Aditya P. Mathur Research, Education, Service, and Vision

1. Aditya P. Mathur Research, Education, Service, and Vision CS Department Colloquium March 26, 2007

2. R’m R’d R’df R’f Reliability Rm Rdf Mutation Rd Dataflow Rf Decision Functional tfs tfe tds tde tdfs tdfe tms tfe True reliability (R) Estimated reliability (R’) Saturation region Testing Effort FUNCTIONAL, DECISION, DATAFLOW AND MUTATION TESTING PROVIDE TEST ADEQUACY CRITERIA. Research: Empirical Studies • Saturation effect [Horgan.Mathur96]

3. Research: Empirical Studies and Reliability • Saturation effect [Horgan.Mathur96] • Microsoft quality gate criteria. Pioneered by Praerit Garg [MS’95] • Guidant test quality assessment for medical devices [recommendation accepted; yet to be implemented] • Software reliability estimation [Chen.Mathur.Rego 95; Krishnamurthy.Mathur 97] • Led to new approaches to software reliability modeling. [Gokhale.Trivedi 98; Singpurwalla.Wilson 99; Goševa-Popstojanova.Trivedi 01; Yacoub et al. 99; Cortellessa et al. 02; Mao.Deng 04]

4. Research: High Performance Testing • Testing on SIMD, Vector, MIMD architectures [joint with Choi, Galiano, Krauser, Rego. 88--92]

5. Research: Feedback Control • Feedback control of software test processes [joint with Cangussu, DeCarlo, Miller. 00--06]

6. Education • Introduction to Microprocessors [80, 85, 89] • Drove curricula in almost every engineering college in India (including all the IITs). • Continues to be recommended mostly as a reference text in many Indian universities. • Over 100,000 students benefited from this book. • Foundations of Software Testing, Vol 1 [07], Vol 2 [08] • First comprehensive (text) book to present software testing and reliability as an integrated discipline with algorithms for test generation, assessment, and enhancement. Is driving testing curricula in CS/ECE departments.

7. Service: Impact • Educational Information Processing System [BITS, Pilani 85] • Led a team of four faculty to design, develop, and deploy from scratch. In use even now(‘06) (code changed from Fortran IV--HP1000-- to C (PC)!) • Software Engineering Research Center (SERC) [94-00] • Started by Conte/Demillo ‘86-87. • Led SERC recovery from six industrial members to 13 and from two university members to four. Over $1.5 Million in research funds awarded to faculty. • Purdue University Research Expertise (PURE) database [06] • Original idea: Dean Vitter. My contribution: Requirements analysis, design, testing, and management; interaction with all 10 colleges. • Over 85% of Purdue (WL) faculty in PURE. Expansion planned to other state universities; enhancement of feature set [with Luo Si]

8. Aditya P. Mathur CS Department Colloquium March 26, 2007

9. Sponsor: National Science Foundation Principle Investigator Aditya Mathur Graduate Student Alok Bakshi, Industrial Engineering Modeling the Auditory Pathway Collaborators: Nina Kraus: Hugh Knowles Professor Sumit Dhar: Assistant Professor, Department of Neurobiology and Physiology, Northwestern Michael Heinz: Assistant Professor, Speech, Language, and Hearing Sciences and Biomedical Engineering, Purdue

10. Objective To construct and validate a model of the auditory pathway that enables us to understand the impact of defects and auditory plasticity along the pathway in children with learning disabilities.

11. Trail What is auditory pathway? Progress so far and the future Existing modeling approaches versus our approach BAEP and childrenwith learning disabilities What is Brainstem Auditory Evoked Potential (BAEP)?

