ODSC Europe 2020 Speakers

We have a fantastic lineup of some of the best and brightest  speakers and core contributors in data science

We are adding speakers and instructors daily to bring the best possible program of top data scientists and instructors.  

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Research ScientistsGoogle Research, NY

Dr. Lora Aroyo

Lora Aroyo is a Research Scientist at Google, NY currently working on human-labeled data quality . She is best known for her work on CrowdTruth crowdsourcing methodology. Throughout her career, Lora was a principal investigator of a large number of research projects bringing together methods and tools from human computation, linked (open) data, data science & human-computer interaction with the goal of building hybrid human-AI systems for understanding text, images, and videos with humans-in-the-loop. Her research projects focussing on personalized access to online multimedia have a major impact and established her as a recognized leader in human computation techniques for digital humanities, cultural heritage, and interactive TV. Prior to joining Google, she worked at the VU University Amsterdam as Full Professor in Computer Science and was Chief Scientist at NY-based startup Tagasauris. She is a four times holder of IBM Faculty Award for her work on CrowdTruth used in adapting the IBM Watson system to the medical domain and in capturing ambiguity in understanding misinformation. She is currently president of the User Modeling Inc, which acts as a steering committee for the UMAP conference series.

Data Excellence: Better Data for Better AI(Keynote)

Head of Data ScienceZopa Ltd.

Jiahang Zhong, PhD

Dr. Jiahang Zhong is the leader of the data science team at Zopa, one of the UK’s earliest fintech company. He has broad experience of data science projects in credit risk, operational optimization and marketing, with keen interests in machine learning, optimization algorithms and big data technologies. Prior to Zopa, he worked as a PhD and Postdoctoral researcher on the Large Hadron Collider Project at CERN, with a focus on data analysis, statistics and distributed computing.

Can Your Model Survive the Crisis: Monitoring, Diagnosis and Mitigation(Talk)

Data ScientistABN AMRO Bank N.V.

Violeta Misheva, PhD

Violeta has been working as a data scientist in the Data Innovation and Analytics department in ABN AMRO bank located in Amsterdam, the Netherlands. In her daily job, she works on projects with different business lines applying the latest machine learning and advanced analytics technologies and algorithms. Before that, she worked for about 1.5 years as a data science consultant in Accenture, the Netherlands. Violeta enjoyed helping clients solve their problems with the use of data and data science but wanted to be able to develop more sophisticated tools, therefore the switch. Before her position at Accenture, she worked on her PhD, which she obtained from Erasmus University, Rotterdam in the area of Applied Microeconometrics. In her research, she used data to investigate the causal effect of negative experiences on human capital, education, problematic behavior and crime commitment.

Explainable ML: Application of Different Approaches(Talk)

Head of Data ScienceSAS UK&I

Dr. Iain Brown

Dr. Iain Brown is the Head of Data Science for SAS UK&I and Adjunct Professor of Marketing Analytics at University of Southampton. Over the past decade he has worked across all sectors, providing thought leadership on the topics of Risk, AI and Machine Learning and their ethical applications.

During his time at SAS he has delivered numerous projects and driven innovation in the fields of AI and the corresponding fields of machine learning, deep learning and natural language understanding.

As an experienced public speaker and published author he has presented at a number of internationally renowned conferences and conventions and has papers published in the European Journal of Operational Research, International Journal of Forecasting and the Journal of Expert Systems with Applications on the aforementioned areas of expertise. As of March 2020 Dr Brown was also recognised as one of the 100 most influential people in Data by dataIQTM.

Data Science Change Is Inevitable, Growth Is Optional(Talk)

Director | ProfessorLeuven.AI | KU Leuven

Luc De Raedt, PhD

Luc De Raedt is full professor, now at KU Leuven, formerly at the Albert-Ludwigs-University Freiburg. He direct the KU Leuven Institute for Artificial intelligence. He is well known for his contributions on learning and reasoning, in particular, for contributions to statistical relational learning, probabilistic and inductive programming. He is a AAAI and a EurAI fellow and he received an ERC Advanced Grant in 2016 on the topic of automating data science. He is action editor for Artificial Intelligence, Machine Learning and the Journal of Machine Learning Research. He was or will be a pc-(co)chair of flagship conferences in machine learning and in artificial intelligence (including ECMLPKDD 2001, ICML 2005, ECAI 2012 and IJCAI 2022). He was coordinator of 7 European projects (most recently FP7-FET ICON en Chist-Era ReGround), and a principle investigator in many further European and other projects. He has co-authored more than 300 publications, including 3 books, 10 papers in Artificial Intelligence, 17 in Machine Learning, and 36 at core AI conferences such as IJCAI, AAAI and ECAI. He has delivered numerous tutorials at major AI and machine learning conferences such as IJCAI, AAAI, ECCAI, ICML, NeurIPS, SIGKDD, …. According to google scholar he is cited more than 18000 times and his h-index is 65. He has (co)advised 27 Phd students and 17 post-docs. Many of his students have won prestigious awards, including 4 ECCAI (&1 ACP) Dissertation awards for the best European thesis in AI (resp. Constraint Programming).

On the Automation of Data Science(Track Keynote)

General ManagerNEC Corporation

Tsvi Lev

Tsvi Lev is the General Manager of NEC Corporation’s Israeli Research Center, the first Open Innovation center of NEC globally. The center is engaged in applications of AI to Cyber, Physical Safety and Medical. Before that Tsvi was VP of Strategy at Amdocs, leading the Amdocs team in the AT&T Open Innovation Organization – the AT&T Foundry. Previously, Tsvi managed the R&D Center of Samsung Electronics in Israel, a center devoted to innovation and cutting-edge development for Samsung’s future consumer electronics and cloud products. Tsvi was an Entrepreneur in the Mobile Multimedia and Computer Vision space and sold his company to Emblaze Systems. A Talpiot Graduate, Tsvi holds a Masters in Theoretical Physics, is the author of numerous patents, and a frequent speaker at events related to technological innovation.

Practical, Rigorous Explainability in AI(Talk)

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)

Professor of Computer Science and Head of the Department of InformaticsKing's College

Luc Moreau, PhD

Luc Moreau is a Professor of Computer Science and Head of the department of Informatics, at King’s College London. Before joining King’s, Luc was Head of the Web and Internet Science, in the department of Electronics and Computer Science, at the University of Southampton.

Luc was co-chair of the W3C Provenance Working Group, which resulted in four W3C Recommendations and nine W3C Notes, specifying PROV, a conceptual data model for provenance the Web, and its serializations in various Web languages. Previously, he initiated the successful Provenance Challenge series, which saw the involvement of over 20 institutions investigating provenance inter-operability in 3 successive challenges, and which resulted in the specification of the community Open Provenance Model (OPM). Before that, he led the development of provenance technology in the FP6 Provenance project and the Provenance Aware Service Oriented Architecture (PASOA) project.

He is on the editorial board of “PeerJ Computer Science” and previously he was editor-in-chief of the journal “Concurrency and Computation: Practice and Experience” and on the editorial board of “ACM Transactions on Internet Technology

Provenance: a Fundamental Data Governance Tool ⎯ a Case Study for Data Science Pipelines and Their Explanations(Track Keynote)

Chair in AI, ProfessorCSML, University College London

John Shawe-Taylor, PhD

John Shawe-Taylor is professor of Computational Statistics and Machine Learning at University College London. He has helped to drive a fundamental rebirth in the field of machine learning, with applications in novel domains including computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 250 papers and two books that have attracted over 70000 citations.
He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing. He was appointed UNESCO Chair of Artificial Intelligence in November 2018 and is the leading trustee of the UK Charity, Knowledge 4 All Foundation, promoting open education and helping to establish a network of AI researchers and practitioners in sub-Saharan Africa.

Leveraging Artificial Intelligence to Better Exploit Open Educational Resources(Track Keynote)

Principal Program Manager, Cloud and AIMicrosoft

Manash Goswami

Manash Goswami is a product management leader with a passion to solve problems and build products that meet end-user needs. Grown from development – coding, test & verification, systems design & architecture, to product management and business development. Delivered consumer electronics products from smartwatch to tablets. Deep understanding of the Android, Chrome & Windows eco-system. Demonstrated leadership by managing through influence across a matrixed org structure. Capable of driving new initiative/agenda internally or with customers, enable collaboration across functional teams and drive for financial results. Excel at dealing with ambiguity and managing complex, cross-group interactions. Can successfully tie long-term strategic thinking with near-term execution.

ML Inference on Edge with ONNX Runtim(Tutorial)

Head of Data ScienceObjectivity

Marta Markiewicz

Head of Data Science at Objectivity with background in Mathematical Statistics. For about 9 years, she has been discovering the potential of data in various business domains, from medical data, through retail, HR, finance, aviation, real estate, … She deeply believes in the power of data in every area of life. Articles’ writer, conference speaker and privately – passionate dancer.

Multivariate (Flight) Anomalies Detection (Talk)

Researcher, University of WarwickRenowned for AI Discovering 50 New Planets from NASA Data

Dr. David Armstrong

David Armstrong is an STFC Ernest Rutherford Fellow working on exoplanet detection, characterization and populations with the TESS mission data. He develops and use automated methods of planet detection to improve planet population statistics, both in general and with a past focus on circumbinary planets. David’s other interests include studying the habitability of known exoplanets, time-variability in planetary phase curves, and the identification and classification of eclipsing binaries and variable stars in large-scale surveys.

