June 8 - 10, 2021

ODSC Europe 2021 Speakers

ODSC hosts a fantastic lineup of some of the best and brightest expert speakers and core contributors in data science
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ODSC Europe Speakers

For 2021 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.

Full list will be coming soon

Current Europe Speakers

Corporate Vice President, Program Management, AI PlatformMicrosoft

John Montgomery

John leads Program Management for Microsoft Azure AI and is responsible for designing products and services that data scientists and ML experts around the world love and use. He leads a team of program managers, researchers, and designers responsible for products and services including Azure Machine Learning, Azure Cognitive Services, ML.NET, and ONNX Runtime. Prior to this role, John has led the Program Management team for Microsoft’s Developer Division, including Visual Studio, Visual Studio Code, and Azure Notebooks. He has also held positions as director of marketing for Visual Studio, as well as a program manager for Microsoft’s participation in several standards organizations, including ISO, IETF, and ECMA.
Prior to joining Microsoft in 1998, John worked as a writer and editor for several computer and technology publications, including BYTE Magazine, PC/Computing, and Corporate Computing, as well as being the Chief Information Officer for Imagine Publishing.

Fostering AI Innovations Through Open Source Projects(Keynote)

Associate Professor and Senior Research Fellow, Law and Ethics of AIOxford Internet Institute, University of Oxford

Sandra Wachter, PhD

Professor Sandra Wachter is an Associate Professor and Senior Research Fellow focusing on law and ethics of AI, Big Data, and robotics as well as Internet regulation at the Oxford Internet Institute at the University of Oxford. Professor Wachter is specialising in technology-, IP-, data protection and non-discrimination law as well as European-, International-, (online) human rights,- and medical law. Her current research focuses on the legal and ethical implications of AI, Big Data, and robotics as well as profiling, inferential analytics, explainable AI, algorithmic bias, diversity, and fairness, governmental surveillance, predictive policing, and human rights online.At the OII, Professor Sandra Wachter also coordinates the Governance of Emerging Technologies (GET) Research Programme that investigates legal, ethical, and technical aspects of AI, machine learning, and other emerging technologies.Professor Wachter is also a Fellow at the Alan Turing Institute in London, a Fellow of the World Economic Forum’s Global Futures Council on Values, Ethics and Innovation, a Faculty Associate at The Berkman Klein Center for Internet & Society at Harvard University, an Academic Affiliate at the Bonavero Institute of Human Rights at Oxford’s Law Faculty, a Member of the European Commission’s Expert Group on Autonomous Cars, a member of the Law Committee of the IEEE and a Member of the World Bank’s task force on access to justice and technology.

Accountable AI in Europe: Are We Ready for the Artificial Intelligence Act?(Track Keynote)

Academic Leader, Professor, Author of Fundamentals of Machine Learning for Predictive Data AnalyticsTechnological University Dublin

Prof. John D. Kelleher

Prof. John Kelleher is the academic leader of the Information, Communication, and Entertainment (ICE) research institute at TU Dublin. He is the TU Dublin lead for the Science Foundation Ireland funded ADAPT research centre (https://www.adaptcentre.ie) and PhD Centre for Research Training in Digitally Enhanced Reality (https://d-real.ie), and the H2020 project PRECISE4Q which focuses on using Artificial Intelligence to develop clinical decision support systems for the treatment of stroke (https://precise4q.eu). He has published three books on Machine Learning: Deep Learning (https://mitpress.mit.edu/books/deep-learning-1), Data Science (https://mitpress.mit.edu/books/data-science), and Fundamentals of Machine Learning for Predictive Data Analytics (https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics-second-edition).

Sustainable AI: Why We Need to Reduce AI’s Carbon Footprint?(Keynote)

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 to Fight Against COVID-19(Keynote)

 

Director, Artificial Intelligence, Data Analytics, and Data ScienceTurkcell

İnanç Çakıroğlu

İnanç Çakıroğlu is an executive manager with more than 20 years of experience. He joined Turkcell Group as a business analyst in 2000 and took various managerial positions at ICT including establishment of Lifecell (Turkcell Ukraine) and Turkcell Europe operations. He’s currently working as “Director, Artificial Intelligence, Data Analytics and Data Science” in Turkcell, specialized on new technologies and disruptive concepts such as digital transformation, big data, AI and machine learning. İnanç Çakiroğlu graduated from Bogazici University Electrical Electronics Engineering and studied MBA in Yeditepe University.

How Turkcell Democratizes Data Science and Accelerates AI Innovation to Transform Customer Experience(Keynote)

Global Head of Advanced Analytics & AICSL Behring

John K. Thompson

John is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. Currently, John is responsible for the global Advanced Analytics & Artificial Intelligence team and efforts at CSL.

Prior to CSL, John was an Executive Partner at Gartner, where he was management consultant to market leading companies in the areas of digital transformation, data monetization and advanced analytics. Before Gartner, John was responsible for the advanced analytics business unit of the Dell Software Group.

John is the author of the new book – Analytics Teams: Leveraging analytics and artificial intelligence for business improvement. The book was published in June 2020 and outlines how to hire and manage high performance advanced analytics teams. The book outlines how to engage with executives and senior managers. How to select and undertake analytics projects that change and improve how a business operates.

John is co-author of the bestselling book – Analytics: How to win with Intelligence, which debuted on Amazon as the #1 new book in Analytics in 2017. Analytics is a book that guides non-technical executives through the journey of creating an analytics function, funding initiatives and driving change in business operations through data and applied analytical applications.

Mr. Thompson’s technology expertise includes all aspects of advanced analytics and information management including – descriptive, predictive and prescriptive analytics, artificial intelligence, analytical applications, deep learning, cognitive computing, big data, data warehousing, business intelligence systems, and high performance computing.

One of John’s primary areas of focus and interest has been to create innovative technologies to increase the value derived by organizations around the world.

John has built start-up organizations from the ground up and he has reengineered business units of Fortune 500 firms to reach their potential. He has directly managed and run – sales, marketing, consulting, support and product development organizations.

He is a technology leader with expertise and experience spanning all operational areas with a focus on strategy, product innovation, growth and efficient execution.

Thompson holds a Bachelor of Science degree in Computer Science from Ferris State University and a MBA in Marketing from DePaul University.

Building and Managing Advanced Analytics & AI Teams(Talk)

Professor Of Computer Science | University of FreiburgUniversity of Freiburg

Frank Hutter, PhD

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), as well as Chief Expert AutoML at the Bosch Center for Artificial Intelligence.
Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq. MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is the recipient of a 2013 Emmy Noether Fellowship, a 2016 ERC Starting Grant, a 2018 Google Faculty Research Award, a 2020 ERC PoC Award, and he is a Fellow of ELLIS. Frank’s recent research focuses on automated machine learning (AutoML), where he co-organized the ICML workshop series on AutoML every year since its inception in 2014, co-authored the prominent AutoML tools Auto-WEKA, Auto-sklearn, and Auto-PyTorch, won the first two AutoML challenges with his team, co-authored the first book on AutoML, worked extensively on efficient hyperparameter optimization and neural architecture search, and gave a NeurIPS 2018 tutorial with over 3000 attendees.

Evolution of Efficient and Robust AutoML Systems(Talk)

Senior Manager, Product MarketingRed Hat

Abhinav Joshi

Abhinav Joshi is a Senior Manager, Product Marketing in the Cloud Platforms Business Unit at Red Hat. He and his team are focused on cloud-native workloads on OpenShift, including AI/ML, data analytics, databases, etc. Abhinav has over 20 years of industry experience around Hybrid Cloud, AI/ML, Data Analytics, Digital Workspace, and Software Defined Data Center focused products and solutions. Throughout his career, Abhinav has held strategic roles in Product Management, Product Marketing, Sales, and Consulting Services at Red Hat, VMware, Cisco, NetApp, etc. He holds MS in Systems Engineering from the University of Maryland College Park, Strategic Management Graduate Certificate from Harvard University, and a B.Tech in Chemical Engineering from Nagpur University, India. is a Senior Manager, Product Marketing in the Cloud Platforms Business Unit at Red Hat. He and his team are focused on cloud-native workloads on OpenShift, including AI/ML, data analytics, databases, etc. Abhinav has over 20 years of industry experience around Hybrid Cloud, AI/ML, Data Analytics, Digital Workspace, and Software Defined Data Center focused products and solutions. Throughout his career, Abhinav has held strategic roles in Product Management, Product Marketing, Sales, and Consulting Services at Red Hat, VMware, Cisco, NetApp, etc. He holds MS in Systems Engineering from the University of Maryland College Park, Strategic Management Graduate Certificate from Harvard University, and a B.Tech in Chemical Engineering from Nagpur University, India.

