June, 2023

ODSC Europe 2023 Speakers

ODSC hosts a fantastic lineup of some of the best and brightest expert speakers and core contributors in data science
THANK YOU ALL SEAKERS FOR THE INSIGHTFUL SESSIONS. WE HOPE TO SEE YOU SPEAKING AGAIN

ODSC Europe Speakers

For 2023 we will have some of the best and brightest minds speaking at ODSC Europe. ODSC will host more than 150 presenters. Speaker profiles are added weekly. Check back for updates.

Featured Europe Speakers

Partner Technical AdvisorMicrosoft

Luis Vargas, PhD

Luis Vargas is a Partner Technical Advisor to the CTO of Microsoft. Responsible for Microsoft’s AI at Scale initiative coordinating efforts across infrastructure, systems software, models, and products. He bootstrapped the productization of Automated ML and Reinforcement Learning in the Azure AI Platform, worked on the launch of Azure Database Services, and lead the high-availability area for SQL Server. Luis has a PhD in Computer Science from Cambridge University.

The Big Wave of AI at Scale(Keynote)

Head of Data Science at Scouts Consulting Group LLC, Z by HP Global Data Science Ambassador

Ken Jee

As the Head of Data Science at Scouts Consulting Group, Ken spends his workdays improving the performance of athletes and teams by analyzing the data collected on them. He also dabbles in entrepreneurship and content creation, best known for his YouTube channel where he helps over 80,000 people navigate the data science landscape. More recently, Ken is focused on project-based learning through Kaggle. He hopes to share the processes that data scientists take when approaching Kaggle competitions and new datasets. He started the #66DaysOfData challenge to help people create the habit of learning and working on projects every day.

Bridging the Gap Between Data Scientists and Decision Makers(Keynote)

DeepMind Professor of AI | Head of Graph Learning ResearchUniversity of Oxford | Twitter

Michael Bronstein, PhD

Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head of Graph Learning Research at Twitter. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).

Physics-inspired Learning on Graph(Keynote)

 

Research Director | Director, Scikit-learnINRIA

Gaël Varoquaux, PhD

Gaël Varoquaux is a research director working on data science and health at Inria (French Computer Science National research). His research focuses on using data and machine learning for scientific inference, with applications to health and social science, as well as developing tools that make it easier for non-specialists to use machine learning. He has long applied it to brain-imaging data to understand cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.

Prediction with Missing Values(Tutorial)

 

ProfessorWeizmann Institute of Science

Yonina Eldar

Yonina C. Eldar is a Professor in the Department of Math and Computer Science at the Weizmann Institute of Science, Rehovot, Israel, where she heads the center for Biomedical Engineering and Signal Processing. She is also a Visiting Professor at MIT and at the Broad Institute and an Adjunct Professor at Duke University, and was a Visiting Professor at Stanford University. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award, the IEEE/AESS Fred Nathanson Memorial Radar Award, the IEEE Kiyo Tomiyasu Award, the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, and the Wolf Foundation Krill Prize for Excellence in Scientific Research. She is the Editor in Chief of Foundations and Trends in Signal Processing, and serves the IEEE on several technical and award committees. She heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel.

Professor of Computational Statistics and Machine LearningUCL Centre for Artificial Intelligence

John Shawe-Taylor, PhD

John Shawe-Taylor is professor of Computational Statistics and Machine Learning at University College London and Director of the International Research Centre on Artificial Intelligence (IRCAI) under the auspices of UNESCO at the Jozef Stefan Institute in Slovenia. He has helped to drive a fundamental rebirth in the field of machine learning, with applications in novel domains including computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have attracted over 84000 citations.

He has assembled a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing. More recently he coordinated the X5gon (x5gon.org) European project developing infrastructure and portals for AI enhanced delivery of open educational materials.

Towards Human-Centric Education with Artificial Intelligence(Talk)

CEOPyMC Labs

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

Bayesian Modeling in Industry(Talk)

Lead Research ScientistBosch Center for AI

Daria Stepanova, PhD

Daria Stepanova is a lead research scientist at Bosch Center for Artificial Intelligence. Her research interests include knowledge representation and reasoning, machine learning and neuro-symbolic AI. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on semantic data. Daria got her PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.

Rule Induction and Reasoning in Knowledge Graphs(Tutorial)

 

Co-founder, CTOPeg

Alex Peattie

Alex Peattie is the co-founder and CTO of Peg, a technology platform helping multinational brands and agencies to find and work with top YouTubers. Peg is used by over 1500 organisations worldwide including Coca-Cola, L’Oreal and Google.

An experienced digital entrepreneur, Alex spent six years as a developer and consultant for the likes of Grubwithus, Huckberry, UNICEF and Nike, before joining coding bootcamp Makers Academy as senior coach, where he trained hundreds of junior developers. Alex was also a technical judge at this year’s TechCrunch Disrupt conference.

Hearing is Believing: Generating Realistic Speech with Deep Learning(Workshop)

 

FounderFire Plus Algebra

Alan Rutter

Alan Rutter is the founder of consultancy Fire Plus Algebra and is a specialist in communicating complex subjects through data visualization, writing, and design. He has worked as a journalist, product owner, and trainer for brands and organizations including Guardian Masterclasses, WIRED, Time Out, the Home Office, the Biotechnology and Biological Sciences Research Council, and Liverpool School of Tropical Medicine.

Communicating Data Insights with Impact(Talk)

Lead Artificial Intelligence AdvocateGoogle

Laurence Moroney

Laurence Moroney leads AI Advocacy at Google, working with the Google AI Research and product development teams. He’s the best-selling author of ‘AI and Machine Learning for Coders,’ as well as the instructor on the Fundamentals of TinyML course at HarvardX, and the popular TensorFlow specializations with deeplearning.ai and Coursera. He’s passionate about empowering software developers to succeed in Machine Learning, democratizing AI as a result. Laurence is based on Washington State in the USA.

A Hands-on Guide to Machine Learning with TensorFlow(Tutorial)

Global Artificial Intelligence Lead | AI-Emtech Lead in Labs | Partner in Analytics InsightsPwC

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

Digital Twins: Not All Digital Twins are Identical(Talk)

Data and Machine Learning Scientist

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)

 

Associate Director – Business Tech Analysis, ML/AIMSD

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. 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

Not Just Deep Fakes: Applications of Visual Generative Models in Pharma Manufacturing(Tutorial)

Senior Research Associate in Biomedical Data Science and University Research LecturerUniversity of Oxford

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.

Ethnicity, Equity, and AI(Talk)

Head of Data Science and AnalyticsBadoo (now MagicLab, which owns several apps)

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 and Manchester United, and with large social networks, like Justgiving. His previous experience includes Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through an acquisition and an IPO. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning and Machine Learning training. He is also a contractor for several companies and for the European Commission, as an expert in AI and Machine Learning. As an author he wrote “Hands On Deep Learning” and he authored an online training course for O’Reilly, Introduction to Reinforcement Learning. In the academic world, he also helped set up the PhD center on Interactive Artificial Intelligence and will take part in the Inner Assessment Board to assign funding to Irish research in AI.

Lead Data Scientist / Google Developer Expert - MLSIT Academy, Zurich

Dipanjan (DJ) Sarkar

Dipanjan (DJ) Sarkar is a data science consultant and published author, and was recognized as a Google Developer Expert in Machine Learning by Google in 2019. He currently works as a lead data science consultant at Schaffhausen Institute of Technology Academy, 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. Dipanjan has also been recognized as one of the top ten Data Scientists in India in 2020, 40 under 40 Data Scientists, 2021 and Top 50 AI Thought Leaders by Global AI Hub, Switzerland. In his spare time he loves reading, gaming, watching interesting documentaries, football. He is also a strong supporter of open-source and publishes his code and analyses from his books, articles and experience on GitHub at https://github.com/dipanjanS and LinkedIn at https://www.linkedin.com/in/dipanzan

Advanced NLP: Deep Learning and Transfer Learning for Natural Language Processing(Workshop)

 

Data Scientist, Software Engineer, Author of Hands-On Data Analysis with PandasBloomberg

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, 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. She is currently pursuing 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.