One of his particular interests is in applying machine learning techniques to astrophysical problems. Recently this has included the automatic selection of real candidates in transiting planet surveys, as well as creating fast detection and classification tools for eclipsing binaries and variable stars in the K2 survey. David works with the Computer Science and Statistics departments at Warwick, with the aim of applying the latest machine learning techniques to problems in the exoplanet field.

David Armstrong is currently an academic co-lead of Warwick’s Habitability GRP, a new initiative at Warwick aimed at promoting multi-disciplinary research in habitability, and he is involved with the Centre for Exoplanets and Habitability. Our current projects include analyses of galactic habitability, dynamical and biological constraints on panspermia, and the stability of DNA in non-Earth environments.

Machine Learning for Exoplanet Discovery(Keynote)

Senior AI DeveloperMicrosoft

Wolfgang Pauli, PhD

Wolfgang Pauli is an AI developer at Microsoft, with 15+ years of experience with Machine Learning and Artificial Intelligence research. He received his Ph.D. in Computational Neuroscience from the University of Colorado and has published numerous high-profile articles in scientific journals on Computational Neuroscience, Reinforcement Learning, and Neural Networks. Before joining the Microsoft AI Platform team in 2018, he was a research scientist at the California Institute of Technology. He supports the democratization of AI by developing open-source solutions that apply recent breakthroughs to real-world problems.

ML Inference on Edge with ONNX Runtime(Tutorial)

 

ML and Project LeadFrontier Development Lab

Alfredo Kalaitzis, PhD

Alfredo joined Element AI as a Research Engineer in the AI for Good lab in London, working on applications that enable NGOs and non-profits.
He is one of the primary co-authors of the first technical report made in partnership with Amnesty International, on the large-scale study of online abuse against women on Twitter from crowd-sourced data. He’s been a Machine Learning mentor at NASA’s Frontier Development Program, helping teams apply AI for scientific space problems. More recently, he led the joint-research with Mila Montreal on Multi-Frame Super-Resolution, which was awarded by the European Space Agency for their top performance on the PROBA-V Super-Resolution challenge.His research interests lie in computer vision for satellite imagery, probabilistic modeling, and AI for Social Good. Prior to joining Element AI, he was a Senior Data Scientist in Digital Shadows, specializing in cyber-security and digital risk management, and a consulting Data Scientist in Microsoft’s Xbox EMEA team, where he also collaborated with Microsoft Research Cambridge. He has been a research scientist with the Department of Statistical Science at University College London, working on probability models to better understand ordinal data coming from surveys, and later on the interface of Machine Learning and Signal Processing to detect faults in the low-voltage power-line grid. During his time with UCL, his team won the first data challenge competition organized by the Royal Statistical Society, for which he designed and developed his team’s algorithm for the analysis of resting f-MRI time-series data. He earned his MSc in Artificial Intelligence from the University of Edinburgh, and his PhD in Machine Learning from the University of Sheffield under the supervision of Professor Neil Lawrence. His PhD research led to contributions in probability methods for dimensionality reduction of data and developed methods for gene-expression time-series to discover genetic factors of disease.

Mapping the Waters of The United States(Talk)

Data Science ManagerAmsterdam Data Collective

Joanneke Meijer

Joanneke is a Manager at Amsterdam Data Collective and initiator of the sustainable benchmark. She is an experienced data-science consultant, focusing on forecasting, pricing, operational research and text mining. By coaching teams towards delivering actionable insights from data, she continuously manages to create sustainable impact.

Sustainable Retail Through Open Source, Scraping and NLP(Talk)

Professor of Condition MonitoringLuleå University of Technology

Diego Galar, PhD

Dr. Diego Galar is 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 University of Sunderland (UK), University of Maryland (USA), and Chongqing University in China.

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

Manager, Financial Advisory Analytics, DeanDeloitte | Data Science Retreat

Daniel Voigt Godoy

Daniel Voigt Godoy has20+ years experience in developing solutions, programs and models using analytical skills across different industries: software development, government, fintech, retail and mobility. 7+ years experience with data processing, data analysis, machine learning and statistical tools: Python (numpy, scipy, pandas, scikit-learn), Spark, R Studio, MatLab and Statistica. Experience in stochastic simulation and agent-based modeling. Experienced programmer in SQL, Python, Java, R, PowerBuilder, PHP. Strong programming skills and eagerness to learn different languages, frameworks and tools. Solid background in statistics, economics, capital markets, debt management and financial instruments.

PyTorch 101: Building A Model Step-by-Step(Workshop)

Data ScientistFarfetch

Davide Sarra

Davide is a Data Scientist with experience in Ad Bidding, Customer Behaviour, Pricing, and A/B testing tools. By working on large machine learning products, he has developed skills and a passion for Software Engineering.

Practical Methods to Optimise Model Stability: A Case Study Using Customer-Lifetime Value at Farfetch(Talk)

Lead Data ScientistBumble

Laura Mitchell

With over 10 years of experience in the tech and data science space, I am the Lead Data Scientist at Bumble. I am also the founder of the DataScienceGuidance.com platform which provides advice for people in the field from industry experts. I am a published author in the field of Artificial Intelligence and have hands-on and leadership experience in the delivery of projects using natural language processing, computer vision, and recommender systems, from initial conception through to production.

Image Detection as a Service: How We Use APIs and Deep Learning to Support Our Products(Talk)

Lead Data ScientistFarfetch

Kishan Manani, PhD

Kishan is a Data Scientist with 5+ years of experience in applying machine learning and statistics in finance, e-commerce, and healthcare. He has worked on a variety of data science problems including time-series forecasting, customer targeting, and pricing optimisation. His research specialism is in applying large scale time-series analysis and modelling to healthcare.

Practical Methods to Optimise Model Stability: A Case Study Using Customer-Lifetime Value at Farfetch(Talk)

Senior Data Science ManagerBlueVine

Ido Shlomo

Ido Shlomo is the head of BlueVine’s data science team in the US, where he works on applying machine learning and other automation solutions for risk management, fraud detection and marketing purposes. Recent work is focused on implementing complex NLP tasks in production systems, and specifically on dealing with the the challenge of consuming unstructured data. Previously Ido worked in the Economics department at Tel Aviv University as a researcher in structural macroeconomic modeling. Ido holds a dual BA in mathematics and philosophy and an MA in economics, both from Tel Aviv University.

Building an Industry Classifier With The Latest Scraping, NLP and Deployment Tools(Workshop)

Machine Learning Research Engineerjalammar.github.io

Jay Alammar

Passionate analytical expert in building and scaling great Internet companies and products. Learns, codes, illustrates, and teaches machine learning topics at every opportunity. Jay’s hands-on expertise covers the entire product life cycle from initial research, focus groups, user experience design, product prototyping, user testing, up to product release, marketing, and acting on deep analytics insights.

A Gentle Intro to Transformer Neural Networks(Talk)

Research ScientistNokia Bell Labs

Anna Zakrzewska, PhD

Anna Zakrzewska is a research scientist at Nokia Bell Labs in Dublin, Ireland. Her main research interests are in the area of wireless communications and focus on self-organising networks, which greatly benefit from data analytics and machine learning. Several of her patented network management mechanisms are used to automate configuration of millions of small cells and Wi-Fi access points around the world. Anna holds M.Sc. Eng. degree in information and communication technology from Wroclaw University of Technology, Poland and Ph.D. degree from Technical University of Denmark. Prior to that she was with NTT Communication Science Laboratories in Japan and European Commission Joint Research Centre in Italy. She is a Senior Member of IEEE. Anna enjoys speaking about technology and often participates in STEM outreach activities.

Towards Fully Autonomous Networks(Talk)

Co-Founder, CEO & CTOELSE Corp

Andrey Golub, PhD

FashionTech & Digital Entrepreneur for 10+ years. AI and Digital Twins, AI-Driven Design enthusiast. Andrey Golub is a Business Transformation and Technology Innovation professional, 17+ years as R&D manager, contract professor, and a strategy consultant, specialized in the development and launch of innovative DeepTech products and services, mainly Cloud Computing and Artificial Intelligence.

As of technology entrepreneur since 2010, Andrey’s best expertise lay in the field of Fashion/Luxury Retail and Manufacturing with ELSE Corp since 2015), now acting as General Manager and Head of AI R&D of ICOL
Group: Smart RoboFactories & Digital Ecosystems.

Integrating Small Data, Synthetic Data in AI and Data Strategy for Fashion Retail(Talk)

Assistant LecturerTU Dublin

Bojan Božić, PhD

Dr. Bojan Božić is an Assistant Lecturer in Computer Science at TU Dublin and has worked on Semantic Web, Machine Learning, and Data Science technologies since 2010. He was a Software Development Engineer in Speech Recognition at Philips Speech Recognition Systems in Vienna, a Scientist at the Austrian Institute of Technology, an External Lecturer at the University of Applied Sciences Burgenland and University of Vienna, and a Research Fellow at Trinity College Dublin and the Centre for Applied Data Analytics Research at Dublin Institute of Technology. At AIT’s Safety and Security department, he worked in leading roles in European research projects such as SANY (Sensor Web Enablement), TaToo (Tagging Tools for Semantic Discovery), Europeana Creative (Cultural Inheritage), PELAGIOS (Linked Data), and C2-SENSE (Sensor Web and Interoperability). At Trinity College he worked on the H2020 project ALIGNED, modelling data and software engineering processes through ontologies and annotations for the Dacura platform. He joined CeADAR and the Dublin Institute of Technology to commence research in Data Science and approach innovation and collaboration opportunities in new projects and is now lecturing, performing leading research and colaborating on several academic and industrial research projects. His current research interests are Semantic Web, Machine Learning, and Natural Language Processing.