How Turkcell Democratizes Data Science and Accelerates AI Innovation to Transform Customer Experience(Keynote)

AI Product ManagerScaleway

Olga Petrova, PhD

A former theoretical physicist turned machine learning engineer, Olga is now building a smart data annotation platform at Scaleway as a technical product manager. On the community side, she enjoys blogging about the latest advancements in AI both in and out of working hours. Some of her writing can be seen on medium.com/@olgapetrova_92798.

Introduction to Transformers for NLP: Where We Are and How We Got Here(Tutorial)

Manager, Financial Advisory Analytics, DeanDeloitte, Data Science Retreat

Daniel Voigt Godoy

Daniel is a data scientist, teacher, and author of “Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide”.
He has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, since 2016, helping more than 150 students advance their careers. 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)

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.

Machine Learning for Planetary Health: Challenges, Opportunities, and Doing Our Bit(Talk)

David Talby: NLP for healthcare
CTOJohn Snow Labs

David Talby, PhD

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in both computer science and business administration.

Model Governance: A Checklist for Getting AI Safely to Production(Talk)

CEOPyMC Labs

Thomas Wiecki, PhD

Thomas Wiecki is the Chief Executive Officer at PyMC Labs (www.pymc-labs.io), the world’s first Bayesian consultancy. Prior to that Thomas was the VP of Data Science at Quantopian, where he used probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. He is an author of the popular PyMC3 package — a probabilistic programming framework written in Python. He holds a PhD from Brown University.

Bayesian Modeling without the Math(Track Keynote)

Assistant ProfessorFogarty International Center at NIH

Judy Gichoya

Dr. Gichoya is a multidisciplinary researcher, trained as both an informatician and a clinically active radiologist. She is an assistant professor at Emory university, and works in Interventional Radiology and Informatics. She has been funded through the Grand Challenges Canada, NBIB and NSF ECCS. She is seconded to the National Institutes of Health as a data scholar to help with the Open Data Science Platform (OSDP) component of the DSI Africa Initiative to “Harness Data Science for Health In Africa”. Her career focus is on validating machine learning models for health in real clinical settings, exploring explainability, fairness, and a specific focus on how algorithms fail. She has worked on the curation of datasets for the SIIM (Society for Imaging Informatics in Medicine) hackathon and ML committee. She volunteers on the ACR and RSNA machine learning committees to support the AI ecosystem to advance development and use of AI in medicine. She is currently working on the sociotechnical context for AI explainability for radiology, especially the dimensions of human factors that govern user perceptions and preferences of XAI systems.

Fairness in Medical Algorithms: Threats and Opportunities(Talk)

Associate Director – Business Tech Analysis, IT & AIMSD

Guglielmo Iozzia

Guglielmo is part of MSD (Merck & Co. in North America). He is currently busy unlocking business value through Computer Vision and other ML/DL/AI applications to the biotech manufacturing space. He has an extensive background in Software Engineering and Data Science across other big organizations including IBM, Optum and FAO of the UN in diverse contexts (such as Healthcare, DevOps, Cyber Security). Guglielmo 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. Since 2018 he is also an international speaker (almost 30 international conferences so far, including Big Things 2019 and 2020, Spark+AI Summit 2019, Annual Cyber Security and AI Summit 2019 and 2020), author of a tech book on distributed Deep Learning with Apache Spark and planning about a second tech book which should be probably released at the end of 2021.

Adversarial Attacks and Defence in Computer Vision 101(Workshop)

Research ScientistBosch Center for AI

Daria Stepanova, PhD

Daria Stepanova is a research scientist at Bosch Center for Artificial Intelligence. Her research interests include Knowledge Representation and Reasoning with a special focus on the automatic acquisition of rules from structured knowledge. 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 diploma degree in Applied Computer Science from the Department of Mathematics and Mechanics of St. Petersburg State University (Russia) in 2010 and a 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.

Teaching Machines to Talk: Modern Speech Synthesis with Deep Learning(Workshop)

Senior Data Scientist | LecturerInPost | Wrocław University of Economics and Business

Marta Markiewicz

Currently Senior (Big) Data Scientist at InPost and Lecturer at Wroclaw University of Economics and Business, previously Head of Data Science at Objectivity, with background in Mathematical Statistics. For almost 10 years, she has been discovering the potential of data in various business domains, from medical data, through retail, HR, finance, aviation, real estate, logistics, … She deeply believes in the power of data in every area of life. Articles’ writer, conference speaker and privately – passionate dancer and hand-made jewellery creator.

The Colours of Cleaning(Talk)

Professor of Condition MonitoringLuleå University of Technology

Diego Galar, PhD

Dr. Diego Galar is a Full Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber-physical systems, Industry 4.0, IoT or Industrial AI and Big Data. He was also involved in the SKF UTC center located in Lulea focused on SMART bearings and also actively involved in national projects with the Swedish industry or funded by Swedish national agencies like Vinnova. He is also a principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group within the Division of Industry and Transport. He has authored more than five hundred journal and conference papers, books and technical reports in the field of maintenance, working also as a member of editorial boards, scientific committees and chairing international journals and conferences and actively participating in national and international committees for standardization and R&D in the topics of reliability and maintenance. In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA), and the Universidad Pontificia Católica de Chile. Currently, he is visiting professor at the University of Sunderland (UK), the University of Maryland (USA), and Chongqing University in China.

Industrial Artificial Intelligence – From automated Process to Cognitive Analytics (Talk)

FounderScalar Research

Gabriel Bianconi

Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics. Previously, Gabriel was a B.S. & M.S. student in computer science at Stanford, where he conducted research on computer vision, deep learning, and quantum computing. He’s also spent time at Google, Facebook, startups, and investment firms.

Introduction To Face Processing With Computer Vision(Tutorial)

Research EngineerCloudera Fast Forward Labs

Nisha Muktewar

Nisha Muktewar is a Research Engineer at Cloudera Fast Forward Labs, where she spends time researching latest ideas in machine learning, builds prototypes that showcase these capabilities when applied to real-world use cases, and advises clients in this space. Prior to joining Cloudera, she worked as a Manager in Deloitte’s Actuarial & Modeling practice leading teams in designing, building, and implementing predictive modeling solutions for pricing, consumer behavior, marketing mix, and customer segmentation use cases for insurance and retail/consumer businesses.

Meta-Learning: Learning to Learn(Talk)

Head of Data Science EvangelismCoiled

Hugo Bowne-Anderson, PhD

Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled, a company that makes it simple for organizations to scale their data science and machine learning in Python. He has extensive experience as a data scientist, educator, evangelist, content marketer, and data strategy consultant at DataCamp, the online education platform for all things data. He also has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 500,000 learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open source software, both for individuals and the enterprise.

Head of Machine Learning, Author of "Practical Natural Language Processing"Vahan Inc.

Anuj Gupta, PhD

Anuj is a seasoned Machine Learning leader, having incubated and led multiple ML teams. During his career, he has worked in academia, early stage startups as well as Fortune 100. He has successfully led efforts for building commercially viable products in a wide spectrum of verticals and functions. He has authored over a dozen research papers and patents.
Anuj is a major speaker at various international forums. He has delivered technical talks, bootcamps and participated in panel discussions at prestigious forums like ODSC, Anthill inside, PyData DC, Fifth Elephant, ICDCN, PODC, to name a few. He was Co-editor of “Anthill inside 2018”. He is a well-known name in the Machine Learning ecosystem.

Advanced NLP: From Essentials to Deep Transfer Learning(Full-Day Training)

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/).