Beyond the Basics: Data Visualization in Python(Training)

Co-founder & CEOOuterbounds

Ville Tuulos

Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of an upcoming book, Effective Data Science Infrastructure, published by Manning.

Data Scientist and Author of Deep Learning with PyTorch Step-by-Step

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.

PyTorch 101: Building a Model Step-by-step(Workshop)

GANs N’ Roses: Understanding Generative Models(Workshop)

Head of Data Science Evangelism and MarketingOuterbounds

Hugo Bowne-Anderson, PhD

Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.

 

Head of Data Scienceloveholidays

Ben Auffarth, PhD

Ben Auffarth is the head of data science at loveholidays. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analysed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores. More recently, he’s built production systems processing hundreds of thousands of transactions per day, and trained neural networks on millions of text documents. He’s authored two books on machine learning. When he’s not at work, you might find him on a playground with his young son in West London. He co-founded and is the former president of Data Science Speakers, London.

Time-Series in Python – Preprocessing and Machine Learning(Workshop)

Data Science InstructorMetis

Julia Lintern

Julia Lintern currently works as an instructor for the Metis Data Science Flex Program. 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)

Senior Data ScientistAstraZeneca

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)

Senior Software EngineerNVIDIA

Jacob Tomlinson

Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source chatbot automation framework Opsdroid in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.

GPU Development with Python 101(Workshop)

Director of Machine LearningVara

Christian Leibig, PhD

Christian Leibig is Director of Machine Learning at Vara, leading the development of methods from research to production. He obtained a Ph.D. in Neural Information Processing from the International Max Planck Research School in Tübingen and a diploma in physics from the University of Konstanz. Before joining Vara, he worked as a Postdoctoral Researcher at the University Clinics in Tübingen on the applicability of Bayesian Deep Learning and machine learning applications for the healthcare space for ZEISS and held research and internship positions with Max Planck, LMU Munich and the Natural and Medical Sciences Institute in Reutlingen. The method and software of his PhD work, an unsupervised solution for neural spike sorting from HDCMOS-MEA data is distributed by Multichannel Systems (Harvard Bioscience). His work on applying and assessing uncertainty methods to large scale medical imaging was among the first in the field and awarded with key note speaker invitations. He enjoys all of theory, software engineering, and people management, in particular for applications that have a meaningful impact, such as diagnosing cancer early.

Towards a Scalable Deployment of AI Models via Uncertainty Quantification(Workshop)

Assistant Professor; Director, Center for Computational HealthYale University

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.

Healthcare Predictive Modeling with Graph Networks(Talk)

Research Scientist | Scientific Manager of ELLIS AmsterdamFacebook AI Research | ELLIS

Dieuwke Hupkes, PhD

Dieuwke Hupkes is a research scientist at Facebook AI Research in FAIR. Her work centers around the evaluation of models of natural language processing (NLP), with a specific focus on how such models can show more human-like behaviour, where they fail and what are areas where they should still improved. In the recent past, she has focussed specifically on large language models (LLMs) and neural machine translation (NMT) models.

Evaluating Generalisation in Natural Language Processing Models(Talk)

Senior Data Science EvangelistH2O.ai

Jo-fai Chow, PhD

Jo-fai (or Joe) has multiple roles (data scientist / evangelist / community manager) at H2O.ai. Since joining the company in 2016, Joe has delivered H2O talks/workshops in 40+ cities around Europe, US, and Asia. Nowadays, he is best known as the H2O #360Selfie guy. He is also the co-organiser of H2O’s EMEA meetup groups including London Artificial Intelligence & Deep Learning – one of the biggest data science communities in the world with more than 11,000 members (https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/).

Admissible AutoML – Efficient, Fair, and Interpretable Machine Learning with H2O(Workshop)

Senior Data Scientist - PhD Applied MathsCybelAngel

Thomas Galtier, PhD

Thomas Galtier, PhD, is the Data Science and Analytics Director at CybelAngel with a PhD in Applied Mathematics. He is passionate about data, especially “telling stories with the right data”. As a former researcher he believes that reproducibility is a key concept in Machine Learning workflow and that sharing will allow us to tackle tomorrow’s challenges in AI field.

Reproducible and Shareable Notebooks Across a Data Science Team(Talk)

Principal Data Architect4 Mile Analytics

Dan Sullivan, PhD

Dan Sullivan is a Principal Data Architect at 4 Mile Analytics. He has a PhD in Genetics, Bioinformatics and Computational Biology and is a former research scientist in infectious disease genomics. He is the author of Google Cloud Certification Study Guides for the Professional Data Engineer, Professional Architect, and Associate Cloud Engineer certifications and an instructor on LinkedIn Learning and Udemy where he provides courses on data science, data modeling, and cloud computing.

Data Science, Meet Data Mesh: What We Can Learn from Bioinformatics about the Power of Standardization in Distributed Systems(Talk)

Machine Learning EngineerCybelAngel

Mike Tapi Nzali, PhD

Mike Tapi Nzali PhD, is a machine learning engineer at CybelAngel with a PhD in Computer Science. He likes to work in a startup environment, also leading the development of machine learning products from idea to production. He is interested in cutting-edge technology, sharing knowledge and industrialization of Machine Learning.

Reproducible and Shareable Notebooks Across a Data Science Team(Talk)

Research EngineerSeldon

Alex Athorne

Alex Athorne is a Research Engineer at Seldon, where he works on open-source libraries for explainability and drift detection. He studied mathematics at Warwick and went on to do a PhD at Imperial College London in dynamical systems. He’s passionate about open-source development and writing about his experiences in ML.

Open Source Explainability – Understanding Model Decisions Using Alibi(Talk)

Data Science and Technology Leadimpactvise

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)

Senior Principal Data ScientistRed Hat

Tuhin Sharma

Tuhin Sharma is Senior Principal Data Scientist at Redhat in the Corporate Development and Strategy group. Prior that he worked at Hpersonix as AI Architect. He also co-founded and has been CEO of Binaize, a website conversion intelligence product for e-commerce SMBs. He received master’s degree from Indian Institute of Technology Roorkee in Computer Science with specialization in Data Mining. He received bachelor’s degree from Indian Institute of Engineering Science and Technology Shibpur in Computer Science. He loves to code and collaborate on open source and research projects. He has 4 research papers and 5 patents in the field of AI and NLP. He is reviewer of IEEE MASS conference in the AI track. He writes deep learning articles for O’reilly with the collaboration with AWS MXNET team. He loves to play TT and Guitar in his leisure time. His favorite quote is “Life is Beautiful”.

Eagleeye: Data Pipeline for Anomaly Detection in Cyber Security(Talk)

Senior NLP Engineer - author of bestseller Mastering spaCyDeepgram

Duygu Altinok, PhD

Duygu Altinok is a senior NLP engineer with 12 years of experience in almost all areas of NLP including search engine technology, speech recognition, text analytics and conversational AI. She authored several publications in NLP area at conferences such as LREC and CLNLP. She also enjoys working for open-source projects and a contributor of spaCy library.

Duygu earned her undergraduate degree in Computer Engineering from METU, Ankara in 2010 and later earned her Master’s degree in Mathematics from Bilkent University, Ankara in 2012. She spent 2 years at University of Bonn for her PhD studies. She is currently a senior engineer at Deepgram with a focus on conversational AI and speech technology.