Knowledge Graphs for the Greater Good(Talk)

Phd CandidateUniversity College London

Sonja Tilly, CFA

Sonja Tilly is a phd candidate at UCL. Her research focuses on forecasting macro-economic variables and stock market movements using narrative and emotions from global newspapers.
Sonja has over a decade experience working in asset management, most recently at Quoniam Asset Management, where she contributed to the development of trading strategies using media sentiment. Prior to that, she was a Quantitative Analyst at Hiscox. Sonja is a CFA Charterholder.

Forecasting the Economy with Fifty Shades of Emotions(Talk)

Sr. Data ScientistJohn Snow Labs

Veysel Kocaman, PhD

Veysel Kocaman is a Senior Data Scientist and ML Engineer at John Snow Labs and has a decade long industry experience. He is also pursuing his PhD in CS as well as giving lectures at Leiden University (NL) and holds an MS degree in Operations Research from Penn State University. He is affiliated with Google as a Developer Expert in Machine Learning.

Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems(Workshop)

Lecturer and Vice-Director of the Data Science InstituteNUI Galway

Dr. Paul Buitelaar

Paul Buitelaar is Senior Lecturer and vice-director of the Data Science Institute (DSI) at NUI Galway where he leads a team in Natural Language Processing. Paul is also co-PI of the Insight Centre for Data Analytics where he lead the Smart Enterprise demonstrator program and co-lead the Multi-modal Data Analysis research program, which brings together interdisciplinary research from across the Insight Centre in close collaboration with industry partners. Paul’s main research interests are in the development and use of Natural Language Processing methods for knowledge extraction and semantic-based information access. He has been involved in a large number of national and international funded projects in this area.

Knowledge Graph Extraction for the Enterprise(Talk)

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.

Deep Learning for Anomaly Detection(Talk)

Lead Data ScientistCenter for Computation and Visualization, Brown University

Andras Zsom, PhD

Andras Zsom is a Lead Data Scientist in the Center for Computation and Visualization group at Brown University, Providence, RI. He works with high-level academic administrators to tackle predictive modeling problems, he collaborates with faculty members on data-intensive research projects, and he was the instructor of a data science course offered to the data science master students at Brown.

Missing Data in Supervised Machine Learning(Tutorial)

Lead Machine Learning EngineerNovetta

Brian Sacash

Brian Sacash is a Machine Learning Engineer in Novetta’s Machine Learning Center of Excellence. He helps various organizations discover the best ways to extract value from data. His interests are in the areas of Natural Language Processing, Machine Learning, Big Data, and Statistical Methods. Brian holds a Master of Science in Quantitative Analysis from the University of Cincinnati and a Bachelor of Science in Physics from Ohio Northern University.

Building an Industry Classifier With The Latest Scraping, NLP and Deployment Tools(Workshop)

Senior Machine Learning EngineerNovetta

Andrew Chang

Andrew Chang is an Applied Machine Learning Researcher in Novetta’s Machine Learning (ML) Center of Excellence. Andrew is a graduate from Carnegie Mellon University who has a focus on researching state of the art machine learning models and rapid prototyping ML technologies and solutions across the scope of customer problems. He has an interest in open source projects and research in natural language processing, geometric deep learning, reinforcement learning, and computer vision. Andrew is the author and creator of NovettaNLP.

State-of-the-art NLP Made Easy with AdaptNLP(Tutorial)

Data Science Technical LeadJournera

Stephanie Kirmer

Stephanie Kirmer is a Data Science Technical Lead at Journera, an early stage startup that helps companies in the travel industry use data efficiently and securely to create better travel experiences for customers. Previously she worked as a Senior Data Scientist at Uptake, where she developed tools for analyzing diesel engine fuel efficiency, and constructed predictive models for diagnosing and preventing mechanical failure. Before joining Uptake, she worked on data science for social policy research at the University of Chicago and taught sociology and health policy at DePaul University.

Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow(Talk)

PresidentEnplus Advisors, Inc.

Daniel Gerlanc

Daniel Gerlanc has worked as a data scientist for more than decade and been writing sofware for nearly 20 years. He frequently teaches live trainings on oreilly.com and is the author of the video course Programming with Data: Python and Pandas. He has coauthored several open source R packages, published in peer-reviewed journals, and is a graduate of Williams College.

Programming with Data: Python and Pandas(Training)

Kirk borne
Principal Data ScientistBooz Allen Hamilton

Dr. Kirk Borne

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples(Training)

Atypical Applications of Typical Machine Learning Algorithms(Training)

Data ScientistGreenhouse Software

Mona Khalil

Mona is a Data Scientist at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology and are passionate about contributing to the ethical use of data science methodology in the public and private sector.

SQL for Data Science(Training)

Chief Data Scientist, Author of "R for Everyone"Lander Analytics

Jared Lander

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

Machine Learning in R Part I(Training)

Machine Learning in R Part II(Training)

Managing Partner, Distinguished FacultyBetaVector, General Assembly

Matt Brems

Matt is currently Managing Partner and Principal Data Scientist at BetaVector. His full-time professional data work spans finance, education, consumer-packaged goods, and politics and he earned General Assembly’s 2019 “Distinguished Faculty Member of the Year” award. Matt earned his Master’s degree in statistics from Ohio State. Matt is passionate about responsibly putting the power of machine learning into the hands of as many people as possible and mentoring folx in data and tech careers. Matt also volunteers with Statistics Without Borders and currently serves on their Executive Committee as the Marketing & Communications Director.

Good, Fast, Cheap: How to do Data Science with Missing Data(Half-Day Training)

Professor of Computer ScienceHarvard University

Michael Mitzenmacher, PhD

Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Michael
has authored or co-authored over 200 conference and journal publications on a variety of topics, including algorithms for the Internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. He is interested in both algorithms for AI applications, and how predictors from AI systems can yield better algorithms with rigorous performance bounds. He is an ACM Fellow and an IEEE Fellow. He has a widely used textbook on randomized algorithms and probabilistic techniques in computer science published by Cambridge University Press.

Michael Mitzenmacher graduated summa cum laude with a B.A. in mathematics and computer science from Harvard in 1991. After studying
mathematics for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999. He served as the chair for computer science from 2010 to 2013 and co-chair in the 2018-2019 academic year.

Algorithms with Predictions(Tutorial)

Data Science ManagerAurubis AG

Dr. Katharina Glass

I am data scientist and digital transformation enthusiast, employed as a manager data scientist at Aurubis AG, Europe biggest multimetal producer and #3 in the world. I am empirical econometrician, was empirical researcher for 10 years with focus on forecasting and uncertainty. Since 2016 I match both science and industry world together. I have expertise in statistics, machine learning and AI, among other. The portfolio of my projects is very broad – from KPI (and other reports) assessment, development and deployment projects, to deep learning model and AI development and implementation for relevant business cases.

Dare to Start Simple(Talk)

FounderBeehive Media

Bill Shander

Bill is an information designer, helping clients turn their data into compelling visual and often interactive experiences. He teaches data storytelling, information design and data visualization on LinkedIn Learning and in workshops for clients around the world. Bill is also Communications Director on the board of the Data Visualization Society. His clients include the World Bank, United Nations, Starbucks, American Express, Deloitte, and various agencies of the U.S. Government.

From Numbers to Narrative: Turning Raw Data into Compelling Visual Stories with Impact(Training)

Post-doc, IGERT Big Data FellowUniversity of Washington

Jacob Schreiber

Jacob Schreiber is a post-doc and IGERT big data fellow in the Genome Science department at the University of Washington. His research focus is on the application of machine learning methods, primarily deep learning ones, to the massive amount of data being generated in the field of genome science. His research projects have involved predicting the three-dimensional structure of the genome using convolutional neural networks and learning a latent representation of the human epigenome using deep tensor factorization. He also routinely contributes to the Python open-source community as the core developer of pomegranate, a package for flexible probabilistic modeling, apricot, a package for data summarization for machine learning, and in the past as a core developer for the scikit-learn project.

Pomegranate: Fast and Flexible Probabilistic Modeling in Python(Workshop)

Developer AdvocateVerverica

Marta Paes

Marta is a Developer Advocate at Ververica (formerly data Artisans) and a contributor to Apache Flink. After finding her mojo in open source, she is committed to making sense of Data Engineering through the eyes of those using its by-products. Marta holds a Master’s in Biomedical Engineering, where she developed a particular taste for multi-dimensional data visualization, and previously worked as a Data Warehouse Engineer at Zalando and Accenture.

Snakes on a Plane: Interactive Data Exploration with PyFlink and Zeppelin Notebooks(Talk)

Lead InstructorGeneral Assembly

Max Humber

Max is a distinguished faculty member at General Assembly and the creator of several popular open source packages, including: gazpacho, gif, and mummify.

Animating Data: From matplotlib plots to GIFs(Training)

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)

Global Head of Artificial IntelligenceThoughtWorks, Inc.

Christoph Windheuser, PhD

Christoph Windheuser is the Global Head of Artificial Intelligence at ThoughtWorks Inc. Before joining ThoughtWorks, he gained more than 20 years of experience in the industry in several positions at SAP and Capgemini. Prior to that, he completed his Ph.D. in Neural Networks with a focus on Speech Recognition at the University of Bonn, Germany, Carnegie Mellon University in Pittsburgh, USA, Waseda University in Tokyo, Japan, and France Telekom (E.N.S.T.) in Paris, France.

Data Science Best Practices: Continuous Delivery for Machine Learning(Training)

Principal Data ScientistThoughtWorks, Inc.