12. 100,000,000 Comparison across sounds 570,000 Medial geniculate body Gateway for AC 392,000 Sensory integration (e.g. head movement) Pitch discrimination (VCN) Range,timing, intervals Input for sound localization Spatial map?, Spectral analysis Onset neurons 8,800 42,000 Azimuth, integration from both ears; ITD and ILD computation Transport frequency, intensity Information; rate encoding/temporal encoding What is (ascending) auditory pathway? http://www.iurc.montp.inserm.fr/cric/audition/english/audiometry/ex_ptw/voies_potentiel.jpg http://www.iurc.montp.inserm.fr/cric/audition/english/audiometry/ex_ptw/e_pea2_ok.gif

13. What is Brainstem Auditory Evoked Potential (BAEP)? Q: What is the effect of learning disability on ABR? ABR [1.5-15ms]: Brainstem Slow AC response MLR [25-50ms]: Upper brainstem and/or Auditory Cortex ABR: Auditory Brainstem Response MLR: Middle Latency Response Source: http://www.audiospeech.ubc.ca/haplab/aep.htm

14. BAEP for normal and language impaired children Stimulus: Synthesized /da/ 6.2ms 7.2ms V: lateral lemniscal input to inferior colliculus Vn: dendritic processing in the inferior colliculus Normal children Language impaired children Observation: Duration of V-Vn found to be more prolonged for children with learning problems than for normal children. Notice also the difference in the slope of V-Vn. Source: Wible, Nicol, Kraus; Brain 2005.

15. FFR BAEP for normal and language impaired children Onset and formant structure of speech sounds in children Stimulus: Train of /da/ FFR: Frequency Following Response Normal children Language impaired children Observation: Mean V-Vn slope was smaller for children with language-based learning problems. Source: Wible, Nicol, Kraus; Biological Psychology, 2004.

16. FFR for Musicians and Non-musicians Stimulus: /mi1/, /mi2/, /mi3/ [Mandarin] F0: Stimulus fundamental frequency Observation: Musicians showed more faithful representation of the F0 contour than non-musicians. Source: Wong, Skoe, Russo, Dees, Kraus; Nature Neuroscience, 2007.

17. Importance of the BAEP • Neural activity in the auditory pathway, measured via the BAEP, seems to be a strong indicator of learning disabilities in children. • Auditory pathway is “tuned” by tonal experience.

18. Why model the auditory pathway? • BAEP is an external measurement (black box) of an internal activity. • Direct observation of internal activity is almost impossible in humans. • A validated model will allow direct observation of (simulated) internal activity and offer insights into the relationship between such activity and the BAEP. • This might lead to better diagnosis. • Several other advantages too.

19. Research questions • How can neuro-computational models be used to encode, and mimic, the auditory neural behavior exhibited by children with learning disabilities? • How can such models be used to accurately predict the impact of treatments for learning impairments?

20. Existing approaches • Connectionist models: • Surface and deep dyslexia: Hinton.Shallice’91, Plaut.Shallice’93 • Spatial firing patterns: Nomoto’79 • Phenomenological models [P-models]: • Sound localization: Neti.Young.Schneider’93 • Response to amplitude modulated tones: Nelson.Carney’04 • Cochlear model: Kates’93 • Speech recognition: Lee.Kim.Wong.Park’03 • Simulation models: • External ear to cochlear nucleus: Guérin.Bès.Jeannès.’03

21. Simulation ……. P-model P-model P-model Anatomy Equations Assumptions Our approach

22. Progress INFERIOR COLLICULUS Not Implemented SUPERIOR OLIVARY COMPLEX Not Implemented Medial Superior Olive Medial Nucleus of the Trapezoid Body Lateral Superior Olive COCHLEAR NUCLEUS Pyramidal Cell Stellate Cell Inter-Neurons Bushy Cell Fusiform Cell Octopus Cell Implemented AN Fibres [Zhang et al.] HRTF [Lookup table/person] Not Implemented

23. Bushy Cell (in Anteroventral Cochlear Nucleus) Preserves timing information for the computation of ITD. AN spikes Bushy Cell Time Bushy Cell spikes Receives excitatory input from 1-20 AN fibers in the same frequency range Latent period Time

24. Bushy Cell Model [Rothman ‘93] Slow low threshold potassium conductance Some constants associated with Bushy cell: Fast high threshold potassium conductance Passive leakage conductance Inhibitory synaptic conductance

25. Bushy Cell Model • The cell potential (V) is given by: Where Reverse potentials for corresponding ions Membrane capacitance Leakage conductance