Originally from Istanbul, Duygu currently resides in Berlin, DE with her cute dog Adele.

Sentiment Analysis Tricks with Keras, spaCy and Transformers(Tutorial)

Principal Data EngineerThoughtworks

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.

Data Mesh: From Concept to Code(Talk)

Data Science in the Industry: Continuous Delivery for Machine Learning(Training)

Principal Data EngineerThoughtworks (Spain)

Shawn 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 13 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.

Scaling Machine Learning with Data Mesh(Talk)

Professor in the Department of Computer ScienceUniversity of Liverpool

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.

Social Biases in Text Representations and their Mitigation(Talk)

CPO and Head of Customer SuccessMona

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)

Director of ProductIguazio

Gilad Shaham

Gilad has over 15 years of experience in product management and a solid R&D background. He combines analytical skills and technical innovation with Data Science market experience. Gilad’s passion is to define a product vision and turn it into reality. As Director of Product Management at Iguazio, Gilad manages both the Enterprise MLOps Platform product as well as MLRun, Iguazio’s open source MLOps orchestration framework. Prior to joining Iguazio, Gilad managed several different products at NICE-Actimize, a leading vendor of financial crime prevention solutions, including coverage of machine-learning based solutions, formation of a marketplace and addressing customer needs across different domains. Gilad holds a B.A in Computer Science, M.Sc. in Biomedical Engineering and MBA from Tel-Aviv University.

“It worked on my Laptop, now what?” Using OS tool MLRun to Automate the Path to Production(Demo Talk)

Head of Developer CommunityStarTree

Karin Wolok

Karin is currently the leading developer community programming in the Developer Relations team at StarTree. Karin initially began her career in entertainment marketing working with the likes of names like Eminem and Live Nation. She also launched a successful professional women’s network in two major cities in the U.S., organized events for her local Data Science meetup, and helped lead a on-going hackathon to put machine learning in the hands of cancer biologists. Her journey working in data eventually let her to a position as Program Manager for Community Development for the leading graph database in the world, Neo4j. Most recently, she was brought on to StarTree to improve the adoption and success of the overall developer community.

Real-Time Analytics: Going Beyond Stream Processing with Apache Pinot(Workshop)

 

Senior Product Marketing ManagerCloudera

Jake Bengtson

Jake is currently working as a Senior Product Marketing Manager over ML Lifecycle products at Cloudera. Before joining Cloudera, Jake worked as a Data Scientist and then as a Data Science and Analytics Solution Architect at ExxonMobil. Additionally, he worked as a Senior Data Scientist at FarmersEdge. Before starting his professional career, Jake obtained his bachelor’s and master’s degree from Brigham Young University. When he isn’t working, Jake enjoys skiing, golfing, and spending time with his family in the mountains.

Predicting the Next NBA Champion with Cloudera’s Applied ML Prototypes (AMPs)(Demo Talk)

Founder | Author of “Hands-on Image Generation with TensorFlow”Sooner.ai

Soon Yau Cheong, PhD

Soon-Yau Cheong is the founder of Sooner.ai, an AI consulting and training company specialises in image/video generation and manipulation. Past projects include face swapping, portrait cartoonisation, shoes virtual try-on etc. He is well-versed in generative AI techniques which include GANs, autoregressive transformer and diffusion models. He authored the book “Hands-on Image Generation with TensorFlow” which is well-received for its hands-on approach in making difficult mathematical theories easy to understand. Soon-Yau is also currently doing PhD in AI digital media creation at University of Surrey.

Diffusion Models for Text-to-Image Generation(Workshop)

FounderAuquan

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)

Sales EngineerRun:Ai

Robert Magno

Rob Magno is a Sales Engineer/Solution Architect at Run:AI based in New Jersey. He has been working in the Docker and Kubernetes space for the past five years. He enjoys tackling the diverse customer challenges that come with orchestrating AI/ML workloads through Kubernetes.

Building the Best AI Infrastructure Stack to Accelerate Your Data Science(Demo Talk)

Data ScientistWhyLabs

Felipe de Pontes

Felipe is a Data Scientist in 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.

Visually Inspecting Data Profiles for Data Distribution Shifts(Workshop)

Head of Artificial IntelligenceOPEN KNOWLEDGE GmbH

Oliver Zeigermann

Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.

Image Recognition with OpenCV and TensorFlow(Training)

 

Director, AI ResearchDataminr

Aoife Cahill, PhD

Aoife Cahill is a Natural Language Processing (NLP) expert and a director of AI research at Dataminr, the leading real-time information discovery platform. Since joining in 2021, Aoife has led a team of data scientists focused on the efficient iterative process of developing and evaluating AI technology that supports the expansion of Dataminr’s internal and external products.

Prior to Dataminr, Aoife led a team of research scientists and engineers working on high-stakes NLP applications in the educational domain at the Educational Testing Service (ETS). The NLP teams at ETS are known leaders in the field of developing and deploying robust, well-documented, scalable NLP prototypes that maintain fairness across user groups.

Aoife holds a PhD in Computational Linguistics from Dublin City University, Ireland, and has also spent time conducting NLP research in Germany, Norway and in the U.S. As an active member of the computational linguistics research community, her research has been published in top-tier journals including Computational Linguistics and the Journal of Research on Language and Computation, as well as conference proceedings at the annual conference for the Association for Computational Linguistics (ACL), the International Conference on Computational Linguistics (COLING) and the Conference on Empirical Methods in Natural Language Processing (EMNLP).

AI for Emergency Response(Demo Talk)

Senior EconomistResearch Division of Sveriges Riksbank

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)

Data ScientistSAS Institute

Kyriakos Fistos

Kyriakos is a Data Scientist and works across a broad set of industries contributing to large scale digital transformation projects for the world’s largest organizations. He specializes in data visualization, predictive modelling and model management.

With his academic background in Mathematics and Operational Research, Applied Statistics and Financial Risk, his day-to-day includes helping clients make better business decisions through Data Management, Business Intelligence, Machine Learning and Artificial Intelligence solutions.

Optimizing Your Analytics Life Cycle with Machine Learning and Open Source(Demo Talk)

Director of AIAdvancing Analytics

Terry McCann

Terry is the Director of AI for Advancing Analytics and Microsoft Artificial Intelligence MVP with a focus on all things AI and Data Science. Terry has a passion for applying traditional Software Engineering techniques to Data, to improve the way teams deliver Machine Learning projects. Terry is the host of the popular podcasts Data Science in Production and Totally Skewed, and organises the Global AI Bootcamp London event.

Simplifying Model Production with MLFlow Pipelines and Delta(Demo Talk)

General Secretary AI Infrastructure Alliance

Lee Baker

Lee is the General Secretary for the AI Infrastructure Alliance. Based out of the UK, he is responsible for crafting and nurturing relationships with companies to build a canonical stack for AI and ML. When not shuttling his 3 children around, he can most often be found cycling, running and swimming around England’s South Coast.

The Rapid Evolution of the Canonical Stack for Machine Learning(Demo Talk)

Lead ML EngineerVortexa

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)

Machine Learning EngineerHugging Face

Adrin Jalali, PhD

He is a computer scientist / bioinformatician who has turned to be a core developer of `scikit-learn` and `fairlearn`, and work as a Machine Learning Engineer at Hugging Face. He is also an organizer of PyData Berlin.

These days he mostly focus on aspects of machine learning and tools which help with creating more ethical and fair decision making systems. This trend has influenced him to work on `fairlearn`, and to work on aspects of `scikit-learn` which would help tools such as `fairlearn` to work more fluently with the package; and at Hugging Face, his focus is to enable the community of these libraries to be able to share their models more easily and be more open about their work.

