Overview of Graduate Program at CS SFSU February 2006

1. Overview of Graduate Program at CS SFSUFebruary 2006 Prof. D. Petkovic

2. Overview of Graduate program • New program started Fall 2004 • New degrees (MS with Conc. In CLS, SW Eng, General) • More emphasis on project and student research • Many new courses • http://cs.sfsu.edu/grad/graduate.html • Program doing well! • New program started Fall 2004 • New degrees (MS with Conc. In CLS, SW Eng, General) • More emphasis on project and student research • Many new courses • Graduate WWW page – check it often • http://cs.sfsu.edu/grad/graduate.html • Program doing well!

3. Objectives • Prepare students for job market • Prepare students for Ph. D. studies • MS is becoming a necessity in today job market • Education goals: • Fundamentals • Discipline specialty (CLS, SW Eng, general) • Project, industry cooperation, teamwork • Individual research • Thesis is mandatory

4. Computer Scientist of the future • Knowledge of technical material (up to date) • Knowledge of some specific domains 9finacials, biotech, games…) • Project and teamwork skills • Verbal and written communication and presentation skills • Ability to work in a global and open SW environment

5. Our “niche” • Address large gap between concepts (addressed by Ph. D. schools) and products (addressed by industry) • Provide true multidisciplinary and teamwork projects: students engaged in working with professors from various disciplines solving real applications • Address work that industry would like to be done, but has no resources • Provide minority training in multidisciplinary projects and biotech (usually not covered by Ph. D. universities) • Joint grants with top Ph. D. schools in a complementary fashion • Attractive niche for funding agencies (due to impact, breadth and minority training)

6. Center for Computing for Life Sciences (CCLS) • CCLS is proposed as official multidisciplinary SFSU Center for addressing problems in broad area of Computing for Life Sciences such as: bioinformatics, imaging, collaborative tools, UI, visualization, databases, computational biology and chemistry, applications in drug discovery, collaborative tools, algorithms etc. • Goal is to develop CCLS into signature “marquee” program of SFSU • CCLS is joint proposal between Computer Science, Biology, Chemistry and Biochemistry, Math, Physics and Astronomy – all are in COSE • http://www.cs.sfsu.edu/ccls/index.html

7. Center for Computing for Life Sciences: Role and Activities • Provides SFSU focus in Computing for Life Sciences: • Place for students to find and do culminating experience and other projects in CCLS area, following individual department policies • Feedback to curriculum development • Hosts projects with local industry • Provides focus on funding and increases chances for getting grants • Complements other degree programs at SFSU ( I.e. MSCS with Concentration in Computing for Life Sciences) • Incubator for commercialization efforts • Consists of faculty, students and external partners (industry collaborators, visitors) from all relevant departments, part or full time, working together

8. CCLS Status • Funded for 3 years by SFSU • Review after 3 years – idea is to become self funded • One FT position • One SW position • Money for seed grants (e.g. faculty release, grad students) • Money for travel and conf. support • Space and new furniture

9. Graduate seminar series - PERNET • Focus on taking this in one semester (your first or second) • Excellent way to broaden up, learn, get an idea for MS thesis and meet/hear great people • Need 10 stickers (can compensate with attending graduate seminars talks in other semesters) • Very exciting speakers coming

10. Thesis and research • Be careful when selecting thesis and advisor – any change requires all signatures • In the new program two 899s are allowed. Both or only one can be used as part of the thesis • Follow up on all steps, as requested by your advisor • Expected time about 3 semesters (with writing and defense) • OIP issues for foreign students

11. How to select the thesis • Talk to the professor who you know and who knows you (e.g. take his/her class) • Have some ideas what type of work you ant to do: application area, UI vs. DB etc. • Discuss with several professors; do some research (e.g. check professor’s WWW pages) • Follow the process and stick with it

12. Some project ideas/people • DB/UI/Applications in biology and bioinformatics and CLS area: Profs. Singh, Yoon, Petkovic) • DB Tools, Multimedia databases: profs. Murphy, Singh, Petkovic • Visulisation/Graphics: prof. Yoon • WWW Applications, Community applications: Prof. Levine • Performance, WWW, Info Retrieval: Prof. Dujmovic • Algorithms, compilers, WWW search: Profs. Wong, Dujmovic • Distributed Systems, Open Source: Prof. Puder • Multimedia sound, music: Prof. Hsu

