams of various sizes. Before Qwak, Yuval was an ML Specialist at AWS , where he helped AWS Customers across EMEA with their ML challenges. Previous to that, he was the CTO of the IT department of the IDF (“Mamram”).
Dipanjan (DJ) Sarkar
Dipanjan (DJ) Sarkar is an acknowledged Data Scientist, published Author and Consultant with over nine years of industry experience in all things data. He was recognized as a Google Developer Expert in Machine Learning by Google in 2019, and a Champion Innovator in Cloud AI\ML by Google in 2022. He currently works as a Lead Data Scientist at Constructor Learning (formerly Schaffhausen Institute of Technology (SIT) Learning), Zurich.
Dipanjan has led advanced analytics initiatives working with Fortune 500 companies like Intel, Applied Materials, Red Hat / IBM. He works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. Dipanjan also works as an independent consultant, mentor and AI advisor in his spare time collaborating with multiple universities, organizations and startups across the globe. His passion includes solving challenging data problems as well as educating and helping people upskill in all things data. Find more about him at https://djsarkar.com
Eleni Triantafillou, PhD
Eleni is a Research Scientist at Google DeepMind, based in London UK. She obtained her PhD from the University of Toronto, advised by Professors Richard Zemel and Raquel Urtasun. Her research is centered around creating methods that allow efficient and effective adaptation of deep neural networks to cope with distribution shifts, introduction of new concepts, or removal of outdated or harmful knowledge, falling in the areas of few-shot learning, meta-learning, domain adaptation and machine unlearning.
Bernardo Caldas
Bernardo is a Data & AI leader, passionate about powering data transformation in companies and promoting social good in society using data.
He is specialized in Data Science, Machine Learning and AI, having won two awards in this field (Innovation in Big Data Award by Thomson Reuters and Machine Learning & Neural Computation Award by Imperial College London). His goal is to be able to take any challenge, no matter how complex, and to solve it using a fusion of art & science, business & technology capabilities, data & analytics to make it happen.
Bernardo has an MRes in Advanced Computing from Imperial College London, with a specialization in Machine Learning and a BSc in Electrical and Computer Engineering from Instituto Superior Técnico.
Beyond Correlation: Unraveling Causality with Machine Learning
(Talk)
Peter Ableda
With over 10 years of working with data management and advanced analytics products, Peter Ableda is serving as the Director of Product Management for Cloudera Machine Learning at Cloudera. Peter holds a Master of Science degree in Computer Science from Budapest University of Technology and is an 8-year veteran of Cloudera — recognized across the industry for his work managing big data technology products and cutting-edge data-driven applications for high-growth organizations. https://www.linkedin.com/in/peterableda/?originalSubdomain=hu
Demo Session Title: LLM Chatbot Augmented with Enterprise Data
Abstract:
In this demo, we will showcase the use of our newest Applied ML Prototype (AMP), which demonstrates how to use an open source pre-trained instruction-following LLM (Large Language Model) to build a ChatBot-like web application. By leveraging a Vector Database populated with relevant documentation for context retrieval, the application enhances the LLM’s responses, creating a subject matter expert Chatbot. All components run within Cloudera Machine Learning (CML), eliminating the need for external model APIs or additional LLM training. Attendees will see how this Retrieval Augmented Generation (RAG) approach improves response accuracy for industry specific use cases.
Sonali Parbhoo, PhD
Sonali is an Assistant Professor and leader of the AI for Actionable Impact Group at Imperial College London. Her research focuses on decision-making in uncertainty, causal inference and building interpretable models to improve clinical care and deepen our understanding of human health, with applications in areas such as HIV and critical care. Prior to this, Sonali was a postdoctoral research fellow at Harvard. Her work has been published at a number of machine learning conferences (NeurIPS, AAAI, ICML, AISTATS) and medical journals (Nature Medicine, Nature Communications, AMIA, PLoS One, JAIDS). She was also a Swiss National Science Fellow and was named a Rising Star in AI in 2021. Sonali received her PhD (summa cum laude) in 2019 from the University of Basel, Switzerland, where she built intelligent models for understanding the interplay between host and virus in the fight against HIV. Apart from her research, Sonali is also passionate about encouraging more discussion about the role of ethics in developing machine learning technologies to improve society.
