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Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing

Available Speakers on this Topic

Rizwan Ahmed – Nagpur, India

Enrique Alba – Malaga, Spain

  • Intelligent Systems for Smart Cities
    The concept of Smart Cities can be understood as a holistic approach to improve the level of development and management of the city in a broad range of services by using information and...
  • Parallel Experiences in Solving Complex Problems
    This lecture introduces the basic concepts of two fields of research: parallelism and metaheuristics. We will revise the main concepts, tools, metrics, open issues, and application domains related...
  • Parallel Multiobjective Optimization
    This lecture introduces the basic concepts of two fields of research: Parallelism and Multiobjective optimization. Dealing with complex problems means optimizing a given metric, like the...
  • World Strategies for Artificial Intelligence
    In this lecture we will make an introduction to Artificial Intelligence (AI), its impact in our lives, and the need for a regulation in modern societies. We will then move to anlyze the different...

Ricardo Baeza-Yates – Palo Alto, CA, United States

  • Bias and the Web
    The Web is the most powerful communication medium and the largest public data repository that humankind has created. Its content ranges from great reference sources such as Wikipedia to ugly fake...
  • Big Data or Right Data? Opportunities and Challenges
    Big data nowadays is a fashionable topic, independently of what people mean when they use this term. But being big is just a matter of volume, although there is no clear agreement in the size...
  • Distributed Web Search
    In the ocean of Web data, Web search engines are the primary way to access content. As the data is on the order of petabytes, current search engines are very large centralized systems based on...
  • Semantic Search
    Semantic search lies in the cross roads of information retrieval and natural language
    processing and is the current frontier of search technology. The first part consists in...

Debnath Bhattacharyya – Visakhapatnam, India

  • Design and Analysis of Graphical Password
    Most common computer authentication method is traditional ‘User Name’ and ‘password’. There are numerous Biometric authentication methods also proposed. In...

Siddhartha Bhattacharyya – Kolkat, India

  • Optimized Image Thresholding using Multilevel Quantum Systems
    Segmentation is a popular technique whose aim is to segregate a set of data points into groups. Thresholding remains the simplest method for image segmentation. The determination of the...
  • Quantum Entanglement and the W State
    Quantum Computing promises to be the computing of the future. Quantum entanglement is the property which generally gives rise to non-local interaction among bipartite correlated states....
  • Quantum Inspired Automatic Image Clustering
    Cluster analysis is a popular technique whose aim is to segregate a set of data points into groups, called clusters where the number of clusters are predefined. Automatic determination of...
  • Understanding Quantum Backpropagation
    Backpropagation techniques are widely used in neural networks for the purpose of weight adjustment and system error compensation. The lecture discusses the basics of backpropagation...

Debnath Bhattacharyya – Visakhapatnam, India

  • Unsupervised Learning in Data Mining
    The meaning of Unsupervised learning is to find hidden structure in unlabeled data. Approaches of Unsupervised learning can be used in Data Mining. Clustering is an important and popular approach...

Albert Bifet – Paris, France

Michael Bronstein – Lugano, Switzerland

  • Computational metric geometry
    Many important problems in computer vision and pattern recognition revolve around the notion of "similarity" or "distance". Metric geometry is a branch of theoretical...
  • Geometric deep learning
    In recent years, more and more data science applications have to deal with a somewhat unusual kind of data residing on non-Euclidean geometric domains such as manifolds or graphs. For...
  • Spectral methods for 3D data analysis
    In recent years, geometric data is gaining increasing interest both in the academia and industry. In computer graphics and vision, this interest is owed to the rapid development of 3D acquisition...
  • Start thinking in 3D!
    The last decade has witnessed a series of technological breakthroughs in the acquisition, processing, and analysis of 3D geometric data, enabling applications that are revolutionizing our way of...

Geeta Chauhan – Santa Clara, CA, United States

  • AI @ Edge using Intel NCS
    The new generation of hardware accelerators are enabling rich AI driven, Intelligent IoT solutions @ the edge.

    The talk will showcase how to use...
  • Best Practices for On-Demand HPC
    Traditionally HPC has been popular in Scientific domains, but not in most other Enterprises. With the advent of on-demand-HPC in cloud and growing adoption of Deep Learning, HPC should now...
  • Decentralized AI: Convergence of Blockchain and AI
    As we move into a world where User's will own their own data, and companies will use "Ethically Sourced Data", there will be a rampant need for Decentralized AI. And,...
  • Deep Learning for Medical Imaging
    The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis. You will learn about:

    - Use cases for Deep...
  • Distributed Deep Learning Optimizations
    This talk will cover how to build and deploy distributed deep learning models at scale. You will learn how to parallelize your models, and techniques for optimizing your cluster for faster...

Deming Chen – Urbana, IL, United States

Li Chen – Washington, DC, United States

Deming Chen – Urbana, IL, United States

Li Chen – Washington, DC, United States

Deming Chen – Urbana, IL, United States

Aswani Kumar Cherukuri – Vellore, India

Cristina Conati – Vancouver, BC, Canada

Kerstin Dautenhahn – Hatfield, Canada

Gianluca Demartini – Queensland, Australia

  • Artificial Intelligence with Humans in the Loop
    Crowdsourcing is a novel approach used to obtain manual data processing and annotation at scale. This can be used to create Artificial Intelligence systems with humans in the loop that can...

Chuck Easttom – Plano, TX, United States

  • The role of machine learning in cybersecurity
    This talk explores the role that machine learning plays in cybersecurity. The latest trends are included.  Defensive uses are explored. This includes using machine learning to improve the...