How to Write a Scikit-learn Compatible Estimator(Tutorial)

Developer Advocatedeepset

Tuana Çelik

Tuana is a Developer Advocate at deepset. She works on improving the developer experience and adoption of deepset’s Open Source NLP framework: Haystack. Originally from Istanbul, she moved to the UK in 2014 where she obtained a Master’s degree in Computer Science from the University of Bristol (in 2018). She initially started her career as a Software Engineer but then decided to become more involved with open source communities and educating people. This led her to developer relations. She worked as a developer advocate at Cumul.io before moving to deepset in 2022.

Semantic Search in NLP – How to Build Question Answering with Haystack(Talk)

Lead Software EngineerSuperwise

Oryan Omer

Oryan is a ֿLead Software Engineer with a passion for Machine Learning and DevOps, with 7 years of experience developing services for production and development environments and leading teams.

Data-driven ML Retraining with Production Insights(Demo Talk)

Product ManagerLyft

Mihir Mathur

Mihir Mathur is the lead Product Manager for Machine Learning at Lyft, where he works on building ML and AI tools that power automated intelligent decisions across realtime pricing, ETAs, fraud detection, safety classification etc. In the past Mihir has worked on building delightful products for millions of users at Quora, Houzz, and Thomson Reuters. Mihir graduated magna cum laude from UCLA with a Bachelor’s and Master’s in Computer Science.

A Systematic Approach for Building Full-Spectrum Model Monitoring(Talk)

Chief Data Science AdvisorCloudFactory

Keith McCormick

Keith McCormick serves as CloudFactory’s Chief Data Science Advisor. He’s also an author, LinkedIn Learning contributor, university instructor, and conference speaker. Keith has been building predictive analytics models since the late 90s. More recently his focus has shifted to helping organizations build and manage their data science teams.

Computer Perception Challenges in Drone Applications Using Quality Data Annotation(Talk)

Director, AI / ML PracticeCognizant

Carl Osipov

Carl implemented his first neural net in 2000. He is a senior director of the AI / ML practice at Cognizant, focusing on communications, technology, and media customers. Previously he worked on deep learning and machine learning at Google and IBM. Carl is an author of over 20 articles in professional, trade, and academic journals, an inventor with 6 patents at USPTO, and holds 3 corporate awards from IBM for his innovative work. His machine learning book, “MLOps Engineering at Scale” continues to receive reader acclaim. You can find out more about Carl from his blog www.cloudswithcarl.com

Revealing the Inner Self: Automatic Differentiation (Autodiff) Clearly Explained(Workshop)

Product ManagerMicrosoft

Doris Zhong

Doris Zhong is a Product Manager in Azure AI Platform organization at Microsoft, and she is focusing on the area of machine learning in hybrid cloud. She loves to communicate with customer to get deep insights, and help solve the real problem. In her early career, she worked on building Microsoft internal GPU training platform, that managed tens of thousands of GPUs, and served thousands of users.

Run Azure Machine Learning Anywhere in Multi-cloud or on Premises(Demo Talk)

Senior Developer Advocate for Tensorflow.jsGoogle

Jason Mayes

Jason is the public face of TensorFlow.js, helping web engineers globally take their first steps with machine learning in JavaScript. He also combines his knowledge of the technical and creative worlds to develop innovative prototypes for Google’s largest customers and internal teams with over 15 years experience working within web engineering and investigating emerging technologies.

He holds an MEng in Computer Science, is a member of the British Computing Society, and is a certified information privacy technologist. Jason loves sharing knowledge online which has attracted a global following. In his spare time he can be found walking the wings of flying aircraft being one of the few people in the world who has been trained in the art of wing walking.

Next Generation Web Apps: Create a Machine Learning Powered Smart Cam in the Browser with TensorFlow.js(Workshop)

Machine Learning AdvocateMeta AI

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.

Leaner and Greener AI with Quantization in PyTorch(Talk)

Director of EngineeringAdvancing Analytics

Simon Whiteley

Simon is the Director of Engineering for Advancing Analytics, a Microsoft Data Platform MVP and one of the few Databricks Beacons Globally. Simon has pioneered Lakehouse Architectures for a some of the world’s largest companies, challenging traditional analytical solutions and pushing for the very best for the data industry. Simon runs the Advancing Spark YouTube channel, where he can often be found digging into Spark features, investigating new Microsoft technologies and cheering on the Delta Lake project.

A Dive into Delta Lake: A Modern File Format for the Next-Generation Lake(Workshop)

Data ScientistKNIME

Corey Weisinger

Corey studied Mathematics at Michigan State University and works as a Data Scientist with KNIME where he focuses on Time Series Analysis, Forecasting, and Signal Analytics. He is the creator and instructor of the KNIME Time Series Analysis course, author of the e-book: Alteryx to KNIME, creator of the KNIME Time Series Analysis components, and Co-Author of the upcoming Codeless Time Series Analysis Book with Packt.

Moving into the Frequency Domain with the Fourier Transform(Tutorial)

Data ScientistSAS Institute

Prathiba Krishna

Prathiba is an experienced Data Scientist with a rich background in the Insurance industry. With a Master’s degree in Operational Research with Applied Statistics and Risk, her passion takes form through seeing the varying applications of Machine Learning and AI techniques, and how they propel data scientists to build better models and solutions. Skilled in data analysis and modelling, she utilizes SAS software and Open Source to assess and address problems within enterprise organizations.

Interpretability vs Explainability: Unpacking the Role of Human Morality in AI Models(Talk)

VP, ProductIguazio

Adi Hirschtein

Adi Hirschtein brings 20 years of experience as an executive, product manager and entrepreneur building and driving innovation in technology companies. As the VP of Product at Iguazio, the MLOps platform built for production and real-time use cases, he leads the product roadmap and strategy. His previous roles spanned technology companies such as Dell EMC, Zettapoint and InfraGate, in diverse positions including product management, business development, marketing, sales and execution, with a strong focus on machine learning, database and storage technology. When working with startups and corporates, Adi’s passion lies in taking a team’s ideas from their very first day, through a successful market penetration, all the way to an established business. Adi holds a B.A. in Business Administration and Information Technology from the College of Management Academic Studies.

Building an ML Factory: How Enterprises use MLOps Automation to Continuously Roll out New AI Services(Talk)

Co-Founder and CTOIguazio

Yaron Haviv

Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases. He also initiated and built Nuclio, a leading open source serverless platform with over 4,000 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. He was also the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.

MLOps Beyond Training: The Production-First Approach to AI(Track Keynote)

Director of Technical Product MarketingRun:Ai

Gijsbert Janssen Van Doorn

Gijsbert Janssen van Doorn is Director of Technical Product Marketing at Run:AI. He is a passionate advocate for technology that will shape the future of how organizations run AI. Gijsbert comes from a technical engineering background, with six years in multiple roles at Zerto, a Cloud Data Management and Protection vendor.

GPUs vs CPUs. Flawless Victory?(Talk)

Data Scientist Tech Lead Imperva

Johnathan Azaria

Bio Coming Soon!

AI in a Minefield: Learning from Poisoned Data(Talk)

The AI Quant | CEO The Python Quants & The AI Machine | Adjunct Professor of Computational Finance

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.

AI-Powered Algorithmic Trading with Python(Training)

Data ScientistFiverr

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)

FounderWright Research

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)

Senior Research EngineerGrid AI | PyTorch Lightning

Kaushik Bokka

Kaushik Bokka is a Senior Research Engineer at Grid.ai and one of the core maintainers of the PyTorch Lightning library. He has prior experience in building production scale Machine Learning and Computer Vision systems for several products ranging from Video Analytics to Fashion AI workflows. He has also been a contributor for few other open source projects and aims to empower the way people and organizations build AI applications.

