Vikas Ashok
Picture of Vikas Ashok

Vikas G Ashok
Assistant Professor
Department of Computer Science
Old Dominion University Norfolk, VA 23529

E&CS Bldg., Room 3107
vganjigu AT cs DOT odu DOT edu
(757) 683-7797
Twitter

Brief Bio

Before joining Old Dominion University, I was a post-doctoral associate at Stony Brook University, from where I graduated with a PhD in Computer Science, under the supervision of Prof. I.V. Ramakrishnan. My primary research focus is in Accessible Computing, and I am particularly interested in designing and developing personalized semantics-aware assistive technologies for people with vision impairments.

News

  • Decemeber 2019: 2 papers accepted for publication at ACM Intelligent User Interfaces (IUI) 2020.
  • July 2019: Joined ODU CS department as an Assistant Professor.
  • July 2019: Completed my post-doc at Stony Brook University.
  • January 2019: Joined Stony Brook University as Postdoctoral Associate.
  • Decemeber 2018: Graduated with a Ph.D. in Computer Science from Stony Brook University.

Curriculum Vitae

Full CV: Vikas_cv

Recent Publications Google Scholar DBLP

  1. Vikas Ashok, Syed Billah, Yevgen Borodin, I V Ramakrishnan, Auto-Suggesting Browsing Actions for Personalized Web Screen Reading, in 27th ACM Conference on User Modeling, Adaptation, and Personalization, UMAP 2019, Larnaca, Cyprus, 2019.
  2. Shirin Feiz, Syed M Billah, Vikas Ashok, Roy Shilkrot, I V Ramakrishnan, Towards Enabling Blind People to Independently Fill out Paper Forms, in 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland UK.
  3. Syed M Billah, Yu-jung Ko, Vikas Ashok, Xiaojun Bi, I V Ramakrishnan, Accessible Gesture Typing for Non-Visual Text Entry on Smartphones, in 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland UK.
  4. Syed M Billah, Vikas Ashok, Donald E Porter, I V Ramakrishnan, SteeringWheel: A Locality Preserving Magnification Interface for Low Vision Web Browsing, in 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, Montreal, Canada.
  5. Syed M Billah, Vikas Ashok, I V Ramakrishnan, Write-it-Yourself with the Aid of SmartWatches: A Wizard-of-Oz Experiment with Blind People, in 23rd International Conference on Intelligent User Interfaces, IUI 2018, Tokyo, Japan.
  6. Syed M Billah, Vikas Ashok, Donald E Porter, I V Ramakrishnan, Speed-Dial: A Surrogate Mouse for Non-Visual Web Browsing, in 19th international ACM SIGACCESS conference on Computers & accessibility, ASSETS 2017, Baltimore, USA.
  7. I V Ramakrishnan, Vikas Ashok, Syed Masum Billah. Non-visual Web Browsing: Beyond Web Accessibility International Conference on Universal Access in Human-Computer Interaction. Springer, Cham, HCII 2017.
  8. Syed M Billah, Vikas Ashok, Donald E Porter, I V Ramakrishnan, Ubiquitous Accessibility for People with Visual Impairments: Are We There Yet?, in 2017 CHI Conference on Human Factors in Computing Systems, CHI 2017, Denver, USA.
  9. Vikas Ashok, Yury Puzis, Yevgen Borodin, I V Ramakrishnan, Web Screen Reading Automation Assistance using Semantic Abstraction, in 22nd International Conference on Intelligent User Interfaces, IUI 2017, Limassol, Cyprus.
  10. Andrii Soviak, Anatoliy Borodin, Vikas Ashok, Yevgen Borodin, Yury Puzis, I V Ramakrishnan, Tactile Accessibility: Who Needs a Haptic Glove?, 18th international ACM SIGACCESS conference on Computers & accessibility, ASSETS 2016, Reno, USA.

CSE 480/580: Introduction to Artificial Intelligence

Course Description

The class will cover fundamental concepts, principles, and techniques in Artificial Intelligence.

Office Hours

Tuesday, Thursday: 2:00 pm - 3:00 pm and by appointment

Topics

  • Problem Solving Agents
  • Intelligent Search
  • Knowledge Representation
  • Machine Learning
  • Natural Language Processing
  • Vision Processing

Textbook

The course content will be based on the following textbook:

  • Stuart Russell, Peter Norvig. Artificial Intelligence: A Modern Approach. (3rd Edition)

Grading Criteria

  • Homework Assignments: 50%
  • Midterm Exam: 15%
  • Final Exam: 20%
  • Class Project: 15%

Attendance

Attendance is highly recommended. It is your responsibility to obtain any information given out in class. The instructor does not give out class notes. Some material presented in the lecture is not covered by the text. Students with special needs (e.g. hearing or vision difficulties) should inform the instructor at the beginning of the semester.

Honor Code

Please refer to the statement on academic integrity given below.

By attending Old Dominion University you have accepted the responsibility to abide by the honor code. If you are uncertain about how the honor code applies to any course activity, you should request clarification from the instructor. The honor code is as follows:

“I pledge to support the honor system of Old Dominion University. I will refrain from any form of academic dishonesty or deception, such as cheating or plagiarism. I am aware that as a member if the academic community, it is my responsibility to turn in all suspected violators of the honor system. I will report to Honor Council hearings if summoned.”

In particular, submitting anything that is not your own work without proper attribution (giving credit to the original author) is plagiarism and is considered to be an honor code violation. It is not acceptable to copy source code or written work from any other source (including other students), unless explicitly allowed in the assignment statement. In cases where using resources such as the Internet is allowed, proper attribution must be given.

Any evidence of an honor code violation (cheating) will result in a 0 grade for the assignment/exam, and the incident will be submitted to the Department of Computer Science for further review. Evidence of cheating may include a student being unable to satisfactorily answer questions asked by the instructor about a submitted solution. Cheating includes not only receiving unauthorized assistance, but also giving unauthorized assistance.

Students may still provide legitimate assistance to one another. You are encouraged to form study groups to discuss course topics. Students should avoid discussions of solutions to ongoing assignments and should not, under any circumstances, show or share code solutions for an ongoing assignment.

Please see the ODU Honor Council’s webpage for other concrete examples of what constitutes cheating, plagiarism, and unauthorized collaboration. All students are responsible for knowing the rules. If you are unclear about whether a certain activity is allowed or not, please contact the instructor.

Slides

Jan 14 2020, Lecture1: Introduction