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.  

AllAdvancedBeginnerDeep LearningIntermediateMachine LearningTalksMLOps & Data EngineeringTrainings, Workshops & TutorialsData for GoodOpen-sourceNLPKick StartML for ProgrammersData VisualizationComputer Vision
Research Scientist, Professor in Computer ScienceGoogle, Columbia University

Dr. Lora Aroyo

Lora Aroyo is a Full Professor in Computer Science. Currently, she is a visiting professor at the Data Science Institute at Columbia University. She is also Chief of Science for a NY-based startup Tagasauris, and the president of the User Modeling community (UM Inc) which serves as a steering committee for the ACM Conference Series “User Modeling, Adaptation and Personalization” (UMAP) part of both SIGCHI and SIGWEB. She is also member of the ACM SIGCHI conferences board, in her role as the UMAP steering committee chair. She headed the User-centric Data Science group at the Department of Computer Science, Vrije Universiteit Amsterdam, The Netherlands, and was member of the Amsterdam Data Science and the Network Institute. She is involved in a number of research projects and in the organization of conferences, workshops and tutorials focussing on crowdsourcing and human computation, collecting data, data quality assessment, and especially hybrid human-AI systems for understand text, image and video. She has led major research projects in semantic search, recommendation systems, event-driven access to online multimedia collections, and through these has become a recognized leader in human computation techniques for specific domains, such as digital humanities, cultural heritage, and interactive TV.

Professor, DirectorArtificial Intelligence, Spanish National Research Council

Carles Sierra, PhD

Carles Sierra is Director of the Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council (CSIC) located in Barcelona. He has been contributing to Artificial Intelligence research since 1985 in the areas of Knowledge Representation, Auctions, Electronic Institutions, Autonomous Agents, Multiagent Systems and Agreement Technologies. He has participated in more than 20 projects funded by the European Union, is or has been a member of several editorial boards of journals, including AIJ and JAIR, two of the most prestigious generalist journals, and was the editor in chief of the JAAMAS journal, specialized in autonomous agents. He organized IJCAI, the most important international artificial intelligence conference in 2011 in Barcelona and was the President of the IJCAI Program Committee in 2017 in Melbourne. He is a Fellow of the European Association of AI, EurAI, and recipient of the ACM/SIGAI Autonomous Agents Research Award 2019.

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.

Deep Learning Survival Analysis for Consumer Credit Risk Modelling(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.

Not Always a Black Box: Explainability Applications for a Real Estate Problem(Talk)

Director | ProfessorLeuven.AI | KU Leuven

Luc De Raedt, PhD

Luc De Raedt is a full professor (of research) at the University of Leuven (KU Leuven) in the Department of Computer Science and a former chair of Machine Learning at the Albert-Ludwigs-University in Freiburg. Luc De Raedt has been working in the areas of artificial intelligence and computer science, especially on computational logic, machine learning and data mining, probabilistic reasoning and constraint programming and their applications in bio- and chemoinformatics, vision and robotics, natural language processing, and engineering. His work has typically crossed boundaries between different research areas, often working towards an integration of their principles. He is well-known for his early work on inductive logic programming (combining logic with learning). Since 2000, he has been working towards a further integration of logical and relational learning with probabilistic reasoning (statistical relational learning and probabilistic programming) and on inductive querying in databases. During the last three years he has been fascinated by the possibility of combining constraint programming principles with data mining and machine learning. He is currently coordinating a European IST FET project in this area (ICON — Inductive Constraint Programming) and is the program chair of the 20th European Conference on Artificial Intelligence (Montpellier, 2012). He was a program co-chair of ICML 2005 and ECML/PKDD 2001.

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.

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.

Chair in AI, ProfessorCSML, University College London

John Shawe-Taylor, PhD

John S Shawe-Taylor is a professor at University College London (UK) where he is Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from Neural Networks, to Machine Learning, to Graph Theory. John Shawe-Taylor obtained a PhD in Mathematics at Royal Holloway, University of London in 1986. He subsequently completed an MSc in the Foundations of Advanced Information Technology at Imperial College. He was promoted to Professor of Computing Science in 1996. He has published over 150 research papers. He moved to the University of Southampton in 2003 to lead the ISIS research group. He has been appointed the Director of the Centre for Computational Statistics and Machine Learning at University College, London from July 2006. He has coordinated a number of European wide projects investigating the theory and practice of Machine Learning, including the NeuroCOLT projects. He is currently the scientific coordinator of a Framework VI Network of Excellence in Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL) involving 57 partners.

