Oct 31st – Nov 3rd, 2023
Machine Learning & Deep Learning Track
Learn the latest models, advancements, and trends from the creators and top practitioners behind two of data science’s hottest topics
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 Previous Machine Learning & Deep Learning Speakers
Pieter Abbeel, PhD
Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, which has his lab push the frontiers of deep reinforcement learning, deep unsupervised learning, especially as it pertains to robotics. Abbeel’s Intro to AI class has been taken by over 100K students through edX, and his Deep Unsupervised Learning materials are standard references for AI researchers. Abbeel has founded several companies, including Gradescope (AI to help instructors with grading homework, projects and exams) and Covariant (AI for robotic automation of warehouses and factories). He advises many AI and robotics start-ups, and is a frequently sought after speaker worldwide for C-suite sessions on AI future and strategy. Abbeel has received many awards and honors, including ACM Prize, IEEE Fellow, PECASE, NSF-CAREER, ONR-YIP, AFOSR-YIP, Darpa-YFA, TR35, and 10+ best paper awards/finalists. His work is frequently featured in the press, including the New York Times, Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review.
Mosharaf Chowdhury, PhD
Mosharaf Chowdhury is a Morris Wellman associate professor of CSE at the University of Michigan, Ann Arbor, where he leads the SymbioticLab. His work improves application performance and system efficiency of machine learning and big data workloads. He is also building software solutions to monitor and optimize the impact of machine learning systems on energy consumption and data privacy. His group developed Infiniswap, the first scalable software solution for memory disaggregation; Salus, the first software-only GPU sharing system for deep learning; FedScale, the largest federated learning benchmark and a scalable and extensible federated learning engine; and Zeus, the first GPU energy-vs-training performance tradeoff optimizer for DNN training. In the past, Mosharaf did seminal works on coflows and virtual network embedding, and he was a co-creator of Apache Spark. He has received many individual awards and fellowships, thanks to his stellar students and collaborators. His works have received seven paper awards from top venues, including NSDI, OSDI, and ATC, and over 22,000 citations. Mosharaf received his Ph.D. from UC Berkeley in 2015.
James Demmel, PhD
James Demmel is the Dr. Richard Carl Dehmel Distinguished Professor of Computer Science and Mathematics at the University of California at Berkeley, and former Chair of the EECS Dept. He also serves as Chief Strategy Officer for the start-up HPC-AI Tech, whose goal is to make large-scale machine learning much more efficient, with little programming effort required by users. Demmel’s research is in high performance computing, numerical linear algebra, and communication avoiding algorithms. He is known for his work on the widely used LAPACK and ScaLAPACK linear algebra libraries. He is a member of the National Academy of Sciences, National Academy of Engineering, and American Academy of Arts and Sciences; a Fellow of the AAAS, ACM, AMS, IEEE and SIAM; and winner of the IPDPS Charles Babbage Award, IEEE Computer Society Sidney Fernbach Award, the ACM Paris Kanellakis Award, the J. H. Wilkinson Prize in Numerical Analysis and Scientific Computing, and numerous best paper prizes.
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training(Tutorial)
Kirstin Aschbacher, PhD
Kirstin Aschbacher is a Data Scientist, with a background in PsychoNeuroImmunology Research from her days as an Associate Professor at the University of California, San Francisco (UCSF), Department of Psychology, Weill Institute for Neurosciences, and the Division of Cardiology. She has a PhD in Clinical Psychology and is also a licensed Psychologist with a certificate in HRV Biofeedback. She uses her cross-functional skill-sets to drive innovative, AI-based products that enhance user well-being and stress-resilience. In her current role as Senior Director of Health Data Science at Meru Health, she has focused on HRV Biofeedback and Precision Care algorithms.
Daniel Lenton, PhD
Daniel Lenton is the creator of Ivy, which is an open-source framework with an ambitious mission to unify all other ML frameworks. Prior to starting Ivy, Daniel was a PhD student at Imperial College London, where he published research in the areas of machine learning, robotics and computer vision.