Automatic and Explainable Machine Learning with H2O(Workshop)

Head of Data and InformaticsRehab Neural Engineeering Labs, University of Pittsburgh

Max Novelli

Max Novelli is a Software Engineer, Data Architect, and Data Manager with a “Laurea” degree from Politecnico di Milano, Italy. He currently works as “Head of Informatics and Data” at Rehab Neural Engineering Lab at the University of Pittsburgh.
His latest interests are in Big Data and its management, structure, visualization, and curation applied to research data — although he never lets go any opportunity to play with hardware and customized experimental equipment. In his current position, he is responsible for the entire lab’s IT infrastructure and the safety, integrity, validation, and curation of experimental data. He is also leading R&D projects spanning from data visualization to data analysis and translating them into viable production tools. His focus is in developing visual tools to explore data structure and to assess the integrity of complex experimental data as well as using neural networks to further study and prove specific experimental results. He has been heavily involved in publishing open-access large datasets into public domain under the Open Science initiative of National Institutes of Health.
When Max is not lost in “computer land,” he enjoys spending time with his family and friends, mountain–biking, hiking trails, swimming, walking (better on the beach), cross-country skiing, eating good food, sipping good wines, and drinking good espresso. He also invests a considerable amount of energy practicing, teaching, and experimenting with yoga and body movement.  Lately, he has discovered rock climbing and is trying to perfect his climbing skills.

What Do I See in This Data? Visual Tools to Enhance Data Understanding(Talk)

Founder & Lead Data ScientistApplied Artificial Intelligence

Stefan Jansen

Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.

How to Build and Test a Trading Strategy Using ML(Half-Day Training)

Associate Professor of AnalyticsInstitute for Advanced Analytics at NC State University

Aric LaBarr, PhD

A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation’s first Master of Science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

Finding that Needle! Modern Approaches to Fraud and Anomaly Detection(Talk)

Software Engineer, Core DeveloperInria, Scikit-learn

Olivier Grisel

Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners.

Hands-on Machine Learning Engineer with scikit-learn(Full-Day Training)

AI Engineerde Volksbank

Daan Knoope

Daan Knoope works as an AI Engineer at de Volksbank, the Dutch parent company of several banks and mortgage providers. He has a background in Computer Science (MSc) and has specialized in Algorithmic Data Analysis. During his studies, he researched the application of Dynamic Bayesian Networks on practical use cases to help further the development of explainable AI. Currently, he is focusing on developing AI-models for the bank as well as providing fellow AI Engineers the tools they need to efficiently explore data and build production-ready models

Responsible Data Science Using Bias-Dashboards(Workshop)

Managing DirectorUntrite

Kamila Hankiewicz

Kamila Hankiewicz is a Managing Director of Untrite, an AI company helping companies make better use of data they already have; we provide an AI engine which pulls data from silos and understands the links and relevance between them. Kamila is a vivid advocate for diversity and empowering women in technology; she co-founded NGO Girls in Tech London and Poland. The local chapter has an active member base of more than 9 000 people, with more than 125 000 globally.
Kamila’s past life includes working as a Management Consultant with a focus on banking, where she was involved in digital transformation projects (such as Santander’s £1.65bn worth project Rainbow). Kamila is a frequent speaker on the subject of humanising work with use of AI. Her latest talks include those for BigDataLDN, Rasa, Future of AI and Women in AI. She hosts “Humans of AI” video interviews with prominent people solving some of the world’s toughest problems with the use of AI – https://www.youtube.com/channel/UC7qPUVnjrzb4oFwtAmDOTtw. Some of her guests include: Lord Tim Clement-Jones from House of Lords, David Barber from UCL, Rana el Kaliouby from Affectiva and many more.

The Missing Link: How AI Can Help Create a Safer Society and Better Businesses (Talk)

AI & Ethics Specialistde Volksbank

Joris Krijger

Joris Krijger (1991) works as an Ethics & AI specialist at the Dutch bank de Volksbank while also holding a PhD position at the Erasmus University Rotterdam on that topic. He has a background in Economic Psychology (MA), Philosophy (MA) and Film and Literary Studies (BA) and studied in Glasgow, Buenos Aires and Leiden, where he was awarded a national thesis prize in 2017 by the Royal Dutch Society of the Sciences for his graduation thesis on technology ethics and the financial crisis of 2008. He co-founded high-tech startup Condi Food (Rabobank Wijffels Innovation Award 2014) and was involved in various biomedical initiatives related to bacteriophages. He presently works on bridging the gap between principle and practice in AI Ethics by studying the operationalization of ethical principles from an academic and practical perspective and is reviewer for the AI Ethics Journal, Subject Matter Expert for CertNexus’ ‘Certified Ethical Emerging Technologist’ and Editorial Board Member for Springer’s AI and Ethics Journal.

Responsible Data Science Using Bias-Dashboards(Workshop)

PhD StudentEindhoven University of Technology

Pieter Gijsbers

Pieter Gijsbers is a PhD student at Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.

Automated Machine Learning(Workshop)

Chief ScientistThe Institute for Ethical AI & Machine Learning

Alejandro Saucedo

Alejandro is the Director of Machine Learning Engineering at Seldon Technologies, where he leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. Alejandro is also the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads thedevelopment of industry standards on machine learning bias, adversarial attacks and differential privacy. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has delivered multi-national projects with top tier investment banks, magic circle law firms and global insurance companies. He has a strong track record building departments of machine learning engineers from scratch, and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).

Production Machine Learning Monitoring: Principles, Patterns and Techniques(Talk)

Principal Data Scientist | Associate Professor, Econometricsde Volksbank | Tilburg University

Ramon van den Akker, PhD

Ramon van den Akker works as a data scientist at the AI Center of Excellence and the Risk Modelling departments of de Volksbank, a Dutch retail bank located in Utrecht. He also works, as an associate professor, at the econometrics group of Tilburg University. His research interests cover various fields in data science, machine learning, econometrics and statistics and his research findings have been published in leading journals in econometrics and statistics. Ramon has taught courses in data science, econometrics, life insurance, machine learning, mathematics, probability theory, quantitative finance, and statistics at Tilburg University, Tias business school, Tilburg Professional Learning, the Jheronimus Academy for Data Science (JADS), and the Dutch Actuarial Institute. In his work at de Volksbank, Ramon mainly works on projects related to data-driven innovation, but also on governance aspects like frameworks for responsible AI & data science and the use of techniques for privacy-preserving data analytics.

Responsible Data Science Using Bias-Dashboards(Workshop)

Research and Product LeadTractable

Franziska Kirschner, PhD

Franziska Kirschner is the Research and Product Lead of Car Inspection at Tractable. Her team uses machine learning to automate car damage appraisal across a range of applications. Her research interests include domain adaptation, and multitask- and multi-instance learning. In a previous life, she did a PhD in Physics at the University of Oxford. In her spare time, she enjoys cooking and making bad puns.

Overcoming the Cold Start Problem: How to Make New Tasks Tractable(Talk)

Head of the Institute of Computational Biology | ProfessorHelmholtz Munich | Technical University of Munich

Dr. Fabian Theis

Fabian Theis is director of the Institute of Computational Biology at the Helmholtz Center Munich and scientific director of the Helmholtz Artificial Intelligence Cooperation Unit (HelmholtzAI) which was launched in 2019. He is a full professor at the Technical University of Munich, holding the chair ‘Mathematical Modelling of Biological Systems’, associate faculty at the Wellcome Trust Sanger Institute as well as adjunct faculty at the Northwestern University. Fabian Theis holds a Master’s degree in Mathematics and Physics and Ph.D. Degrees in Physics and Computer Science. After different research stays. He worked as visiting researcher at the Department of Architecture and Computer Technology (University of Granada, Spain), at the RIKEN Brain Science Institute (Wako, Japan), at FAMU-FSU (Florida State University, USA), and TUAT’s Laboratory for Signal and Image Processing (Tokyo, Japan), and headed the ‘signal processing & information theory’ group at the Institute of Biophysics (Regensburg, Germany). In 2006, he started working as a Bernstein fellow leading a junior research group at the Bernstein Center for Computational Neuroscience, located at the Max Planck Institute for Dynamics and Self-Organisation at Göttingen. In summer 2007, Fabian Theis became working group head of CMB at the Institute of Bioinformatics at the Helmholtz Center Munich. In spring 2009, he became associate Professor for Mathematics in Systems Biology at the Math Department of the TU Munich. 2009-2014 he was a member of the ‘Young Academy’ (founded by the Berlin-Brandenburg Academy of Sciences and Humanities and the German Academy of Natural Scientists Leopoldina) and was awarded an ERC starting grant in 2010. In 2017 he was awarded the Erwin Schrödinger prize together within an interdisciplinary team at the ETH Zürich. Fabian Theis is part of and also coordinates various consortia (i.e. sparse2big involving 8 Helmholtz Centers) and founded the network SingleCellOmics Germany (SCOG). Furthermore, he coordinates 2019 launched Munich School for Data Science (MUDS) and is co-directing the ELLIS Munich Unit, the local hub of the European Machine Learning network ELLIS. Since 2020, he holds the position of co-chair of the Bavarian AI Council of the Bavarian Ministry for Science and Art and supports the TUM with his expertise as start-up Ambassador.

