QATAR Science & Technology Park

The Research To Startup program (RTS) is an initiative of the Qatar Science & Technology Park that aims to facilitate the creation, acceleration and scaling of startups that leverage technologies developed by leading research institutes anduniversities in Qatar.

Selected entrepreneurs will have the opportunity to explore new market applications for technologies developed by the Qatar Computing Research Institute (QCRI), Qatar University (QU) and Virginia Commonwealth University Qatar (VCU-Q).

Wasabi Ventures has partnered with Qatar Science and Technology Park to run a customized version of our Technology Commercialization Accelerator Program suited to the needs of QSTP and its portfolio of technologies. We recruit are recruiting entrepreneurs globally and will lead the process of matching them with technologies, forming teams, and moving them forward to explore the commercialization potential of their ideas, ideally culminating in the formation of a fundable early-stage technology company.

About the program

We are inviting seasoned entrepreneurs and product managers to apply for the Research to Startup Program to compete for the opportunity to launch the next generation of tech companies in Qatar. Those ultimately chosen to continue in the program will receive a generous support package from the Qatar Foundation to help turn their ideas into reality.

Program Phases

Phase 1: Bootcamp (April TBC 2018)  

During this 5 Day Bootcamp, get to know the QSTP technology you’ve expressed interest in better, meet the team researchers who developed it, understand the current market application of the technology, and develop business model concepts to scale it internationally. If there’s a good team fit, mutual interest and alignment on strategy, you may be invited to return as an EIR.

Phase 2: Planning (1 – 3 months)

Based on the outcome of Phase 1, the next is to work towards the goal of creating a fundable operational plan. To facilitate this process you may be offered a position as Entrepreneur In Residence to work intensively alongside the local team, and possibly other EIRs. The program ends with a pitch for $500,000 in seed-stage capital and other benefits from Qatar Foundation. The expected outcome is that EIRs assume C Level roles in the startup business and work towards identifying external investors to participate in a Series A round.

Phase 3: Startup and Acceleration  

If selected, return to build a team and execute your business model in Doha’s free-zone, a highly favorable business jurisdiction, and begin your operation with a $500,000 seed-stage investment from QSTP to accelerate your venture.

Be a Co-founder of MENA’s hottest

Tech Startups

Are you a serial techpreneur or experienced senior tech professional?

Application open until April 1st 2018.

Applications and admission decisions are made on a rolling, first-come first-served basis.

Application Process:  

  1.  Fully complete and submit the online application
  2.  Applications will be screened by QSTP and Wasabi Ventures Global
  3.  First round telephone interview
  4.  Second round telephone interview
  5.  Final selection decisions made by no later than April 5th.

Please note that the Bootcamp begins on Saturday April 21st 2018 (TBC).  By applying to the program, you acknowledge that, if invited to participate, you are available to participate in the Bootcamp from April 21-26 (TBC) in Doha.

Technologies available to EIRs

QCRI Speech Translation Technologies



People speaking different languages cannot understand each other.  Human interpreters are not always available or are too expensive.  The technologies developed enable communication across language barriers by providing automatic interpretation.

Application & Level of Development

Currently, the full speech translation technology is not deployed in any commercial setting.  The (Arabic) speech recognition component has been provided as a paid service to Al Jazeera for subtitling videos.  The combination of speech recognition and machine translation (Ar<->En) is demonstrated through an online demo (link see below) which so far has been accessed by more than 170 unique users from more than 30 countries.

Potential industry applications:  media translation, lecture translation, meeting translation, speech translation in specific domains such as medical, law tourism, law enforcement, legal, etc.

Description of Technology

The speech translation system has 2 major components, 1. ASR (Automatic Speech Translation), 2. MT (Machine Translation).  Both components are based on open source toolkits like Kaldi for ASR and Tensorflow. Theano, and other deep leaning frameworks for MT.  The main effort goes into training specific models.  Currently, we have models for Arabic speech recognition, English speech recognition, Arabic to English translation, and English to Arabic Translation.  These components have been integrated into an online demo, which is targeted towards lecture translation, i.e. one person giving a talk and multiple users can access transcription and translation through an online portal.  In addition, APIs have been developed which allow to access the different speech recognition and translation systems.