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)

Data and Applied Scientist, AI FrameworksMicrosoft

Prabhat Roy

Prabhat Roy is a Data and Applied Scientist at Microsoft, where he is a leading contributor to the scikit-learn to ONNX converter project (https://github.com/onnx/sklearn-onnx). In the past, he worked on ML.NET, an open-source ML library for .NET developers, focusing on customer engagements for text and image classification problems.

ML Inference on Edge with ONNX Runtime(Tutorial)

 

Applied Research ScientistElement AI

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.

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.

Lead Data ScientistMagicLab

Laura Mitchell

With over 10 years of experience in the tech and data science space, I am the Lead Data Scientist at MagicLab whose brands have connected the lives of over 500 million people through dating, social and business. I have hands-on 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)

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 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)

Machine Learning Research EngineerWelocalize

Dr. Natasha Latysheva

Dr. Natasha Latysheva is a machine learning engineer in the NLP group at Welocalize, a leading language services provider. Her work focuses on machine translation and natural language processing. She previously worked as a data scientist in a video game studio, and before that on a PhD in computational biology at Cambridge.

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)

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.

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.

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)

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)

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.

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)

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)

Head of AIAxel Springer SE

Dat Tran

Dat is heading Axel Springer AI, the artificial intelligence unit of Axel Springer SE which is the largest digital publishing house in Europe. His goal is to make AI more accessible within Axel Springer and hence drive innovation within the group. His ultimate plan is to turn Axel Springer into an AI first company.

Dat’s interests are diverse from traditional machine learning, deep learning, AI in general to computer vision and NLP. He is a regular speaker and has presented at several renowned conferences. He also blogs about his work on Medium.

Training a Machine to See What’s Beautiful (esp. for Hotel Photos)(Talk)

Lecturer and Vice-Director of the Data Science InstituteNUI Galway

Paul Buitelaar, PhD

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.

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)

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.

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 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)

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 ScientistDataCamp

Hugo Bowne-Anderson, PhD

Hugo Bowne-Anderson is a Data Scientist at DataCamp and has had extensive experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and with organizations such as Data Carpentry. He has developed over 25 courses on the DataCamp platform, impacting over 300,000 learners worldwide through his own courses. He previously also hosted DataFramed, the DataCamp podcast, loves teaching Bayesian data analysis and aspires to reduce as much “computational anxiety’ in the world as he can through pedagogy.

Bayesian Data Science: Probabilistic Programming(Training)

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)

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: Penalized Regression and Boosted Trees(Training)

Machine Learning in R Part II: Using workflows to build an ML optimization pipeline(Training)

Machine Learning in R Part III: Forecasting Time Series Data(Training)

Machine Learning in R Part IV: Putting R-Based Machine Learning into Production with Plumber and Docker(Training)

Global Lead Data Science InstructorGeneral Assembly

Matt Brems

Matt currently is a senior data scientist with FINRA, the Financial Industry Regulatory Authority, tackling bias and fairness in machine learning. Matt also is a managing partner at a data science consultancy named BetaVector, volunteers with Statistics Without Borders, and is a member of the Distinguished Faculty program at General Assembly, earning their 2019 Distinguished Faculty Member of the Year award. Matt previously applied data science to politics and to peanut butter, and earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about responsibly putting the revolutionary power of machine learning into the hands of as many people as possible

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.

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)

Founder & Lead Data Scientist, Lead InstructorGeneral Assembly

Stefan Jansen

Stefan is Lead Data Scientist at Applied AI where he advises Fortune 500 companies and startups on translating business goals into a data & AI strategy, building data science teams, and developing machine learning solutions. Prior to his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. Stefan holds Master degrees from Harvard and Free University Berlin, and a CFA Charter. He is the author for ‘Machine Learning for Algorithmic Trading’ and teaches data science at General Assembly.

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

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)

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)

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)

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)

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)

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)

Big Data & ML Expert | Data AdvisorIncubhealth

Inés Huertas Freire

Ines Huertas is Big Data and Machine Learning expert, she is a coordinator in Rladies Madrid and Datanauts in the Open Data NASA Program. She participates as a speaker in different conferences, in 2015 she founded her own Big Data and ML startups, of which she was CEO for 4 years. She currently advises companies within the data area.