Interpretable Machine Learning to Model Drug Perturbations in Single Cell Genomics(Talk)

CEO and Co-founderScaleout

Daniel Zakrisson

At Scaleout, we are solving the data access challenge in AI. We are developing a world leading solution for federated learning. In federated learning, you distribute the training of machine learning models to the data. You avoid collecting all data in one place.

Daniel is the CEO and co-founder of Scaleout and has a long background as an entrepreneur and leader in deep tech companies. He co-founded Scandinavia’s first personal DNA-testing company in 2008, was CTO at a multinational growing medtech company for 7 years and then co-founded the first international accelerator for blockchain startups. As CTO and CEO, he has many years of experience in leading deep tech projects and taking them to market.

Federated Learning from Scratch to Production with Scaleout(Workshop)

Applied Research ScientistGeorgian

Azin Asgarian

Azin is currently an applied research scientist on Georgian’s R&D team where she works with Georgian’s portfolio companies to help adopt applied research techniques to overcome business challenges. Azin holds a Master of Science in Computer Science from University of Toronto and a Bachelor of Computer Science from University of Tehran. Prior to joining Georgian, Azin was a research assistant at the University of Toronto and University Health Network (UHN) where she was working on the intersection of Machine Learning, Transfer Learning, and Computer Vision.

Overcoming the Cold Start Problem: How to Make New Tasks Tractable(Talk)

Software ArchitectDouble Yard

Jaime Buelta

Jaime Buelta has been a professional programmer since 2002 and a full-time Python developer since 2010. He has developed software for a variety of fields, focusing, in the last 10 years, on developing web services in Python in the gaming and finance industries. He is a strong proponent of automating everything to make computers do most of the heavy lifting, so humans can focus on the important stuff. He published his first book, “Python Automation Cookbook”, in 2018 (now updated recently with an extended second edition), followed by “Hands-On Docker for Microservices with Python” the following year. He is currently working as Software Architect in Double Yard in Dublin, Ireland, and is a regular speaker at PyCon Ireland.

Basic Python for Data Processing(Half-Day Training)

Research Staff MemberIBM Research and MIT-IBM Watson AI Lab

Mikhail Yurochkin, PhD

Mikhail is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor’s degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.

Machine Learning EngineerDataworkz

Laurens Koppenol

I am Laurens, Machine Learning Engineer at Dataworkz. I have a background in Artificial Intelligence and worked for several years as lead data scientist (at ProRail). I like to be on the edge where business meets tech, where AI and machine learning make an impact. Currently I am working as a data engineer / data architect at the Port of Rotterdam, via Dataworkz. I am intrigued by the creative side of artificial intelligence; can a machine show symptoms of creativity or can it only reproduce what it has seen before? How is that different from what we can do? The subject we are talking about is inspired by David’s love for music and my urge to do cool stuff in the field of data science.

Exploring the Rimworld of Sound Space Using Generative Adversarial Networks(Talk)

AI ProfessorUniversity of Utrecht

David Isaacs Paternostro

I’m David, I work as a teacher, teaching artificial intelligence, at the applied university of Utrecht. Before I started teaching I’ve worked as a prototyping developer for AI research at Philips Research developing intelligent agents using machine learning and reinforcement learning for consumer healthcare products. It was very interesting but my need for more social engagement has driven me towards teaching. A step that has made me a very happy person so far. I love how artificial intelligence is like a lens through which we can see the world. Whether the models we make of the world are realistic or not – do brains really work like a neural network? Not really – they are still inspiring and often usefull. It’s just fun to try and recreate everything. There is only one thing that I probably like even more and that’s making music. I’ve been playing piano and guitar since the age of 7 and it is a big part of who I am. Later on in life I’ve gotten more interested in the electronic world of music too and now have arrived at a point where I can combine my two favorite things. Create instruments using AI.

Exploring the Rimworld of Sound Space Using Generative Adversarial Networks(Talk)

DirectorNeuron Labs

Alex Honchar

Alex Honchar is a tech entrepreneur and educator. Currently, he is co-founder and ML director at Neurons Lab – a consulting firm specializing in healthcare, finance, and IoT. Also, he writes a popular blog on Medium about machine learning applications and leadership. Previously he worked as an independent consultant with SMBs and startups on rapid go-to-market ML solutions and taught machine learning courses at the University of Verona and Ukrainian Catholic University.

Assistant Professor of Machine LearningEindhoven University of Technology

Joaquin Vanschoren, PhD

Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.

Tutorial on Automated Machine Learning(Workshop)

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 data science platform and leads the shift towards real-time AI. He also initiated and built Nuclio, a leading open source serverless platform with over 3,400 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 Orchestration: Your Highway to Accelerating Deployment of AI(Workshop)

Building Real-Time ML Pipelines the Easy Way(Talk)

CTOCodete

Karol Przystalski

Karol Przystalski obtained a PhD degree in Computer Science in 2015 at the Jagiellonian University in Cracow. He is the CTO and founder of Codete where he’s leading and mentoring teams as they work with Fortune 500 companies on data science projects. He also built a research lab for machine learning methods and big data solutions at Codete. Karol gives speeches and trainings in data science with a focus on applied machine learning in German, Polish, and English.

Explainable Artificial Intelligence Explained(Workshop)

Stanislav Kirillov
Head of ML Systems GroupYandex

Stanislav Kirillov

Stanislav Kirillov is the leading developer in the group of ML-platforms in Yandex where he develops machine learning tools, supporting and developing infrastructure for them.

Mastering Gradient Boosting with CatBoost(Workshop)

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.

He now works as Head of Data Scientist and Analytics in Badoo, the largest dating site with over 420 million users. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning, and Machine Learning training.

NLP Fundamentals(Full-Day Training)

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 on 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.

Introduction to Data Analysis Using Pandas(Half-Day Training)

Marketing Operations LeadProject Management Institute

Ayemya Moe

Ayemya (Mina) Moe is a Marketing Operations Lead at Project Management Institute and also holds Director of Marketing position. She graduated from UCLA with Economics major and is also serving as a reviewer for IEEE CIS (IEEE Transactions on AI).

Benefits of Convolutional Neural Network for Healthcare Shortage Classification in Underserved Community (Talk)

Marie Skłodowska-Curie Research FellowUniversity of Munich

Julia Schulte-Cloos, PhD

Julia Schulte-Cloos is a Marie Skłodowska-Curie funded LMU Research Fellow at the Geschwister Scholl Institute of Political Science at LMU Munich. Her main research interests lie in comparative politics, political behavior, research methodology, and reproducibility. Julia Schulte-Cloos has earned her PhD from the European University Institute.

Head Big Data FoundationsSwiss Re

Lukáš Csóka

Lukas Csoka, working as Head Big Data Foundations in Swiss Re, one of the biggest global reinsurers, combines data science, consultative thinking and business partnering as the main skills used daily in his job to push design and develop new AI products. His MS in software engineering from Slovak University of Technology and MBA in global management from City University of Seattle are supporting him during this journey. His projects include innovative solutions for farmers using satellites, the application of predictive methods to manage costs inside Swiss Re or the analysis of thousands of client meetings from text and the provision of recommendations to Swiss Re’s traders and many others.

AI Risk to Companies(Workshop)

Kubernetes Contributor and CNCF Ambassador | Founder & CEOWeaveworks | luxas Labs Ltd.

Lucas Käldström

Lucas is a cloud native expert who’s serving the CNCF community in lead positions for 5 years. He’s awarded Top CNCF Ambassador 2017 with Sarah Novotny. Lucas was a co-lead for SIG Cluster Lifecycle, co-created kubeadm, Cluster API & Weave Ignite and ported Kubernetes to ARM. Lucas runs 3 meetups, and has spoken at 7 KubeCons. Most recently, Lucas co-created Racklet besides his university studies.