David Johnston, PhD

David Johnston is a Principal Data Scientist and founding data scientist of the ThoughtWorks Data Science & Engineering practice. David has over 25 years of experience working with data, data processing pipelines, algorithms, optimization and statistical and machine learning models. David has a Ph.D. in physics and worked previously as a researcher at top universities, NASA and US government labs in the field of cosmology. Since leaving academia he has specialized in helping clients apply these techniques in their business environments with a focus on end-to-end delivery of valuable data-driven products and creating working, maintainable production systems. David is a frequent writer and speaker on data science, artificial intelligence and the importance of applying quality software development best practices toward data science-driven applications.

Data Science Best Practices: Continuous Delivery for Machine Learning(Training)

Lead Data EngineerThoughtWorks, Inc.

Eric Nagler

Eric Nagler serves ThoughtWorks, Inc. as a Lead Data Engineer with eight years of developing innovative batch and real-time data solutions for multiple different clients in multiple different domains. Eric holds a Masters Degree in Computer Science with a focus in Parallel and Distributed Algorithms. Eric’s data interests include Big Data Application Architecture and Design, Parallel Computation, Big Data ETL, NoSQL Data Storage, Natural Language Processing, Geospatial Analytics, Web Services, and Data Visualization.

Data Science Best Practices: Continuous Delivery for Machine Learning(Training)

Assistant ProfessorUniversity of California San Diego

Rose Yu, PhD

Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at the University of Southern California in 2017. She was subsequently a Postdoctoral Fellow at the California Institute of Technology. She was an assistant professor at Northeastern University prior to her appointment at UCSD. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. Among her awards, she has won Google Faculty Research Award, Adobe Data Science Research Award, NSF CRII Award, Best Dissertation Award in USC, and was nominated as one of the ’MIT Rising Stars in EECS’.

Learning and Mining Large-Scale Spatiotemporal Data(Tutorial)

Founder, Assistant ProfessorEasyAsPie.ai, Carleton College

Yaniv Ben-Ami, PhD

Yaniv Ben-Ami loves seeing data with my own eyes. It’s frustrating to him that it’s impossible to observe the geometry of multidimensional data that’s more than 3d. Yaniv first became obsessed with this challenge as a Bioinformatics undergrad at Tel Aviv University. Little did Yaniv knows that my obsession would become an epic journey. As an Economics Master student, Yaniv met the ultimate beast of a data set. This beast is The American Time Use Survey (ATUS), which has 389 dimensions. To Yaniv, it appeared as a 389-head Hydra that nobody would dare to slay. Gazing upon that beast, Yaniv initially fell into despair. Fast forward 15 years, 3 kids and a Ph.D. in Economics from Yale, Yaniv reads Deep Learning in Python by François Chollet. Within this tome of wisdom, Yaniv found a gift from the Gods. A deep learning algorithm by the name of Variational Auto-Encoder. This mythical weapon can map multidimensional data to a one-dimensional range and maintain relative distances. Finally, Yaniv Ben-Ami can see the whole data set. Yaniv will show you how he used this weapon to slay the ATUS monster. Just in case there’s a monster you want to slay.

Senior LecturerNewcastle University

Dr. Colin Gillespie

Colin Gillespie is the co-founder of Jumping Rivers, a data science & machine learning consultancy. He has been using R since 1999 and Python since 2002.
He’s the author of a number of R packages and has published the book Efficient R Programming with O’Reilly.

R for Python Programmers(Training)

Lead Instructor, Data and AnalyticsGeneral Assembly

Nathaniel Tucker

Nathaniel earned his AB/SM in Computer Science from Harvard. He previously worked as a Quant and Trader at Jane Street and Goldman Sachs before transitioning into the pure tech industry. Nathaniel worked as a Data Scientist at Facebook, a Product Manager at Microsoft and a Software Engineer at Google before joining Vicarious. He is an avid reader and learner. He teaches part time at General Assembly and is developing open source teaching material for data science, machine learning, and web development.

Deep Learning Building Blocks(Training)

Data Science ManagerBoostrs

Gabrielle Fournet, PhD

Gabrielle is the Head of Data Science at Boost.rs, a startup focusing on people professional development. In this role, Gabrielle is responsible for building and maintaining a world-class database of jobs and skills across 27 major industries. In addition, she develops algorithms to recommend meaningful career paths to the Boost.rs users and help them progress in their careers. Prior to her startup experience, Gabrielle was a medical researcher in the field of brain imaging. She holds a PhD in physics from the Paris-Saclay University and an engineering degree from ESPCI ParisTech.

Your Future, Today. Using NLP to Advance Your Career.(Talk)

CTOManifold

Sourav Dey, PhD

As CTO for Manifold, Sourav is responsible for the overall delivery of data science and data product services to make clients successful. Before Manifold, Sourav led teams to build data products across the technology stack, from smart thermostats and security cams (Google / Nest) to power grid forecasting (AutoGrid) to wireless communication chips (Qualcomm). He holds patents for his work, has been published in several IEEE journals, and has won numerous awards. He earned his PhD, MS, and BS degrees from MIT in Electrical Engineering and Computer Science.

MLOps: ML Engineering Best Practices from the Trenches(Workshop)

Senior Data EngineerManifold

Alex Ng

Alexander Ng is a Senior Data Engineer at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Prior to Manifold, Alex served as both a Sales Engineering Tech Lead and a DevOps Tech Lead for Kyruus, a startup that built SaaS products for enterprise healthcare organizations. Alex got his start as a Software Systems Engineer at the MITRE Corporation and the Naval Undersea Warfare Center in Newport, RI. His recent projects at the intersection of systems and machine learning continue to combine a deep understanding of the entire development lifecycle with cutting-edge tools and techniques. Alex earned his Bachelor of Science degree in Electrical Engineering from Boston University, and is an AWS Certified Solutions Architect.

MLOps: ML Engineering Best Practices from the Trenches(Workshop)

Brian Lucena
Consulting Data ScientistAgentero

Brian Lucena, PhD

Brian Lucena is a Principal at Lucena Consulting and a consulting Data Scientist at Agentero. An applied mathematician in every sense, he is passionate about applying modern machine learning techniques to understand the world and act upon it. In previous roles, he has served as SVP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.

Interpreting and Explaining XGBoost Models(Workshop)

Data & AI Developer AdvocateIBM

Margriet Groenendijk, PhD

Margriet is the global data science developer advocacy focal at IBM. As a Data Scientist she has a passion for exploring different ways to work with and understand diverse data by using open-source tools. She is active in developer communities through attending and presenting at conferences and organising meetups. She has a background as a climate scientist researching large observational datasets of carbon uptake by forests and the output of global scale weather and climate models.

Removing Unfair Bias in Machine Learning(Workshop)

Explain Machine Learning Models(Workshop)

Building Fair and Explainable AI Pipelines(Talk)

 

Head of AI & ResearchAira

Anirudh Koul

Anirudh Koul is a noted AI expert, ML Lead for NASA FDL, UN/TEDx speaker, author of O’Reilly’s Practical Deep Learning book and a former scientist at Microsoft AI & Research, where he founded Seeing AI, considered the most used technology among the blind community after the iPhone. With features shipped to a billion users, he brings over a decade of production-oriented applied research experience on petabyte-scale datasets. He also coaches a team for Roborace, the Formula One championship of autonomous driving @200mph. His work in the AI for Good field, which IEEE has called ‘life-changing’, has received awards from CES, FCC, MIT, Cannes Lions, American Council of the Blind, showcased at events by UN, World Economic Forum, White House, House of Lords, Netflix, National Geographic, and lauded by world leaders including Justin Trudeau and Theresa May.

Deep Learning on Mobile(Tutorial)

Co-FounderTreebeard Technologies

Laurence Watson

Laurence Watson is a data scientist with 9 years experience, primarily in energy and climate change. He runs Treebeard, a startup helping data scientists to automatically test their work. At financial think tank Carbon Tracker, Laurence used machine learning and satellite data to track coal power plants, and built technology with Ember Climate to reform emissions trading in Europe. He previously worked on energy policy in Westminster for the Shadow Minister for Energy and Climate Change. He has a degree in physics from the University of Cambridge and an MSc in Energy Systems from UCL.

Tracking Coal and Solar Power with Machine Learning and Satellites(Talk)

Manager Data SciencePublicis Sapient

Sray Agarwal

Sray Agarwal is based in London and works for Publicis Sapient as a Manager of Data Science. His expertise lies in Predictive Modelling, Forecasting and advanced Machine Learning. He possesses a deep understanding of algorithms and advanced statistics. He has a background in management, economic and has done a master equivalent program in Data Science and Analytics. His current areas of interest are Fair and Explainable ML.

Ensuring Ethical Practice in AI(Talk)

Head of Artificial Intelligence / Digital Product School UnternehmerTUM

Afsaneh Asaei, PhD

Afsaneh has more than 18 years of professional experience in applied Artificial Intelligence R&D. She completed her Ph.D. at the Swiss Federal Institute of Technology Lausanne (EPFL) on the robustness of machine learning and pattern recognition in commercial applications. Her contributions were recognized by community awards and granted several million Euros of Swiss, European & American funding. At Digital Product School of UnternehmerTUM, she supports her teams with exploiting the AI capabilities, their feasibility, and risks for problem-solution fits. In a multi-disciplinary setup, Afsaneh guides them throughout the challenges on how a data-driven solution should be developed and evaluated and how to transform the assumptions and data requirements to product vision and market opportunities.