26. Bushy Cell Model Factor to scale rate constants to body temperature General expression for scaling rate constants to temperature T The three conductance mentioned earlier are given as:

27. Bushy Cell Model Here themselves depend on voltage of soma V Here denotes the arrival time for spike and synaptic Conductance reaches its peak value of at time Variation is given as: Here and are given as:

28. Bushy Cell Model

29. Bushy Cell Model - Output • Response of Bushy cell for different number of input AN fibers (N), and synaptic conductance (A) • Fig. A shows the response of our implemented model for N=1 and A= 9.1, while the output obtained by Rothman et. al. is shown in D for same parameter.

30. Next Step • Implement the IID circuit and find out the correlation between neural output and sound source (azimuth angle) Carney et al. H&H LSO LSO Constant delay Cochlear Nucleus Cochlear Nucleus Rothman et al. SBC GBC MNTB MNTB GBC SBC Spirou et al. Zhang et al. Cochlea Cochlea

31. Next Step • Implement the ITD circuit and find out the correlation between neural output and sound source (azimuth angle) MSO MSO LNTB LNTB Cochlear Nucleus Cochlear Nucleus SBC GBC MNTB MNTB GBC SBC Cochlea Cochlea

32. Next Step • Implement the dorsal cochlear nucleus neurons and find out the correlation between vertical angle and neural output in DCN region

33. Model Validation • Interconnected P-models • Functional • Sound localization; in collaboration with Professor Sumit Dhar, Northwestern

34. Outside Iext IK INa IL C K+ ion channel gK gNa gL VK VNa VL Inside ( At potential V ) http://personal.tmlp.com/Jimr57/textbook/chapter3/images/pro5.gif Hodgkin Huxley Model m, n and h depend on V

35. Aditya P. Mathur CS Department Colloquium March 26, 2007

36. Vision as in the Strategic Plan [2003] • The faculty will be preeminent in creating and disseminating new knowledge on computing and communication. The department will prepare students to be leaders in computer science and its applications. Multidisciplinary activities that strengthen the impact of computation in other disciplines will play an essential role. …..

37. Vision as in the Strategic Plan [2003] • The department will be known for: • Faculty who are recognized worldwide as leaders. They will set and implement the national agenda for discovery and education in computer science. • A superior and diverse student body learning the values, vision, knowledge, and skills of computer science. • Graduates who go on to be faculty at highly ranked departments, researchers at internationally recognized labs, and leaders and innovators in industry and government. • Involvement and leadership in university institutes and centers that foster multidisciplinary research. • Collaboration with public and private enterprises in Indiana, the nation, and the world.

38. Goals Offer a broader set of options to our undergraduate students. 2. Strengthen interdisciplinary research and educational programs. 3. Improve upon the existing research environment for faculty and students, in particular for tenure-track assistant professors. 4. Meet our implicit obligations to the state and the nation, in particular to our customers. 5. Maintain excellence where it already exists.

39. Aditya P. Mathur CS Department Colloquium March 26, 2007 Thanks!

40. Faculty: Hiring • Look to the future of CS. • Continue support for research in core areas but aim to establish collaborative groups that are radically different in their perspective and aspirations. • Consider CS as a discipline essential to finding solutions to problems of key significance to humans: cancer and other diseases, large scale information processing, finance, health care, etc. • Aim at creating strengths in new and challenging areas while retaining current strength in core areas. Goal: Strengthen interdisciplinary research and educational programs.

41. Faculty: Tenure • Reduce the uncertainty for an Assistant Professor. • Focus (primarily) on scholarship; identify quantitative and qualitative indicators of scholarship. Consider “quality” as a multi-dimensional attribute. • Identify and communicate ways of measuring impact/potential impact. • Create a “Tenure card” that aids in (accurate) self assessment. • Strengthen the third year review process. Goal: Improve upon the existing research environment for faculty and students, in particular for tenure-track assistant professors.

42. Other programs/staff • Outreach programs • All staff • Facilities • Corporate Partners Program • Development Goal: Maintain excellence where it exists.