Associate Professor Pavel Loskot
ZJU-UIUC Institute
BIO: Pavel Loskot joined the ZJU-UIUC Institute in January 2021 as the Associate Professor after being nearly 14 years with Swansea University in the UK. 25+ years of experience in design, analysis, implementation and deployment of telecommunication systems through numerous academic and industrial collaborative projects and consultancy contracts. Expert level knowledge of digital and statistical signal processing, algorithms and methods. Solid background in applied probability and statistics. Avid Linux programmer and user since 1996. In 2014/2015, as a Visiting Researcher at CSRC of the Chinese Academy of Engineering Physics started working on computational molecular biology. In 1999-2001, Research Scientist and Project Manager at CWC, Oulu, Finland. A Fellow of the Higher Education Academy of the UK, and the Recognised Research Supervisor of the UK Council for Graduate Education. A Senior Member of the IEEE since 2013.
Title: Mperfect Reasoning: Cognitive Biases
Abstract: The environment is naturally uncertain. It inevitably leads to decisions that can only be good statistically. Thus, at best, we can hope that our decisions are good enough in most cases. The statistical reasoning is prone to various statistical phenomena, which can easily bias our decisions and the related outcomes. In this talk, I will discuss the problem of cognitive biases, why they occur, and their consequences. Understanding the cognitive biases is important in scientific research, in designing AI systems as well as when doing business.
Kannimuthu Subramaniyam
Anna University, India
BIO: Kannimuthu Subramaniyam is currently working as Professor in the Department of Computer Science and Engineering at Karpagam College of Engineering, Coimbatore, Tamil Nadu, India. He is also an In-Charge for the Center of Excellence in Algorithms. He is an IBM Certified Cybersecurity Analyst. He did PhD in Computer Science and Engineering at Anna University, Chennai. He did his M.E (CSE) and B.Tech (IT) at Anna University, Chennai. He has more than 16 years of teaching and industrial experience. He is the recognized supervisor of Anna University, Chennai. Three PhD candidate is completed their research under his guidance. He is now guiding 11 PhD Research Scholars. He has published 59 research articles in various International Journals. He published 2 books ("Artificial Intelligence" & “LinkedList Demystified-A Placement Perspective” and 3 Book Chapters (WOS / Scopus Indexed). He is acting as mentor / consultant for DeepLearning.AI, Hubino, MaxByte Technologies and Dhanvi Info Tech, Coimbatore. He is the expert member for AICTE Student learning Assessment Project (ASLAP). He has presented a number of papers in various National and International conferences. He has visited more than 100 Engineering colleges and delivered more than 138 Guest Lectures on various topics. He is the reviewer for 50 Journals and 3 Books. He has successfully completed the consultancy project through Industry-Institute Interaction for ZF Wind Power Antwerpen Ltd., Belgium. He has received funds from CSIR, DRDO and ISRO to conduct workshops and seminars. He has completed more than 610 Certifications (41 Specializations and 4 Professional Certifications) in Coursera, Hackerrank and NPTEL on various domains. He has guided a number of research-oriented as well as application-oriented projects organized by well-known companies like IBM. He is actively involving in setting up lab for Cloud Computing, Big Data Analytics, Open-Source Software, Internet Technologies etc., His research interests include Artificial Intelligence, Data Structures and Algorithms, Machine Learning, Big Data Analytics, Virtual Reality & Blockchain. One of his research works is incorporated SPMF Open-Source Data Mining Tool. Source: http://www.philippe-fournier-viger.com/spmf/index.php?link=algorithms.php. He Conferred Second Best Team in NLP Challenge as part of FIRE 2019 conference. He secured first Position in NLP Challenge as part of FIRE 2018 Conference.
Title: Recent Trends and Research Challenges in Applying Machine Learning for Cyber Threat Detection and Mitigation
Abstract: Cyber security refers to every aspect of protecting an organization and its employees and assets against cyber threats. As cyberattacks become more common and sophisticated and corporate networks grow more complex, a variety of cyber security solutions are required to mitigate corporate cyber risk. In the rapidly evolving nature of cybersecurity, the integration of machine learning plays a pivotal role in securing our digital defenses. This keynote talk investigates into the multifaceted relationship between machine learning and cyber threat detection, highlighting both its remarkable advancements and the consequential challenges that accompany its implementation.
The talk begins with an exploration of the fundamental role of machine learning in cybersecurity. It illuminates the significant applications of machine learning algorithms, elucidating their capacity to detect anomalies, unearth patterns within network traffic, and undertake predictive analyses to preempt potential threats. However, the rise of machine learning in cybersecurity is not without its complexities. Ethical considerations materialize large, encompassing concerns of data privacy, bias within models, and the everlasting battle to adapt to evolving cyber threats. Furthermore, the speech offers insights into the future prospects of cybersecurity associated with machine learning innovations. As a outcome, listeners will obtain a comprehensive understanding of the relationship between machine learning and cybersecurity, insights into its current state, future possibilities, and the imperative need for ethical and technical consideration in this field.