Distributed Training with PyTorch Lightning(Workshop)

Director of Airflow EngineeringAstronomer

Kaxil Naik

Kaxil is currently working as the Director of Airflow Engineering Team @ Astronomer. Currently, he is one of the top three committers of the Airflow Project based on the number of commits. He is one of the release managers of Airflow. Most prominent works include co-authoring DAG Serialization, Scheduler HA, Secrets Backend.

He did his Masters in Data Science & Analytics from Royal Holloway, University of London. Started as a Data Scientist and then gained experience in Data Engineering, BigData and DevOps space. He began working on Airflow in 2017 while working at Data Reply as a BigData consultant and became a PMC member in 2018 and now works full-time at astronomer.io making Airflow better for everyone. He is a huge cricket fan and his favourite cricketers are Rahul Dravid and Virat Kohli.

What’s new in Apache Airflow 2.3?(Talk)

VP of Research and DevelopmentcausaLens

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)

Data ScientistSeMI Technologies

Laura Ham

Laura is a ML Product Researcher at SeMI Technologies, the company behind the open-source vector search engine Weaviate. She researches new machine learning features for Weaviate and works on everything UX/DX related to Weaviate. For example, she is responsible for the GraphQL API design. She is in close contact with our open source community. Additionally, she likes to solve custom use cases with Weaviate, and introduces Weaviate to other people by means of Meetups, talks and presentations.

An Intro to Vector Databases and Vector Search(Talk)

Senior Data ScientistLyst

Seyed Saeid Masoumzadeh, PhD

Seyed Saeid Masoumzadeh is a senior data scientist at Lyst, a world largest fashion search platform. He has extensive experience in researching and developing Machine Learning, Deep Learning and NLP, and delivering them into production. Saeid is also the Co-founder of Cyra, a smart AI-based recruiting assistant, backed by “Entrepreneur First”, an international Talent Investor. Saeid has received his master degree in artificial intelligence and his PhD in computer science from the University of Vienna. He has published several peer-reviewed papers in reputed international journals and conferences.

Building a Fashion Recommender System from Learned Embeddings(Workshop)

Engineering Director, Machine LearningSeldon

Alejandro Saucedo

Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he contributes to policy and industry standards on the responsible design, development and operation of AI, including the fields of explainability, GPU acceleration, privacy preserving ML and other key machine learning research areas. Alejandro Saucedo is also the Director of Engineering at Seldon Technologies, where he leads teams of machine learning engineers focused on the scalability and extensibility of machine learning deployment and monitoring products. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has a strong track record building cross-functional teams of software engineers. He is currently appointed as governing council Member-at-Large at the Association for Computing Machinery, and is currently the Chairperson of the GPU Acceleration Kompute Committee at the Linux Foundation.

Flawed Machine Learning Security: The Top Security Flaws in the ML Lifecycle (and how to avoid them)(Talk)

VP of ProductiMerit

Glen Ford

Glen Ford is VP of Product at iMerit — a leading AI data solutions company — where he leads the product management and design teams. Glen holds more than two decades of product development experience across the technology sector. A Graduate of Texas A&M University—Commerce, Glen began his career as a consultant where he handled full-stack web programming and architecture for clients including Time Warner and AIM Funds. Over the years, he has held senior and director-level product management roles at several companies including Demand Media, WP Engine and Humanify. Most recently, Glen spent four years at Alegion — an ML-powered data annotation platform — where he helped the company grow from eight full-time employees to more than 100 in a challenging, emerging market.

The Hidden Layers of Tech Behind Successful Data Labeling(Demo Talk)

Research ScientistSeMI Technologies

Connor Shorten

Connor is a Research Scientist at SeMI Technologies, where he works on the Weaviate Vector Search Engine. He is thrilled about the opportunity of Vector Search to extend Database functionality! Connor was originally introduced to Vector Search while researching his publication “Deep Learning applications for COVID-19”. As a part of his Ph.D. research group at FAU, including the FAU College of Nursing and the Memorial Healthcare System, Connor will present his work on Vector Search for personalized treatment planning. Connor is also an avid content creator, having published over 300 YouTube videos on Henry AI Labs which have accumulated roughly 2 million views and 40,000 subscribers. Connor is currently continuing this work with the Weaviate Podcast. He will be presenting how Vector Search can aid in content performance analytics.

Vector Search for Data Scientists(Talk)

Senior Manager, Business Development UK & EuropeAppen

Romain Chapman

Romain is one of Appen’s Senior Manager’s, overseeing and supporting their European client-base with Appen’s breadth of Data for the AI Lifecycle services (data sourcing, annotation/labelling, and model evaluation). Romain came from the localization industry and noted firsthand the advancements and impacts of ML/AI via Machine Translation/Transcription, ASR and TTS offerings. Therefore, he saw a transition into the world of AI/ML as the next logical step. Passionate about ethically sourced, high-quality labelled data, which powers Machine Learning/AI programmes for good.

Data-centric AI: The Winning Combination of Human-in-the-Loop Real-World Data and Synthetic Data for Edge Cases(Demo Talk)

Product Manager, AI Strategy & Business TransformationAppen

Rachel Kellam

Rachel is a Product Manager in Appen’s Autonomous Vehicles working group. In that role, she is working to provide high quality data on all levels of autonomy for motor vehicle clients. Prior to joining Appen, Rachel worked on data science tools to enable model interpretability, fairness testing and automated machine learning. Other passions of hers include using AI and technology to act as a catalyst towards solving humanitarian-centered problems for non-profits around the world.

The Data Science Behind the Data Science: Why Automated Labelling and Responsible AI are the Foundation of High-Quality Predictive Modeling.(Talk)

Sr. Specialist – AI Global Black BeltMicrosoft

Manu Kanwarpal

Manu Kanwarpal is a Senior Specialist in the EMEA AI Global Black Belt team at Microsoft. He specialises in Azure Machine Learning and works with some of Microsoft’s largest customers on establishing their end to end processes for Data Science & MLOps. He is one of the authors of Microsoft’s unified MLOps accelerator called “MLOps v2”.

Microsoft’s Accelerator for MLOps(Workshop)

Senior Solutions Consultant Cloudfactory

Matt Beale

Matt Beale is one of CloudFactorys Senior Solutions Consultants. He helps clients around the world overcome their data challenges with AI and ML projects across Autonomous Vehicles, Green Energy and FinTech. Matt joined CloudFactory due his interest in the ethical issue that impact AI and CloudFactorys mission to create meaningful work in the developing world. Away from work Matt has a passion for photography, travelling and unusual cars. In fact his passion for unusual cars bought him to import a Nissan Stagea from Japan to the UK.

Ethics aren’t just for AI algorithms(Talk)

Co-Founder & CEOWatchful

Shayan Mohanty

Shayan Mohanty is the CEO and Co-Founder of Watchful, a company that largely automates the process of creating labeled training data. He’s spent over a decade of leading data engineering teams at various companies including Facebook, where he served as lead for the stream processing team responsible for processing 100% of the ads metrics data for all FB products. He is also a Guest Scientist at Los Alamos National Laboratory and has given talks on topics ranging from Automata Theory to Machine Teaching.

Bias is Good: Arguments for Programmatic Labeling(Demo Talk)

Sr. Cloud Solution Architect – Data & AIMicrosoft

Cindy Weng

Cindy Weng is a Senior Cloud Solution Architect at Microsoft in Data & AI. She specializes in architecting MLOps solutions for customers across a variety of industries including retail, financial services, consumer goods, and tech. She is one of the authors of the MLOps V2 unified accelerator by Microsoft.

Microsoft’s Accelerator for MLOps(Workshop)

Success ML Engineer, EMEAWeights and Biases

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)

Developer Relations EngineerStar Tree Ai

Mark Needham

Bio Coming Soon!

