Testimonianza dI Alessandro Tommasi lab innovation

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Testimonianza dI Alessandro Tommasi lab innovation

  1. 1. CryptoLabTN Active links Expectations Obstacles Pathways to Innovation: Academia and Industry Alex Tomasi University of Trento Department of Mathematics May 30, 2013 Alex Tomasi Industrial Math Lab
  2. 2. CryptoLabTN Active links Expectations Obstacles Outline 1 CryptoLabTN 2 Active links between academia and the private sector 3 Expectations: private sector vs. academia 4 Obstacles to be overcome Alex Tomasi Industrial Math Lab
  3. 3. CryptoLabTN Active links Expectations Obstacles Members 1 Director: Massimiliano Sala 3 Research Fellows: Riccardo Aragona, Pietro Peterlongo, Alessandro Tomasi 6 PhD students: Matteo Piva, Chiara Marcolla, Emanuele Bellini, Marco Calderini, Federico Pintore, Claudia Tinnirello 9 Master’s students and young graduates 2 Software engineers: Lorenzo Nicolodi, Alessio Parzian 2 Administration: Francesca Stanca, Elisa Cermignani Alex Tomasi Industrial Math Lab
  4. 4. CryptoLabTN Active links Expectations Obstacles Activities The Lab’s main activities can be summarized as follows: we provide several courses for cryptographers and experts in security systems; we develop projects funded directly by private companies; we pursue academic research in fields closely linked to real-life applications. Alex Tomasi Industrial Math Lab
  5. 5. CryptoLabTN Active links Expectations Obstacles Introduction The Lab investigates two types of cryptography: Commercial cryptography e-commerce, online banking, privacy protection use of standard solutions and protocols upper bounds on security level Cryptography suitable for defense applications protection of classified information use of dedicated solutions and own protocols lower bounds on security level (provable security) Alex Tomasi Industrial Math Lab
  6. 6. CryptoLabTN Active links Expectations Obstacles Active links between academia and the private sector Alex Tomasi Industrial Math Lab
  7. 7. CryptoLabTN Active links Expectations Obstacles Stage Students spend a continuous period of time at a company’s office. Requirements : adequate provision of facilities at the company’s site Pros : allows the company to integrate the student in day-to-day routine over a period of time Cons : the experience can be somewhat disjoint from academic studies Alex Tomasi Industrial Math Lab
  8. 8. CryptoLabTN Active links Expectations Obstacles Internships Students spend some of their time at a company’s office, and some working at dedicated stations at their academic home institution. Requirements : adequate provision of facilities at both sites Pros : allows the student to use research tools on the job and discuss problems immediately with both professional and academic staff Cons : requires more careful management of tasks and expectations Alex Tomasi Industrial Math Lab
  9. 9. CryptoLabTN Active links Expectations Obstacles PhDs Companies can provide the funds for a student’s PhD if the student is willing to spend a significant amount of time, e.g. 12 months, working at the company’s premises. Requirements : a company with sufficiently complex needs, either due to scale or advanced technology, e.g. finance, energy, software Pros : allows the company to have access to dedicated cutting-edge scientific research for a long period of time; allows the student to acquire highly valuable work experience Cons : requires negotiation of property rights and disclosure agreements; requires balancing the company’s goals with research objectives Alex Tomasi Industrial Math Lab
  10. 10. CryptoLabTN Active links Expectations Obstacles Research contracts I A company commissions a specific piece of research or software. Requirements : a company with sufficiently specific and specialised needs Pros : allows the company to have access to cutting-edge scientific research without necessarily devoting facilities or man-hours to the task; allows the student to acquire highly valuable work experience and teamwork skills Alex Tomasi Industrial Math Lab
  11. 11. CryptoLabTN Active links Expectations Obstacles Research contracts II Cons : requires negotiation of property rights and disclosure agreements; requires commitment to a commercial contract that must be honoured on pain of legal recourse; requires the company to bear the full commercial risk; requires careful management of tasks and expectations; requires public contracts if specific hires are necessary Alex Tomasi Industrial Math Lab
  12. 12. CryptoLabTN Active links Expectations Obstacles Expectations: private sector vs. academia Alex Tomasi Industrial Math Lab
  13. 13. CryptoLabTN Active links Expectations Obstacles Private sector I Concreteness : Give specific answers to the right degree of detail; don’t just state something may be possible in theory, say what it would take to achieve a specific goal and whether it would make any sense to try Quantitative measures : applied problems require exact answers; be mindful of constants and orders of magnitude Time management : meet deadlines; don’t promise more than you can deliver; prepare for delays Alex Tomasi Industrial Math Lab
