ODSC APAC Virtual Conference 2020 | December 8 - 9, 2020

Machine Learning & Deep Learning Track

Learn the latest models, advancements, and trends from the top practitioners behind two of data science’s hottest topics
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Machine Learning & Deep Learning

The Machine Learning & Deep Learning Conference is where 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 demonstrate and teach the latest developments in Machine Learning and Deep Learning that solve problems in business and society today.

What You'll Learn

Talks & Workshops 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..

ODSC APAC Virtual Conference 2020 | December 8 - 9, 2020

Register Now

Some Current Speakers

Head of Data Science Evangelism and MarketingCoiled

Hugo Bowne-Anderson, PhD

Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled, a company that makes it simple for organizations to scale their data science and machine learning in Python. He has extensive experience as a data scientist, educator, evangelist, content marketer, and data strategy consultant at DataCamp, the online education platform for all things data. He also has 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 ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 500,000 learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open source software, both for individuals and the enterprise.

Bayesian Data Science: Probabilistic Programming(Half-Day Training)

Chief Machine Learning ScientistAppier

Shou-De Lin, PhD

Dr. Shou-de Lin is Appier’s Chief Machine Learning (ML) Scientist. He joined Appier from National Taiwan University (NTU), where he served as a full-time professor in the Department of Computer Science and Information Engineering. Prior to joining NTU, Dr. Lin was a postdoctoral research fellow at the Los Alamos National Lab. Dr. Lin specializes in areas including artificial intelligence, knowledge discovery, and natural language processing. He is an expert in solving practical challenges in machine learning applications. Dr. Lin joined Appier in February 2020 and leads the AI, research team. His focus is on the development and application of advanced machine learning technology to help customers smoothly implement AI solutions and optimize performance. Dr. Lin has collaborated with more than 50 companies and has won several awards in recognition of his work, including the 2007 Google Research Awards; the Microsoft Research Awards (which Dr. Lin won three times); and the IBM Research Awards.
During his time at NTU, Dr. Lin established the Machine Discovery and Social Network Mining Lab. He is the all-time winner of the ACM KDD Cup, for which he either led or co-led the NTU team to win 6 championships. He also led a team to win the WSDM Cup in 2016. Dr. Lin is passionate about developing young AI talent in Taiwan and has supervised more than 100 graduate students, and several of which have gone on to become university professors, founders of start-up companies, or key people at software and hardware companies.

Machine Learning as a Service: Challenges and Opportunities(Talk)

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)

CEOVingyani

Parthiban Srinivasan, PhD

Parthiban Srinivasan holds a dual Masters Degree- one in Science and the other in Engineering. Then, Ph.D. in Computational Chemistry from Indian Institute of Science, Bangalore. After his Ph.D., he continued research at NASA Ames Research Center (USA) and Weizmann Institute of Science (Israel). Then he worked at AstraZeneca in the area of Computer-Aided Drug Design for Tuberculosis. Later, he headed informatics business units in Jubilant Biosys and then in GvkBio before he floated the company, Parthys Reverse Informatics. Now his recent venture is VINGYANI, a data science company, with a focus on AI guided drug design and health.
Now, Parthi is also serving as Adjunct Faculty at the Indian Institute of Science Education and Research (IISER) Bhopal, teaching Artificial Intelligence.
Attended 5-day ODSC West, San Francisco, October 2019, and ODSC east, Boston, April 2020 (Virtual).

Coronavirus: Through The Lens Of AI(Talk)

Head of Federated Learning LabAI Singapore

Jianshu Weng, PhD

Jianshu is Head of Federated Learning at AI Singapore where he leads the team to develop a platform to support Federated Learning, a new paradigm of privacy-preserving Machine Learning. As a national initiative, AISG brings together the strength of AI research bodies in Singapore’s Autonomous Universities and research institutes, together with the vibrant ecosystem of AI start-ups and companies developing AI products, to perform use-inspired research, create innovative AI solutions, and develop the talent to power Singapore’s AI efforts.
Jianshu has many years of AI/Data Science research and consulting experience. In recent years, he has spent most of his time in putting AI/ML into real-world usage and promoting ethical aspects of AI/ML, e.g. explainability, fairness, robustness, and privacy-preserving AI/ML models. Before joining AISG, he was the Head of Insights and Modelling of a leading global reinsurer where he led his team to deliver a number of significant data science projects for their key clients in the Asia Pacific region. Jianshu obtained his Ph.D. in Computer Science from Nanyang Technological University (NTU), Singapore in 2008.

