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Introduction for i-Teams

2022-06-11 02:16:00 【SyncStudy】

Introduction for i-Teams

Jan 17

Online platform

moodle
- recording
- accouncements
zoom
- online meeting rooms
slack
- team communication
google drive
- Storage
- all materials needed
Micro.com
- online interactive whiteboard
- workshop
- next week’s brainstorming

Covid

symptoms and isolations
masks
pizza
- 6:30

Zoom rooms

lectures
team meetings

Amy Weatherup

STNC
Microsoft
Quuaalcomm
Start up business development
commercial and charitable boards
university teaching
created and run iteams
university of cambridge primary school
birthlight

Histroy

MIT
2006
Pre-spin-out stage
Essex
UoL
200 projects
1300 students
90 start ups founded
2/3 still actively commercialising

samples

raspberry pi

no 1 reason for start up to fail

overconfidence
not creating what customer’s need
poor team structure
lack of capital
1. no market need ！！！！！
2. no market
3. not right tea
4. get completed

No market need (42%)

Sanity check
if someone can not find it
which people
which company
how they will pay for it?
take a different approach at it

timing is everything

Time
note
- not taking the

nvoy [an iphone in 2003]

What went wrong?

right product, wrong time
no viable route-to-market
Did not listen, did not solve current problems
Customers are not reasdy to take it

STNC- the first mobile web browser

brining information to people on the move

What went right?

Right product, right time
Route-to-market
business model
fast and flexible
paying customer

Time, timing and money is the most important things in life

Dr. andy richards
chiroscience, DanioLabs, Arakis and others

Chance favors only the prepared mind—Louis Pasteur

https://carey.jhu.edu/one/2010/fall/the-mind-of-the-enterpreneur

ITEAM FORMATION

new technology from cambridge lab

What

Analysis and recommend
- best target applications for new technologies
  - no commercial application
    - valid and useful answers
use real feedback
- industry experts
  - not just an interest exercise
your maintask real world feedback on your technologies
Geoffrey Moore in Crossing the Chasm
- What is the one application that best captures the power and value of your technology?

How does it help?

Work on real inventions while still in the lab
look for real world uses that solve real customer problems
- is it good match

Iteams works like like real startup

iteams
- interdisciplinary
- Mentoring
- Contact
- repeated revision of conclusions required
startup
- complimentary skills
- Feedback
- Contact
- Re[eayed review of progress and direction to ensure success

What skills are you bringing to your teams?

Customers

paying money to your product
sustainable business need paying customers
government grants and investment can help you started, but they are not customers
get the people willing to pay
people willing to pay

Benefits not features

do something new
do something better
behaviour change takes a generation
beware of assumptions
- cheaper
- safer
- cleaner
- quicker
- less manual efforts
LARGE AND SMALL ONE
- actually they are not the key things
- actually, the industry want to see the best performance of the sensors
- make the associated insights
- problems for our technologies to make the implications

Finding contacts

Identify companies working in the relevant area
- try different search terms
- wikipedia
- relevant industry market report
- patent searching for relevant technical
  - Ways to looking for fields in the domain
finding out more about them
- company websites
- company annual reports
- Pre releases and technology white papers
  - what they are saying what
identify people in your target companies
- conference speakers and attendees
- new items/ pre releases
- linkedin
  - people talking about their companies
  - it is better to give a video call
  - which is much more engaging

Some past iteams projects

Cell cultivator
- what is the technology and what has been done with it?
  - Cell cultivator cultivator created to help inventor’s own research project
  - needed a controllable environment for observing cells over several weeks
  - working model cfreated and used in experiments, refined several times
  - we need to know the existance of others by systematic literature review
What is the technology and what has been done with it?
- South american
  - toothache plant
- plant
- commercial
- phase ii human clinical trials
- I-teams
  - before
    - EU legislation ti
  - during
  - After

