Call For Doctoral Symposium Papers
Guidelines for Abstract Submission:
We invite applicants to submit an extended 2 page abstract compiling your research towards your dissertation in a coherent manner. The contents of the abstract may include both already published work and work in progress. We highly recommend that the abstract highlights the following about your research:
- Motivation
- Major contributions of the thesis
- Key challenges identified or addressed
- Modeling challenges when deployed as systems for use
- Empirical and/or qualitative discussion of results
- Timeline and roadmap for thesis plan (may include some projects that are in future plans)
The extended abstract may be upto 2 pages with an additional page for references. Be mindful of the following:
- Describe your progress as of the date of submission (August 2021), and your anticipated progress by the workshop date (October 2021), being careful to distinguish between work that has already been accomplished and work that remains to be done.
- Clearly identify which work is done by you, and which work is done by collaborators, such as your thesis supervisor and/or other students in your research group (if there are any).
- Include a title for your work (please do not title it “Thesis Summary”).
- Remember that the audience for your thesis summary includes people who are knowledgeable about AI and ML, but are not necessarily experts in the narrow topic of your thesis. Introduce the content at a high enough level so that the general AIML audience can understand your story, but also include enough low-level detail so that the experts will appreciate your unique contribution.
- The thesis summary MUST be 2 pages, excluding references (1 additional page for references). Yes, this is tricky to do — but learning how to produce a concise statement of your thesis research is very important!
- A list of up to 5 keywords summarizing your work.
- We encourage you also to include a cover sheet, i.e., one page information (see below).
Cover Sheet
A one-page information sheet that includes the following:
- Your full name
- Contact details (mailing address, email address, daytime phone number)
- Affiliation: department, university, country
- Home page (URL)
- Your gender
- Country of Citizenship
- (Optional) Are you a member of an underrepresented minority ethnic group? If so, please specify.
- Expected graduation date
- Your thesis advisor’s full name
- Your thesis advisor’s contact details (title, department, affiliation, mailing address and email address)
- Please go through the committee members of all the tracks to list upto 5 people in the AI ML community as a suggestion for mentors for yourself during the AIMLSystems Doctoral Symposium.
- List of up to 5 keywords
Submission site: https://cmt3.research.microsoft.com/AIMLSystems2021/.
Upon Acceptance
We will notify the acceptance of your submission via email. Upon acceptance, we invite you to a presentation of your extended abstract to a panel of experts.
The panel along with your peers attending the symposium will provide constructive feedback on your presentation that hopefully hones your thesis.
Please feel free to contact any of the organizing committee for any questions regarding your submissions.
Key Dates
Paper submissions due: August 22, 2021Paper submissions due: August 30, 2021Author notifications (Accept/Reject): September 12, 2021Author notifications (Accept/Reject): September 21, 2021Camera-ready submissions due: October 10, 2021Conference dates: October 21-23, 2021
Schedule
Doctoral Symposium, 22 October 2021, Friday (2:00-4:30 pm)
Session 1: Presentations (2:00-3:30 pm)
2:00-2:30 pm
- Generative Modelling and Representation Learning in Temporal Graphs
- Investigating Interpretability in Deep Learning Models: A Case Study on Cardiac Abnormality Detection from Electrocardiogram Signals
- Explorations into MapReduce based Parallel Reduct Computation
2:30-3:00 pm
- Learning Neural Models for Continuous-Time Sequences
- Cybersecurity Threat Detection using Machine Learning and Deep Learning Techniques
- Exploring Attention-based Deep Learning methods for Classification, Retrieval and Shape Completion of ALS Roof Point Clouds
3:00-3:30 pm
- Evolving Hyperboxes for Enhanced Classification and Scalable Feature Selection
- Data Analysis Techniques for Smart Nudging
- Advanced machine learning techniques for short-term solar irradiance forecasting
