CS 59000BB: Situation Awareness, Adversarial ML, and Explainable AI

Fall 2020, Tuesdays and Thursdays 4:30pm-5:45pm, TBA

Instructor: Prof. Bharat Bhargava. email: bbshail AT purdue.edu

Class repository: CS590BB Drive


Develop tools and systems that apply ML to real applications on real data and try to deal with attacks and explain the decisions of AI powered decision making. In addition, the seminar will discuss methods to make these tools more efficient and more accessible.

Teaching Material

The class provides insights about:

  • Multi-modal data fusion, knowledge graphs, modeling context and situation awareness, user profiling, and matching interests with streaming data ( sensors, text, tweets, video, news articles, emails, phone calls), pattern recognition, data mining, intelligent query processing. Machine learning models to connect user's needs with data based on situation and context awareness.
  • Privacy-enabling frameworks for situational-aware systems. Representing networks and knowledge graphs using graph databases, graph analytics for enhancing search.
  • Machine learning in data cleaning, video data understanding, and data mining and identifying objects and events. Modeling user queries to build knowledge graphs to target data to users. AutoML and self-supervised learning: Deep learning to accelerate the labeling of data and carefully tune hyper-parameters. Learn automatically and learn on unlabeled data to make machine learning accurate and efficient. Labeling and learning with incomplete or limited data.
  • Joint Modeling (Language Modeling, Multi-modal modeling, User Modeling), Open Information Extraction, Attribute Extraction, Relation Extraction, Similarity Learning, Graph Similarity, Pedestrian Attribute and Action Recognition, Intent Classification. The use of tweets as the people's information to visualize topic modeling, study subjectivity, and model the human emotions during the COVID-19 pandemic. Sharing information (e.g., personal opinions, some facts, news, status, etc.) on social media platforms, which can be helpful to understand the various public behavior such as emotions, sentiments, and mobility during the ongoing pandemic and police encounters. Deep Learning approach for tweet classification for disaster management. Hierarchical deep learning model using text embedding via Crisis and GloVe, bidirectional LSTM (BLSTM), attention, and convolution layers.
  • Attacks on ML models, training data, and streaming data. Preserving privacy and security by monitoring attacks, mitigating attacks, dealing with changing behavior of adversary, collaborative attacks, predicting attacks, and intent. Deep learning-based Programming Language Processing for Detecting Evasive Cyber-attacks, Trusted Classification under Poisoning attacks using Semantic Factors. Bias and Fairness in ML, what fairness means, causes that introduce unfairness in ML, Data-driven methods that unintentionally encode existing human biases and introduce new ones.
  • Defensive methods to protect Deep Neural Networks (DNNs).
  • The success DNNs in image-related applications is threatened by their vulnerability to adversarial settings, including trojan and adversarial sample attacks. A detailed explanation of these types of attacks and countering methods are covered.
  • Insider threats are considered one of the most serious and difficult problems to solve, given the privileges and information available to insiders to launch different types of attacks. Current security systems can record and analyze sequences from a deluge of log data, potentially becoming a tool to detect insider threats. The issue is that insiders mix the sequence of attack steps with valid actions, reducing the capacity of security systems to discern long sequences and programmatically detect the executed attacks. M Deep learning-based methods are introduced, which overcome the existing limitations and protect against these types of attacks.
  • Monitoring and learning of attacks on autonomous systems and cyber attribution.
  • Enhancing Cognitive Autonomy through Deep learning. Autonomous systems, explanation of ML, and actions of autonomous activity with humans in the loop.
  • Attacks on space system protocols that use moving target defense and utilizing ML to identify intent and devising methods to mitigate.
  • Graph machine learning, graph structure to understand the social network and extract useful information to analyze social relationships.
  • Building recommendation systems. Deep learning in trading: Deep learning to track the market and make trading decisions.
  • Privacy-preserving predictive modeling in edge networks, privacy issues in ML used in contact tracing.
Class Logistics

Class Format

  • This is a seminar course. Each class will consist of presentations and discussions. Students will be required to do a class project for the course. A significant portion of the grade will be based on projects and class/research contributions, including paper presentations, contributions to paper reviews, and paper discussions.