  14. 14. CryptoLabTN Active links Expectations Obstacles Private sector II Hands-on approach : prioritise solutions that work over elegant and comprehensive ones; be ready to take on responsibility and solve problems yourself, e.g. by writing your own code Professional attitude : take responsibility for your decisions, for better or worse; keep the promises you make and never leave something unfinished; admit to your mistakes and fix them in the shortest possible timeframe Alex Tomasi Industrial Math Lab
  15. 15. CryptoLabTN Active links Expectations Obstacles Academia I Negative results : Proving something is not possible is just as significant a result as the opposite Research needs to be published... : At least some of the money spent by the company will contribute to freely available information, but truly original ideas can lead to valuable intellectual property ... but published research is not infallible : even if you don’t aim for original research but simply implement something published by others, methods that allegedly perform well in academic tests may not fare well when applied to real-world data Alex Tomasi Industrial Math Lab
  16. 16. CryptoLabTN Active links Expectations Obstacles Obstacles to be overcome Alex Tomasi Industrial Math Lab
  17. 17. CryptoLabTN Active links Expectations Obstacles Lack of information I First and foremost, this is the greatest problem to be overcome in all its facets: both the private sector and academia are susceptible to it. Here are some concrete examples and a few suggestions on how to solve it. Private sector : The company does not even realise it has a problem. The company has no idea academia can solve their problems without buying something off-the-shelf or calling in a consultancy firm. Alex Tomasi Industrial Math Lab
  18. 18. CryptoLabTN Active links Expectations Obstacles Lack of information II Academia : The institution does not know which companies operate in the vicinity. The company has no idea academia can solve their problems without buying something off-the-shelf or calling in a consultancy firm. Common : sources of funding. Alex Tomasi Industrial Math Lab
  19. 19. CryptoLabTN Active links Expectations Obstacles Establishing channels I The best way to overcome a lack of information is to establish channels through which it can be spread, from one-to-one channels to comprehensive forums. Conferences : organised by either academia or the private sector, regular events encourage participation Courses : highly specialised and intensive events, one day or at most a week Consortia : some business categories form consortia whose entire reason for being is to provide services to the category that funds them, among which spreading awareness of resources for problem-solving is prominent Alex Tomasi Industrial Math Lab
  20. 20. CryptoLabTN Active links Expectations Obstacles Establishing channels II Career fairs : inviting companies to participate in end-of-academic-year career events makes them aware of graduates’ skill sets Successful collaborations : past performance is not a reliable indicator of future returns - but it is great for spreading word-of-mouth! Alex Tomasi Industrial Math Lab
  21. 21. CryptoLabTN Active links Expectations Obstacles Sources of funding I These largely depend on local arrangements and are highly thematic, but some noteworthy ones include: Public interest, regional : foundations (financial or otherwise), regional government, region-specific national funds Entrepreneurial : Start-up and spin-off funds are sometimes available from universities or business chambers EU : a multitude of options, from individual scholarships to projects involving a dozen companies - these all have trans-national requirements Alex Tomasi Industrial Math Lab
  22. 22. CryptoLabTN Active links Expectations Obstacles Bureaucracy I Another major hurdle to overcome is pervasive and pernicious bureaucracy, to which the only permanent solution is successful lobbying against red tape. Meanwhile, mitigation strategies are essentially based on careful planning to address the following risks: Taxes : transactions are taxed at every level, including cuts taken on contracts by departments and colleges Hiring : if a project requires a specific skill-set, such as software development in a certain language, a formal hiring process may take time and impose unrealistic requirements on candidates Alex Tomasi Industrial Math Lab
  23. 23. CryptoLabTN Active links Expectations Obstacles Bureaucracy II Proliferation of signatories : Ensure you know how many layers of approval are required to pass a contract before you agree on a deadline with companies. Intellectual Property : European law states that IP derived from research contracts belongs to the companies funding the research. Have this argument with your university’s IP office before drafting the contract. Enterprise risk : Italian law states that as universities run on public funds, they cannot bear enterprise risk - in other words, anyone signing a contract with academia must pay up, as long as work has demonstrably been done, regardless of the outcome of the research. Alex Tomasi Industrial Math Lab

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