Real-Time Analytics: Going Beyond Stream Processing with Apache Pinot(Workshop)

Data ScientistVortexa

Tyler Ferguson

Tyler works as a Data Scientist at Vortexa where he focuses on building machine learning models that capture the dynamics of the energy markets. Prior to Vortexa, Tyler was doing research in clinical machine learning and published work in sports injury analytics and mathematical optimisation. He previously worked as a software engineer working for startups and clients in finance and uses this experience to contribute to the full lifecycle of building machine learning pipelines.

Dynamicio (a pandas I/O wrapper); Why you Should Start your ML-Ops Journey with Wrapping your I/O(Talk)

Data ScientistImperva

Adam Reichenthal, PhD

Adam is an experienced Data Scientist at Imperva’s threat research group where he works on creating machine learning algorithms to help protect Imperva’s customers against database attacks. Before joining Imperva, he obtained a PHD in Neuroscience from Ben-Gurion University of the Negev.

ML with Humans: Integrating Experts into the Learning Process(Workshop)

Brian Lucena
PrincipalNumeristical

Brian Lucena, PhD

Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.

StructureBoost: Gradient Boosting with Categorical Structure(Workshop)

Senior ML EngineerThoughtworks

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.

Data Science in the Industry: Continuous Delivery for Machine Learning(Training)

Principal Engineer, Threat ResearchImperva

Ori Nakar

Ori Nakar is a principal cyber-security researcher, a data engineer and a data scientist at Imperva Threat Research group. Ori has a many years experience as a software engineer and engineering manager, focused on cloud technologies and big data infrastructure. At the Threat Research group Ori is responsible for the data infrastructure and involved in analytics projects, machine learning and innovation projects.

How to Tune a Model using Feature Contribution and Simple Analytics(Workshop)

Network Economics (ML), Deliveroo | Global Data Science Z by HP Ambassador

Akram Dweikat

Akram Dweikat is a computer engineer and entrepreneur, specialized in machine learning & AI. He has been recognized by the UK Government as an Exceptional Talent in computer engineering, innovation, and entrepreneurship. Akram is currently the Engineering Manager for Deliveroo’s Network Economics (ML) team. Also, he is a global data science ambassador for Z by HP. He has been appointed as an AI Expert by the World Economic Forum, serving on their Global Future Council on Artificial Intelligence for Humanity. In his spare time, Akram helps build agricultural gardens for income and food security in his native Palestine. Earlier in his career, Akram helped establish the entrepreneurial community in Nablus and was one of eight youth selected to meet US President Barack Obama on his official visit to Palestine.

Introduction to WSL2 for Data Science with Z by HP(Demo Talk)

Data Science Innovation with Z by HP Workstations and Software Stack(Talk)

Data Science & AI Business Development Manager UK&I at HP

Tom Sadler

Tom joined HP earlier in the year to manage HP Inc’s Data Science and AI business for the UK&I. He works with some of the UK&I’s biggest organisations across Private and Public sectors, helping them implement HP’s Data Science and Edge Solutions.Before working for HP Tom has worked in the IT industry for the last decade working with some of the biggest organisations in the UK and Europe. Providing and applying Technology solutions.

Data Science Innovation with Z by HP Workstations and Software Stack(Talk)

Assistant ProfessorMIT

Prof. Pulkit Agrawal

Pulkit is an Assistant Professor in the department of Electrical Engineering and Computer Science (EECS) at MIT. His lab is a part of the Computer Science and Artificial Intelligence Lab (CSAIL), is affiliated with the Laboratory for Information and Decision Systems (LIDS) and involved with NSF AI Institute for Artificial Intelligence and Fundamental Interactions ( IAIFI ).

He completed Ph.D. at UC Berkeley; undergraduate studies from IIT Kanpur. Co-founded SafelyYou Inc. that builds fall prevention technology.

Is Reinforcement Learning the Right Tool for Your Problem? (Demo Talk)

Machine Learning EngineerMindtech Global

Dimitris Trigkakis

Dimitris is a Machine Learning engineer at Mindtech, implementing AI powered computer vision, trained on synthetic data created by their Chameleon platform. Dimitris has held a passion for AI since experiencing the fundamental breakthroughs of the early 2010’s in deep learning as an undergraduate. To pursue this interest, he undertook postgraduate studies in computer vision at Oregon State University. Aware of the limitations of real datasets he became very interested in the potential of replicating real modalities abundantly in a controlled and ethically considerate environment and joined Mindtech to substantiate the use of synthetic data by the next generation of AI.

Data-centric AI: The Winning Combination of Human-in-the-Loop Real-World Data and Synthetic Data for Edge Cases(Demo Talk)

Assistant Professor, Computer Science | DS3 LabETH Zurich

Ce Zhang, PhD

Ce is an Assistant Professor in Computer Science at ETH Zurich. The mission of his research is to make machine learning techniques widely accessible-​​-​-while being cost-​efficient and trustworthy-​​-​-to everyone who wants to use them to make our world a better place. He believes in a system approach to enabling this goal, and his current research focuses on building next-​generation machine learning platforms and systems that are data-​centric, human-​centric, and declaratively scalable. Before joining ETH, Ce finished his PhD at the University of Wisconsin-​​Madison and spent another year as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC Starting Grant, and has been featured and reported by Science, Nature, the Communications of the ACM, and a various media outlets such as Atlantic, WIRED, Quanta Magazine, etc.

Building Machine Learning Systems for the Era of Data-centric Ai (Talk)

Professor School of Computer Science and EngineeringHebrew University of Jerusalem

Yair Weiss, PhD

Yair Weiss is a Professor of Computer Science at the Hebrew University and the former Dean of the School of Computer Science and Engineering. His research interests include Machine Learning, Computer Vision and Neural Computation. He served as the program chair of the Neural Information Processing Systems conference (2004) and the European Conference on Computer Vision (2018). From 2004-2019 He was a Senior Fellow of the Canadian Institute for Advanced Research and he is currently a Fellow of the European Laboratory for Learning and Intelligent Systems. With his students and colleagues he has received best paper awards at UAI, NIPS, CVPR and ECCV.

Scientific Discovery and Unsupervised Disentanglement(Talk)

Co-Founder, CTOJumping Rivers

Dr. Colin Gillespie

Dr Colin Gillespie is the Co-Founder and CTO of Jumping Rivers. A data science consultancy that specialises in all things R and Python. He is also a Senior Statistics lecturer at Newcastle University, has published over eighty peer-reviewed papers, and co-authored the O’Reilly book, Efficient R programming.

Managing your Data Science Git Repositories(Training)

Tenured ResearcherInria

Karteek Alahari, PhD

Karteek Alahari is a senior researcher (known as chargé de recherche in France, which is equivalent to a tenured associate professor) at Inria. He is based in the Thoth research team at the Inria Grenoble – Rhône-Alpes center. He was previously a postdoctoral fellow in the Inria WILLOW team at the Department of Computer Science in ENS (École Normale Supérieure), after completing his PhD in 2010 in the UK. His current research focuses on addressing the visual understanding problem in the context of large-scale datasets. In particular, he works on learning robust and effective visual representations, when only partially-supervised data is available. This includes frameworks such as incremental learning, weakly-supervised learning, adversarial training, etc. Dr. Alahari’s research has been funded by a Google research award, the French national research agency, and other industrial grants, including Facebook, NaverLabs Europe, Valeo.

Continual Visual Learning(Tutorial)

Commissioner to the President, AI Strategy and Data Science | Cofounder and Vice President | Chief Data ScientistValencian Government | ELLIS | ELLIS Alicante Unit Foundation | Data-Pop Alliance

Nuria Oliver, PhD

Nuria Oliver is the Commissioner to the President of the Valencian Government on AI Strategy and Data Science against COVID-19; Cofounder and Vicepresident of ELLIS; Cofounder of the ELLIS Alicante Unit Foundation; Chief Data Scientist at Data-Pop Alliance.

