Machine Learning & Deep Learning
Comprising multiple tracks, this focus area is where leading experts in the rapidly expanding fields of Deep Learning and Machine Learning gather to discuss the latest advances, trends, and models in this exciting field.
Attend talks, tutorials, and workshops and hear from the creators and top practitioners as they teach the latest models and trends in Machine Learning and Deep Learning to solve problems in business and society.
Some of Our Confirmed Machine Learning & Deep Learning Speakers
Jack McCauley
Jack McCauley an Innovator in Residence at Jacobs Institute for Design Innovation at UC Berkeley, Professor at UC Berkeley, Co-Founder of Oculus, an American engineer, hardware designer, inventor, video game developer and philanthropist. Jack is best known for designing the guitars and drums for the Guitar Hero video game series, and as a co-founder and former chief engineer at Oculus VR. At Oculus, Jack designed and built the Oculus DK1 and DK2 virtual reality headsets. Oculus was acquired by Facebook for $2 Billion. McCauley holds numerous U.S. patents for inventions in software, audio effects, virtual reality, motion control, computer peripherals, and video game hardware and controllers. Jack was awarded a full scholarship to attend University of California, Berkeley where he earned a BSc., EECS in Electrical Engineering and Computer Science in 1986. Jack has authored numerous research papers in the field of artificial intelligence (AI) and mathematical modeling of AI-based systems and is currently pursuing new projects at his private R&D facility and hardware incubator in Pleasanton, California.
Jim Dowling
Jim Dowling is CEO of Hopsworks and an Associate Professor at KTH Royal Institute of Technology. He is lead architect of the open-source Hopsworks Feature Store platform. He is the organizer of the annual feature store summit conference and featurestore.org community, as well as co-organizer of PyData Stockholm.
Personalizing LLMs with a Feature Store(Workshop)
Stefanie Molin
Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
Dr. Petar Veličković
Petar Veličković is a Staff Research Scientist at Google DeepMind, Affiliated Lecturer at the University of Cambridge, and an Associate of Clare Hall, Cambridge. Petar holds a PhD in Computer Science from the University of Cambridge (Trinity College), obtained under the supervision of Pietro Liò. His research concerns geometric deep learning—devising neural network architectures that respect the invariances and symmetries in data (a topic I’ve co-written a proto-book about). Petar’s research has been used in substantially improving travel-time predictions in Google Maps, and guiding intuition of mathematicians towards new top-tier theorems and conjectures.
Dr. Andre Franca
Andre joined causaLens from Goldman Sachs, where he was an executive director in the Model Risk Management group in Hong Kong and Frankfurt. Today he is working with industry leading, global organisations to apply cutting edge Causal AI research in production level solutions that empower individuals and teams to make better decisions. Andre received his PhD in theoretical physics from the University of Munich, where he studied the interplay between quantum mechanics and general relativity in black-holes.
Causal AI: from Data to Action(Workshop)
Brian Lucena, PhD
Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Uncertainty Quantification: Approaches and Methods(Training)
Evie Fowler
Evie Fowler is a data scientist based in Pittsburgh, Pennsylvania. She currently works in the healthcare sector leading a team of data scientists who develop predictive models centered on the patient care experience. She holds a particular interest in the ethical application of predictive analytics and in exploring how qualitative methods can inform data science work. She holds an undergraduate degree from Brown University and a master’s degree from Carnegie Mellon.
Bridging the Interpretability Gap in Customer Segmentation(Talk)
Jonas Mueller
Jonas Mueller is Chief Scientist and Co-Founder at Cleanlab, a software company providing data-centric AI tools to efficiently improve ML datasets. Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms which now power ML applications at hundreds of the world’s largest companies. In 2018, he completed his PhD in Machine Learning at MIT, also doing research in NLP, Statistics, and Computational Biology.
Jonas has published over 30 papers in top ML and Data Science venues (NeurIPS, ICML, ICLR, AAAI, JASA, Annals of Statistics, etc). This research has been featured in Wired, VentureBeat, Technology Review, World Economic Forum, and other media. He has also contributed open-source software, including the fastest-growing open-source libraries for AutoML (https://github.com/awslabs/autogluon) and Data-Centric AI (https://github.com/cleanlab/cleanlab).