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 Professor, Computer ScienceNortheastern University College

Rose Yu, PhD

Dr. Rose Yu is an Assistant Professor at Northeastern University Khoury College of Computer Sciences. Previously, she was a postdoctoral researcher in the Department of Computing and Mathematical Sciences at Caltech. She earned her Ph.D. in Computer Science at the University of Southern California and was a visiting researcher at Stanford University. Her research focuses on machine learning for large-scale spatiotemporal data and its applications, especially in the emerging field of computational sustainability. She has over a dozen publications in leading machine learning and data mining conference and several patents. She is the recipient of the USC Best Dissertation Award, “MIT Rising Stars in EECS”, and the Annenberg fellowship.

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)

Data ScientistWalmart Labs, Bangalore

Debanjana Banerjee

Debanjana is a Data Scientist at Walmart Labs, Global Data Value Realization. At Walmart, she has been instrumental in building numerous high-functioning bots in the compliance space dealing heavily in Natural Language Processing, Optimization, Mixture Models and Rare Time Series. Currently, her focus is on extensive Shrink Research where along with her team members she is identifying potential areas of high impact for Retail Shrink. During her 3 years of experience, Debanjana has filed 5 US patents in the field of Clustering & Anomaly Detection, Imbalance Text Classification, Travel Optimization and Stochastic Processes. In addition, she has three published papers to her credit. She presented her paper REDCLAN (Relative Density Based Clustering and Anomaly Detection) in ADCOM’18, CRESST was included at ICMLA’19 and iCASSTLE (Imbalanced Classification Algorithm for Semi Supervised Text Learning) was presented at ICMLA’18 (Orlando, FL), which was later published by IEEE. Debanjana has a master’s degree in Statistics from Indian Institute of Technology (Kanpur).

CRESST: Complete Rare Event Specification Using Stochastic Treatment(Talk)

AI and Cloud Architect & AuthorNew Relic, Inc | Wiley

Dan Sullivan, PhD

Dan Sullivan is a Principal Engineer and Architect with New Relic where he focuses on cloud architecture, data science, machine learning, and data architecture. He is the author of six books, most recently on cloud and NoSQL databases, as well as several online courses on machine learning, data science, and cloud computing that have had over 1 million views. Dan holds a doctorate in genetics, bioinformatics, and computational biology. His peer-reviewed research has been published in PloS One, Nucleic Acid Research, Infection and Immunity, and the Journal of Proteomics and Genomics Research.

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.

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.

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.

StructureBoost: Gradient Boosting with Categorical Structure(Workshop)

Data ScientistCoursera

Vinod Bakthavachalam

Vinod Bakthavachalam is a Data Scientist working with the Content Strategy and Enterprise teams, focusing on using Coursera’s data to understand what are the most valuable skills across roles, industries, and geographies. Prior to Coursera, he worked in quantitative finance and studied Economics, Statistics, and Molecular & Cellular Biology at UC Berkeley.

Causal Inference in Data Science(Workshop)

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.

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.

Eye-Tracking and Visualization(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)

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 InstructorFlatiron School

David John Baker, PhD

David John Baker, PhD is a data scientist and educator passionate about questions at the intersection of music and science.
His research looks to understand how computational musicology and cognitive psychology can explore how music can be used to learn about how people think. He is an RStudio Certified Trainer, previously taught at Flatiron School, London as a Lead Instructor of Data Science, and has worked both on music industry projects as a data scientist as well as volunteered in the charity sector.
He’s spoken at EARL London and is the organiser of the L3 Data Science: A Place to Learn and Grow meet-up group in London.@DavidJohnBaker davidjohnbaker.rbind.io

Introduction to Statistics for Data Science(Training)

Instructor, Data ScientistData Dan

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 | Google Developer ExpertApplied Materials | Google

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. Sachin is a Deputy Director at Applied Materials leading efforts in Data Science and Advanced Analytics with a team of data scientists, working to provide innovative solutions with a focus on AI enablement in various products and systems for the semiconductor equipment manufacturing industry. He has a BS in Information Technology and over 15 years of diverse experience in the Industry around Software, Data Science and Artificial Intelligence.

NLP Crash Course (Full-Day Training)

Software EngineerInria

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)


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