Nuria earned her PhD from MIT; is a Fellow of the IEEE, an ACM Fellow and Fellow; Member of the Spanish Royal Academy of Engineering, SIGCHI Academy and Academia Europaea. She has 25+ years of research experience in human-centric AI and is the author of 160+ widely cited scientific articles as well as an inventor of 40+ patents and a public speaker. Her work is regularly featured in the media and has received numerous recognitions, including the Spanish National Computer Science Award (Angela Ruiz Robles category), the MIT TR100 (today TR35) Young Innovator Award (first Spanish scientist to receive this award); 2019 Data Scientist of the Year in Europe; 2020 Data Scientist of the Year by ESRI. She has recently co-led ValenciaIA4COVID, the winning team of the 500k XPRIZE Pandemic Response Challenge.

Data Science Against COVID-19(Talk)

 

Data Scientist, Senior ConsultantThoughtworks

Andy Symonds, PhD

Andy Symonds is a technologist passionate about using data science in new and interesting ways. With a background in academia before moving into consulting, he loves problem solving and experimentation. Andy works with clients to help them gain insights and drive business value, by developing proof of concepts and moving these solutions into production.

Data Science in the Industry: Continuous Delivery for Machine Learning(Training)

Lecturer in Natural Language ProcessingQueen Mary University of London, UK

Julia Ive, PhD

Julia Ive is a Lecturer in Natural Language Processing at Queen Mary University of London, UK. She is the author of many mono- and multimodal text generation approaches in Machine Translation and Summarisation. Currently, she is working on the theoretical aspects of style preservation and privacy-safety in artificial text generation.

Controlled Text Generation with Transformer-based Language Models(Workshop)

Data Science ManagerJaguar Land Rover

Alice Grout-Smith

Alice Grout-Smith is a Data Science Manager at Jaguar Land Rover (JLR). Over the past 5 years at JLR she has enjoyed being part of the data science team that has enabled £300m+ of value to the business. After presenting at the Open Data Science Conference back in 2019 on Hierarchical Bayesian Models, Alice and the team have been busy pursuing the exciting applications of causal inference. They have observed that data science in a business context often involves making interventions and taking actions, requiring techniques beyond traditional machine learning. Prior to joining JLR, Alice graduated from the University of Oxford with a degree in Chemistry and a Masters specialising in Quantum Mechanics, which was later published.

Causal Inference for Price Elasticity Modelling(Workshop)

Senior Data ScientistJaguar Land Rover

Jamie Hilton

Jamie Hilton is a Senior Data Scientist at Jaguar Land Rover (JLR) with over 5 years of experience realising business value through data insights. At JLR his work focuses on driving digital transformation with data, helping the business to make the right decisions at the right time. Previously, he led advanced analytics initiatives as Head of Customer Science at Manchester-based e-commerce business THG. He holds a MA in Mathematics from the University of Cambridge.

Jamie is particularly passionate about the application of data science to the automotive and motorsport industries. In 2021, he worked with leading Formula 2 team Virtuosi Racing to deliver a competitive advantage by leveraging their data, having studied Advanced Motorsport Engineering at Cranfield University.

Causal Inference for Price Elasticity Modelling(Workshop)

Senior Data ScientistFitch Ratings

Jayeeta Putatunda

Jayeeta is a Senior Data Scientist with 6+ years of industry experience. She received her MS in Quantitative Methods and Modeling from NY, and a BS in Economics and Statistics. Currently, Jayeeta works at Fitch Ratings, a global leader in financial information services. Jayeeta is an avid NLP researcher and gets to explore a lot of state-of-the-art models to build cool products and firmly believes that data, of all forms, is the best storyteller. She also led multiple NLP workshops in association with Women Who Code, GitNation among others. Jayeeta has also been invited to speak at International Conference on Machine Learning (ICML 2020), ODSC East, MLConf EU, WomenTech Global Conference, and Data Summit Connect. Jayeeta is passionate about promoting initiatives to inspire more women to take up STEM. Jayeeta lives in New York, she loves to cook, and spends her summers hiking and traveling with her husband. Connect here – https://linktr.ee/JayeetaP

Data ScientistAdevinta Spain

Didac Fortuny Almiñana, PhD

Didac, PhD in Physics and expert Data Scientist, is part of the Machine Learning team of InfoJobs (the job portal of Adevinta Spain). His work is focused on delivering product solutions based on Artificial Intelligence algorithms, most of them related with NLP. He has also driven several initiatives in Barcelona to leverage the power of data and Machine Learning to social initiatives, for example being a board member of DataForGoodBCN and organising data competitions to tackle problems with a clear social background.

End-to-end CV Parsing Solution Using Transformers(Talk)

Head of Client Success, EMEACloudfactory

Andy Minshall

Andy has 20+ years experience in customer success, consulting, training & coaching for martech, edtech and AI businesses. Commercially focussed & high achiever in client retention, expansion & satisfaction metrics. Builder & leader of high performing CS teams, mentor and advisor on strategic growth tactics. Customer experience first attitude to products, services and interactions. Creative & entrepreneurial with a passion for serving others. Driven to deliver for customers, stakeholders and execs with a consistent focus on providing value.

Is Poorly Labelled Data the Culprit for Failed AI Projects?(Demo Talk)

Co-FounderEncord

Ulrik Stig Hansen

Ulrik Stig Hansen is the co-founder and CEO of Encord, a London-based computer vision training data platform. The company’s platform is used by businesses to make unstructured data readable by machines. Its tools include data annotation, evaluation, and management of training data.

Is Poorly Labelled Data the Culprit for Failed AI Projects?(Demo Talk)

Chief Data Scientist, Author of R for Everyone, ProfessorLander Analytics, Columbia Business School

Jared Lander

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts. He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

ProfessorUniversity College London

Emine Yilmaz, PhD

Emine Yilmaz is a Professor and Turing Fellow at University College London, Department of Computer Science. She also works as an Amazon Scholar for Amazon.

Her research interests lie in the areas of information retrieval, natural language processing and applications of machine learning, probability and statistics. She is a recipient of the Early Career Fellowship from the Engineering and Physical Sciences Research Council (EPSRC). To date, she has received approximately £1.5 million of external funding from funding agencies including European Union, EPSRC, Google, Elsevier and Bloomberg. She has served in various senior roles, including co-editor-in-chief for the Information Retrieval Journal, a member of the editorial board for the AI Journal and an elected member of the executive committee for ACM SIGIR.

Prof Yilmaz was the recipient of the Karen Sparck Jones 2015 Award for her contributions to information retrieval research. She is also one of the recipients of the Google Faculty Research Award in 2015 and the Bloomberg Data Science Research Award in 2018.

Research Challenges in Devising the Next Generation Information Retrieval and Access Systems (Talk)

Head of CV/AI Research, Co-founderStreem Inc, Selerio

Flora Tasse, PhD

Flora is the Head of CV/AR at Streem. She specialises in AI applied to Computer Graphics and Vision problems faced in AR/VR. Her team at Streem is making the mobile phone’s camera more intelligent, by building AI agents that can understand images/videos and augment them with relevant interactive virtual content. She received her PhD degree in 2016 from the University of Cambridge. At Cambridge, Flora research focused on 3D shape retrieval using different query types such as 3D models, images/sketches and range scans. This work was awarded the 2013 Google Doctoral Fellowship in Computer Graphics and published in various top-tier venues, including ICCV and SIGGRAPH Asia. She served on several international program committees such as ICLR and Eurographics. Notably she was Paper Chair of the 2019 & 2020 Black in AI workshops, co-located with NeurIPS. She was named among the Rework Top 30 UK Women in AI and selected in 2021 by Wired UK as one of the world’s 30 innovators building a better future.