  • A research class project: Each of you will work on a research project. Group projects are encouraged. The project will be broken down into three assignments: (1) initial research proposal, (2) intermediate report, (3) final report, and presentation.
  • Feel free to discuss your ideas with the instructor and propose your own project. The project should have a research component. Project ideas will be outlined in class, but you are responsible for proposing your project. Some background reading is associated with each project. The project proposal (due date October 5) should contain the following information:
    • Topic to be addressed and problem definition.
    • State of the art (prior work, what was done and what needs to be done.)
    • The proposed technique to be advanced, developed and implemented/evaluated.
    • How does your work advance the solution to the problem?.
    • Algorithm proposed, Experiments and Data collected, Conclusions/Guidelines.
    Project proposals should be a couple of pages at most. A project status report is due on November 2nd. The status report should include a description of progress to date and what is expected to be accomplished by the final project presentation day.


  • Class time may be adjusted to accomodate external talks releated to the class.
  • Google drive for deliverables: CS590BB Drive


Summary of weights per category

Item Weight
Written research paper/report   20%
Class presentations, participation, and discussion   15%
Projects   65%

Comprehensive Reading list

Situational Knowledge and Knowledge Graphs

    Machine Learning

      Adversarial Machine Learning

        Entity Resolution

          Entity Resolution Explanations

            Situation Awareness, Video and Text Processing

              Data Management For Video Streams

                Attacks and Privacy in Distributed Systems


                    Students' Presentations

                      Science of Artificial Intelligence and Learning for Open-world Novelty (SAIL-ON)

                        Other resources


                            Data Sources
                            Additional Resources on Explainable AI
                            Additional sources of knowledge

                            Outside speakers from MIT, CMU, Northrop Grumman, Sandia, IBM, Missouri Institute of Science and Technology, EPFL, University of Bern and Purdue (ECE, IE, CS) have been invited.

                            Dawn project at Stanford https://dawn.cs.stanford.edu/

                            Office Hours

                            Professor Bhagava: Office: LWSN 2116F and by appointment. email: bbshail at cs purdue edu Phone: (765).413.7312 call anytime.

                            Late Policy and Deliverables
                            There will be no late dates for the project deliverables and no late dates for in class questions. Extensions may be granted in the case of a severe medical or family emergency.
                            Academic Guidance in the Event a Student is Quarantined/Isolated
                            If you become quarantined or isolated at any point in time during the semester, in addition to support from the Protect Purdue Health Center, you will also have access to an Academic Case Manager who can provide you academic support during this time. Your Academic Case Manager can be reached at acmq@purdue.edu and will provide you with general guidelines/resources around communicating with your instructors, be available for academic support, and offer suggestions for how to be successful when learning remotely. Importantly, if you find yourself too sick to progress in the course, notify your academic case manager and notify me via email. We will make arrangements based on your particular situation. The Office of the Dean of Students (odos@purdue.edu) is also available to support you should this situation occur.
                            Classroom Guidance Regarding Protect Purdue
                            The Protect Purdue Plan, which includes the Protect Purdue Pledge, is campus policy and as such all members of the Purdue community must comply with the required health and safety guidelines. Required behaviors in this class include: staying home and contacting the Protect Purdue Health Center (496-INFO) if you feel ill or know you have been exposed to the virus, wearing a mask in classrooms and campus building, at all times (e.g., no eating/drinking in the classroom), disinfecting desk/workspace prior to and after use, maintaining proper social distancing with peers and instructors (including when entering/exiting classrooms), refraining from moving furniture, avoiding shared use of personal items, maintaining robust hygiene (e.g., handwashing, disposal of tissues) prior to, during and after class, and following all safety directions from the instructor. Students who are not engaging in these behaviors (e.g., wearing a mask) will be offered the opportunity to comply. If non-compliance continues, possible results include instructors asking the student to leave class and instructors dismissing the whole class. Students who do not comply with the required health behaviors are violating the University Code of Conduct and will be reported to the Dean of Students Office with sanctions ranging from educational requirements to dismissal from the university. Any student who has substantial reason to believe that another person in a campus room (e.g., classroom) is threatening the safety of others by not complying (e.g., not wearing a mask) may leave the room without consequence. The student is encouraged to report the behavior to and discuss next steps with their instructor. Students also have the option of reporting the behavior to the Office of the Student Rights and Responsibilities. See also Purdue University Bill of Student Rights.
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