Mind Your Evaluation: Towards Practical Off-Policy Evaluation in Safety Critical Settings (Talk)
Tomasz M. Grzegorczyk
Tomasz M. Grzegorczyk is the founder and CEO of Teranalytics, an AI and optimization company specializing in large-scale logistics operations such as production, manufacturing, shipping, and distribution. Before creating Teranalytics, he was a Chief Scientist at BAE Systems and MIT Research Scientist where he worked on computational electromagnetics, scattering in complex media, optical forces, and wave propagation in metamaterials. Tomasz holds a PhD from the Swiss Federal Institute of Technology in Lausanne, an MBA from the Massachusetts Institute of Technology, and is a senior member of the IEEE. He served as editor and board member of two international peer-reviewed journals and one international conference, has authored more than a hundred publications and a book on metamaterials.
Logistics Network Optimization: from Data-focus to Information-focus(Business Talk)
Itai Bar-Sinai
With over 10 years of experience (Google, AI-focused startups) with big data and as the CPO and head of customer success at Mona, the leading AI monitoring intelligence company, Itai has a unique view of the AI industry. Working closely with data science and ML teams applying dozens of solutions in over 10 industries, Itai encounters a wide variety of business use-cases, organizational structures and cultures, and technologies used in today’s AI world.
Utilizing Advanced Monitoring Capabilities to Promote Product-oriented Data Science(Tutorial)
Gal Naamani
Gal Naamani has been working as a data scientist for 4 years, with the past 3 years being at Fiverr. As the Senior Data Scientist, Gal works closely with developers, analysts, product managers, and business owners on growth opportunities and new ideas, from research to production. Gal currently has leading roles in projects that are focused around search engine ranking, promoted ads, online bidding optimization, exploration-exploitation problems, monitoring, and more.
Utilizing Advanced Monitoring Capabilities to Promote Product-oriented Data Science(Tutorial)
Helen Yannakoudakis, PhD
Helen Yannakoudakis is an Assistant Professor in Natural Language Processing (NLP) at the Department of Informatics, King’s College London, and a Visiting Researcher at the Department of Computer Science & Technology, University of Cambridge. She is also a co-founder and Chief Scientific Officer at Kinhub (formerly Kami), translating research outcomes to deployable real-world applications in health and wellbeing. Her research focuses on machine learning for NLP, and specifically on transfer learning, few-shot learning, lifelong learning, multilingual NLP, and societal and health applications, such as language assessment, abusive language detection, misinformation, emotion and mental health detection. Helen is a Fellow of the Higher Education Academy, has received funding awards from both industry and academia, has won international competitions such as the NeurIPS 2020 Hateful Memes Challenge, and currently serves as an Area Chair for NeurIPS 2023.
Todd Cioffi
For more than 20 years, Todd has been highly respected as both a technologist and a trainer. As a tech, he has seen that world from many perspectives: “data guy” and developer; architect, analyst, and consultant. As a trainer, he has designed and covered subject matter from operating systems to databases to machine learning / AI to end-user applications, with an emphasis on data, programming, and results that matter.
As a strong advocate for knowledge sharing, he combines his experience in technology and education to impart real-world use cases to students and users of analytics solutions across multiple industries. He has been a regular contributor to the community of analytics and technology user groups in the Boston area and beyond, writes and teaches on many topics, and looks forward to the next time he can strap on a dive mask and get wet.
Toni Perämäki
Toni Perämäki is the Chief Operating Officer of Valohai, a globally recognized MLOps platform dedicated to automating machine learning workflows. With an academic background in software engineering and economics, he brings a strong blend of technical and business acumen to the table. An advocate of the ‘giving forward’ principle, Toni generously offers pro-bono support to young entrepreneurs, sharing his wealth of experience and knowledge. He understands the challenges faced by emerging startups and is committed to empowering the next generation of leaders. Beyond his professional endeavors, Toni is a passionate sailor and a jiu-jitsu enthusiast. These pursuits reflect his philosophy of balance, discipline, and resilience, which he seamlessly applies to his leadership role at Valohai.
Session Title: Streamlining MLOps with Valohai: Large-Scale Experiments, Data Lineage, Pipelines, and Production Deployment
Abstract:
In this informative session, we invite you to delve into the world of MLOps and explore the intricacies of managing large-scale machine learning experiments, ensuring data lineage, orchestrating efficient pipelines, and deploying models into production. Join us for a practical demonstration of the Valohai MLOps platform, which simplifies and streamlines the entire MLOps lifecycle.
Konstantin Gulin
Konstantin Gulin is a Machine Learning Engineer at Neural Magic working on bringing sparse computation to the forefront of industry. With prior experience in applying machine learning to remote sensing (NASA) and space mission simulation (The Aerospace Corporation), he’s turned his focus to enabling effective model deployment in even the most constrained environments. He’s passionate about technology and ethical engineering and strives for the thoughtful advancement of AI.