Inventor meeting

MarcBax
- I assist clients to turn ideas, inventions and insights into tangible, valuable and sellable products. In some cases this means taking a product from proof-of-concept through to volume manufacture. But it can also mean working out a rough idea into an investable proposition by creating concepts and product demonstrators. Value is not intrinsic to an idea, but is created by making it practical, by execution.
  I focus on ideas where sensors and sensing play an important role - products that detect, monitor, diagnose, measure or image something. In application fields including medical devices, lab equipment, security/defense, and environmental monitoring.
  I’m a mechanical engineer and physicist by education, added a marketing degree through evening study and think I’ve become an all-round product- and business-developer by experience.
  For employers in the medical device and printing equipment industries I’ve taken products from concepts through to commercial success. Since 2003 I work freelance mostly for start-ups and SMEs.
  Apart from working on product design and development through my company Panchromos Limited I collaborate with my brother Laszlo in Bax & Company, an open innovation consultancy with offices in Spain, the UK and the Netherlands. We set up and execute collaborative innovation projects with an EU perspective (e.g. Horizon 2020, Interreg).
  I also volunteer as business mentor for i-Teams projects at the University of Cambridge.
Philip stanley marbell
- Research interests
  NOTE: The publication list above is auto-generated by the University and is outdated and inaccurate. See below or here for an up-to-date list.
  Summary of Recent Research: My research exploits information about the physical world to make more efficient computing systems that interact with nature. This requires a combination of theory (applied mathematics) and hardware (circuits and computer architecture). My research involves equal parts of equations, proofs, circuits, and hardware prototypes. I spent some time in the mid-nineties working at Bell-Labs in the group that created C, C++, and Unix. Partly as a result, I enjoy designing domain-specific programming languages and compilers (inevitably, for problems involving efficient computing systems that interact with nature).
  Capsule Bio: B.Sc., 1999 (Rutgers); M.Sc., 2001 (Rutgers); Ph.D., 2007 (Carnegie Mellon). In the summers of 1995, 1996, and 1999, I worked as an intern / engineer at Bell Labs (Murray Hill, NJ), first in the Microelectronics Division, and then in the Data Networking Division, on a project spun out by the research group that created the C programming language, the Unix, Inferno, and Plan 9 operating systems, and much more. I spent 2006–2008 at Technische Universiteit Eindhoven in the Netherlands, joined IBM Research in Zürich, Switzerland, as a permanent Research Staff Member from 2008–2012, and then joined Apple in Cupertino from 2012–2014. I moved back to academia in 2014: I was in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) from 2014-2017 and joined the University of Cambridge as a faculty member in 2017. Since 2018, I am also a faculty fellow at the Alan Turing Institute for Data Science and Artificial Intelligence in London.
  I lead the Physical Computation Laboratory, a research group with about a dozen members (three postdocs, two directly-supervised PhD students, two PhD project students from the Faculty of Mathematics, and five M.Eng./M.Res./IIB students from the Nano DTC, Graphene CDT, and elsewhere). I teach one third year / IIA project course (RISC-V Processor Design), one fourth-year / IIB course (Embedded Systems), and serve as a cohort leader for the Part IA Integrated Electrical Project. Additionally, I am leading the Embedded Systems Technology-Enabled Learning (TEL) Pilot Program in Cooperation with the Cambridge University Press and edX.
  Recent Professional Service
  - Program committee, USENIX/ACM European Conference on Computer Systems (EuroSys), 2020.
  - Co-Organizer, Dagstuhl International Workshop 20222 on Approximate Systems, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, May 2020.
  - Associate Editor, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2019 to present.
  - Vice Chair, ACM Special Interest Group on Operating Systems (SIGOPS), 2019 to present.
  - Executive Committee, EPSRC Connected Everything NetworkPlus, 2019 to present.
  - Steering Committee, EPSRC Centre for Doctoral Training in Sensor Technologies and Applications (Sensor CDT), 2019 to present.
  - Steering committee, University of Cambridge Trust and Technology Initiative, 2017 to present.
  - Steering committee, USENIX/ACM Hot Topics in Operating Systems (HotOS XVII), 2017, 2019.
  - Program committee, USENIX/ACM European Conference on Computer Systems (EuroSys), 2019.
  - Program committee, IEEE Symposium on High Performance Computer Architecture (HPCA), 2019.
  - Program committee, ACM/IEEE International Symposium on Computer Architecture (ISCA), 2018.
  Selected Recent Research Publications
  - J. T. Meech and P. Stanley-Marbell, “Efficient Programmable Random Variate Generation Accelerator from Sensor Noise". Accepted for publication / to appear in IEEE Embedded Systems Letters, June 2020.
  - N. J. Tye, J. T. Meech, B. A. Bilgin, and P. Stanley-Marbell, “Generating Non-Uniform Random Variates Using Graphene Field-Effect Transistors”. Accepted for publication / to appear in 31st IEEE International Conference on Application-specific Systems, Architectures and Processors, July 2020.
