This assignment asks students to implement several dynamic programming string-matching algorithms to compare DNA sequences. Since the matching is approximate, their goal is to find the closest match to a given query in a database of known sequences. We use real DNA sequence data to ensure that solutions are reasonably robust. Students get hands-on experience with algorithms they may have only seen on paper and learn about the complex world of approximate string matching. The assignment is appropriate for a upper-level Algorithm Analysis course, where students are learning dynamic programming and algorithm analysis. We use in-person code demonstrations as an opportunity to discuss external factors that affect software engineering beyond just algorithm design, tying abstract algorithm knowledge to real-world concerns and broadening student perspectives.
Sat 21 FebDisplayed time zone: Central Time (US & Canada) change
13:40 - 15:00 | |||
13:40 13mTalk | Image Compression / Decompression (The OK Text Image Format)In-Person & OnlineGlobal Nifty Assignments Ben Stephenson University of Calgary | ||
13:53 13mTalk | Password SecurityIn-Person & Online Nifty Assignments | ||
14:06 13mTalk | Nifty Assignments: Tone MatrixIn-Person & Online Nifty Assignments Keith Schwarz Stanford University | ||
14:20 13mTalk | AI in Orbit: Intelligent Classification of Space Weather Events with Machine LearningIn-Person & Online Nifty Assignments John Brown Passaic Schools, James Liporace Rockland County Community College, Katherine G. Herbert Montclair State University, Thomas Marlowe Seton Hall University, Rebecca Goldstein Montclair State University | ||
14:33 13mTalk | Local LLM chatbotIn-Person & OnlineGlobal Nifty Assignments Jason Madar Langara College, Vancouver BC; Capilano University, North Vancouver, BC | ||
14:46 13mTalk | Nifty: DNA Sequence MatchingIn-Person & Online Nifty Assignments | ||