Exploring Modern and Secure Operations of Kubernetes Clusters on the Edge(Talk)

VP ProductIguazio

Adi Hirschtein

Adi Hirschtein contributes 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 data science 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.

Feature Stores: Your MLOps Competitive Advantage(Demo Talk)

Data ScientistSAS

Federica Citterio

Federica is a Data Scientist at SAS, working across EMEA on the latest technologies involving Artificial Intelligence. She is excited about combining business and data science to help companies make better decisions, grounded in trusted data and assisted by analytics. You will likely find her working around Computer Vision or Natural Language Processing, but she enjoys everything that is data-related.

Computer Vision: Seeing the Value for the Pixels – Lessons Learned from Real World Applications(Demo Talk)

Community AdvocateClearML

Stef Telford

Stef has over 25 years of commercial experience in Computing, with half in Financial, and the other half with Startups. Starting out in what would now be called Software Development, going through database admin, network operations, and various other roles all the way to management, he has migrated in the past six years to specialising in NLP. Currently he has been helping ClearML with talking to end users, trying to improve both the user experience as well as corresponding requests back to the developers.

Comprehending ClearML and MLOps – Enabling the new A-Z(Demo Talk)

Chief Data ScientistRavenPack

Peter Hafez

Peter is the head of data science at RavenPack. Since joining RavenPack in 2008, he’s been a pioneer in the field of applied news analytics bringing alternative data insights to the world’s top banks and hedge funds. Peter has more than 15 years of experience in quantitative finance with companies such as Standard & Poor’s, Credit Suisse First Boston, and Saxo Bank.

He holds a Master’s degree in Quantitative Finance from Sir John Cass Business School along with an undergraduate degree in Economics from Copenhagen University. Peter is a recognized speaker at quant finance conferences on alternative data and AI, and has given lectures at some of the world’s top academic institutions including London Business School, Courant Institute of Mathematics at NYU, and Imperial College London.

Using News Analytics & Graphs to Inform Investment Decisions(Demo Talk)

EvangelistClearML

Ariel Biller, PhD

Researcher first, developer second, in the last 5 years Ariel worked on various projects from the realms of quantum chemistry, massively-parallel supercomputing and deep-learning computer-vision. With AllegroAi, he helped build an open-source R&D platform (Allegro Trains), and later went on to lead a data-first transition for a revolutionary nanochemistry startup (StoreDot). Answering his calling to spread the word on state-of-the-art research best practices, He recently took up the mantle of Evangelist at ClearML. Ariel received his PhD in Chemistry in 2014 from the Weizmann Institute of Science. With a broad experience in computational research, he made the transition to the bustling startup scene of Tel-Aviv, and to cutting-edge Deep Learning research.

Deconstructing MLOps (Track Keynote)

Principal Data ScientistSAS

Tamara Fischer

Tamara Fischer, a graduate statistician, has been working for many years in the role of Principal Solutions Architect, Analytics, at SAS in the DACH region. In this role she implemented solutions along the entire analytical life cycle from model development to model deployment. Currently she is working in an international team of Data Scientists and Architects which takes care for the EMEA region. Her work is focused on all analytical aspects of ModelOps. A term that SAS refers to a wider spectrum of models – data driven and business driven – that can be combined for decision-making to drive better business outcomes, a spectrum in which SAS delivers capabilities across.

Mastering Responsible Machine Learning in an Open World(Talk)

Data ScientistSAS EMEA

Matteo Landrò

Matteo is a data scientist at SAS. He supports global initiatives and projects that involves advanced analytics and artificial intelligence technologies. He is passionate about helping companies transforming their business processes with analytics. Matteo has a MSc in Management Engineering from Politecnico di Milano, with a specialization in Industrial Management.

Mastering Responsible Machine Learning in an Open World(Talk)

Lead Data ScientistNeo4j

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.

Relationships Matter: Using Connected Data for Better Machine Learning(Talk)

 

Senior Pre-Sales ConsultantNeo4j

Joe Depeau

Joe has over 20 years of varied experience in the IT industry across a number of industries, domains, and specialties. For most of the last decade he has focused on technical pre-sales and solution architecture for data and analytics. Joe is especially passionate about graph databases and graph data science, and is thrilled to work for the leader in this space – Neo4j.

When not geeking out over data and technology he enjoys camping and hiking with his dog, tending to his garden, reading, and playing all manner of video and tabletop games. He also bakes a mean cheesecake.

Neo4j Demo: Graph-Native Machine Learning and Predictions(Demo Talk)

Principal Product Manager in the Azure AI PlatformMicrosoft

Elena Neroslavskaya

Elena Neroslavskaya – AI Frameworks Program Manager at Microsoft focusing on Open Source frameworks for AI. Elena is passionate about all things Cloud Native and AI and helping developers and data scientists be more efficient. She has 20+ years in IT – building large scale distributed systems, microservices, AI and cloud native applications. Elena holds Master in Computer Science majoring in AI.

The Next Evolution of PyTorch Performance Debugging(Demo Talk)

Performance and Capacity EngineerFacebook

Gisle Dankel

The Next Evolution of PyTorch Performance Debugging(Demo Talk)

AI/Pytorch Partner Engineering HeadFacebook

Geeta Chauhan

Geeta Chauhan leads AI Partnership Engineering at Facebook AI with expertise in building resilient, anti-fragile, large scale distributed platforms for startups and Fortune 500s. As a core member of the PyTorch team, she leads TorchServe and many partner collaborations for building a strong PyTorch ecosystem and community. She is winner of Women in IT – Silicon Valley – CTO of the year 2019, an ACM Distinguished Speaker and thought leader on topics ranging from Ethics in AI, Deep Learning, Blockchain, IoT. She is passionate about promoting use of AI for Good.

Fostering AI Innovations Through Open Source Projects(Keynote)

The Next Evolution of PyTorch Performance Debugging(Demo Talk)

 

 

Senior Product ManagerMicrosoft

Saurya Das

Saurya Das is a PM in the Azure ML organization focused on the Hybrid strategy and Kubernetes.

Enterprise ready ML Model Training on Hybrid Cloud, leveraging Kubernetes (Talk)

Senior Program ManagerMicrosoft

Emma Ning

Emma Ning is a senior Product manager in AI Framework team under Microsoft Cloud + AI Group, focusing on AI model operationalization and acceleration with ONNX/ONNX Runtime for open and interoperable AI. She has more than five years of product experience in search engine taking advantage of machine learning techniques and spent more than three years exploring AI adoption among various businesses. She is passionate about bringing AI solutions to solve business problems as well as enhancing product experience.

Fostering AI Innovations Through Open Source Projects(Keynote)

Head of Decision Analysis and Support Unit | Adjunct Professor, Data Science LaboratoryWarsaw School of Economics | Ryerson University

Bogumił Kamiński

Bogumił Kamiński is Head of Decision Analysis and Support Unit at Warsaw School of Economics, Poland, and Adjunct Professor at Data Science Laboratory, Ryerson University, Canada. His research interests are techniques of large scale mathematical modelling of complex systems combining simulation, optimization, and machine learning. A particular area of his expertise are agent based simulation and modeling and analysis of complex networks.

Bogumił is one of the core developers of DataFrames.jl package for data wrangling in the Julia language. He is also a top answerer for the [julia] tag on StackOverflow and regularly discusses a wide range of data science related topics on his blog https://bkamins.github.io/.

Dataframes.jl: a Perfect Sidekick for Your Next Data Science Project(Workshop)

Senior Researcher, Adaptive Systems and Interaction Microsoft

Besmira Nushi, PhD

Besmira Nushi is a researcher in the Adaptive Systems and Interaction group at Microsoft Research. Her interests lie at the intersection of human and machine intelligence focusing on Reliable Machine Learning and Human-AI Collaboration. In the last five years, she has made practical and scientific contributions on implementing and deploying Responsible AI tools for debugging and troubleshooting ML systems. Prior to Microsoft, Besmira completed her doctoral studies at ETH Zurich in 2016 on optimizing data collection processes for Machine Learning.

