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IT - the Master Key for Success

IT - the Master Key for Success. school will soon be over. next: where to go?. Choice. but what to study?. something interesting. something that pays well. something useful for society. good news 1:. you will change at least 5 times what you do during your career.

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IT - the Master Key for Success

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  1. IT - the Master Key for Success

  2. school will soon be over

  3. next: where to go?

  4. Choice

  5. but what to study?

  6. something interesting

  7. something that pays well

  8. something useful for society

  9. good news 1: you will change at least 5 times what you do during your career

  10. no decision is ultimate!

  11. good news 2: now it is much easier to mix and match courses and come out with combined degrees

  12. so you have a chance to find a career matching all your interests

  13. quick rewind: how it was once…

  14. place: Sofia, Bulgaria

  15. year: 1981 “real socialism”

  16. Julita Vassileva, 18

  17. liked: writing, paintingdreamed of: traveling good at: generally everything…

  18. constraints communism  writing, art, traveling

  19. arts and humanities

  20. engineering TU Sofia Required a 30-min tram ride from home home TU

  21. sciences • Biology: • Chemistry: Never liked the smell • Physics: okay Ex-boyfriend applied there • Math: not so interesting, but powerful… • The laws of Nature are written in the language of mathematics ... (Galileo)

  22. so I chose: Math!

  23. brief history of studenthood • 1981- fresh”woman” in math – horrible! • 6 hours of math per day! So many different kinds of math! • learned 18 hours a day • surrounded by partying geniuses • exams: @#$% -- but actually, not so bad. • 1982- 2nd year in math • favorite subject: Analysis… Geometry nice as well • Computing: just boring… • Many boys around – good. • exams – going well – apart from bloody numeric methods! • 1983 – 3rd year in math • excellent marks overall • the geniuses disappeared • research career in Functional Analysis? • 1984 – 4th year: specialization in cybernetics and system control • “Computing is like knitting” • gives a sense of power • see your creation work in reality – nice  • 1985/86- diploma work (M.Sc.): intelligent tutoring system for Ohm’s law • 1985 - got married!

  24. 1987: PhD program at the Institute of Math (CS), Bulg. Academy of Sciences • practical Intelligent tutoring systems for any domain • A baby (1988)! • joy and horror! • found a nanny (1989)! That was an achievement! • read, read, think, write, think, write, program, play, think, think, read, play, play, program, think, play, think, write, read, think, write, play … • first paper accepted at an international conference in Sofia • 1989: Suddenly the Wall collapsed! • Second paper accepted at a conference in Germany – I am the first one in my family go to the West on business! • And I got the PhD (1991)! • The world was ready for my arrival! after graduation: quickly back to school • 1986: assistant prof. at the Chemical Technology Inst. • too much teaching – 15 hrs/week, 15 weeks/term… • doing this all my life? • what else? – compete to enter a PhD program.

  25. the big journey • 1992-1997: Munich, Germany • research associate at Inst. Technische Informatik • work, work work • the 2-body problem • distance • the horrors of the German kindergarten for a 4 yrs. old not speaking German) • Europe is too small, crowded • 1997-present: Saskatoon, Canada • - research associate (2 years) • - University Faculty Award (1999) – faculty at UofS • - assistant prof 1999-2001 • - associate prof 2001-june 2007 • - full prof Juy 2007 - • Writing and publishing over 100 papers • Conferences and sabbaticals – traveling around the world (some of my dreams came true)

  26. Amsterdam my travels Auckland, NZ Porto Christchurch,NZ Hawaii Madrid, Spain Berlin Santiago, Chile Maceio, Brazil

  27. but this is history…

  28. what is the situation Now and Here?

  29. Example Interdisciplinary Programs Mathematics and Computer Science Actuarial ScienceBioinformaticsBusiness Administration (WLU) and Mathematics (Waterloo) Double DegreeCombinatorics and OptimizationComputational MathematicsComputer Science MathematicsMathematics Teaching OptionMathematics/Business AdministrationMathematics/Chartered AccountancyOperations ResearchPure MathematicsStatisticsMathematical Physics Biology related Biochemistry Bioinformatics Biochemistry and biotechnology Biotechnology Biology Biology and biotechnology Biomolecular structure Microbiology and biotechnology Sociology of Biotechnology Computing and Financial Management Biotechnology/Chartered AccountancyBiotechnology/EconomicsEnvironment and BusinessEnvironment and Resource Studies

  30. Study: programs / disciplines Computer Science Bioinformatics Mathematics Biology Engineering Biochemistry Biotechnology Mathematics & MBA Environment Studies Chemistry Nanotech Sociology Agriculture Business/Commerce Physics International development

  31. Application of knowledge Bioinformatics Mathematics Biology Engineering Biochemistry Biotechnology Mathematics & MBA Environment Studies Chemistry Nanotech Sociology Agriculture Business/Commerce Physics International development Computer Science

  32. programming – though it is deemed necessary to proceed to learn towards really interesting things what is NOT computer science?