Unifying ML With One Line of Code(Tutorial)
Ali Vanderveld, PhD
Ali Vanderveld is a Senior Staff Data Scientist at Wayfair, where she serves as a technical leader for machine learning, currently leading the development of novel search and recommendation technologies. Prior to Wayfair, she led a team focused on language AI at Amazon Web Services and was the Director of Data Science at ShopRunner. She has also worked at Civis Analytics, at Groupon, and as a technical mentor for the Data Science for Social Good Fellowship. Ali has a PhD in theoretical astrophysics from Cornell University and got her start working as an academic researcher at Caltech, the NASA Jet Propulsion Laboratory, and the University of Chicago, working on the development teams for several space telescope missions, including ESA’s Euclid.
Optimizing Recommendations for Competing Business Objectives(Talk)
Oswald Campesato
Oswald is a former PhD Candidate (ABD) in Mathematics, an education fanatic (5 degrees), and an author of 40 technical books. He has worked for Oracle, AAA, and Just Systems of Japan as well as various startups. He has lived/worked in 5 countries on three continents, and in a previous career he worked in South America, Italy, and the French Riviera, and has traveled to 70 countries on five continents. He has worked from C/C++/Java developer to CTO, comfortable in 4 languages, and currently he is an AI (ML,DL,NLP,DRL) adjunct instructor at UCSC and works on NLP-related tasks in a start-up in the Bay Area.
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.
Frank Zickert, PhD
Frank Zickert is Quantum machine learning engineer and the author of Hands-On Quantum Machine Learning With Python. He teaches quantum machine learning in an accessible way to help those without a degree in math or physics to get started in the field.
In his research, Frank strives to use quantum machine learning to advance the field of knowledge graph-based natural language processing. He is also the Chief Technology Officer of Ihr MPE B+C where he supports medical physicists to provide radiation protection services for clinical customers. Previously he worked at Aperto-An IBM Company and Deutsche Bank.
Frank earned his Ph.D. in Information Systems Development from Goethe University Frankfurt am Main, Germany.
Getting Started With Quantum Bayesian Networks in Python and Qiskit(Tutorial)
Andrew Zirm, PhD
Andrew is a Ph.D. Astrophysicist who made the switch from academia to data science (via the Insight Data Science program) in 2014. He was the first data scientist hired at Greenhouse Software where he has worked on many internal data science projects and a few customer-facing data-powered product features. Andrew lives in New Jersey with his wife and son.
Statistics for Data Science(Bootcamp)
Ajay Thampi, PhD
Ajay Thampi is a machine learning engineer at Meta where he works on large recommender systems, responsible AI and fairness. He holds a PhD and his research was focused on signal processing and machine learning. He has published papers at leading conferences and journals on reinforcement learning, convex optimization, and classical machine learning techniques applied to 5G cellular networks.
Interpretable AI or How I Learned to Stop Worrying and Trust AI(Talk)
Jennifer Dawn Davis, PhD
Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics at conferences for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level. Jennifer enjoys working with clients and helping them achieve their goals.
Large Scale Deep Learning using the High-Performance Computing Library OpenMPI and DeepSpeed(Workshop)
Mohit Bansal, PhD
Dr. Mohit Bansal is the John R. & Louise S. Parker Professor and the Director of the MURGe-Lab in the Computer Science department at University of North Carolina (UNC) Chapel Hill. He received his PhD from UC Berkeley and his BTech from IIT Kanpur. His research expertise is in natural language processing and multimodal machine learning, with a particular focus on grounded and embodied semantics, human-like language generation, and interpretable and generalizable deep learning. He is a recipient of DARPA Director’s Fellowship, NSF CAREER Award, Army Young Investigator Award, Google Focused Research Award, Microsoft Investigator Fellowship, and outstanding paper awards at ACL, CVPR, EACL, COLING, and CoNLL. His service includes ACL Executive Committee, ACM Doctoral Dissertation Award Committee, Program Co-Chair for CoNLL 2019, ACL Americas Sponsorship Co-Chair, and Associate/Action Editor for TACL, CL, IEEE/ACM TASLP, and CSL journals. Webpage: https://www.cs.unc.edu/~mbansal/
Unified and Efficient Multimodal Pretraining Across Vision and Language(Talk)
Alex Ratner, PhD
Alex Ratner is the co-founder and CEO at Snorkel AI, and an Assistant Professor of Computer Science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in CS advised by Christopher Ré at Stanford, where he started and led the Snorkel open source project, and where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows such as creating and managing training data and applying this to real-world problems in medicine, knowledge base construction, and more. Previously, he earned his A.B. in Physics from Harvard University.