Robert James Fine – Washington, DC, United States

Charlie Fink – New York, NY, United States

Dan Garcia – Millbrae, CA, United States

Andrew Glassner – Seattle, WA, United States

Jennifer Golbeck – College Park, MD, United States

Javier Gonzalez-Sanchez – Tempe, AZ, United States

  • Artificial Emotional Intelligence
    This lecture presents a pragmatic view of the machine learning workflow to build Emotion AI. Emotion AI (artificial emotional intelligence) is a subset of artificial intelligence that...

Gururaj H L – Mysuru, India

Laura M Haas – San Jose, CA, United States

Min-Yen Kan – Singapore, Singapore

Salil Kanhere – Sydney, NSW, Australia

John Kaufhold – Arlington, VA, United States

  • Deep Learning Past, Present and Near Future
    In the past 5 years, deep learning has become one of the hottest topics in the intersection of data science, society, and business. Google, Facebook, Baidu and other companies have embraced...

Anis Koubaa – Riyadh, Saudi Arabia

Ashish Kundu – San Jose, CA, United States

Dongwon Lee – State College, PA, United States

  • Combating False Information
    The recent world-wide rise of “false information” (that includes rumor, clickbait, fake news, hoax, misinformation, and disinformation) has caused significant confusion and disruption...

Antonio Lieto – Turin, Italy

Jian Ma – Pittsburgh, PA, United States

Lauren Maffeo – Bethesda, MD, United States

  • Erase Unconscious Bias From Your AI Datasets
    Advances in AI techniques like machine learning and deep neural networks have potential to save time and boost productivity. But what if we train these technologies using datasets that...

A. Cristiano I. Malossi – Rüschlikon, Switzerland

Koushik Mondal – Dhanbad, India

Kayal Padmanandam – Hyderabad, India

  • Machine Learning Taxonomy
    Machine learning is an application of Artificial Intelligence that provides the system with the ability to learn and decide automatically through algorithms and statistical models. This...

Jon G Peddie – Tiburon, CA, United States

  • Augmented Reality, where we all will live
    Philosophers, scientists, futurists, and other have speculated about the moment in time when computers will match, and quickly exceed, human processing speed, memory access, and ultimately...

Vishnu S Pendyala – San Jose, CA, United States

  • Machine Learning for Veracity of Big Data
    Machine Learning is increasingly proving itself to be the mortar of modernization. The talk will examine how Machine Learning can be applied to the problem of veracity of Big Data,...

Pearl Pu – Preverenges, Switzerland

  • How People Perceive AI - Trust and Explanation
    Trust is believed to be a central antecedent of any relationship: personal, familial, business, and organizational. Trust is difficult to build, but easy to break. As AI is becoming increasingly...

Junaid Qadir – Lahore, Pakistan

Shrisha Rao – Bangalore, India

  • Ethics in Artificial Intelligence
    Artificial Intelligence (AI) systems are already widely used, and have the capacity to improve lives for billions of people going forward.  However, AI systems, which are machines, are...
  • Social Consequences of Artificial Intelligence
    One aspect of the contemporary world is our large reliance on computation in all aspects of personal life and social interaction. We are likely as not to decide which movie to watch, which...

Johannes Schöning – Bremen, Germany

G R Sinha – Mandalay, Myanmar

Shan Suthaharan – Greensboro, NC, United States

Mauro Vallati – Huddersfield, United Kingdom

  • Knowledge Configuration for AI solvers
    Given an off-the-shelf solver and a symbolic representation of a problem to be solved, a way for improving performance is the configuration of the parameters of the solver. Such parameters allow...
  • Knowledge Engineering for AI Planning
    Automated Planning is one of the most prominent AI challenges; it has been studied extensively for several decades and led to numerous real-world applications. Recently, Automated Planning...
  • Planning and Scheduling Approaches for Urban Traffic Control
    The current increase in urbanisation, coupled with the socio-economic motivation for increasing mobility, is pushing the transport infrastructure well beyond its capacity. Traditional urban...

Michalis Vlachos – Lausanne, Switzerland

  • Interpretable and Scalable Recommender Systems
    In this talk I describe a new class of recommendation techniques based on co-clustering, which is not only fast and accurate but it is also interpretable. I also show how GPU-accelerated versions...
  • Mining Compressed Time-Series Weblogs
    I show techniques for compressing time-series data with very high fidelity. Then I introduce techniques for estimating the similarity between time-series in the compressed domain using...
  • Time Series Analytics
    This is a tutorial that covers topics such such as time-series similarity and search, clustering and classification with applications in web pattern analysis, images, video and others.

Toby Walsh – Kensington, NSW, Australia

  • Allocation in Practice
    How do we allocate scarce sources? How do we fairly allocate costs? These are two pressing challenges facing society today. I discuss three recent projects concerning resource and cost allocation....
  • Deceased Organ Matching
    Thousands of people in Australia are waiting for a donated kidney. Matching donated organ to people on the waiting list is becoming increasingly challenging as road safety improves. In 1989, the...
  • Killer robots: the third revolution in warfare
    The age of autonomous killing machines is closer than you think, and scientists and computer experts are deeply worried. Already autonomous weapons systems are under development, and some...
  • Many Fears About AI Are Wrong
    Should you be worried about progress in Artificial Intelligence? Will Artificial Intelligence destroy jobs? Should we fear killer robots? Does Artificial Intelligence threaten our very...
  • What AI Can (and Can't) Do
    AI is transforming our lives. What can it do? What will it do? What should we welcome or fear? It is clear that he next industrial revolution has started. However, there are many misconceptions...
  • Where the hard computational problems are?
    Some computational problems are easy. We can sort numbers quickly for example. Other problems are hard. Scheduling jobs, routing trucks, these are all hard problems to solve. I will survey...

Ka-Chun Wong – Kowloon Tong, Hong Kong