Adversarial Machine Learning
June 14th, 2023 (CDT)
ITM Department, Illinois Institute of Technology, USA
Dr. Omar's Academic career has consistently focused on applied, industry-relevant cyber security, Data Analytics, machine learning, application of AI to cyber security and digital forensics research and education that delivers real-world results. He brings a unique combination of industry experience as well as teaching experience gained from teaching across different cultures and parts of the world. He has an established self-supporting program in machine learning application to cyber security. He has established a respectable research record in AI and cyber security exemplified in the dozens of published papers and book chapters that have gained recognition among researchers and practitioners (more than 272 Google scholar citations thus far). He is actively involved in graduate as well as undergraduate machine learning education including curriculum development and assessment.
Dr. Omar has recently published two books with Springer on Machine Learning and Cyber Security and has also published research with IEEE conference on Sematic Computing. Additionally, Dr. Omar holds numerous industry certifications including Comptia Sec+, ISACA CDPSE, EC-Council Certified Ethical Hacker, and SANS Advanced Smartphone Forensics Analyst.
Dr. Omar has been very active and productive in both academia as well as the industry and he is currently serving as an associate professor of cyber security at Illinois Institute of Technology.
Background:
The Adversarial Machine Learning workshop provides a forum for researchers, practitioners, and industry experts to exchange ideas, discuss the latest research, and share their experiences in developing and deploying robust machine learning systems. The workshop covers a range of topics related to adversarial attacks and defenses, including the latest attack techniques, their impact on machine learning systems, and the development of effective defense mechanisms.
The Adversarial Machine Learning workshop is a specialized event that focuses on exploring the challenges and latest developments in the field of adversarial attacks and defenses in machine learning systems. This workshop is particularly relevant today, as machine learning algorithms are becoming increasingly ubiquitous in our daily lives, powering a variety of applications, from image and speech recognition to autonomous driving systems. However, these systems are also becoming increasingly vulnerable to malicious attacks that aim to exploit their weaknesses, making the development of effective defenses against these attacks crucial.
Goal/Rationale:
One of the primary goals of this workshop is to foster collaboration and networking among experts in the field. The workshop provides an opportunity for participants to interact with other researchers and practitioners, learn about new techniques and approaches, and share their experiences and challenges. Through interactive sessions and discussions, participants can collaborate on developing new defense mechanisms and identifying new attack techniques, helping to advance the state-of-the-art in Adversarial Machine Learning.
Overall, the Adversarial Machine Learning workshop plays a critical role in advancing the state-of-the-art in defending against adversarial attacks in machine learning systems. With the rapid development of machine learning technologies, the importance of this workshop in protecting these systems from malicious attacks is only set to grow.
Scope and Information for participants
The Adversarial Machine Learning workshop has a broad scope that covers the latest developments and challenges in the field of adversarial attacks and defenses in machine learning systems. The workshop covers a range of topics related to adversarial attacks and defenses, including:
1. Adversarial attacks and their impact on machine learning systems: The workshop explores the latest attack techniques, how they can be used to manipulate machine learning models, and their impact on the performance of these systems.
2. Developing robust machine learning models: The workshop covers the latest techniques and approaches for developing machine learning models that are resistant to adversarial attacks.
3. Evaluation of adversarial defenses: The workshop explores different methods for evaluating the effectiveness of adversarial defenses, including the use of benchmarks and evaluation metrics.
4. Applications of adversarial machine learning: The workshop discusses how adversarial machine learning can be applied to different domains, including computer vision, natural language processing, and autonomous systems.
5. Ethical considerations: The workshop addresses the ethical considerations related to adversarial machine learning, including the potential risks and unintended consequences of using these techniques.
Overall, the scope of the Adversarial Machine Learning workshop is wide-ranging, covering the latest developments and challenges in the field of adversarial attacks and defenses in machine learning systems. The workshop provides a platform for researchers, practitioners, and industry experts to exchange ideas, collaborate on new approaches and techniques, and share their experiences in developing and deploying robust machine learning systems.
The Adversarial Machine Learning workshop is a specialized event that focuses on exploring the challenges and latest developments in the field of adversarial attacks and defenses in machine learning systems. The workshop brings together experts from different disciplines, including machine learning, computer security, and cryptography, to discuss the latest research and advancements in this rapidly evolving field.
The workshop covers a range of topics related to adversarial attacks and defenses, including the latest attack techniques, their impact on machine learning systems, and the development of effective defense mechanisms. Participants will have the opportunity to learn about state-of-the-art techniques for defending against adversarial attacks and explore the latest research and advancements in the field.
The workshop will also include interactive sessions and discussions where participants can exchange ideas and share their experiences in dealing with adversarial attacks. These sessions will provide a forum for participants to collaborate and network with other researchers in the field, helping to build a community of experts dedicated to developing robust machine learning systems.
Overall, the Adversarial Machine Learning workshop is an essential event for researchers and practitioners who want to stay up-to-date with the latest developments and best practices in defending against adversarial attacks in machine learning systems. The workshop will provide a platform for participants to gain new insights, share their experiences, and collaborate with other experts in the field, helping to advance the state-of-the-art in Adversarial Machine Learning.
CONF-CDS 2023 Workshop -- Chicago - YouTube
Illinois Institute of Technology, 10 W 35th St, Chicago, IL 60616
In order to ensure the information is correct and up to date, there may be changes which we are not aware of. And different countries have different rules for the visa application. It is always a good idea to check the latest regulations in your country. This page just gives some general information of the visa application.
The B-1/B-2 visitor visa is for people traveling to the United States temporarily for business (B-1) or for pleasure or medical treatment (B-2). Generally, the B-1 visa is for travelers consulting with business associates; attending scientific, educational, professional, or business conventions/conferences; settling an estate; or negotiating contracts. The B-2 visa is for travel that is recreational in nature, including tourism; visits with friends or relatives; medical treatment; and activities of a fraternal, social, or service nature. Often, the B-1 and B-2 visas are combined and issued as one visa: the B-1/B-2.
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