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

Available Speakers and their Lectures on this Topic

Rizwan Ahmed – Nagpur, India

Sven Apel – Saarbrücken, Germany

  • AI4SE: Adventures in the Promised Land
    AI is about to revolutionize how we develop software. In particular, large language models have shown great promise in assisting programmers in writing and reasoning about source...

David Atienza – Lausanne, Switzerland

Ahmedullah Aziz – Knoxville, TN, USA

  • AI for Better Hardware & Hardware for Better AI
    For years, the rapid ascent of AI has captivated the world, but behind every groundbreaking algorithm lies an unsung hero: hardware. While software and algorithms have stolen the spotlight, the...

Ricardo Baeza-Yates – Palo Alto, CA, USA

  • 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...
  • Ethics in AI: A Challenging Task
    In the first part we cover current specific challenges: (1) discrimination (e.g., facial recognition, justice, sharing economy, language models); (2) phrenology (e.g., biometric based...
  • Responsible AI
    In the first part we cover five current specific problems that motivate the needs of responsible AI: (1) discrimination (e.g., facial recognition, justice, sharing economy, language models); (2)...
  • 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...

Mehdi Bahrami – Santa Clara, CA, USA

Nelly Bencomo – Durham, United Kingdom

Duncan P Brumby – London, United Kingdom

Margaret Burnett – Corvallis, OR, USA

Heloisa Candello – Campinas, São Paulo, Brazil

  • Artificial Intelligence and social impact
    In this talk I invite you to consider how the use of technology can support communities living in vulnerable situations to increase access to financial services. We will discuss and explore...
  • Designing conversational agent(ic) systems
    The human society is undergoing a significant transformation due to advances in artificial intelligence (AI). These technologies enhance operational processes and customer engagement, enabling...
  • Generative AI: Design and HCI perspectives
    This talk invites reflection on essential human factors to consider when designing conversational user interfaces and generative AI.  Through examples of CUI projects and design methods,...
  • User Methods and Approaches to Design conversational user interfaces
    Recent advances in artificial intelligence, natural language processing, and mobile computing, together with the rising popularity of chat and messaging environments, have enabled a boom in the...

Federico Cerutti – Brescia, Italy

Eugenio Cesario – Rende (CS), Italy

Tanmoy Chakraborty – New Delhi, India

Polo Chau – Atlanta, GA, USA

Geeta Chauhan – Santa Clara, CA, USA

  • 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...

Nitesh Chawla – Notre Dame, IN, USA

Deming Chen – Urbana, IL, USA

Junying Chen – Guangzhou, China

Pin-Yu Chen – WHITE PLAINS, NY, USA

Yuxin Chen – Philadelphia, PA, USA

Betty H.C. Cheng – East Lansing, MI, USA

Aswani Kumar Cherukuri – Vellore, India

Yuejie Chi – Pittsburgh, PA, USA

Kenneth W Church – Boston, MA, USA

  • Better Together: Text + Context

    Graph learning has applications in web search (Page Rank), Product Search (Amazon), Biology, Finance and Traffic Analysis for...

Cristina Conati – Vancouver, BC, Canada

Benjamin Richard Cowan – Dublin, Ireland

Rik Das – Ranchi, India

Swagatam Das – Kolkata, India

Dipankar Dasgupta – Memphis, TN, USA

  • AI for Security and Security of AI
    Artificial Intelligence (AI) constitutes an umbrella of techniques, and has proven to provide flexible, adaptable solutions to wide variety of security solutions. These techniques typically...
  • Context-Aware Adaptive Multi-Factor Authentication
    Authentication and access control are merging in continuous authorization of online resources/services by users and IoTs. In this talk, I will first discuss an adaptive multi-factor...
  • Secure Federated Learning: Challenges and mitigations
    Federated Learning (FL) promises collaborative decentralized intelligence without centralized data sharing, although its distributed design introduces some security challenges. This talk...
  • Security issues of Generic Large Language Models
    Generic Large Language Models (GLLMs) are continuously being released with increased size and capabilities, promoting the abilities of these tools as universal problem solvers.  While...
  • Using AI Agents in Intrusion Detection: Historical perspective
    Intrusion/anomaly detection is an important part of cyber security. This is the process of identifying computer or network activity that is malicious or unauthorized. Most of the intrusion...
  • What should GAN in AI stand for?
    GANs were introduced as AI framework where two learning models (a generator and a discriminator) compete in a two-player game. Goodfellow et al. (2014) were the first to explicitly define...