Neural Generative Codes for Clinical Speech Pathology Applications(Talk)

PhD CandidateStony Brook University

Ayush Kumar, PhD

Ayush Kumar is a senior Ph.D. Candidate from the Computer Science Department, Stony Brook University, New York in Visual Analytics and Imaging Lab. He was a visiting researcher in the Visualization Research Center, University of Stuttgart, Germany. He worked as a researcher in Brookhaven National Lab, New York during his Ph.D. and at AT&T Labs during Summer 2019. His main research interests are Data and Visual Analytics, Data Science, Machine Learning, and Eye Tracking.

Visual Analytics: Strengthening Eye Movements Research(Talk)

ArchitectOralytics

Brendan Tierney

Brendan Tierney, Oracle GroundBreaker Ambassador and Oracle ACE Director, is an independent consultant (Oralytics) and lectures on data science, databases, and Big Data at the Technological University Dublin. He has over 27+ years experience working in the areas of data mining, data science, machine learning, big data, and data warehousing. Tierney has published five books,
97 Things about Ethics Everyone in Data Science Should Know, a best seller with MIT Press titled Data Science (Essentials Series), and three books with Oracle Press/McGraw-Hill (Predictive Analytics Using Oracle Data Miner, Oracle R Enterprise: Harnessing the Power of R in Oracle Database, and Real World SQL and PL/SQL: Advice from the Experts). 
Web and blog: www.oralytics.com
Twitter: @brendantierney

Ethical Issues for Data Science, Machine Learning and Artificial Intelligence(Talk)

Machine Learning DevOps EngineerScaleway

Olga Petrova, PhD

Olga is a deep learning R&D engineer at Scaleway, the second largest french cloud provider. She received her PhD in theoretical physics from Johns Hopkins University in 2013, followed by postdoctorate appointments at the Max Planck Institute in Dresden and the École Normale Supérieure in Paris. In the latter, she looked into the possible applications of artificial intelligence to quantum systems, among other things.

Olga’s current interests focus on semi-supervised and active machine learning. 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

Active Learning with a Sprinkle of PyTorch(Tutorial)

Senior Data ScientistVan Lanschot Kempen

Alun Biffin, PhD

Alun is a Senior Data Scientist at Van Lanschot Kempen, an independent wealth manager in the Netherlands which has been helping clients reach their financial goals for almost 300 years. As a data scientist his interests are; the application of AI in Finance, Recommender Systems, and NLP. As a human his interests are; music, poetry and dogs of all shapes and sizes.

Have I Got (Financial) News for You(Talk)

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)

Machine Learning EngineerMyHammer AG

Flavio Clesio

Flavio works with information technology, he has acquired experience in some companies in different businesses. Most of these roles were performed in parallel or throughout my overall experience. Currently works as head and machine learning engineer in a chapter of core machine learning, embedded algorithms at telecommunication business platforms for mobile products.

Machine Learning Operations: Latent Conditions and Active Failures(Talk)

Head of Quant Research SolutionsBloomberg

Dr. Arun Verma

Dr. Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the areas of computer science & applied mathematics. At Bloomberg, Mr. Verma’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing. More recently, he has enjoyed working at the intersection of diverse areas such as data science (with structured & unstructured data), innovative quantitative models across all asset classes & using machine learning methods to help reveal embedded signals in financial data.

Automated Insights in Finance Using Machine Learning & AI(Talk)

Professor of Computer ScienceTrinity College Dublin

Khurshid Ahmad, PhD

Khurshid is the Professor of Computer Science in the School of Statistics and Computer Science, Trinity College Dublin, University of Dublin, Ireland since 2005. He was formerly Professor of Artificial Intelligence at the University of Surrey, UK (1999-2005). He is currently working on spinning out his research in emergency management and decision making as a commercial venture: the product will be a social media monitor for use in emergencies including natural disasters like floods, and health pandemics. Khurshid’s research areas include artificial intelligence, machine learning, social media and networks, fuzzy logic and behavioural finance. He was trained as a nuclear physicist and worked in high-performance computing covering areas such as forecasting, computer-assisted learning, engineering design, and information extraction from continuous information streams comprising texts, images and numbers. His work seeks to maximise the potential of computing systems by enabling these systems to deal with different modalities of human communications, language, vision, and numerical information exchange. Khurshid’s current focus is on the automatic analysis facial expressions and hand gestures of politicians, civil protection officials, and market traders during emergencies. His collaborators have been diverse ranging from National Gallery of Ireland, UK and Irish Police Forces, Bundeskommnado Leipzig, Protezione Civile Veneto, Italy, Siemens AG, Mercedes Benz, AXA Insurance, international banks, and universities across the EU, the USA, and Australia. He has recently completed an EU Framework Programme 7 – Security Area on the use and abuse of social media in disaster emergency (Project Slandail). A book on Social Computing and the Law (Cambridge, 2018), focussing on emergency management, has just been published. In the past by the UK Research Councils (including EPSRC, ESRC, and AHRC) and EU Information Societies Programme have funded his projects. His work on commercialisation of his research on sentiment analysis and on social computing during disasters were funded by Atlantic Bridge Venture Finds and Enterprise Ireland respectively (2018019). He was the founding Head and Professor of Artificial Intelligence of the Department of Computing, University of Surrey, UK (1999-2005); Visiting Professor at the Copenhagen Business School (1998, 2013) and University of Surrey (2005-2008). He has extensive international contacts and has been invited to speak at major conferences across the world. He is currently working on an information system that can find trends in the reporting and blogging about infectious disease. He has published over 250 papers including 6 books; his most recent book is on the topic of Social Computing and the Law (Cambridge) and a five years before he published Affective Computing and Sentiment Analysis: Metaphor, Ontology and Terminology (Springer). He has supervised 45 PhD, 1 MPhil, and 21 MSc students successfully, and is currently supervising 2 PhD students and 6 MSc students. He is an elected Fellow of the British Computer Society and of Trinity College Dublin, and a Chartered Engineer of the UK Engineering Council.

Needles in a Haystack: Big Data and Bigger Promises?(Talk)

Professor, Co-founderUniversity of Oxford, Oxford Semantic Technologies

Ian Horrocks, PhD

Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Visiting Professor in the Department of Informatics at the University of Oslo. He is a Fellow of the Royal Society, a member of Academia Europaea, an ECCAI Fellow, and a Fellow of the British Computer Society. His research interests include logic-based knowledge representation and reasoning and semantic technologies, with a particular focus on ontology languages and applications. He was an author of the OIL, DAML+OIL, and OWL ontology language standards, chaired the W3C working group that standardized OWL 2, and developed many of the algorithms, optimization techniques, and reasoning systems that underpin OWL applications. His recent work includes query answering over ontologies and very large data sets, and applications in domains such as engineering, oil and gas, finance and medicine.

Which is the Tallest Building in Europe? — Representing and Reasoning About Knowledge(Talk)

Head of the Computing Science Faculty, Program DirectorGriffith College Dublin

Dr. Waseem Akhtar

Dr. Waseem Akhtar is Head of Computing Science faculty at Griffith College Dublin. He holds a PhD in Computer Science from University College Dublin (UCD) and an MBA in Higher Education Management from University College London (UCL). Waseem’s current research interests focus on multidisciplinary topics such Big Data Analytics based Business, Process and Management Intelligence Systems, Evidence and Analytics Based Management, Robotic Process Automation and other applications of Artificial Intelligence, Machine Learning and Big Data Analytics. His research interests also include software development for large & complex systems and studying the fundamental nature of information. Waseem is also interested in science communication, history and dissemination of knowledge and studying the impact of emerging technologies on the future of humanity.

Re-imagining Privacy in the Age of Technical and Social Disruption(Talk)

Lead Data ScientistInsideTracker

Svetlana Vinogradova, PhD

Svetlana Vinogradova is a Data Scientist at Inside Tracker, leading the Data Science team to integrate blood biomarkers and DNA data with physiological data from activity trackers to improve lifestyle recommendations and discover new patterns and optimal zones in sleep, heart rate, and blood biomarkers.
Prior to Inside Tracker, Svetlana was a research fellow at Dana Farber Cancer Institute and Harvard Medical School where she worked as a bioinformatician developing statistical methods to study epigenetic mechanisms affecting gene expression.
Svetlana has a PhD in Bioinformatics and Mathematical Biology from Lomonosov Moscow State University. During her PhD, she worked on developing algorithms to study RNA secondary structures and also enjoyed teaching and developing bioinformatics, algorithms, and R programming courses.

Building Personalized Scores for Customers: How to Combine Different Data Types and Learn in the Process (Talk)

Instructor, Data ScientistSIL International

Daniel Whitenack, PhD

Daniel Whitenack (aka Data Dan) is a PhD trained data scientist who has been developing artificial intelligence applications in the real world for over 10 years. He knows how to see beyond the hype of AI and machine learning to build systems that create business value, and he has taught these skills to 1000’s of developers, data scientists, and engineers all around the world. Now with the AI Classroom event, Data Dan is bringing this knowledge to an live, online learning environment so that you can level up your career from anywhere!

State of the art AI Methods with TensorFlow: Transfer Learning, RL and GANs(Training)

Data Science Lead, Applied MaterialsGoogle Developer Expert - ML

Dipanjan (DJ) Sarkar

Dipanjan (DJ) Sarkar is a Data Science Lead at Applied Materials, leading advanced analytics efforts around computer vision, natural language processing and deep learning. He is also a Google Developer Expert in Machine Learning. He has consulted and worked with several startups as well as Fortune 500 companies like Intel and Open Source organizations like Red Hat. He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. Dipanjan is also a published author, having authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.