Error Analysis for Accelerating Responsible Machine Learning(Talk)

Senior Technical Program ManagerMicrosoft

Mehrnoosh Sameki, PhD

Mehrnoosh Sameki is a senior technical program manager at Microsoft, responsible for leading the product efforts on machine learning interpretability and fairness within the Azure Machine Learning platform. She earned her PhD degree in computer science at Boston University, where she currently serves as an adjunct assistant professor and lecturer, offering courses in responsible AI. Previously, she was a data scientist in the retail space, incorporating data science and machine learning to enhance customers’ personalized shopping experiences.

Error Analysis for Accelerating Responsible Machine Learning(Talk)

Principal Data ScientistRed Hat

Sophie Watson, PhD

Sophie Watson is a data scientist at Red Hat, where she helps customers to solve business problems using machine learning in the hybrid cloud. She has previously conducted research in the areas of researched Bayesian Statistics and Recommendation Engines, and is focused on using her data science and statistics skills to inform next-generation infrastructure for intelligent application development.

Integrating Data Science and Application Development(Demo Talk)

Principal Software EngineerRed Hat

Chris Chase

Chris Chase is a developer at Red Hat, where he develops next-generation applications in the hybrid cloud. He is an expert in application development, and has experience in supporting data scientists deploying their models in production applications.

Integrating Data Science and Application Development(Demo Talk)

Sr. Principal Software EngineerRed Hat

Audrey Reznik

Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.

What’s the Deal with Managed Services and Model Delivery?(Talk)

Chief Scientist and Head of Conversational AIGot It AI

Chandra Khatri

Chandra Khatri is the Chief Scientist and Head of AI at Got It AI, wherein, his team is transforming the AI space by leveraging state-of-the-art technologies in order to deliver Self-Discovering, Self-Training, and Self-Optimizing products. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various kinds of applied research projects at Uber AI such as Conversational AI, Multi-modal AI, and Recommendation Systems.

Prior to Uber AI, he was the founding member of the Alexa Prize Competition at Amazon, wherein he was leading the R&D and got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. Prior to Alexa AI, he was driving NLP, Deep Learning, and Recommendation Systems related Applied Research at eBay. He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India.

His current areas of research include Artificial and General Intelligence, Democratization of AI, Reinforcement Learning, Language Understanding, Conversational AI, Multi-modal and Human-agent Interactions, and Introducing Common Sense within Artificial Agents

Advances in Conversational AI and NLP through Large Scale Language Models such as GPT-3(Workshop)

Deep Learning and Machine Learning Instructor

Minerva Singh, PhD

I joined the Center for Environmental Policy (CEP), Imperial College London as a Research Fellow in 2018. Before that, I completed a PhD from the University of Cambridge in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have nearly 10 year’s experience in conducting academic research at the interface of tropical ecology, data science, earth observation (EO) and artificial intelligence (AI)and published 14 first author peer-reviewed papers in international journals since 2013 including PLoS One. I have obtained research funding of approximately £275,000 since 2018 and provided research, consultancy and data science support to startups and industry partners including Treeconomy and Morphobotics LLC. I am a Fellow of the Royal Geographic Society (RGS)and Royal Statistical Society (RSS) as well as serving as an Associate Fellow at the Data Science Institute, Imperial College London. I am also a best selling course-instructor on the online MOOC platform Udemy where I provide online teaching to more than 71,000 students from across the world on machine learning, earth observation and deep learning related topic.

Machine Learning For Remote Sensing Based Landcover Change Detection(Workshop)

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

Soon Yau Cheong

Soon-Yau Cheong is the founder of Sooner.ai. It helps businesses to devise and implement AI strategies. Past projects include helping ARM to research using computer vision in automotive camera, and scaling up federated learning software infrastructure with Samsung Research. He has a wide interest in many AI domains including computer vision, NLP and productizing AI. Soon-Yau is also the author of “Hands-on Image Generation with TensorFlow” where he implemented many state-of-the-art models from scratch.

Generative AI to Create Image and Deepfake Video(Workshop)

 

Data Scientist element61

Ivana Pejeva

Ivana is a data scientist, passionate about machine learning and artificial intelligence. As part of the Data Science and Strategy competence center at element61, she helps organizations build and grow business with data. Ivana is an engineering professional with a Master’s degree in Artificial Intelligence from KU Leuven.

Building Data Lakes(Workshop)

Turing fellow, Associate ProfessorAlan Turing Institute, UCL

Dr. Hao Ni

Dr. Hao Ni is an associate professor in financial mathematics at UCL and a Turing fellow at Alan Turing Institute since September 2016. Prior to this Dr. Hao Ni was a visiting postdoctoral researcher at ICERM and Department of Applied Mathematics at Brown University from 2012/09 to 2013/05 and continued her postdoctoral research at the Oxford-Man Institute of Quantitative Finance until 2016. Dr. Hao Ni finished her D.Phil. in mathematics in 2012 under the supervision of Professor Terry Lyons at University of Oxford.

Senior EconomistResearch Division of Sveriges Riksbank

Isaiah Hull

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)

Lead Data ScientistDataswati

Luis Blanche

Luis spent his education and early career in the realm of Transport (Civil Engineering Diploma, MSc Transport from Imperial College London, French Civil Aviation Authority) but he took the train of data science and machine learning in 2014 with Kaggle and Coursera as teachers. He spent about 3 years as a Data Scientist at Quantmetry, a Data Science and AI Consultancy based in Paris where he helped several big companies in multiple sectors develop solutions involving Natural Language Processing, Geospatial data, and Time series. He has recently joined Dataswati to take the lead of the Data Science team. At Dataswati, they develop a SAAS called PowerOP that offers agro-industrial companies easy integration and visualization of their production data as well as explainable and actionable AI services like quality analysis, smart alerting and recipe or settings recommendation.

Build an ML Pipeline with Airflow and Kubernetes(Workshop)

Full Stack DeveloperValohai

Magdalena Stenius

Magda is a full stack developer at Valohai and one of the organizers of the Turku.py Python meetup. She is passionate about open source, open data and MLOps.

MLOps Will Change Machine Learning(Tutorial)

Senior Product Manager,Appen

Kirsten Gokay

Kirsten has been with Appen for over five years and has seen countless ways in which people succeed – and fail – when implementing AI in their business processes or products. As a Product Manager, she primarily focuses on the Quality and Experience functions of the platform, including new features to drive quality results with ease as well as usability enhancements for enterprise-grade users. She works with Machine Learning Scientists on AI products both internally and as external customer offerings, giving her a deep understanding of the benefits of incorporating AI for numerous use cases.

Data Acquisition and Governance: Considerations for Success(Demo Talk)

Sr. Principal Data Engineering ArchitectRed Hat

Guillaume Moutier

Guillaume Moutier is a Sr. Principal Data Engineering Architect at Red Hat, focusing his work on data services, AI/ML workloads and data science platforms. Former CTO of Laval University in Canada, he is constantly looking for and promoting new and innovative solutions, but always with a focus on usability and business alignment brought by 20 years of IT management experience.

Smart City Data Pipeline, an Edge to Core Data Story(Talk)

ML Engineer, Real World AIIBM Research Singapore Lab

Chaitanya Kumar

Dr. Laura Wynter is the head of the RealWorld AI team at the IBM Research Singapore Lab. Laura has degrees from MIT and the Ecole des Ponts (Paris, France). Her areas of expertise involve the use of AI as well as optmization, equilibrium modeling and statistics-based methods for enabling effective real-time decision making for planning and operational problems in numerous domains. She has been named an IBM Master Inventor. Her work spans the full lifecycle of a research solution from the definition of the research problem and its characterization, to the development of effective algorithms, to collaborations with the IBM software division culminating in the creation of commercial software products from the models and algorithms developed.

Federated Learning: Practice and Modern Algorithms(Workshop)

LeadIBM Research Singapore Lab

Dr. Laura Wynter

Dr. Laura Wynter is the head of the RealWorld AI team at the IBM Research Singapore Lab. Laura has degrees from MIT and the Ecole des Ponts (Paris, France). Her areas of expertise involve the use of AI as well as optmization, equilibrium modeling and statistics-based methods for enabling effective real-time decision making for planning and operational problems in numerous domains. She has been named an IBM Master Inventor. Her work spans the full lifecycle of a research solution from the definition of the research problem and its characterization, to the development of effective algorithms, to collaborations with the IBM software division culminating in the creation of commercial software products from the models and algorithms developed.