Kalyanmoy Deb – MI, USA

Gianluca Demartini – Queensland, QLD, Australia

Ilke Demir – Hermosa Beach, CA, USA

Xin Luna Dong – Seattle, WA, USA

Asif Ekbal – Jodhpur, India

Kaoutar El Maghraoui – Yorktown Heights, NY, USA

Lance Eliot – Palo Alto, CA, USA

Bjoern M Eskofier – Erlangen, Germany

  • AI for Future Healthcare
    The fast-growing costs of acute care are pushing the healthcare systems worldwide to a limit. Globally, we are coming to realize that we cannot afford to provide everybody with access to...
  • Machine Learning: Trends (and Hypes?)
    The talk highlights current trends (and hypes?) in machine learning and artificial intelligence. Example projects in the abovementioned domains will be highlighted, as well as basic technology...
  • Wearable computing systems and machine learning for sports science research
    Wearable computing systems play an increasingly important role in recreational and elite sports. They comprise of two parts. First, sensors for physiological (ECG, EMG, ...) and...

Vagner Figueredo de Santana – São Paulo, Brazil

  • Challenges and Opportunities for Responsible Prompting
    Generative AI systems such as ChatGPT and Midjourney are transforming how we create, learn, and innovate yet the responsible use of these technologies often remains an afterthought. In this talk,...
  • Challenges and Opportunities in Responsible AI Project
    In this talk, I introduce the Responsible and Inclusive Technology Framework, a formative framework designed to guide technologists, researchers, and organizations toward more ethical and...
  • LLM on the Web: Contexts of Creation & Use of Technology
    Large Language Models (LLMs) are transforming how we access, produce, and understand information on the Web yet the distance between their contexts of creation and use is growing rapidly. This...
  • My Journey as an Inventor
    In this talk I present the audience with my journey through the world of patents and innovation. From understanding what patents are and what can be protected, to exploring the differences between...

João Gama – Porto, Portugal

  • Current Trends in Learning from Data Streams
    Learning from data streams is a hot topic in machine learning and data mining. In this talk, we present three different problems and discuss streaming techniques to solve them. The first problem...
  • Data Mining for the XXI Century
    Nowadays, there are applications where data is best modeled not as persistent tables, but rather as transient data streams. In this talk, we discuss the limitations of current machine learning and...
  • Evolving Social Networks: trajectories of communities
    In recent years we witnessed an impressive advance in the social networks field, which became a ”hot” topic and a focus of considerable attention. The development of methods that focus...

Dan Garcia – Millbrae, CA, USA

Nitesh Goyal – Stamford, CT, USA

Sumit Gulwani – Redmond, WA, USA

Bo Han – Hong Kong, Hong Kong

Zhu Han – Houston, TX, USA

David Howard – Brisbane, QLD, Australia

Longbo Huang – Beijing, China

Rasheed Hussain – Bristol, United Kingdom

Letizia Jaccheri – Trondheim, Norway

Anura Jayasumana – Fort Collins, CO, USA

Xiaohua Jia – Hong Kong, Hong Kong

  • Data Authorized Machine Learning and Unlearning
    Data ownership protection is an important task in machine learning (ML). However, it is always difficult to protect data owner’s right in the process of ML and the development of data-driven...

Arijit Khan – Bowling Green, OH, USA

Latifur Rahman Khan – Plano, TX, USA

Irwin King – Hong Kong, Hong Kong

Manoj Kumar Kumar – Sydney, NSW, Australia

  • Unveiling the Power of Generative AI
    In the realm of cutting-edge technology, Generative AI emerges as a formidable force, shaping industries and paving the way for unprecedented innovation. This talk endeavors to demystify...

Sathish A.P. Kumar – Westlake, OH, USA

C.-C. Jay Kuo – Los Angeles, CA, USA

  • On Sustainable Healthcare and Sustainable AI
    This talk addresses two sustainability challenges in our society. The first one is the sustainability of today’s healthcare services. As people’s life is prolonged, the need for...
  • Toward Interpretable and Sustainable AI
    Rapid advances in artificial intelligence (AI) and machine learning (ML) have been attributed to the wide applications of deep learning (DL) technologies. There are, however, concerns with this AI...