Chief Data and Analytics OfficerFNB South Africa

Nollie Maoto

Nollie is a visionary and results-oriented Executive with over 15 years’ experience in the Financial Services industry. A proven track record of defining and executing a data analytics strategy and of envisioning, creating and delivering Business Intelligence and Analytics solutions for stakeholders. A confident, ambitious and fearless Data and Analytics Exec who can deliver independent and insightful solutions to business challenges. Highly analytical thinker who has demonstrated experience in leading teams while driving better performance and results for stakeholders. Nollie as a high performer believes in meritocracy. Nollie is a huge advocate for closing the gender, pay and leadership gaps within the technology industry in Africa and globally. Nollie came 4th overall at the Strategic African Women in Leadership (SAWIL) Trailblazers Awards 2020 for displaying unparalleled leadership values and advocating for gender equality. Nollie is an Inspiring Fifty SA 2021 nominee as one of the 50 most inspiring women in the STEM sectors in South Africa. Nollie is one of the 2022 Corinum Global Top 100 Leaders in Data & Analytics. Nollie has recently been nominated for the inaugural Data Analytics Leader of the Year awards 2022 by the Data Leaders Exchange. Nollie has also recently been promoted to Chief Data and Analytics Officer within FNB South Africa.

Researcher | InnovatorEURECAT

Laia Subirats, PhD

Laia Subirats is a data science researcher at Eurecat – Technology Centre of Catalonia and a part-time lecturer at the Open University of Catalonia. She holds an MSc in Telecommunications Engineering from Pompeu Fabra University, an MSc in Telematics Engineering from the Technical University of Catalonia, and a Ph.D. in Computer Science from the Autonomous University of Barcelona. Since 2006, she has worked in the field of research and innovation, and since 2016, she has also been a lecturer at the university. She has worked in national and international centers (Eurecat, Telefónica R&D, and European Organization of Nuclear Research (CERN)), as an expert evaluator for the European Commission, and in initiatives such as the Google Summer of Code. She has participated in collaborative national and international research projects, and she has been a speaker at different courses, conferences, and congresses. She is interested in spreading science and in encouraging women to pursue technical careers.

North America Business Development Manager, Z by HP

Bradley Franko

Brad leads business development in North America in Data Science for all commercial and enterprise accounts. He brings a unique background in SAAS sales, technology, and leadership to HP’s Advanced Compute Solutions organization.

Data Science Innovation with Z by HP Workstations and Software Stack(Talk)

Data Scientist in cybersecurity, Z by HP Global Data Science Ambassador

Hunter Kempf

Hunter’s Data Science Journey began when working for AT&T. During this time, he was also earning his masters from the University of Notre Dame. In the 4 years he worked for AT&T, Hunter worked on a variety of projects in Planning/Forecasting and Fraud/Cyber Threat Prevention. Today, Hunter works for an Internet Infrastructure and Cybersecurity Company and is finishing up his second Masters in Cybersecurity from Georgia Tech. Outside of work, Hunter enjoys analyzing Entertainment, Video Game and Streaming data.

Data Science Innovation with Z by HP Workstations and Software Stack(Talk)

Strategy and Advising ManagerCloudera

Ade Adewunmi

Ade Adewunmi is responsible for Machine Learning Services at Cloudera Fast Forward. She spends her days advising clients on the data-enabled transformation of their organisations with a particular focus on the systematic integration of machine learning into their business operations.

Prior to joining Cloudera Fast Forward Labs, Ade worked as a consultant, advising organizations on the development and delivery of their data strategies. Before that, she led the UK-based Government Digital Service’s Data Infrastructure programme.

Outside of work, Ade’s interests in the application and impact of data are broader – beyond the boundaries of corporate organisations; she volunteers with civil society organisations such as Datakind UK, mySociety and Foxglove Legal.

She blogs about the ways in which data can be made useful for organisations and wider society as well as the leadership and organisational cultures that make this possible. When she’s not advising, blogging or speaking about these things, she’s almost certainly watching too much TV and justifying it on the grounds of maintaining cultural relevance (as if any justification were needed!).

Forecasting Crypto Currency Prices with Cloudera’s Applied ML Prototypes (AMPs)(Talk)

Founder and CEOODSC

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.

ML EngineerAuquan

Vishal Singh

With a bachelor’s degree in Engineering Physics from IIT Delhi, Vishal has a strong background in mathematics and statistics. He thoroughly enjoys working with Data Science, Machine Learning, and Big Data technologies, with a firm belief in learning by doing.

Aspect-Based Sentiment Analysis: Predict Market Impact of Financial Documents and other Use Cases(Tutorial)

Partner EngineerCohere

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).

Large Language Models for Real-World Applications – A Gentle Intro(Talk)

Data Science SpecialistNeo4j

Kristof Neys

I am a senior Pre-sales engineer responsible for Neo4j’s Graph Data Science product across EMEA and APAC. I am also a Machine Learning practitioner – TensorFlow Developer certified – with a deep interest in Graph Neural Networks and pursuing a PhD in this field (part-time), you can check out some of my work on Medium: https://kristof-neys-58246.medium.com/
Delivering value to clients as a trusted advisor is what excites me, and I have a 15y+ proven track record in this area.

A Graph Data Science Framework for the Enterprise (Demo Talk)

SVP of ProductsNeo4j

Philip Rathle

Philip is SVP of Products for Neo4j, the company behind the open source Neo4j graph database&data science platform. Philip has a deep understanding of how graphs are used, where the space is going, and what uniquely differentiates graph technologies from others and from each other. With 10+ years as Neo4j’s head of product, he thrives on helping users solve intractable problems using the power of connected context, and offers a uniquely rich perspective on the increasingly popular space that he helped to pioneer and grow. Philip is a contributor to the book Graph Databases, published by O’Reilly Media, and he frequently speaks and writes about the technologies, tools, and techniques for deriving value from data. Prior to Neo4j Philip co-founded a data infrastructure startup, worked as Head of Product for database tooling vendor Embarcadero Technologies, and worked at many global companies as a consultant on large-scale mission-critical data projects.

Production Ready Graph Data Science: Better Predictions With The Data You Already Have(Talk)

Director, Graph Data ScienceNeo4j

Dr. Alicia Frame

Alicia Frame is the lead product manager for data science at Neo4j. She’s spent the last year translating input from customers, early adopters, and the community into the first truly enterprise product for doing data science with graphs: Neo4j’s Graph Data Science Library. She has a Ph.D. in computational biology from UNC Chapel Hill, and her background is in data science applications in healthcare and life sciences.

She’s worked in academia, government, and the private sector to leverage graph techniques for drug discovery, molecular optimization, and risk assessments — and is super excited to be making it possible for anyone to use advanced graph techniques with Neo4j.

Graph Data Science: What’s the Big Deal?(Talk)

 

Senior PreSales ConsultantNeo4j

Nicolas Rouyer

Bio Coming Soon!

Senior Data Scientist WhyLabs

Bernease Herman

Bernease Herman is a senior data scientist at WhyLabs, the AI Observability company, and a research scientist at the University of Washington eScience Institute. At WhyLabs, she is building model and data monitoring solutions using approximate statistics techniques. Earlier in her career, Bernease built ML-driven solutions for inventory planning at Amazon and conducted quantitative research at Morgan Stanley. Her academic research focuses on evaluation metrics and interpretable ML with specialty on synthetic data and societal implications. She has published work in top machine learning conferences and workshops such as NeurIPS, ICLR, and FAccT.

Visually Inspecting Data Profiles for Data Distribution Shifts(Workshop)


ODSC EUROPE Hybrid Conference 2023 | June

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