Federated Learning: Practice and Modern Algorithms(Workshop)

Industrial PostdocDemant Enterprise A/S and Aalborg University

Dr. Daniel Michelsanti

Daniel Michelsanti is an Industrial Postdoctoral Researcher at Demant and Aalborg University. He has a PhD in Electrical and Electronic Engineering obtained at Aalborg University. Currently, he is investigating cutting-edge technologies for next-generation hearing assistive devices, with the goal of improving the life quality of people with hearing loss.

Audio-Visual Speech Enhancement and Separation Based on Deep Learning(Half-Day Training)

Professor of Machine Learning and Speech ProcessingAalborg University

Prof. Zheng-Hua Tan

Prof. Zheng-Hua Tan is a Professor of Machine Learning and Speech Processing, a Co-Head of the Centre for Acoustic Signal Processing Research (CASPR), and Machine Learning Research Group Leader in the Department of Electronic Systems at Aalborg University, Denmark.

Prof. Zheng-Hua Tan
was a Visiting Scientist/Professor at the Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, USA, an Associate Professor in the Department of Electronic Engineering at Shanghai Jiao Tong University, China, and a postdoctoral fellow at AI Spoken Language Lab, in the Department of Computer Science at KAIST, Korea.

He received the B.S. and M.S. degrees in electrical engineering from Hunan University, China, in 1990 and 1996, respectively, and the Ph.D. degree in electronic engineering from Shanghai Jiao Tong University, China, in 1999.

His research interests include machine learning, deep learning, pattern recognition, speech and speaker recognition, noise-robust speech processing, multimodal signal processing, and social robotics. He has over 200 publications. He edited the book Automatic Speech Recognition on Mobile Devices and over Communication Networks (Springer, 2008).

He is the elected Chair of the IEEE Machine Learning for Signal Processing Technical Committee. He is an Associate Editor for the IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING. He has served as an Editorial Board Member/Associate Editor for several journals including Computer Speech and Language, and Digital Signal Processing. He was a Lead Guest Editor of the IEEE Journal of STSP and a Guest Editor of several journals including Neurocomputing. He was the General Chair of IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP 2018), Aalborg, Denmark, and was a Program Co-Chair for IEEE Workshop on Spoken Language Technology (SLT 2016), San Diego, California, USA. He has served as a Chair, Program Co-chair, Area and Session Chair, and Tutorial Speaker of many international conferences. He is a Senior Member of the IEEE.

Audio-Visual Speech Enhancement and Separation Based on Deep Learning(Half-Day Training)

Principal Solution ArchitectRed Hat

John Senegal

John Senegal is a Principal Solution Architect for IoT, Edge, and AI/ML as part of Red Hat’s Global Partners and Alliances team. His responsibilities include building technology and solution partnerships with Red Hat’s strategic partners.

Parsing Engineering Diagrams for Industrial AI Applications using Deep Learning and Graph Search(Track Keynote)

Senior Data ScientistC3.ai

Shouvik Mani

Shouvik Mani is a Data Scientist at C3.ai, where he has built AI applications for customers in the defense, manufacturing, and oil and gas industries. He studied statistics and machine learning at Carnegie Mellon University. In his free time, he enjoys running and playing soccer.

Parsing Engineering Diagrams for Industrial AI Applications using Deep Learning and Graph Search(Track Keynote)

Developer AdvocateIBM

Yamini Rao

Yamini Rao is a Developer Advocate for IBM. She complies various developer scenarios and training material, including demos, writing blog posts, creating audio-visual artefacts and giving hands-on workshops and training sessions based on IBM Cloud technologies. She works with various developer communities across the UK. As a developer advocate, Yamini is involved in public speaking engagements at conferences and meetups and also organises some of these events as part of the role. The other part of Advocacy involves closing the loop, to collect feedback from the developer community about how they are using the technology and channel this back to the engineering and product management teams. She has a background in computer science and has previously worked as an Implementation Engineer for various IBM Analytical tools.

Classification Algorithms using Python and Scikit-Learn(Workshop)

Group Head/PI, Integrative Computational Biology and Machine Learning, MRC WIMM, RDMUniversity of Oxford

Aleksandr Sahakyan, PhD

Alex is a principal investigator at the University of Oxford, leading a group focused on integrative computational biology and machine learning in the MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine. In past, he did his undergraduate studies in pharmaceutical sciences with a research focus on quantum/structural chemistry and NMR spectroscopy. He next moved to the University of Cambridge, first obtaining an MPhil degree in computational biology (Department of Applied Mathematics and Theoretical Physics), followed by a PhD in theoretical chemical biology (Department of Chemistry). He then became an interdisciplinary research fellow in computational genomics and epigenetics (Department of Chemistry and Cancer Research UK Cambridge Institute), before joining the University of Oxford. His research aims at combining machine learning, computational biology, computational chemistry, data from experimental genomics and biophysical techniques to reach a new level of precision in biology at both genome and proteome levels.

Machine Learning in Processing Genomic Sequence Information (XGBoost/R)(Workshop)

Director of Data Science, UK&IDataRobot

Kayne Putman

Kayne is the Director of Data Science in the UK&I. He has 10 years of experience in helping organisations get value from data science, working both client-side and vendor-side. In his spare time, he is an avid sports player, and is always seeking his next travel adventure…which is currently particularly challenging!

Why No Model is a Black Box(Track Keynote)

Data ScientistDataRobot

Vyas Adhikari

Vyas Adhikari is a Customer Facing Data Scientist at DataRobot, working with customers to solve data science problems across numerous industries.

Why No Model is a Black Box(Track Keynote)

WW Sales EngineeringDataRobot

Asger Pedersen

Asger holds a master of Computer Science and has for many years been leading software development teams as the CTO of several startup companies. Following that he has been helping many of the biggest enterprises around the world becoming successful with monitoring critical application services in production, and today at DataRobot he is focused on bringing some of the best practices from the DevOps world into the new growing field of MLOps, where some of the principles can be reused but where there is also a great need to have AI and Machine Learning specific capabilities to be able to scale in a standardised and governed way.

Closing the Production Gap with MLOps(Workshop)

Data ScientistDataRobot

Qian Zhao, PhD

Qian Zhao is a London-based data scientist at DataRobot who helps fintech, banking, and healthcare customers accelerate their machine learning capabilities by using the DataRobot platform. Before he joined DataRobot, Qian was a data science manager at PwC and Deloitte, where he led a team of data scientists, delivering innovative and cutting-edge AI and machine learning solutions to enterprise businesses. Qian received his Ph.D. in Neuroscience from UCL in 2012.

Closing the Production Gap with MLOps(Workshop)

VP of Data Science, Data Engineering, and Platform EngineeringStore Associate Technology of Walmart Global Technology

Jeffrey Yau, PhD

Jeffrey is a VP of Data Science, Data Engineering, and Platform Engineering at the Store Associate Technology of Walmart Global Technology. His prior roles include the Chief Data Scientist at AllianceBernstein, a global asset-management firm that managed nearly $700 billion, Vice President and Head of Data Science at Silicon Valley Data Science, and senior leadership position at Charles Schwab Corporation and KPMG. He has also taught econometrics, statistics, and machine learning at UC Berkeley, Cornell, NYU, University of Pennsylvania, and Virginia Tech. Jeffrey is active in the data science community and often speaks at data science conferences and local events. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, businesses, and policy institutions. Jeffrey holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.

The Fundamentals of Statistical Time Series Forecasting(Workshop)

Sr. Systems EngineerImply Data, Inc.

Danny Leybzon

Danny lives and breathes data science. He is constantly finding connections and similarities between the world of data science and the world around us. Danny loves what he does. The feeling of solving interesting problems is exhilarating to Danny, and he loves seeing the impact of machine learning and advanced analytics to improve society and lives.

Augmented Intelligence: How Machine Learning + Human Analytics Catches Criminals(Demo Talk)

Data Management Director & Chief EvangelistDenodo

Alexey Sidorov, PhD

Alexey started his career 27-years ago, building a volumetric model of radioactive pollution of the Chernobyl nuclear disaster zone with the Russian Academy of Science. Since then, he’s earned his reputation as the strategic adviser in all aspects of data management.

