ODSC Europe Hybrid Conference | June 15 - 16, 2022
AI in Life Sciences and Pharma
The Most Essential Techniques in Healthcare
Learn New AI Techniques in Life Sciences & Pharma
The Ai for Life Science Track is where top pharma and medtech experts gather to discuss the latest advances, trends, and models in the rapidly expanding life science sector.
These industry experts will teach you essential machine learning and deep learning techniques in healthcare. You will be able to analyze how AI affects patient outcomes and research, understand the tools needed to solve problems in the business of medicine, and how to apply AI to innovate and understand emerging technologies in drug discovery projects.
ODSC EUROPE Hybrid Conference 2022 | June 15 - 16
Register and save 75%
What You'll Learn
Talks + Workshops + Special Events on these topics:
Disease identification and classification
Machine Learning in Clinical Trials
Machine Learning for Drug Discovery
Precision Medicine with Machine Learning
Predictive Analytics for Clinical Trails
Machine Learning for Genomics
Advancing Diagnostics
Improving Patient Outcomes
Digitization of Data in Healthcare
Imaging Diagnostics
Supply Chain in Pharma
Managing Product Pipelines
Risk Management
and more…
ODSC EUROPE Hybrid Conference 2022 | June 15 - 16
Register and save 75%Some of Our Previous Speakers
Sara Khalid
Sara is a Senior Research Associate in Biomedical Data Science and University Research Lecturer at the University of Oxford, where she is the Machine Learning Lead in the Centre for Statistics in Medicine. She has 12 years of experience in machine learning, signal processing, and intelligent remote monitoring research, with applications in biomedical and planetary health informatics. Sara has served on the NASA Frontier Development Lab Artificial Intelligence Panel and the NASA Climate Challenge Big Think. She is a National Geographic Society Explorer in Tracking Plastic Pollution with Remote Monitoring and Machine Learning. Sara is also a University of Oxford Ambassador for Women in Data Science.
Judy Gichoya
Dr. Gichoya is a multidisciplinary researcher, trained as both an informatician and a clinically active radiologist. She is an assistant professor at Emory university, and works in Interventional Radiology and Informatics. She has been funded through the Grand Challenges Canada, NBIB and NSF ECCS. She is seconded to the National Institutes of Health as a data scholar to help with the Open Data Science Platform (OSDP) component of the DSI Africa Initiative to “Harness Data Science for Health In Africa”. Her career focus is on validating machine learning models for health in real clinical settings, exploring explainability, fairness, and a specific focus on how algorithms fail. She has worked on the curation of datasets for the SIIM (Society for Imaging Informatics in Medicine) hackathon and ML committee. She volunteers on the ACR and RSNA machine learning committees to support the AI ecosystem to advance development and use of AI in medicine. She is currently working on the sociotechnical context for AI explainability for radiology, especially the dimensions of human factors that govern user perceptions and preferences of XAI systems.
Fairness in Medical Algorithms: Threats and Opportunities(Talk)
Dr. Fabian Theis
Fabian Theis is director of the Institute of Computational Biology at the Helmholtz Center Munich and scientific director of the Helmholtz Artificial Intelligence Cooperation Unit (HelmholtzAI) which was launched in 2019. He is a full professor at the Technical University of Munich, holding the chair ‘Mathematical Modelling of Biological Systems’, associate faculty at the Wellcome Trust Sanger Institute as well as adjunct faculty at the Northwestern University. Fabian Theis holds a Master’s degree in Mathematics and Physics and Ph.D. Degrees in Physics and Computer Science. After different research stays. He worked as visiting researcher at the Department of Architecture and Computer Technology (University of Granada, Spain), at the RIKEN Brain Science Institute (Wako, Japan), at FAMU-FSU (Florida State University, USA), and TUAT’s Laboratory for Signal and Image Processing (Tokyo, Japan), and headed the ‘signal processing & information theory’ group at the Institute of Biophysics (Regensburg, Germany). In 2006, he started working as a Bernstein fellow leading a junior research group at the Bernstein Center for Computational Neuroscience, located at the Max Planck Institute for Dynamics and Self-Organisation at Göttingen. In summer 2007, Fabian Theis became working group head of CMB at the Institute of Bioinformatics at the Helmholtz Center Munich. In spring 2009, he became associate Professor for Mathematics in Systems Biology at the Math Department of the TU Munich. 2009-2014 he was a member of the ‘Young Academy’ (founded by the Berlin-Brandenburg Academy of Sciences and Humanities and the German Academy of Natural Scientists Leopoldina) and was awarded an ERC starting grant in 2010. In 2017 he was awarded the Erwin Schrödinger prize together within an interdisciplinary team at the ETH Zürich. Fabian Theis is part of and also coordinates various consortia (i.e. sparse2big involving 8 Helmholtz Centers) and founded the network SingleCellOmics Germany (SCOG). Furthermore, he coordinates 2019 launched Munich School for Data Science (MUDS) and is co-directing the ELLIS Munich Unit, the local hub of the European Machine Learning network ELLIS. Since 2020, he holds the position of co-chair of the Bavarian AI Council of the Bavarian Ministry for Science and Art and supports the TUM with his expertise as start-up Ambassador.