13. 694/893 – co-op • For foreign students: counts as an elective • Foreign students have limit of 30 units, so be careful how you sue them – 694/893 counts • 694/893 can not be taken if: • All electives are compelled • Student working on a thesis • 694/893 would delay the graduation • Check http://cs.sfsu.edu/forms/student%20forms/893-694%20Course%20Requirments.pdf

14. Practical training – foreign students • Must have GPA > 3 • Must not delay graduation • Thesis practical training will be harder to justify • Post-completion practical training can start only when the thesis is completed • Starting work under practical training before graduation is not advisable and not good for you! • New process: • http://www.cs.sfsu.edu/forms/student%20forms/opt_cpt_letter_instructions.html

15. CSC 895 • Please follow the schedule for all the related forms, like GAP, Human Subjects etc. – they need to be done well before you are registering for 895 • Foreign students – watch the reduced load etc. – need to get a form approving reduced load • OIP issues – we do not easily approve starting outside jobs before the thesis is complete

16. Other stuff • “Biology for CS” is now is prereq for “Bioinformatics Computing” • Policy on cheating and plagiarism will be strictly enforced – (students are getting inventive…) • http://www.cs.sfsu.edu/plagarism.html

17. Project examples: • NetBEAMS • http://www.netbeams.org/ • HH Signaling pathway site • http://hedgehog.sfsu.edu/ • HR Tools • https://dbsgrad.sfsu.edu/~erdesigntools/ • Machine learning for drug discovery • http://prdownloads.sourceforge.net/phasemachine/ • FreeFlow DB for drug discovery (with UCSF) • Annotaions

18. FreeFlow: Information Management for High-Throughput Drug DiscoveryPI: Rahul Singh

19. Motivations and Scope of the Project • Modern drug discovery is a complex high-throughput process • Heterogeneous data: Molecules, Assay Outputs, Imagery, … • Each data type has its own logic for interaction • Large volumes of information require novel ways of interacting with the • data. • Information needs are both data oriented and process oriented • Opportunity for broad impact • Collaboration with Joseph DeRisi, UCSF for designing a high-throughput • information management system for anti-malarial drug discovery. • Project started in January 2005. First version of the system is in use • at UCSF

20. Overview of the System FreeFlow is a system for managing and interacting with information generated during pharmaceutical drug discovery. It is designed to enable drug discovery efforts by: • Storing large amounts of raw data generated from high-throughput screening experiments. • Allowing researchers to ubiquitously input and interact with biological data. • Performing post-processing of the raw data files efficiently and storing the result in database along with other heterogeneous data: structures, graphs, images, alpha-numeric, etc. • Providing a set of online tools for researchers to perform complex data analysis, data visualization, data mining, report generation from a web interface. • Providing a way for researchers to access data and share and collaborate on the web.

21. Example Functionality: Plate-based assay setup and visual data query Figure 1. FCS Loader Interface. Aids in managing experimental information and uploading raw data files. Figure 2. Plate Builder Interface. Aids in the configuration of well properties, such as drug id, cell type, and malarial strain. Figure 4. Data Visualization. Data can be visualized in numerous ways once processed and loaded into the database. Figure 3. FCS Processor Interface. Aids in the post-processing of raw data files.

22. Example of Visualization in FreeFlow • Visualization of raw data in the form of histograms and dot plots. • QC reports for each plate– event counts, data processing statistics, and FITC-gate calculations. • Reports for various queries done by the user, e.g. by malarial strain. • Processed plate visuals for drug sensitivity in each well within the plate. • IC50 curves for dose response experiments. Uninfectedred blood cells Infected red blood cells Fluorescent intensity

23. P. Malik, T. Chan, J. Vandergriff, J. Weisman, J. DeRisi, and R. Singh, “Information Management and Interaction in High-Throughput Screening for Drug Discovery”, Book Chapter in Database Modeling in Biology: Practices and Challenges Z. Ma, and J. Chen, eds., Springer Verlag, 2006 Contact: Dr. Rahul Singh rsingh@cs.sfsu.edu For Further Information