Prior to joining Denodo as Chief Evangelist, Alexey built and managed enterprise-wide data integration platforms for industry giants like Nokia and Deutsche Bank. During the last 15 years, Dr. Alexey Sidorov has been helping many companies across 3 continents to digitally transform their businesses with edge-cutting data technologies from Teradata and Informatica.

The Role of Data Virtualization in AI/ML Projects – A Demonstration (Talk)

Director of Product Marketing (EMEA & LATAM)Denodo

Robin Tandon

Robin Tandon is the Director of Product Marketing (EMEA & LATAM) at Denodo. Robin has over 20 years of industry experience working with enterprise and cloud software organizations. He is responsible for all Denodo product marketing activities in his supported regions. Robin is a keen agile practitioner and a certified Scrum product owner; he also holds a B.Sc. (Hons) in Computer Science and Geology from the University of Sunderland

Why is Data Virtualization a Data Scientist’s Best Friend? (Demo Talk)

Senior Research EngineerDeepMind

Edgar A Duenez-Guzman, PhD

Edgar is a senior research engineer at DeepMind working at the intersection of machine learning, evolutionary biology, and game theory. During his time in academia, his research focused on the evolution of cooperation, task partitioning, punishment, and their interaction with population structure. He has contributed to a wide range of applied machine learning projects at Google, including the index for Google Image Search, the development of the new voice of the Google Assistant using WaveNet, and the energy prediction for Google’s wind farms. More recently, he has brought together his applied ML expertise with his expertise in cooperation and sociality into his work in multi-agent reinforcement learning, where he aims to understand and tackle the biggest (social) challenges humanity faces.

Incentives for Sociality: Intrinsic and Extrinsic Motivations of Social Dilemmas in Multi-Agent Systems(Talk)

Chief Product OfficerArize AI

Aparna Dhinakaran

Aparna Dhinakaran is Chief Product Officer at Arize AI; a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and Tubemogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michaelangelo. She has a bachelor’s from Berkeley’s Electrical Engineering and Computer Science program, where she published research with Berkeley’s AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

ML Observability: A Critical Piece in Making Models Work in the Real World(Demo Talk)

Senior Technical ConsultantIW Connect

Cvetanka Eftimoska

Cvetanka is a data science enthusiast with knowledge in Machine learning, Python, R, Statistics, Big Data, and Spark. Seeking to increase data efficiency. She is an efficient Database Developer with a vast knowledge of high availability SQL Server solutions. Cvetanka is an adaptable professional with a background in workflow processes, creating database objects, and overseeing security tasks and has expertise in ETL and Data warehousing, including Data management.

How we can use PySpark for building and training a Machine Learning model(Women in Ignite)

Senior Data ScientistTBC Bank

Nutsa Abazadze

Nutsa Abazadze is a Senior Data Scientist at TBC, the biggest Bank in the Caucasus region. In that role she manages technical side of multiple projects, mentors and supervises other data scientists and helps leadership teams to translate analytical findings into actionable insights. During her time at TBC she has contributed to the analytical transformation projects that aimed to transform various business processes using Machine learning, such as: retail deposit profitability, motor insurance pricing, Digital Payments strategy and anti-money laundering. Nutsa holds a master’s degree in survey statistics from the University of Bamberg, Germany. Before joining TBC, she worked in market research industry and applied machine learning models to survey data.

Putting Machine Learning in AML(Women in Ignite)

Senior Manager Data SciencePublicis Sapient

Sharmistha Chatterjee

Sharmistha is currently working as a Senior Manager at Publicis Sapient where she is working on different AI, machine learning, and data governance problems. She holds a Master’s Degree in Computer Science and Engineering from Aalto University. Her Master’s thesis was done in collaboration with Nokia Research center under the supervision of Professor Joerg Ott (https://www.netlab.tkk.fi/~jo/), who is also the co-chair of SIP working group and other telecommunication research organizations and startups in Europe.


Sharmistha has in-depth expertise in productionizing and scaling AI solutions on AWS and Google Cloud. She is a certified Professional Google Cloud Architect. Prior to joining Publicis Sapient she has worked at a startup called Datami Mobile Solutions, sprung from Princeton University. Here she has filed 5 patents (https://patents.justia.com/inventor/sharmistha-chatterjee). Her work in SIP has been cited by Microsoft patent (https://patents.google.com/patent/US8682889B2/en).


She holds expertise in both research and productionizing scalable AI solutions, and skillset to bridge the gap between theory and practice. She has a proven track record of delivering complex innovative solutions to fortune 500 companies increasing revenue to $5M (in early-stage start-ups: Princeton University start-up, Ittiam Systems, SAP labs) to larger than $500M. She has worked with direct stakeholders, the world’s most renowned, award-winning research scientists in the field of telecom, media, advertising, and IOT domain. She is also a medium blogger.

AI Ethics in Federated Learning(Women in Ignite)

Chief Revenue OfficeriMerit

Jeff Mills

Jeff brings vast experience in marketing, sales and organizational management. In his current role as the CRO at iMerit Technologies, a company that specializes in data annotation services, he brings unique insights on the challenges AI companies are trying to solve today through unique solutioning and their pursuit of high quality data. Prior to iMerit Jeff was the Chief Revenue Officer at 1-Page which he helped bring public in 2014. Jeff was also the CRO of Gengo, which was acquired by Lionsbridge.Ai. He’s also held management positions at Criteo, Yahoo, Sojern and SideStep (which was acquired by Kayak).

ML Data Challenges and Market Insights(Demo Talk)

Lead Solutions EngineerSeldon

Tom Farrand

Tom Farrand leads the Solutions Engineering team at Seldon, where he knits client needs with Seldon’s deployment capabilities to help clients realise the potential of ML. Previously, he was the lead Machine Learning Engineer for the UKI SystemsAI team at IBM. Tom has a Master’s degree in Chemical Engineering from the University of Bath.

Intro to Seldon Deploy: Deployment, Management and Monitoring of ML Models in Production(Demo Talk)

Solutions ArchitectiMerit

Natasha Montagu

Natasha Montagu is a Solutions Architect in the field AI and data annotation. She has worked on numerous end-to-end AI projects across multiple industries helping companies unlock the power of their data and turn it into cost-effective, scalable models. She also brings her expertise from the client’s perspective from building out in-house teams to outsourcing whilst retaining high-quality data outputs.

Most Popular Reasons ML Projects Fail and How to Avoid Them(Talk)

Artificial Intelligence and Data Science ConsultantDeloitte Luxembourg

Hajar Khizou

Hajar is an Artificial Intelligence and Data Science Consultant @Deloitte Luxembourg. As part of her industry experience, she delivered end-to-end solutions by utilizing modeling and machine learning methodologies to answer some of the most complex data questions the business is facing currently. Her areas of expertise include Data Governance, Data science, Data Visualization, and Data Storytelling. As an IEEE volunteer for five years now, she is focused on advanced technologies to engineer a better future and leverage value. She organized the first AI Arab teleconference with the ARISS, led a talk at IEEE International Congress on Information Science and Technology (CIST’19), and held various positions within the IEEE as a VP of IEEE Young Professionals Morocco and the Chair of the IEEE SYP Congress – Moroccan edition. This year, she leads the regional Women in Data Science (WiDS) – Luxembourg edition to collaborate with field experts and empower women in the field.

Lead AI EngineerDataRobot

Pavel Ustinov, PhD

Pavel Ustinov is a Lead AI Engineer at DataRobot, where he applies his scientific background and more than 15 years’ experience in software engineering to successfully deliver end-to-end Augmented Intelligence solutions driven by the DataRobot platform. He takes care of EMEA region customers. Pavel began his career as a researcher in the nonlinear dynamics of energy conversion systems before moving to the software engineering and machine learning industry. Prior to joining DataRobot, he also spent time at Anaplan, Société Générale CIB, BNP Paribas Securities Services, and ML/DL startups holding senior/leading technical positions and delivering large scale analytical projects. Pavel received his PhD from Orel State Technical University (Orel, Russia) in Automation and Control of Industrial Systems and PhD from University of Reims Champagne-Ardenne (Reims, France) in Computer Science, Automation and Signal Processing. When not working out over technology he enjoys a high altitude mountaineering.

Closing the Production Gap with MLOps(Workshop)


ODSC Europe Virtual Conference 2021 | June 8th – 10th

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