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.

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 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.

Project

• 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.

Misc

• 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%

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

Talks

Students' Presentations

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

Other resources

CERIAS

• List of Projects
• Optimize Integration of Software-Defined Networking and Mobile Edge Computing in Vehicular Network with Reinforcement Learning
• Reinforcement Learning in Monopoly Games
• Anomaly Detection in Multimodal Data Leveraging Semantic Distance From Knowledge Graphs
• Knowldege Representation for Multimodal Streaming Data and Data at Rest for Situational Awareness
• Autism Information for Law Enforcement and other first Responders

• SNAP
• OpenAG Data Alliance (OADA)
• Videos from NYC
• The Stanford Open Policing Project

• Explainable AI Darpa
• DARPA-BAA-16-53
• XAI Program Portfolio DARPA
• Paper on Explainable AI
• Darpa XAI: A retrospective
• Trusted AI
• Bias in AI
• Trustworthy AI
• Triage proposal
• Explainable AI, Vishnu S. Penubarthi and Lalana Kagel, MIT, 2020
• Towards Effective Interpretation of Deep Neural Networks: Algorithms and Applications, Hu, Texas A&M University, 2020
• Trusting Autonomous Cars [Talk], CERIAS, 07/08/2020
• The Ghost in the Machine: Reconciling AI and Trust in the Connected World [Talk - Wednesday, Jul 22, 2020 1:00pm ET], CERIAS, 2020
• A Snapshot of the Frontiers of Fairness in Machine Learning, Chouldechova and Roth, CACM, 2020
• A Snapshot of the Frontiers of Fairness in Machine Learning [Video], Chouldechova and Roth, CACM, 2020
• Techniques for Interpretable Machine Learning, Du, Liu, and Hu, 2020
• Techniques for Interpretable Machine Learning [Video], Du, Liu, and Hu, 2020
• Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning [Paper], Gilpin et al., arXiv, 2018
• Trustworthy Autonomous Systems [Presentation Slides], Kagal et al., MIT, 2018
• Trustworthy Autonomous Systems [Abstract], Kagal et al., MIT, 2018
• Explainable AI - An Overview (Presentation 1) by Trevor Bonjour
• Explainable AI - An Overview (Presentation 2) by Trevor Bonjour
• Multimodal Explainable AI by Trevor Bonjour
• Bias in ML by Mijanur Palash

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/

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

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.

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.

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.

Academic integrity is one of the highest values that Purdue University holds. Individuals are encouraged to alert university officials to potential breaches of this value by either emailing integrity@purdue.edu or by calling 765-494-8778. While information may be submitted anonymously, the more information is submitted the greater the opportunity for the university to investigate the concern. More details are available on our course Brightspace table of contents, under University Policies.

Purdue University is committed to maintaining a community which recognizes and values the inherent worth and dignity of every person; fosters tolerance, sensitivity, understanding, and mutual respect among its members; and encourages each individual to strive to reach his or her own potential. In pursuit of its goal of academic excellence, the University seeks to develop and nurture diversity. The University believes that diversity among its many members strengthens the institution, stimulates creativity, promotes the exchange of ideas, and enriches campus life. More details are available on our course Brightspace table of contents, under University Policies.

The Disability Resource Center (DRC) is a resource for students and instructors. Students may present a “Letter of Accommodation” to you at any point in the semester. Should you have questions about accommodations, please contact the DRC at 765-494-1247 or email. In many cases, the DRC can partner with you to develop inclusive teaching strategies that benefit all students in your class. Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, you are welcome to let me know so that we can discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: 765-494-1247. More details are available on our course Brightspace under Accessibility Information.

If you find yourself beginning to feel some stress, anxiety and/or feeling slightly overwhelmed, try WellTrack. Sign in and find information and tools at your fingertips, available to you at any time. If you need support and information about options and resources, please contact or see the Office of the Dean of Students. Call 765-494-1747. Hours of operation are M-F, 8 am- 5 pm. If you find yourself struggling to find a healthy balance between academics, social life, stress, etc. sign up for free one-on-one virtual or in-person sessions. with a Purdue Wellness Coach at RecWell. Student coaches can help you navigate through barriers and challenges toward your goals throughout the semester. Sign up is completely free and can be done on BoilerConnect. If you have any questions, please contact Purdue Wellness at evans240@purdue.edu. If you’re struggling and need mental health services: Purdue University is committed to advancing the mental health and well-being of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of mental health support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at 765-494-6995 during and after hours, on weekends and holidays, or by going to the CAPS office of the second floor of the Purdue University Student Health Center (PUSH) during business hours.

In the event of a major campus emergency, course requirements, deadlines and grading percentages are subject to changes that may be necessitated by a revised semester calendar or other circumstances beyond the instructor's control. Relevant changes to this course will be posted onto the course website or can be obtained by contacting the instructors or TAs via email or phone. You are expected to read your @purdue.edu email on a frequent basis.

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