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

Co-founderMACTY.EU | Content A

Susana Zoghbi, PhD

Susana Zoghbi, Co-Founder & CEO, Macty. Susana is a researcher and entrepreneur in a quest to help businesses grow with Artificial Intelligence. She received a PhD in Computer Science and her research focused on cross-modal processing of textual and visual Information. She designed deep neural network architectures and probabilistic graphical models to understand visual and textual content from e-commerce and social media. Her work has been published in top conferences and journals in Artificial Intelligence. She has worked for NASA’s Frontier Development Lab as a Deep Learning Researcher to automatically search for long-period comets that might impact Earth. She has also worked for Microsoft Research in Cambridge, where she focused on machine learning for optimizing environments for large scale software development. Before her PhD, she obtained two Masters degrees, one in Mechanical Engineering from the University of British Columbia, where her research focused on human-robot interaction technologies, and one in Mathematical Physics, where she focused on gravitational fluctuations in Domain Wall Spacetimes. In 2014, she was granted a Google Anita Borg award for her contributions in Computer Science and her community.

A Deep Dive into Convolutional Neural Networks(Tutorial)

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)

Head of Data Science and AnalyticsBadoo (now MagicLab)

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.

Modern and Old Reinforcement Learning Part 1(Training)

Modern and Old Reinforcement Learning Part 2(Training)

Director, R&DUBS

Michał Chromiak, PhD

Dr Michał Chromiak is a Director at UBS, contributing to text document analytics and leading efforts to democratize AI in financial sector and investigate applications for multiple ML based tasks. Michal is also a member of Department of Intelligent Systems at Maria Curie-Skłodowska University. Past research in integrating big data from distributed and heterogeneous sources, brought him to concentrate on data perception using deep learning. He is interested in improving and understanding ways to generalize the modern deep learning algorithms and finding their best suited AI applications. He is strongly fascinated with understanding how deep learning can be improved to match, and exceed biological forms of intelligence, in terms of performance.

Transformers Know More Than Meets the Eye(Tutorial)

Software EngineerQuantumBlack

Kiyohito Kunii

Kiyo is a software engineer at QuantumBlack, an advanced analytics firm operating at the intersection of strategy, technology and design to improve performance outcomes for organizations. Kiyo is one of the core contributors and maintainers of Kedro, a Python library that implements software engineering best-practice for data and ML pipelines.
Kiyo holds MSc in Computing Science from Imperial College London, and MA in Economics from The University of Edinburgh.

Building a Production-level Data Pipeline Using Kedro(Tutorial)

Doctoral StudentThe Alan Turing Institute

Ioana Bica

Ioana Bica is a PhD student at the University of Oxford and at the Alan Turing Institute. Her PhD research focuses on building machine learning methods for causal inference and individualised treatment effect estimation from observational data. In particular, she has developed methods capable of estimating the heterogeneous effects of time-dependent treatments, thus enabling us to determine when to give treatments to patients and how to select among multiple treatments over time. Recently, Ioana has started working on methods for understanding and modelling clinical decision making through causality, inverse reinforcement learning and imitation learning.
She has previously completed a Bachelor’s degree and a Master’s degree in Computer Science at the University of Cambridge where she has worked on multi-modal data integration and unsupervised learning for genomics data.

From Longitudinal Patient Observational Data to Individualized Treatments Effects Using Causal Inference(Talk)

Data and Machine Learning Scientist

Hadrien Jean, PhD

Hadrien Jean is a machine learning scientist. He’s currently working on the book “Essential Math for Data Science” with O’Reilly (https://learning.oreilly.com/library/view/essential-math-for/9781098115555/). He previously worked at Ava on speech diarization. He also works on a bird detection project using deep learning. He completed his Ph.D. in cognitive science at the École Normale Supérieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https://hadrienj.github.io/posts/).

Introduction to Linear Algebra for Data Science and Machine Learning With Python(Training)

Data ScientistKNIME

Maarit Widmann

Maarit Widmann is a data scientist at KNIME. She started with quantitative sociology and she holds her Bachelors degree in Social Sciences. The University of Konstanz made her drop the “social” as a Master of Science. Her ambition is to communicate concepts behind data science to others in videos and blog posts.

Introduction to Time Series Analysis with KNIME(Training)

Co-Founder and CTOXAIN AG

Michael Huth, PhD

Professor Michael Huth (Ph.D.) is Co-Founder and CTO of the technology company XAIN and teaches at Imperial College London. His research focuses on Cybersecurity, Cryptography, Mathematical Modeling, as well as security and privacy in Machine Learning. He served as the technical lead of the Harnessing Economic Value theme at PETRAS IoT Cybersecurity Research Hub in the UK. In 2017, he founded XAIN AG together with Leif-Nissen Lundbæk and Felix Hahmann. The Berlin-based company aims to solve the challenge of combining AI with privacy with an emphasis on Federated Learning. XAIN won the first Porsche Innovation Contest and has already worked successfully with Porsche AG, Daimler AG, Deutsche Bahn, and Siemens.

Professor Huth studied Mathematics at TU Darmstadt and obtained his Ph.D. at Tulane University, New Orleans. He worked at TU Darmstadt, Kansas State University and spent a research sabbatical at The University of Oxford. Huth has authored several scientific publications and is an experienced speaker on international stages.

Federated Learning: AI for the Privacism Movement(Workshop)

Software EngineerArrikto

Yannis Zarkadas

Yannis is a software engineer at Arrikto, working with Kubeflow, the cloud-native machine learning platform built on Kubernetes. He loves contributing to open source projects and has authored the Cassandra Operator in Rook and the official Scylla Operator, which he is currently maintaining.

GitOps and Multi-Tenancy Combined for an Enterprise Data Science Experience on Kubeflow(Tutorial)

Professor Of Computer ScienceUniversity of Tübingen

Dr. Zeynep Akata

Zeynep Akata is a professor of Computer Science within the Cluster of Excellence Machine Learning at the University of Tübingen. After completing her PhD at the INRIA Rhone Alpes with Prof Cordelia Schmid (2014), she worked as a post-doctoral researcher at the Max Planck Institute for Informatics with Prof Bernt Schiele (2014-17) and at University of California Berkeley with Prof Trevor Darrell (2016-17). Before moving to Tübingen in October 2019, she was an assistant professor at the University of Amsterdam with Prof Max Welling (2017-19). She received a Lise-Meitner Award for Excellent Women in Computer Science from Max Planck Society in 2014, a young scientist honour from the Werner-von-Siemens-Ring foundation in 2019 and an ERC-2019 Starting Grant from the European Commission. Her research interests include multimodal learning and explainable AI.

Learning and Explaining with Limited Labels(Talk)

Software EngineerArrikto

Stefano Fioravanzo

Stefano Fioravanzo is a Software Engineer at Arrikto, his background is in Data Science and ML Research. He understands the value of building robust Machine Learning infrastructure and providing Data Scientists with the necessary tools to scale up their workflows. He works as a full-time contributor to Kubeflow and he is the creator of the Kubeflow Kale project which enables Jupyter Notebooks deployments to Kubeflow Pipelines.

GitOps and Multi-Tenancy Combined for an Enterprise Data Science Experience on Kubeflow(Tutorial)

Software EngineerArrikto

Kimonas Sotirchos

Kimonas is a Software Engineer at Arrikto, working on storage solutions on the cloud. He loves Open Source and has been a core contributor to the Kubeflow project for more than a year. Kimonas is the owner of the platform’s Jupyter infrastructure and his main goal is to improve the way users manage the lifecycle of their ML tools, like Notebooks, and data on top of Kubeflow. He is also a mentor at the Kubeflow project at Google Summer of Code 2020 providing guidance for adding seamless support for launching Tensorboard instances.

Model Training with GPUs and Live Metrics Tracking with Tensorboard on Kubeflow(Tutorial)

StudentNational Technical University of Athens

Konstantinos Andriopoulos

Konstantinos is an undergraduate student at the National Technical University of Athens in the school of Electronic and Computer Engineering. His current interests involve the constant development and optimization of emoFeatExtract: an open-source Python package and feature set for Speech Emotional Recognition. Also, he’s currently a Google Summer of Code student for Kubeflow. In his free time he enjoys playing and listening to music, as well as swimming.

Model Training with GPUs and Live Metrics Tracking with Tensorboard on Kubeflow(Tutorial)

Head of Data ScienceVertica

Badr Ouali

Badr Ouali works as a Data Scientist for Vertica worldwide. He can embrace data projects end to end through a clear understanding of the “big picture” as well as attention to details, resulting in achieving great business outcomes – a distinctive differentiator in his role. Prior to Vertica, Badr received both an undergraduate and a Master degree in Computer Science/Mathematics from the National School of Computer Science and Applied Mathematics in Grenoble, France. Badr enjoys sharing knowledge and insights related to data analytics with colleagues & peers and has a sweet spot for Python. He loves helping customers finding the best value from their data and empower them to solve their use-cases.

VerticaPy: Demystifying Machine Learning Complexity with Python at Scale(Talk)

VerticaPy Demo : Building a Prediction Churn Model Using Random Forest & Logistic Regression (Demo talk)

David Talby: NLP for healthcare
CTOPacific AI, John 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)

FounderFire Plus Algebra

Alan Rutter

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

Building a Better Data Visualization Culture(Tutorial)

Head of Department, Social ResarchEötvös Loránd University

Ildikó Barna, PhD

Ildikó B is a sociologist and a survey statistician. She is an Associate Professor at ELTE University Faculty of Social Sciences (ELTE FSS) Budapest, where she also serves as Chair of the Department of Social Research Methodology. She is the co-leader of the Research Center for Computational Social Science at ELTE FSS (https://rc2s2.eu/en). Her research topics include NLP in sociology, antisemitism, and xenophobia.