  - R. Hopper, D. Popa, V. Tsoutsouras, F. Udrea, and P. Stanley-Marbell, “Miniaturized Thermal Acoustic Gas Sensor based on a CMOS Micro-hotplate and MEMS Microphone”. Proceedings of 4th Functional Integrated NanoSystems (NanoFIS), May 2020.
  - P. Stanley-Marbell, A. Alaghi, M. Carbin, E. Darulova, L. Dolecek, A. Gerstlauer, G. Gillani, D. Jevdjic, T. Moreau, M. Cacciotti, A. Daglis, N. Enright Jerger, B. Falsafi, A. Misailovic, A. Sampson, and D. Zufferey, “Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms”. ACM Computing Surveys Vol. 53, No. 3, Article 51., 2020. (Nominated for best paper award.) Available as preprint ArXiv:1809.05859.
  - P. Stanley-Marbell and M. Rinard, "Warp: A Hardware Platform for Efficient Multimodal Sensing With Adaptive Approximation. IEEE Micro, vol. 40, no. 1, pp. 57-66, 1 Jan.-Feb. 2020.
  - Y. Wang, S. Willis, V. Tsoutsouras, and P. Stanley-Marbell, “Deriving Equations from Sensor Data Using Dimensional Function Synthesis". ACM Transactions on Embedded Computing Systems (best paper award winner), volume 18, issue 5s, (22 pages) October 2019.
  - P. Stanley-Marbell and M. Rinard, “Perceived Color Approximation Transforms for Programs that Draw". In IEEE Micro Journal, vol. 38, no. 4, pp. 20-29, July/August 2018.
  Selected Recent Patent Grants
  - P. Stanley-Marbell and M. Rinard, “Method and Apparatus for Reducing Sensor Power Dissipation". US Patent number 10,539,419, granted 21st January 2020.
  - D. Chan, J. Iarocci, G. Kapoor, K.-M.Wan, P. Stanley-Marbell et al. (Apple, Inc.), “Initiating background updates based on user activity”. US Patent number 10,223,156, granted 5th March 2019.
  - P. Stanley-Marbell and M. Rinard, “System, method, and apparatus for reducing power dissipation of sensor data on bit-serial communication interfaces". US Patent number 10,135,471 B2, granted 20th November 2018.
  - C. de la Cropte de Chanterac, P. Stanley-Marbell, K. Venkatraman, and G. Kapoor (Apple, Inc.). “Smart Advice To Charge Notification”. US Patent 10,083,105, granted 25th September 2018.
  - P. Stanley-Marbell, G. Kapoor, and U. Vaishampayan (Apple, Inc.). “Dynamic adjustment of mobile device based on voter feedback”. US Patent 9,813,990, granted November 7, 2017.
  Research projects
  - Programmable Sensing Composites
    Funder: EPSRC (EP/V004654/1). Investigators: P. Stanley-Marbell (PI), A. Barbalace (Co-I), S. Pattinson (Co-I)
  - OLED Color Power Optimization
    Funder: Industrial Sponsor. Investigators: P. Stanley-Marbell (PI).
  - Graphene-Based Ambient Light Sensing and Signal Processing
    Funder: Industrial Sponsor. Investigators: S. Hofmann (PI) and P. Stanley-Marbell (Co-I).
  - Uncertainty Propagating Processor (UPP) Industrial Demonstrator
    Funder: EPSRC Impact Acceleration Account (EP/R511675/1). Investigators: P. Stanley-Marbell (PI).
  - New Industrial Systems: Optimising Me Manufacturing Systems
    Funder: EPSRC EP/R022534/1. Investigators: P. Stanley-Marbell (Co-I). Collaborators: Kent (PI), Bath (Co-I), UWE (Co-I), Imperial College London (Co-I), University College London (Co-I).
  - Computational and Sensing Vitamins for Construction and Infrastructure
    Funder: Industrial Sponsor. Investigators: P. Stanley-Marbell (PI).
  - Programmable In-Powder Sensors (PIPS) for Real Time Metrology and Data Analysis in Powder Processes
    Funder: EPSRC (via MAPP Hub). Investigators: P. Stanley-Marbell (PI).
  - Continuous In Situ Microstructure and Composition Analysis within 3D-Printed Structures Using In-Chamber Sensors
    Funder: EPSRC (via Connected Everything Network Plus). Investigators: P. Stanley-Marbell (PI). Collaborators: Imperial College London (Co-I), Sheffield-Hallam University (Co-I).
  - Energy, Information-Leakage, and Noise Characterization for Sensor Fingerprinting and Sensor Privacy Guards
    Funder: Royal Society Grant RG170136. Investigators: P. Stanley-Marbell (PI).
  - Graphical Programming with Physical Laws for Engineering Students (Grapples)
    Funder: Teaching and Learning Innovation Fund (TLIF) award. Investigators: P. Stanley-Marbell (PI).
  Teaching activity
  - Module lecturer, CUED 4B25 (Embedded Systems)
  - Project lecturer/leader, CUED GB3 (RISC-V Processor Design)
  - Course instructor, MIT 6.S194/IAP (Error-Efficient Computing Systems) in IAP 2017.
  - Workshop instructor, MIT 6.S977 (Technical Communication Skills for Graduate Students), spring 2016.
  - Course instructor, MIT 6.S194/IAP (Error-Efficient Computing Systems) in IAP 2016.
  - Advisor, MIT EECS Communication Laboratory (Communication Skills for Engineers), 2015 to 2017.
  - MIT Kaufman Teaching Certificate Program, spring 2015.
  - From about 2002 to 2005, I took part in the Carnegie Mellon Eberly Center for Teaching Excellence program.
  Research opportunities
  - Compile-Time Transformations to Induce Optical Illusions in a Vector Drawing Language (available, starting 2020)
  - Machine Learning for Sensor Transducer Conversion Routines (available, starting 2020)
  - Synthetic Sensors and Digital Sensor Substitution (available, starting 2020)
  - Feature Extraction in Multi-Modal Sensor Data by Dimensional Function Synthesis on FPGAs (available, starting 2020)
  - Performance and Power Analysis of Sensor Access Schedulers (available, starting 2020)
  - Sensor access schedulers (available, starting 2020)
  - Virtual Machine / Interpreter for C-like Language on Microcontrollers with Less Than 128k RAM (available, starting in 2020)
  Department role and responsibilities
  - Designed and introduced new 4th-year undergraduate course, 4B25 Embedded Systems (2017/2018)
  - Designed and introduced a new 3rd-year undergraduate project-based course on Computer Architecture with RISC-V, Verilog, and the iCE40 FPGA (2018/2019)
  - Cohort leader for IA IEP