  33. so what IS computer science? Informatics – the science of information: patterns, structures, processes Intelligence - understanding and constructing intelligent behaviours

  34. Business Computing Dr. Maya Daneva Telus

  35. Business Process Modelling E.g. SAP – the largest German Software Company allows integrated: Budget monitoring: Gives managers seamless access to the financial data they need to make better decisions. Time management: Enables employees to record work and billable hours using their calendar, and automatically synchronizes and updates appointments with the application for enterprise resource planning (ERP). Leave management: Enables employees to submit personal leave requests and handle management approvals processes. Organization management: Allows employees and managers to access organization information and HR-related tasks. • Main aims of process models: • descriptive • traces what actually happens during a process • takes the point of view of an external observer who looks at the way a process has been performed and determines the improvements that have to be made to make it perform more effectively or efficiently • prescriptive • defines desired processes and how they should/could/might be performed • lays down rules, guidelines, and behavior patterns which, if followed, would lead to the desired process performance. They range from strict enforcement to flexible guidance. • explanatory • provides explanations about the rationale of processes • explores and evaluates several possible courses of action based on rational arguments • establishes an explicit link between processes and the requirements that they are to fulfill

  36. BioComputing Professor Lila Kari Computer Science, University of Western Ontario

  37. Computational Biology • = tries to solve biological problems with computational modelling methods and tools . • Examples include simulation programs applied to looking at protein-protein interactions, • protein folding, drug binding site elucidation, etc. • Bioinformatics • = the application of data management, data mining, data modeling and algorithmic techniques to biological databases, such as genome databases and related sequencing information. • Examples include using computer models to predict method gene function • and data mining for inferring and determining sequence homology information. • Biomolecular Computation • = exploit biological macromolecules to implement relatively standard methods of computation. • Examples are DNA computing, storage media using bacteria rhodopsin and biologically • altered cells that do rudimentary operations within the paradigm of traditional computation. • Biological Computation • = how biology computes from the sub-cellular level to the systems and population level.

  38. My name is danah boyd and i'm a PhD student in SIMS at Berkeley and a social media researcher at Yahoo! Research Berkeley. Buzzwords in my world include: identity, context, social networks, youth culture, social software, performance, Friendster, MySpace. Social Computing Barry Wellman is a Professor at the University of Toronto. He studies networks: community, communication, computer, and social. His research examines virtual community, the virtual workplace, social support, community, kinship, friendship, and social network theory and methods. Paul Resnick, University of Michigan: We are drawing on theories and data from social psychology and public goods economics to drive design decisions about on-line communities with the goal of increasing participants' contributions to the communal good.

  39. Affective Computing is computing that relates to, arises from, or deliberately influences emotions Affective Computing Rosalind Picard MIT Media Lab “Our approach, grounded in findings from cognitive science, psychology, neuroscience, medicine, psychophysiology, sociology, and ethics, is to develop engineering tools for measuring, modeling, reasoning about, and responding to affect. Thus, we develop new sensors, algorithms, systems, and theories that enable new forms of machine intelligence as well as new forms of human understanding.”

  40. Artificial Intelligence Game computing Author of the program Chinook, the World Man-Machine Checkers Champion. Chinook has been recognized by the Guiness Book of World Records as the first computer to win a human world championship in any game. We have done a lot of work on (nearly optimally) solving Sokoban problems. We think we have the strongest poker program (Texas Hold'em). An awesome Lines of Action program. One of the best Hex programs.

  41. but I can also study biology, psychology or sociology and then do all the computational stuff

  42. yes, but it will be harder think of all the Math that you will have forgotten in the meantime learn the harder stuff when you are younger and smarter!

  43. “studying CS is hard”

  44. well, yes, but just the first 2 years! and every study at the University is hard in the 1st year!

  45. This is Just a Temporary Appearance

  46. “need a lot of patience to succeed”

  47. yes, patience is needed to debug programs.

  48. a small attitude test • Imagine you are at a math exam • You’re simplifying an equation and you get stuck at the following: 0*x = 2 • Your classmates are whispering around you: “The right answer is 12”… • What do you do?

  49. options • Check carefully to find your error – spend the remainder of the exam on this, leaving the other problems • Check briefly and go to the next problem, then check again if there is time left • Think “they must be wrong”, even though you can’t really get any answer from where you are now • Write an explanation note to the examiner, that you have worked really hard and even though it seems to be wrong it isn’t really your fault

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