Women in Data Science Ignite: AI for Social Good(Women in Ignite)

Head of Machine Learning, Vahan IncAuthor of "Practical Natural Language Processing"

Anuj Gupta

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)

Head of Data SciencePROWLER.io

Sarah Jarvis, PhD

Sarah is both an engineer and a researcher, with experience in bioengineering and health economics. Growing up between the UK and Australia, she completed her PhD in Germany. As a data scientist, she bridges the research, software engineering and product teams to identify how best to build application software.

Principal Data Scientist, Co-founderPyData London

Ian Ozsvald

Ian is a Chief Data Scientist and has worked in AI and Data Science building teams and high-value IP since 1999. He’s published the 2nd edition of his High-Performance Python book with O’Reilly, speaks and gives keynote talks internationally and co-founded the 11,000 member PyDataLondon community which has delivered 7 years of volunteer-run meetups and conferences to the community

Sprinting Pandas(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)

Senior Data ScientistAttest

Gokhan Ciflikli, PhD

Gokhan is a senior data scientist at Attest. He is also a member of the Quanteda Initiative, and a guest lecturer for the LSE summer school course Introduction to Data Science and Machine Learning. As a computational social scientist, his core expertise lies in latent variable analysis, predictive modelling, and causal inference. Prior to industry, he was a postdoctoral researcher in analytic software development at the London School of Economics, where he received his PhD. Previously, he held research positions at UCL and Uppsala University, primarily developing machine learning pipelines and working on large-scale NLP problems.

Data Annotation at Scale: Active and Semi-Supervised Learning in Python(Training)

Decision ScientistSAS

Véronique Van Vlasselaer, PhD

Véronique is a Decision Scientist at SAS, and a true data science enthusiast. In her job, she passionately helps companies to envision and prepare for an AI-driven future, embrace the power of data science to support intelligent decisioning, and discover the real value in their data. Before she joined SAS, she graduated as Doctor in Business Economics at the KU Leuven (Belgium) with the department of Information Management and Decision Sciences under prof. dr. Bart Baesens. Her Ph.D. is oriented towards the development of fraud detection frameworks and solutions from a data science perspective. She is co-author of the book “Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection”.

First Aid Kit for Data Science: Keeping Machine Learning Alive (Demo Talk)

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)

CEO & Founder | High Level Ai ExpertOKRA Technologies | High Level Ai Expert EU Commission

Loubna Bouarfa, PhD

Dr. Loubna Bouarfa is a machine learning scientist turned entrepreneur. She is the founder and CEO of OKRA Technologies – an artificial intelligence data analytics company for healthcare. OKRA allows healthcare professionals to combine all their data in one place and generate actionable, evidence-based insights in real-time, to save and improve human lives. Loubna is currently a member of the European Union High-Level Expert Group on Artificial Intelligence, where she is particularly focused on healthcare and achieving a competitive business impact with AI. She was named an MIT Technology Review Top Innovator Under 35, a Forbes 50 Top Women In Tech, and won several prizes, including CEO of the Year 2019 at the Cambridge Independent Science and Technology Awards and Best Female-Led Startup at the StartUp Europe Awards. On a personal level, she is a strong advocate for diversity, women, and challenging the status quo.

Pythagoras Was Wrong(Women in Ignite)

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)

Executive Head: Business Intelligence, Analytics, Planning and MonitoringFNB

Nollie Maoto

Nollie is a savvy business intelligence and business analytics leader with over 13 years of diverse financial services background including performance-driven results in strategic planning, action plan execution, leadership, project/programme management, business intelligence and analytics, operational efficiency, business and target operating models and process engineering and re-engineering. As a dynamic leader, has consistently developed high performing teams through effective coaching, team building, motivation, strength driven performance, and 360-degree performance focus. Nollie is a highly motivated, innovative and performance-driven Head of Business Intelligence and Analytics offering comprehensive achievements and experience in Retail and Corporate Investment Banking, Wealth Management, Insurance, Financial Services, and Management Consulting particularly in the contact center environment, operations, mobile payments, operating models, electronic delivery channels, digitization, cash management, and financial regulatory compliance in South Africa and Rest of Africa. Nollie holds a Master’s in Business Administration (MBA) from the Gordon Institute of Business Science (GIBS) with a focus in Strategy and Digital Innovation. She recently completed her Data Science and Business Analytics post-graduate program with the University of Texas at Austin McCombs School of Business. The focus of this program was advanced statistics, machine learning, and predictive modelling.

The Gender Gap in Data Science and What You Can Do About It!(Women in Ignite)

Principal Business Solutions ManagerSAS

Hans-Joachim Edert

Hans has been supporting SAS customers in Germany, Austria and Switzerland as a Presales consultant and Solution Architect since he joined SAS in 2002. Currently he is working in an international team of architects and developers which takes care for the EMEA region. His work is focused on enterprise architectures, Kubernetes and cloud technologies.

ModelOps in Action with SAS Viya(Workshop)

Postdoctoral FellowUniversity of Warwick

Orsolya Vasarhelyi, Ph.D

Orsolya is a Postdoctoral Fellow at the University of Warwick. Her research focuses on how network and data science can lead to a better understanding of unconscious bias in STEM. Currently, she is interested in developing data-driven methods to quantify inclusion and explore the role of gendered network formation in success and creativity. Orsolya is an ambassador of the Global Women in Data Science Initiative and a member of the Data Science for Social Good Alumni.

Why Diversity is Not Enough?(Women in Ignite)

Senior Associate Data ScientistSAS Global Technology Practice

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.

ModelOps in Action with SAS Viya(Workshop)

Assistant ProfessorDuke University

Allison Chaney, PhD

Allison Chaney is an Assistant Professor of Marketing in the Fuqua School of Business at Duke University. Her research is at the intersection of machine learning and marketing, focusing on developing scalable and interpretable machine learning methods and understanding the impacts of these methods on individuals and society when they are deployed in real-world markets. She received her Ph.D. in Computer Science at Princeton University and holds a B.A. in Computer Science and a B.S. in Engineering from Swarthmore College. She has worked for Pixar Animation Studios and the Yorba Foundation for open-source software and has collaborated with teams at many companies including Microsoft Research, eBay/Hunch, and Etsy.

Algorithmic Confounding in Recommendation Systems(Talk)

 

Senior Research Fellow in Data ScienceUniversity of Oxford

Ning Wang, PhD

Ning Wang (Ph.D.) works as Senior Research Fellow in Data Science at the Oxford-NIE financial Big Data Lab, Mathematical Institute, University of Oxford. He also works as Research Associate at the Oxford Internet Institute. His research is driven by a deep interest in analyzing a wide range of social and economic problems by exploiting data science approaches. His research interest lies in machine/deep learning in finance, social media, and mobile trading platform, online sentiment analysis and financial market, trading behaviors, and performance evaluation. He is also interested in the broad areas of behavioral finance, social computing, data mining, and social networks analysis. Prior to Oxford, he was a postdoctoral researcher at the Computer Laboratory, University of Cambridge. He worked on the Social-based Forwarding Algorithm. He was involved in research to analyze a large number of real-world data on social networks in an effort to use centrality and community structure to optimize online communications. He is also guest editor of a forthcoming Policy and Internet journal special issue of Chinese Web Data, program chair of the 14th IEEE Conference on Computer and Information Technology (CIT2014), Co-Chair of the 2014 IEEE Conference on Cyber, Physical and Social Computing (CPSCom2014), and TCP members of 2013 AAAI Conference on Weblogs and Social Media (ICWSM13) and reviewer for IEEE Communications Magazine.

Predicting Future Decisions with Deep learning for Financial Trading(Talk)

PhD student in Data ScienceOxford-NIE financial Big Data Lab

Yuting Fu

Yuting Fu is a PhD student in Data Science at the Oxford-NIE financial Big Data Lab, Mathematical Institute at the University of Oxford. Her research interest lies in machine/deep learning in finance, including trading behaviors and performance evaluation. Prior to entering Oxford University, Yuting won the first prize in Sichuan Province in the 2014 China National Olympiad in Mathematics and second prize of Beijing in the 2017 China Undergraduate Mathematical Contest in Modeling. She graduated from Tsinghua University in 2019 with a first class honours Bachelor’s degree in Pure and Applied Mathematics at Tsinghua University.

Predicting Future Decisions with Deep learning for Financial Trading(Talk)

Software EngineerRed Hat

Matteo Mortari

Matteo Mortari is a Software Engineer at Red Hat, where he contributes in Drools development and support for the DMN standard. Matteo graduated from Engineering with focus on enterprise systems with a thesis involving rule engines which sparked his interests and influenced his professional career since. He believes there is a whole new range of unexplored applications for Expert Systems (AI) within the Corporate business; additionally, he believes defining the Business Rules on the BRMS system not only enables knowledge inference from raw data but, most importantly, helps to shorten the distance between experts and analysts, between developers and end-users, business stakeholders.

eXplainable Predictive Decisioning: Combine ML and Decision Management to Promote Trust on Automated Decision Making(Demo Talk)

Lead Sport ScientistNC State University

Christopher Connelly

Christopher Connelly has a Masters in Exercise Science and Nutrition from Sacred Heart University. He has been working with NC State Strength and Conditioning for over two years where he has built a platform for athlete data monitoring in python. He has formerly worked with the NC Courage professional soccer team doing GPS and data monitoring. His graduate work was primarily in biomechanics and movement analysis where he did data analysis with 3D movement testing and electromyography. He has his CSCS certification with the NSCA as well as level 1 coach with USA weightlifting. Chris took part in the HPCC Systems summer internship program working on a project for cleaning and analysis of collegiate soccer GPS data in HPCC Systems.