Content

five years ago
program
- Mit-translation program
- technology for embedded system
- accurate system sensors
- interesting things
patent filing
lent to spin out-
examples
picture description of the problem
keep the slides brief

Signaloid

tesla
swirlesland
Auto
coop
computing system
how uncertainty they are
algorithms
large and growing challenge across markets
- computational finance
  - customers can we analyze the POE solution
  - volatity
  - equity
  - commity
  - models they have today
  - predictions
  - Price of the stock
  - with the signaloid’s technology
  - distribution and the uncertainity of the information
    - how sure the data things will do
      - will you have the single number
      - in reality , you have a distribution
      - at least, they want you to know the probability of the price distribution
- healthy
  - customer
    - can we make repp indicate prediction uncertainty??? what makes the different in normal ai?
      - pixals
      - estimate the temperature of the skin
      - response
      - 55 beats
    - a distribution for the thing
- ai in autonomous systems
  - customer
    - can we explain uncertainty in decisions of legacy sensor-driven firmware?
    - the distribution of the
representation of the uncertainity
taking existing software on the computing platforms
on the implementations of the algorithms to be implemented today
intermediate
context

question
what do we want to find out
primary from the renewable energy sectors
building a sub sea auto veheicles
behaviour of the auto veicles
Taking our technology to make their system safer
automaous system
automous system interecting with the human
human robotic
uav
driveless car
full auto

–

identify specific domain, people, company
this people is the boss in the domain
this people is the best in this domain
they would benefit their
cloud deployment for analyszing their stage
they want to see their systems
find out their person
who they can
a lead on the people who will use our product
if you
Get

signaloid

trustworthy data-driven system
by providing technologies for
- capturing
- storing
- transmitting
- computation
  - empirical data
    - have uncertainties
The process
- source of empirical data
- technologies for representing uncertain data
- technologies for storing and i/o uncertain data
- technologies for computing on uncertain data
- legacy host software
every measurement in nature is related to uncertainty
Estimate of uncertainity of the things
specific point to the questions
- seperate from the product
- integrated circuit
- interfaces
- number of standard interfaces
- standard equippments
- integrated circuits for sit behind it
- quantification of the measurement of the uncertainity
- Camera of the phone has the csi
allow the characteristiction for th

is it a hardware?
- Scale through cloud api’s growth in services integrating it
- indicator of success: get other cloud platforms / apps building on top of our infracturcture
- Lead market in hardware modules for trustworthy automous
- indicator of success: get autonomous systems integrating our GPU/fpga

what is your business model?