Eren Kurshan – New York, NY, USA

Antonio Lieto – Salerno, Italy

David Lo – Singapore, Singapore

Seng Loke – Melbourne, VIC, Australia

Walid Maalej – Hamburg, Germany

A. Cristiano I. Malossi – Rüschlikon, Switzerland

Maja Mataric´ – South Pasadena, CA, USA

Ujjwal Maulik – Kolkata, India

Bertrand Meyer – Zurich, Switzerland

Ajmal Saeed Mian – Crawley, WN, Australia

Kenny Mitchell – Burbank, CA, USA

Peyman Moghadam – Brisbane, QLD, Australia

San Murugesan – Sydney, NSW, Australia

Olfa Nasraoui – Louisville, KY, USA

Corina Pasareanu – Sunnyvale, CA, USA

Sudeep Pasricha – Fort Collins, CO, USA

  • Artificial Intelligence at the Speed of Light
    The massive data deluge from mobile, IoT, and edge devices, together with powerful innovations in data science and hardware processing, have established artificial intelligence (AI) as the...

Steven Pemberton – Amsterdam, Netherlands

  • There's no I in AI (yet)
    There's no intelligence in current AI systems, but apparently we think there is, and then get surprised when it gives wrong answers.  Why is this, and what will happen when we get...

Junaid Qadir – Doha, Qatar

Nitendra Rajput – Gurgaon, India

Danda B Rawat – Washington, DC, USA

Sripana Saha – Bihta Patna District, India

Albert Ali Salah – Utrecht, Netherlands

KC (Casey) Santosh – SD, USA

Federica Sarro – London, United Kingdom

  • MEG: Multi-objective Ensemble Generation
    Recent studies have found that ensemble prediction models (i.e., aggregation of multiple base classifiers) can achieve more accurate results than those that would have been obtained by...
  • Search-based Software Engineering for Modern Software Systems
    Realizing modern software systems poses new challenges to the software engineers: Users of applications running on limited capability devices still demand acceptable performance, users of...
  • Software Fairness
    Software Fairness is an emerging property of modern AI-enabled software systems.
    Many real-world software is vulnerable to fairness bugs and frequently exhibit unfair...

Nishanth Sastry – London, United Kingdom

Björn Schuller – Munich, Germany

  • Computer Audition The Era of Large Models
    In this lecture, we delve into the fascinating field of Computer Audition Ð that is, hearing, understanding, and generating audio by computers powered by the latest advancements in Artificial...

Abhronil Sengupta – University Park, PA, USA

Chirag Shah – Kenmore, WA, USA

Yiyu Shi – Notre Dame, IN, USA

RK Shyamasundar – Mumbai, India

Houbing Song – Baltimore, MD, USA

Ram Sriram – Gaithersburg, MD, USA

Hanghang Tong – Urbana, IL, USA

  • Graph Neural Networks Beyond Homophily
    The emergence of deep learning models designed for graph and network data, often under an umbrella term named graph neural networks (GNNs for short), has largely streamlined many graph learning...
  • Optimal Deep Graph Learning: Towards a New Frontier
    The emergence of deep learning models designed for graph and network data, often under an umbrella term named graph neural networks (GNNs for short), has largely streamlined many graph learning...

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...

Sunil Kumar Vuppala – Bangalore, India

Haixun Wang – ISSAQUAH, WA, USA

Zhangyang "Atlas" Wang – Austin, TX, USA

Allison Woodruff – Mountain View, CA, USA

Ming Xiao – Stockholm, Sweden

Xing Xie – BEIJINGSHI, China

Guoliang Xing – Hong Kong, Hong Kong

Elad Yom-Tov – Hoshaya, Israel

Daron Yondem – Istanbul, Turkiye

  • Agentic LLM Workflows with AutoGen
    In the era of artificial intelligence, Large Language Models (LLMs) have catalyzed a transformative shift across multiple domains, heralding a new age of computational ingenuity and...
  • Mastering Prompt Engineering Techniques
    This session provides an in-depth exploration of prompt engineering techniques with recent large language models. A comprehensive walkthrough of various prompt engineering strategies are...
  • Meet my AI Sidekick!
    It has been a long time since we have access to LLMs. I have been playing with it not only for customers but for myself as well. How could I increase my own productivity? What value could I...

Dong Yu – Bothell, WA, USA

  • Audio/Speech Enhancement and Separation

    We have seen significant progress in audio and speech processing in the past several years. In this talk, I will introduce a series of techniques we developed on...

Jingren Zhou – HANGZHOU, ZHEJIANG, China

Justin Zobel – Melbourne, VIC, Australia

  • Smoke or mirrors: A perspective on emerging AIs
    AI technologies seem to be poised to disrupt a huge range of industries and activities. The abrupt rise in awareness of AI, following the appearance of publicly available generative tools...