Sports Analytics – Leveraging Open Source Technology to Improve Athlete Performance(Demo Talk)

Co-Founder & Research Director, AI Applications & Platforms451 Research

Nick Patience

Nick Patience is 451 Research’s lead analyst for AI and machine learning, an area he has been researching since 2001. He is part of the company’s Data, AI & Analytics research channel but also works across the entire research team to uncover and understand use cases for machine learning. He is a co-founder of 451 Research and rejoined the team in 2015 after almost three years running product marketing at machine learning-driven eDiscovery and information governance software company Recommind (now part of OpenText).

Is Infrastructure Holding Back Adoption of AI at Scale? (Demo Talk)

Director of Applications EngineeringJulia Computing, Inc.

Dr. Matt Bauman

Matt Bauman is the Director of Applications Engineering at Julia Computing. He has developed machine learning pipelines in C, Matlab, Python, and now Julia, targeting problems ranging from neural engineering at the University of Pittsburgh (where he received his PhD in 2018) to social services at the University of Chicago’s Center for Data Science and Public Policy. He’s been a contributor to the Julia language and its packages for over 6 years and now helps guide Julia Computing’s products and trainings.

Learn How to Train and Deploy Machine Learning Models in Julia(Workshop)

Learn How to Seamlessly Use Julia for Your Machine Learning Tasks(Demo Talk)

 

Developer AdvocateIBM

Yamini Rao

Yamini Rao is a Developer Advocate for IBM, focusing on Data Science and AI. She compiles developer scenarios, workshops and training material based on IBM Cloud technologies. She works with local developer communities, presenting workshops and organising meetups and conferences.
She has a background in computer science and has worked extensively as an Implementation Engineer for various IBM Analytical tools.

Build and Deploy Custom AI Predictive Models(Demo Talk)

Principal Software Engineer Red Hat

Daniele Zonca

Matteo Mortari is a Software Engineer at Red Hat, where he contributes in Drools development and support for the DMN standard. Matteo graduated from Engineering with a focus on enterprise systems with a thesis involving rule engines which sparked his interests and influenced his professional career since. He believes there is a whole new range of unexplored applications for Expert Systems (AI) within the Corporate business; additionally, he believes defining the Business Rules on the BRMS system not only enables knowledge inference from raw data but, most importantly, helps to shorten the distance between experts and analysts, between developers and end-users, business stakeholders.

eXplainable Predictive Decisioning: Combine ML and Decision Management to Promote Trust on Automated Decision Making(Demo Talk)

Data ScientistJulia Computing, Inc.

Dhairya Gandhi

Dhairya got his Bachelors in Electrical and Electronic Engineering from Birla Institute of Technology, Pilani and is presently a data scientist at Julia Computing Inc. He is responsible for developing and maintaining performant differentiable programming routines and providing core tooling through Flux.jl, Zygote.jl etc. He has been contributing to various parts of the Julia ecosystem for more than 2 years, and helped develop differentiable pipelines for scientific computing and more recently on materials exploration.

Learn How to Train and Deploy Machine Learning Models in Julia(Workshop)

Senior DirectorRed Hat AI Center of Excellence

Steven Huels

Steven is the Senior Director of the Red Hat AI Center of Excellence with responsibility for Red Hat’s AI strategy and how that impacts internal use cases, products, customers, and partner ecosystem.
Steven has been involved with real-time data management and analytic platforms for over 20 years as a consultant, developer, product owner, and engineering manager.

Cloud Platforms for AI – Why you should care about DevOps, Containers and Kubernetes(Track Keynote)

Senior Business Solutions ManagerSAS

Piotr Kaczyński

Piotr Kaczyński is a Senior Business Solutions Manager in the analytical team. Responsible for business development in the area of ​​analytics and system integration, also at the interface between SAS and open source tools. For almost 8 years, as a researcher, he conducted research on the convergence of linearization algorithms for stochastic processes. Since 2003, he has been associated with analytics through the practical use of predictive modeling for forecasting in the energy sector. He also participated in projects in the field of data mining, BI and forecasting for sea transport. His main interests are artificial intelligence, and in particular its practical use in image processing.

AI Assisting in Traffic Relief(Tutorial)

Senior Data ScientistSAS

Wioletta Stobieniecka

Wioletta Stobieniecka is a Data Scientist with over 5 years of experience. Open-minded individual with deep passion for knowledge discovery and solving real-world problems using advanced statistical and machine learning tools. Strongly self-motivated and eager to constant development of skills including both technical and social with major in analytics. Deep theoretical and practical knowledge of statistical learning methods. Inquisitive mind searching for optimal solutions and ready to demonstrate own initiative and ambition to gain experience across a variety of industries. Having experience in fraud analytics with strong focus on building predictive models for banking and insurance. Looking for opportunities with exposure to projects involving development of recommender systems and image recognition. Striving to find strong mentorship and inspiring environment with well-rounded people open to knowledge transfer. Having long exposure to both open source and commercial software (R, Python, SAS, Oracle).

AI Assisting in Traffic Relief(Tutorial)

LecturerInsight Centre for Data Analytics, Data Science Institute, NUI Galway

Dr. John McCrae

John McCraeHe’s a lecturer above-the-bar at the Insight Centre for Data Analytics at the National University of Ireland Galway. He is currently working with Paul Buitelaar in the Unit for Natural Language Processing.Until 2015 he was a post-doctoral researcher at the University of Bielefeld in Bielefeld, Germany in Prof. Philipp Cimiano’s group, AG Semantic Computing. His research interests include the following: Ontologies, lexicography and the lexicon-ontology interface Collaborative development and publishing of language resources Big data and data science Linked data and the Semantic Web Machine translation and multilingualism Machine learning methods for NLP Digital Humanities

Knowledge Graph Extraction for the Enterprise(Talk)

Director in AI Center of ExcellenceRed Hat

Sherard Griffin

Sherard Griffin is a Director at Red Hat in the AI Center of Excellence. His primary responsibility is the development of Open Data Hub, a community-driven reference architecture for building an AI-as-a-service platform on OpenShift. Sherard is also responsible for the deployment of Open Data Hub in Red Hat’s internal data center where hundreds of gigabytes of data per day is processed, stored and made available to analysts, developers and data scientists across the company. Additionally, he works with hardware and software partners to build out an ecosystem of AI technologies optimized to run on OpenShift as certified operators.

Democratizing Data for the Enterprise(Track Keynote)

CTOTerminusDB

Gavin Mendel-Gleason

Dr. Gavin Mendel-Gleason is the CTO of TerminusDB. He is a former research fellow at Trinity College Dublin in the School of Statistics and Computer Science. His research focuses on databases, logic, and verification in software engineering. His work includes contributing to the Seshat global historical databank, an ambitious project to record and analyze patterns in human history. He is the inventor of the Web Object Query Language and the primary architect of TerminusDB. He is interested in improving the best practices of the software development community and a strong believer in formal methods and the use of mathematics and logic as disciplines to increase the quality and robustness of the software.

Revision Control for Structured Data(Demo Talk)

 

CEO and data scientistCubonacci

Jan van der Vegt

During his career at KLM, Jan van der Vegt worked on several large machine learning projects. Technical challenges turned his focus more to the engineering side, with him initiating and leading an internal MLOps project. Afterward, he founded Cubonacci to create the Cubonacci MLOps platform.

Creating Efficiency and Trust with MLOps(Demo Talk)

Data Scientist DataRobot

Thodoris Petropoulos

Theo is a Customer Facing Data Scientist working for DataRobot where he helps customers achieve their AI goals. Previously, he worked as a Lead Data Scientist in a major Telco Company where he spent the majority of his time building machine learning pipelines using Apache Spark. Furthermore, he is currently completing his Ph.D. at the Athens University of Economics and Business. Theo loves discussing AI and is a frequent contributor to the DataRobot community.

At Last, a Good Night’s Sleep! Operationalizing your Models the Correct Way(Talk)

Head of Developer Ecosystems, EuropeIBM

Dr. Mo Haghighi

Dr Mo Haghighi is IBM’s head of Developer Ecosystems in Europe, former Research Scientist at Intel and former Java and Open Source developer at Sun Microsystems. Mo leads several teams of developer advocates in Benelux, DACH, UKI, SPGI, France, Italy, and CEE, as well as overseeing all advocacy and DevRel activities across Europe. Mo’s expertise primarily lies in the areas of Edge Processing, Distributed Computing, Cloud Native Development, Embedded Systems and AI, with several publications and patents in those areas. He has also been involved in various projects involving the UK government, European Commission and tech corporations around Edge, AI and Network Security. Mo has been a regular keynote speaker at various conferences including The Next Web, AiWorld, DevNexus, IEEE/ACM, JavaOne, DevRelCon, CloudConf, Open Data Science Conference, Pycon and O’Reilly.

Build Your Own Cloud Native Covid-19 Data Analytics with Kubernetes and OpenShift(Demo Talk)

Co-FounderCord

Eric Landau

Prior to co-founding Cord, Eric spent 8 years in high-frequency trading where he was the lead quantitative researcher on an equity desk at DRW. Eric is now leading Cord’s deep learning research and technology. Eric holds an M.S. in Applied Physics from Harvard University and an M.S. in Electrical Engineering and B.S. in Physics from Stanford University.

Annotating Data with AI-assisted Labelling(Demo Talk)


ODSC Europe Virtual Conference 2020 | September 17th – 19th

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