AI Singapore’s Journey into the World of Federated Learning(Talk)

Principal Data ScientistGuavus Networks

Priyanshu Jain

Priyanshu Jain is a Data Scientist and a Machine Learning enthusiast with 6 years of experience. He graduated from IIT Delhi in 2014. He did B.Tech and M.Tech in Electrical engineering. He has worked with Barclays and Opera Solutions in the past. Currently, he is employed with Guavus Networks.

Automated Ticket Routing for Large Enterprises(Talk)

 

ML Architect

Kuldeep Singh

Kuldeep Singh has 11 years of professional experience in the Technology domain with DevOps specialisation. Currently, he is working to explore Data Science world with BigData, Machine learning and AI.

Simplify Experimentation, Deployment and Collaboration for ML and AI Models (Talk)

Lead Data ScientistTiger Analytics

Sri Vallabha Deevi, PhD

Dr. Sri Vallabha Deevi is a Data Scientist at Tiger Analytics and lead teams in building analytics solutions – from simple statistical models to AI/Deep Learning models. His expertise is in scientific computing, machine learning & reduced-order modeling of physical systems. He is interested in teaching and talk on data science and machine learning regularly at various colleges & conferences. He finished B.Tech from IIT Madras and Ph.D. from IISc Bangalore.

Machine Health Monitoring with AI(Talk)


Some Previous Speakers

Professor, ResearcherUniversity of Wisconsin-Madison

Sebastian Raschka, PhD

Sebastian Raschka is a machine learning researcher developing new deep learning architectures to solve problems in the field of biometrics with a focus on face recognition and privacy protection. Among others, his research activities include applications of machine learning to solve problems in (computational) biology. After receiving his doctorate from Michigan State University, Sebastian recently joined the University of Wisconsin-Madison as Assistant Professor of Statistics. Sebastian Raschka is also the author of the bestselling book “Python Machine Learning”, which received the ACM Best of Computing award in 2016 and was translated into many different languages, including German, Korean, Chinese, Japanese, Russian, Polish, and Italian. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle.

Machine Learning and AI in 2021: Recent Trends, Technologies, and Challenges(Talk)

Data ScientistVeeva Systems

Sihem Romdhani

Sihem Romdhani received her MASc degree in Machine Learning from the department of Electrical and Computer Engineering at the University of Waterloo-Canada, where her research was focused on Deep Learning for Speech Recognition. She is currently working with Veeva Systems as a Data Scientist, where she is building ML models for Natural Language Processing. She has led multiple projects on text parsing, sequence tagging, and information extraction from unstructured text data. She has also worked on recommendation systems using different ML algorithms including Reinforcement Learning. Sihem is very interested in AI and how to solve new and challenging problems. Throughout her education, academic research, and work in industry, she gathered experiences and knowledge that she enjoys sharing by actively doing public presentations.

Self-Supervised Learning and Natural Language Processing for Hate Speech Detection(Talk)

Deep Learning and Software GPU expert Nir Shavit
CEO, ProfessorNeural Magic, MIT

Nir Shavit, PhD

Nir Shavit is the CEO of Neural Magic, a machine learning startup that is currently in stealth mode and is a professor in the Department of Electrical Engineering and Computer Science at MIT. Shavit is a co-author of the book The Art of Multiprocessor Programming, is a recipient of the 2004 Gödel Prize in theoretical computer science and of the 2012 Dijkstra Prize in Distributed Computing, and is an ACM fellow. His recent interests include systems issues in machine learning and techniques for understanding how neural tissue computes by extracting connectivity maps of neural tissue, a field called connectomics.