How to Practice Data-Centric AI and Have AI improve its Own Dataset(Tutorial)
Parul Pandey
Parul Pandey has a background in Electrical Engineering and currently works as a Principal Data Scientist at H2O.ai. Prior to this, she was working as a Machine Learning Engineer at Weights & Biases. Parul is one of the co-authors of Machine Learning for High-Risk Applications book, which focuses on the responsible implementation of AI. She is also a Kaggle Grandmaster in the notebooks category and was one of Linkedin’s Top Voices in the Software Development category in 2019. Parul has written multiple articles focused on Data Science and Software development for various publications and mentors, speaks, and delivers workshops on topics related to Responsible AI.
Machine Learning for High-Risk Applications – Techniques for Responsible AI(Tutorial)
Joep Kokkeler
Joep has more than 12 years experience of developing, engineering, architecting and visualising data products in various markets ranging from energy to clothing manufacturing. He’s focussing on enabling teams to be better at handling data and providing the teams with the tools and the knowledge needed to go live and to stay in production.
He was member of the Teqnation program committee), did a presentation on Kafka and Hue usage during football, developing and deploying on Hololens, Total Devops using Gitlab, Evolution of a datascience product, Using the elastic stack from PoC to Production, Xbox Kinect on a bike at Devoxx London.
Jeff Tao
Jeff Tao is the founder and CEO of TDengine. He has a background as a technologist and serial entrepreneur, having previously conducted research and development on mobile Internet at Motorola and 3Com and established two successful tech startups. Foreseeing the explosive growth of time-series data generated by machines and sensors now taking place, he founded TDengine in May 2017 to develop a high-performance time-series database purpose-built for modern IoT and IIoT businesses.
What is a Time-series Database and Why do I Need One?(Workshop)
Chuying Ma
Chuying (Annie) Ma is a senior data scientist in Walmart Inkiru team, where she works on developing and implementing machine learning models and strategies for real-time fraud detection and risk mitigation. She has a Master of Science degree in Biostatistics from Harvard University and gets her bachelor’s degree in Statistics and Mathematics from University of Michigan – Ann Arbor. In her spare time, she enjoys playing violin and ukulele.
A Semi-Supervised Anomaly Detection System Through Ensemble Stacking Algorithm(Talk)
Vincent Granville
Bio Coming Soon!
Massively Speed-Up your Learning Algorithm, with Stochastic Thinning(Tutorial)
Alison Cossette
Alison Cossette is a dynamic Data Science Strategist, Educator, and Podcast Host. As a Developer Advocate at Neo4j specializing in Graph Data Science, she brings a wealth of expertise to the field. With her strong technical background and exceptional communication skills, Alison bridges the gap between complex data science concepts and practical applications.
Alison’s passion for responsible AI shines through in her work. She actively promotes ethical and transparent AI practices and believes in the transformative potential of responsible AI for industries and society. Through her engagements with industry professionals, policymakers, and the public, she advocates for the responsible development and deployment of AI technologies.
Alison’s academic journey includes pursuing her Master of Science in Data Science program, specializing in Artificial Intelligence, at Northwestern University and research with Stanford University Human-Computer Interaction Crowd Research Collective. Alison combines academic knowledge with real-world experience. She leverages this expertise to educate and empower individuals and organizations in the field of data science.
Overall, Alison Cossette’s multifaceted background, commitment to responsible AI, and expertise in data science make her a respected figure in the field. Through her role as a Developer Advocate at Neo4j and her podcast, she continues to drive innovation, education, and responsible practices in the exciting realm of data science and AI.
Bridging the Gap: Light Code Solutions to Uniting Social Science and Modern Knowledge Graphs(Workshop)
Angad Arora
Angad Arora is a seasoned professional with a wealth of expertise in manufacturing and quality for leading tech companies. With his innovative approach to lean manufacturing using data science, he has successfully saved millions of dollars for consumer electronics manufacturing lines. Currently serving as a Product Quality Manager at Google Inc., Angad continues to drive excellence and ensure top-notch product quality in the dynamic world of technology.