Operationalizing Organizational Knowledge with Data-Centric AI(Talk)
Guy Van den Broeck, PhD
Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.
Artificial Intelligence Can Learn from Data. But Can It Learn to Reason?(Talk)
Diego Klabjan, PhD
Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences. He is also Founding Director, Master of Science in Analytics, and the Deep Learning Lab. His expertise is focused on data science and deep learning with a concentration in finance, insurance, and healthcare. Professor Klabjan has led projects with large companies such as The Chicago Mercantile Exchange Group, Intel, General Motors and many others, and he is also assisting numerous start-ups with their analytics needs. He is also a founder of Opex Analytics.
MLOps for Deep Learning(Talk)
Matt Harrison
Matt Harrison has been using Python since 2000. He runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage.
He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.
Machine Learning with XGBoost(Workshop)
Idiomatic Pandas(Workshop)
Amita Kapoor, PhD
Amita Kapoor, is the author of best-selling books in the field of Artificial Intelligence and Deep Learning. She mentors students at different online platforms such as Udacity and Coursera and is a research and tech advisor to organizations like DeepSight AI Labs and MarkTechPost. She started her academic career in the Department of Electronics, SRCASW, the University of Delhi, where she was an Associate Professor. She has over 20 years of experience in actively researching and teaching neural networks and artificial intelligence at the university level. A DAAD fellow, she has won many accolades with the most recent being Intel AI Spotlight award 2019, Europe. An active researcher, she has more than 50 publications in international journals and conferences. Extremely passionate about using AI for the betterment of society and humanity in general, she is ready to embark on her second innings as a digital nomad.
Deep Learning with Python and Keras (Tensorflow 2)(Training)
Clinton Brownley, PhD
Clinton Brownley, Ph.D., is a data scientist at Meta (formerly Facebook), where he’s responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook.
As an avid student and teacher of modern data analysis and visualization techniques, Clinton teaches a graduate course in interactive data visualization for UC Berkeley’s MIDS program, taught a short-term graduate course in regression analysis and machine learning workshop for NYU’s A3SR program, leads an annual machine learning in Python workshop, and is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis”.
Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.
Machine Learning with Python: A Hands-On Introduction(Training)
Dr. Anju Kambadur
Dr. Prabhanjan (Anju) Kambadur heads the AI Engineering group at Bloomberg. Anju leads a group of 100+ researchers and engineers who build solutions for Bloomberg clients in the areas of machine learning, natural language processing (NLP) and natural language understanding, information extraction, knowledge graphs, question answering, and table understanding. Previously, Anju was a research staff member in the Business Analytics and Mathematical Sciences Department at IBM Research’s Thomas J. Watson Research Center, where he worked on problems in machine learning, such as matrix sketching, genome-wide association studies, temporal causal modeling, and high-performance computing. He received his PhD from Indiana University. Anju has published peer-reviewed articles in the fields of high-performance computing, machine learning, and natural language processing.
Johnathan Roy Azaria
Experienced Data Scientist and Tech Lead at Imperva’s threat research group where I work on creating machine learning algorithms to help protect our customers against web app and DDoS attacks. Before joining Imperva, I obtained a B.Sc and M.Sc in Bioinformatics from Bar Ilan University.
Oliver Zeigermann
Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.
Autoencoders – a Magical Approach to Unsupervised Machine Learning(Workshop)
Michelle Hoogenhout
Michelle Hoogenhout is the lead data scientist at Hydrostasis, Inc. Hydrostasis is pioneering hydration monitoring by collecting optical changes in blood flow and water content from wrist-worn sensors. Michelle holds a PhD in Psychology (Neuropsychology) from the University of Cape Town and a neuropsychiatric genetics training fellowship from the Harvard T.H. Chan School of Public Health. She has over 10 years of experience in machine learning and insight generation from physiological and psychological data. Her research interests include the intersection between physical states and emotional and cognitive performance, as well as developmental disorders and empathy. Michelle also loves teaching and instructional design: she’s taught data science, psychology, and statistics. In her free time Michelle loves hiking, board games and swimming.