Interpretable Machine Learning to Model Drug Perturbations in Single Cell Genomics(Talk)
Besmira Nushi, PhD
Besmira Nushi is a researcher in the Adaptive Systems and Interaction group at Microsoft Research. Her interests lie at the intersection of human and machine intelligence focusing on Reliable Machine Learning and Human-AI Collaboration. In the last five years, she has made practical and scientific contributions on implementing and deploying Responsible AI tools for debugging and troubleshooting ML systems. Prior to Microsoft, Besmira completed her doctoral studies at ETH Zurich in 2016 on optimizing data collection processes for Machine Learning.
Error Analysis for Accelerating Responsible Machine Learning(Talk)
Thomas Wiecki, PhD
Thomas Wiecki is co-creator of PyMC, the industry-standard tool for statistical data science in Python. To help businesses solve advanced analytical problems he founded PyMC Labs (www.pymc-labs.io) consisting of world-class experts in Bayesian modeling.
Bayesian Marketing Science: Solving Marketing’s 3 Biggest Problems(Track Keynote)
Mehrnoosh Sameki, PhD
Mehrnoosh Sameki is a principal PM manager at Microsoft, where she leads emerging Responsible AI technology and tools and for the Azure Machine Learning platform. She has cofounded Error Analysis, Fairlearn and Responsible AI Toolbox and has been a contributor to the InterpretML offering. She earned her PhD degree in computer science at Boston University, where she currently serves as an adjunct assistant professor, offering courses in responsible AI. Previously, she was a data scientist in the retail space, incorporating data science and machine learning to enhance customers’ personalized shopping experiences.
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.
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)
Ayemya Moe
Ayemya (Mina) Moe is a Marketing Operations Lead at Project Management Institute and also holds Director of Marketing position. She graduated from UCLA with Economics major and is also serving as a reviewer for IEEE CIS (IEEE Transactions on AI).
Kamila Hankiewicz
Kamila Hankiewicz is a Managing Director of Untrite, an AI company helping companies make better use of data they already have; we provide an AI engine which pulls data from silos and understands the links and relevance between them. Kamila is a vivid advocate for diversity and empowering women in technology; she co-founded NGO Girls in Tech London and Poland. The local chapter has an active member base of more than 9 000 people, with more than 125 000 globally.
Kamila’s past life includes working as a Management Consultant with a focus on banking, where she was involved in digital transformation projects (such as Santander’s £1.65bn worth project Rainbow). Kamila is a frequent speaker on the subject of humanising work with use of AI. Her latest talks include those for BigDataLDN, Rasa, Future of AI and Women in AI. She hosts “Humans of AI” video interviews with prominent people solving some of the world’s toughest problems with the use of AI – https://www.youtube.com/channel/UC7qPUVnjrzb4oFwtAmDOTtw. Some of her guests include: Lord Tim Clement-Jones from House of Lords, David Barber from UCL, Rana el Kaliouby from Affectiva and many more.
The Missing Link: How AI Can Help Create a Safer Society and Better Businesses (Talk)
Why Attend
Accelerate and broaden your knowledge of key areas in Life Science and Pharma, including deep learning, machine learning, and predictive analytics
With numerous introductory level workshops, get hands-on experience to quickly build up your skills
Post-conference, get access to recorded talks online and learn from over 100+ high-quality recording sessions that let you review content at your own pace
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science
Learn directly from world-class instructors who are the authors of and contributors to many of the tools and frameworks
Meet hiring companies ranging from hot startups to Fortune 500s looking to hire professionals with data science skills at all levels
Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms
Get access to other focus area content, including ML/DL, Data Visualization Big Data, and Open Data Science
More Reasons To Attend?
Download the why attend guideWho Should Attend
The Life Sciences and BioPharma track will prove invaluable to those looking to quickly understand in detail the topics that matter most in data science now
Data scientists looking to develop and apply Deep Learning and Machine Learning models across a variety of Life Science problems and datasets
Data scientists seeking to learn how to leverage machine learning in drug discovery, genomics, and medicine
Anyone interested in understanding underlying AI/ML technologies & hands-on techniques across life science
Business professionals and industry experts looking to understand data science in practice
Software engineers and technologists who need to configure and administer deep learning in the construction/training of algorithms for biological data
CTO, CDS, and other managerial roles that require a bigger picture view of data science
Technologists in the field of Life Sciences & Pharma looking to break into data science
Students and academics looking for more practical applied training in data science tools and techniques