The Software GPU: Making Inference Scale in the Real World(Talk)

Director of Data ScienceShopRunner

Ali Vanderveld, PhD

Ali Vanderveld is the Director of Data Science at ShopRunner, where her team leverages data from a network of over 100 retailers to build products for their 6 million members. Prior to ShopRunner, she was a staff data scientist at Civis Analytics, a consulting and software startup that helps companies, nonprofits, and political organizations better utilize their data. She has also worked 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 to 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.

Using Computer Vision and NLP Together for Fashion Classification(Talk)

Co-creator of the Julia language, Co-founder & CTOJulia Computing, Inc.

Jeff Bezanson, PhD

Jeff is one of the creators of Julia, co-founding the project at MIT in 2009 and eventually receiving a Ph.D. related to the language in 2015. He continues to work on the compiler and system internals, while also working to expand Julia’s commercial reach as a co-founder of Julia Computing, Inc.

Advances in Julia for Data Science and ML(Workshop)

Author & Maintainer of PyTorch

Adam Paszke

Adam is an author and maintainer of PyTorch. He has worked with large organizations like Facebook AI Research, NVIDIA and Google, despite the fact that he has graduated from the Master’s program in Computer Science at the University of Warsaw only last year. Currently, he is also finishing his second major in Mathematics. His general interests include graph theory, programming languages, numerical computing and machine learning.

Step Up Your PyTorch with Custom Extension(Workshop)

Head of Data SciencePodium Education

Robert Alvarez, PhD

Robert loves to break deep technical concepts down to be as simple as possible, but no simpler.

Robert has data science experience in companies both large and small. He is currently Head of Data Science for Podium Education, where he builds models to improve student outcomes and an Adjunct Professor at Santa Clara University’s Leavey School of Business. Prior to Podium Education, he was a Senior Data Scientist at Metis teaching Data Science and Machine Learning. At Intel, he tackled problems in data center optimization using cluster analysis, enriched market sizing models by implementing sentiment analysis from social media feeds, and improved data-driven decision making in one of the top 5 global supply chains. At Tamr, he built models to unify large amounts of messy data across multiple silos for some of the largest corporations in the world. He earned a PhD in Applied Mathematics from Arizona State University where his research spanned image reconstruction, dynamical systems, mathematical epidemiology, and oncology.

Understanding the PyTorch Framework with Applications to Deep Learning(Training)

Supervisor, Data EducationChildren's Hospital of Philadelphia

Joy Payton

Joy Payton is a cloud engineer, data scientist, and adjunct professor who specializes in helping biomedical professionals conduct reproducible computational research. In addition to moving medicine forward through principles of open science and reproducibility, Joy also enjoys teaching citizen scientists how to use public data repositories to understand their own communities better and advocate for change from a data-centric perspective. Her various roles allow Joy to lead efforts to teach people how to write their first line of code and help anyone who’s interested climb the data science learning curve. Currently employed by the Children’s Hospital of Philadelphia and Yeshiva University, Joy is always open to hearing about open-source, data-centric volunteer opportunities for herself and her students.

Using Google BigQuery Public Data With Colab Notebooks: From Insight to Knowledge Using Cloud Computing, SQL, and Python to Map NYC Fall Tree Color!(Workshop)


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

Previous Sessions

  • Machine Learning with R
  • Deep Learning with Tensorflow for Absolute Beginners
  • Deep Learning for Developers
  • Small to Big Data and Deep Learning
  • Leveraging Better User Experience through Chatbots
  • Guided Generative Adversarial Neural Network
  • Deep Learning in Keras
  • Distributed Deep Learning on Hops
  • Deep Learning with MXnet
  • Recommendation System Architecture and Algorithms

Who will attend

  • 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

ODSC APAC Virtual Conference 2020 | December 8 - 9, 2020

Register Now
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info@odsc.com

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