product vision
- Go to market
- via cloud deployment
- indicator of succes

CHIP DESIGN
- PHASE I
  - VHDL
  - R-T-L; Register-Transistor-Logical design
- PHASE II
  - Physical design
  - actual circuits

Leads in

route to market
- product line
  - prototypes
    - partners
- product line
- product line

cloud platforms
edge hardware
working to make these solutions that happen

i-teams
really who have the pains today

people who understand that they have the problem in the autonomous systems

you can identify
have the need to make that
understand their problem better
dealing with the problems on the problem they are selling

eu legistration
business a lot of businesses in your directions
gdpr
Sub industry

security

what are your description on how your product
- Outcome 1
  - A large company some
- Outcome 2
  - A small company really benefits for your
  - real customer who has the real pain point
I-teams
- a clear distinctions
  - not here to sell the technologies
- i-teams
  - gather information about the types of the institutions
  - get opinions from the technologies
  - the roadmap identifided
  - get their perceptions in their mind
  - where they will deverications from
  - intelligence
  - produce
  - a conversations about the preselling processs
  - how do you sell your product?
  - which benefits for your products?
  - what development chocies do you have to make in order to make it possible to happen
we help you get the information on how other people think about it
solve your uncertainties problems
insulted to
do you have an issue about the uncertainity
the conservations
the business development on their side

16 times compared using monte carlo simulations

Ecosystems and competition
ver
- deployment model sophistication
  - cloud+hardware
  - hardware only
  - cloud compute
  - Library/os
  - End-user service
hor
- technology solipholicatoion and risk
  - classical computation
  - confidence intervals
  - distribution track
  - classiscal interface for NISQ
  - Quantum
Signaled: cloud-navie hardware
Over 2000 x faster than the state of art at uncertainity tracking

computer archiecture
CMU
half of the decade in ibm
apple
what other people think about the
8 publications
since the first publication
top picks in the
key
Gchq
the world has the

uncertainity of the sensor side
machine learning

a = 0.754354

b = 14.985515

c = -1.106014

use my technologies for making perictions
values
Appreciation
floating values

step things
representing to make it
different
go beyond sensor data
sensors

variables
Uncertainity and distribution data
uncertainity data
Uncertainity does not come only from data
a probability distribution about the
rador
flight sensors
analog
creating the 3d imagies
have the measurement uncertainities
multi paths
objects in the room
what you know you should be
noises
just because the measurement system, which is ill formed
signoid sub
tracking uncertainty
emperical market data
push that to the black sore equity for
making the decision about the trust

great
think

brainstroming
applications of the technologies

1 what

summary of technologies

sentense 1: it is a technology to visualize the uncertainty
sentense 2: starting from the cloud computing services now to the edge computing platforms in the future for higher performances.
elivator plitch
have 20 seconds to hook someone

2 why do not

dimensions or questions
potential aspects for that
- it is useful for data analysists for making the interperations
- it is useful for the real data responses
considerations people will have
- people who need to quanitiy the uncertainity to make the interperations will benefit most
some applications for high
possible relative modest edge devices
what does it mean for the automatic distributions
what are the imagine the systems
auto

le
- urban planning
- smart cities
- computer visions
- computer imagines
- archectures
- cost living lab
- tool of understanding of it
- sensors
- how to get people involved in the ICT problems
- How the culture archive them in the databases in a highly
- digital twin for in phd at digital twin
- urban planning
- urban modelling
peter
- ai in logistics
franscrio
- digital twin
- living model of
- engine
- Operating at certain minutes per
- cool thins in the
- complex
- harvessing data
- manufacturing
- and data gathering
- making it as broad as possible
- new cities
- new costs
- crm
- one big problem
- ideal to digital twin
- exact copy of this things
- billion of pound
- government product
- trying to make the practical solutions
- digital twins in organizational learning processes
- how digital twins for their knowledge interperinal
  - machine learning staff in the staff
- one being the phd
  - veronica

Adium-
- eng
- bachelor
- master
- Electronic
- information
- theory
- machine learning
- second
- natural language processing group
- information extraction
- how well people on