Mark Saroufim
Mark Saroufim is an engineer on PyTorch at Meta working on open infrastructure, compilers and community. Mark is fond of hot takes and shares them on his blog https://marksaroufim.substack.com/. Prior to Meta, Mark worked as a Machine Learning engineer at Graphcore, Microsoft and yuri.ai.
Sanjay Jinturkar
Sanjay Jinturkar is Senior Director, MySQL HeatWave, focused on building machine learning capabilities inside the MySQL HeatWave database. These capabilities enable the user to automatically develop and deploy machine learning models inside the data base using AutoML in a cloud native environment for a variety of use cases, without the need to pull the data or the model outside the database. In past, he has held multiple technology and engineering management positions in systems software, mobile communications software, applications development and diagnostics. His interests include machine learning, cloud computing, database and architecture. Sanjay has a PhD in Computer Science from University of Virginia.
A Unified and User Friendly Approach to Develop ML Solutions in MySQL HeatWave AutoML(Talk)
Geeta Shankar
Geeta Shankar is a software engineer who specializes in leveraging data for business success. With expertise in computer science, data science, machine learning, and artificial intelligence, she stays updated with the latest data-driven innovations. Her Indian classical music background has taught her the value of sharp thinking, spontaneity, and connecting with diverse individuals. Geeta uses these skills to translate complex data into meaningful insights that enhance performance and customer experiences.
Emmanuel Turlay
Emmanuel Turlay is the founder and CEO of Sematic, an open-source ML infrastructure company. Emmanuel started his career in academia researching particle physics at CERN, before branching out into tech and moving to the US in 2014. He led engineering teams at Instacart, and then Cruise, where he led the ML Infrastructure team for four years. In 2022, Emmanuel founded Sematic to bring learnings from building ML infrastructure for robotaxis to the rest of the industry in an open-source manner.
Battle Scars from the MLOps Trenches of the Robotaxi Industry(Talk)
Kabir Nagrecha
Kabir Nagrecha is a Ph.D. candidate at UC San Diego, working with Professors Arun Kumar & Hao Zhang. His work focuses on systems infrastructure to support deep learning at scale, aiming to democratize large models and amplify the impact of machine learning applications. He is the recipient of the Meta Research Fellowship, as well as fellowships from the Halicioglu Data Science Institute and Jacobs School of Engineering at UCSD.
Kabir is the youngest-ever Ph.D. student at UCSD, having started his doctorate at the age of 17. He’s previously worked with companies such as Apple, Meta, & Netflix to build the core infrastructure that supports widely-used services such as Siri & Netflix’s recommendation algorithms. Most recently, he’s been working on Saturn, a new system to support automatic parallelization, scheduling, and resource apportioning for training large neural networks.
Democratizing Fine-tuning of Open-Source Large Models with Joint Systems Optimization(Talk)
What You'll Learn
Talks + Workshops + Special Events on these topics:
Topics
Machine Learning
Deep Learning
Artificial Intelligence
Neural Networks
Natural Language Processing
Computer Vision
Pattern Recognition
Tools & Languages
R
Python SciPy, Pandas, etc
Scikit-learn
Tensorflow
Spark
MLlib
H20
Tools & Languages
WEKA
Pylearn2
Theano
Caffe
Torch
Azure Machine Learning API
and many more..
You Will Meet
Top speakers and practitioners in Machine Learning and Deep Learning
Data Scientists and Data Analysts
Decision makers
Software Developers focused on Machine Learning and Deep Learning
Data Science Innovators
CEOs, CTOs, CIOs
Industry leaders
Core contributors in the fields of Machine Learning and Deep Learning
Data Science Enthusiasts
Why Attend?
Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models and advanced trends
Expand your network and connect with like-minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career
Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning
Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and impacting the domains of business, society, healthcare, finance, manufacturing, and more
ODSC WEST 2023 - Oct 30th – Nov 2nd, 2023
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