Victor Zitian Chen, PhD, CFA
Dr. Victor Zitian Chen, CFA, is a believer and action-taker on the idea of a world brain. Dr. Chen is currently the Director of Data Analytics and Insights, Experimental Design and Causal Inference at Fidelity Investments. He leads the causal analytics efforts across the personal investing business at the Fidelity, including experimentation, prescriptive analytics, and causal knowledge graph-based applications. Before joining Fidelity, Dr. Chen was a tenured professor in management and data science at the University of North Carolina, Charlotte, and a visiting professor in international business at Copenhagen Business School, Denmark. He led two major National Science Foundation (NSF) grants focusing on causal knowledge graph-based explainable AI and analytics applications. He founded and led the Global OpenLabs for Performance Enhancement-Analytics and Knowledge System (GoPeaks) – a startup to advance and commercialize knowledge synthesis and causal/prescriptive analytics solutions for business decisions.
Causal/Prescriptive Analytics in Business Decisions(Business Talk)
Balaji Lakshminarayanan, PhD
Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.
Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning(Tutorial)
Jacob Schreiber
Jacob Schreiber is a post-doctoral researcher at the Stanford School of Medicine. As a researcher, he has developed machine learning approaches to integrate thousands of genomics data sets, to design biological sequences with desired characteristics, and has described how statistical pitfalls can be encountered and accounted for in genomics data sets. As an engineer, he has contributed to the community as a core contributor to scikit-learn and as the developer of several machine learning toolkits, including pomegranate for probabilistic modeling and apricot for submodular optimization.
Navigating the Pitfalls of Applying Machine Learning in Practice(Talk)
Julia Lintern
Julia Lintern currently works as a Director of Data Science at Gartner. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.
Introduction to Machine Learning(Bootcamp)
Yang You, PhD
Yang You is a Presidential Young Professor at National University of Singapore. He is on an early career track at NUS for exceptional young academic talents with great potential to excel. He received his PhD in Computer Science from UC Berkeley. His advisor is Prof. James Demmel, who was the former chair of the Computer Science Division and EECS Department. Yang You’s research interests include Parallel/Distributed Algorithms, High Performance Computing, and Machine Learning. The focus of his current research is scaling up deep neural networks training on distributed systems or supercomputers. In 2017, his team broke the world record of ImageNet training speed, which was covered by the technology media like NSF, ScienceDaily, Science NewsLine, and i-programmer. In 2019, his team broke the world record of BERT training speed. The BERT training techniques have been used by many tech giants like Google, Microsoft, and NVIDIA. Yang You’s LARS and LAMB optimizers are available in industry benchmark MLPerf. He is a winner of IPDPS 2015 Best Paper Award (0.8%), ICPP 2018 Best Paper Award (0.3%) and ACM/IEEE George Michael HPC Fellowship. Yang You is a Siebel Scholar and a winner of Lotfi A. Zadeh Prize. Yang You was nominated by UC Berkeley for ACM Doctoral Dissertation Award (2 out of 81 Berkeley EECS PhD students graduated in 2020). He also made Forbes 30 Under 30 Asia list (2021) and won IEEE CS TCHPC Early Career Researchers Award for Excellence in High Performance Computing. For more information, please check his lab’s homepage at https://ai.comp.nus.edu.sg/
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training(Tutorial)
Leonidas Souliotis, PhD
Leonidas (Leo) is a Senior Data Scientist at Astrazeneca. His work is focused around machine learning in oncology, including clinical and non clinical applications. He is also enthusiastic about NLP applications in oncology and how this can be used to leverage patient treatment. He is also a workshop facilitator in the European Leadership University (ELU), NL and has also been a data science educator at DataCamp. He holds a PhD from the University of Warwick, UK. in bioinformatics and ML, an MSc in statistics from Imperial College London, UK and a BSc in Statistics and Insurance Science from the University of Piraeus, GR.
Introduction to Python for Data Analysis(Bootcamp)
Yegna Jambunath
Yegna Jambunath is a Researcher at Centre for Deep Learning, Northwestern University. Yegna has six years of total work experience with four years of industry focused research experience in ML and Data Science. His areas of interest are MLOps, ML in Healthcare and RL.
MLOps for Deep Learning(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 31st – Nov 3rd, 2023
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