QCRI Live Speech Translation System.

Fahim Dalvi, Yifan Zhang, Sameer Khurana, Nadir Durrani, Hassan Sajjad, Ahmed Abdelali, Hamdy Mubarak, Ahmed Ali, Stephan Vogel.

European Chapter of the Association for Computational Linguistics (EACL), Valencia, 3-7 April 2017.


  • Stephan Vogel (PI)
  • Hassan Sajjad (machine translation)
  • Ahmed Ali (speech recognition)
  • Fahim Imaduddin (system integration)

Automated Persona Generator



Our technology algorithmically identifies groups of people, and automatically generates personas, which are descriptions of fictional persons embodying the attributes of particular market segments, all while still providing access to the underlying data.

Current objectives

To develop techniques for leveraging real user behavior and related demographic data concerning users of a product, service, or content rapidly and inexpensively collected from variety of social platforms and analyzed in order to generate personas in real-time. Achieving this objective means that the resulting personas are:

1. Representative of the current users of the product and

2. Sensitive that usage can dramatically change based on audience interests or shifts over time.

Research/development team:

  • Dr. Jim Jansen
  • Dr. Haewoon Kwak
  • Dr. Jisun An
  • Soon-gyo Jung

A general solution for personalized big data analytics



The big data ecosystem is diverse – one system is unlikely to cater to every need. In addition, solving business problems increasingly requires applications to go beyond the limits of a single data processing platform, such as Hadoop or a DBMS.


Rheem has being developed to cover the need for a general-purpose cross-platform data processing system. It decouples applications from the underlying platforms. In a glance, Rheem splits an incoming task into subtasks and assigns each to a specific platform to minimize its overall runtime.

Application & Level of Development

Rheem have been piloted by a leading airline, as well as preparing a proposal to make Rheem an Apache project.

​The Team:

  • Sanjay Chawla
  • Bertty Contreras
  • Ji Lucas
  • Yasser Idris
  • Zoi Kaoudi
  • Jorge-Arnulfo Quiane-Ruiz
  • Anis Troudi

Low-cost aerogel synthesis and usage



Aerogel is a novel material that hold the potential to improve a range of products and processes, due to its superior properties related to insulation and structural integrity. Current adoption of aerogel has so far been limited due the ability to produce it cost-efficiently and in custom shapes and forms.

Application & Level of Development

The technology can be applied to a range of applications, including:

  • Thermal insulation coating for structural building, glass, cars, boats and clothes.

  • Custom printed and flexible insulation for structural applications

  • The thermal protective cloths (thermal protection, military clothing for severe weather, and firefighters, and astronauts),

  • Soundproofing and sound absorbing applications in buildings.

  • Creative 3D printed ultra-light material for low-cost orthotics and assistive devices for a disabled population

  • Creative 3D printed ultra-light carbon aerogel material for capacitors and batteries.

Description of Technology

Due to the risk, cost and limitation of the supercritical drying step in aerogel production, the industrial and commercial use of silica aerogels have been limited. On the other hand, ambient pressure drying is safer and less expensive than the supercritical drying process. This process involves the evaporation of the liquid in the pores that causes shrinkage and cracking as result of the high capillary pressure at the menisci of the solid−liquid−vapor interfaces inside the gel structure during drying especially when the contact angle of the menisci is lower than 90°. This can be done by overcoming the capillary surface tension using ambient pressure drying (APD) through a multistep process that exchanges the liquid inside wet-gels for an organic drying solvent. The technology allows low cost aerogel material using Ambient Pressure Drying (APD) using different approaches such as Inorganic Ambient Pressure Drying (IAPD), this process is very promising, and it can cut the cost by 80% and eliminate lengthy drying and processing steps and eliminate the use of dangerous solvents. In addition, a new technique which allows fabricating ultr-light and mechanically strong aerogels and aerogels composites with various shapes and dimensions without using molds have been developed. This technology can be used in rapid additive manufacturing and overcome many obstacles in many industries.


Smart Filtering of Research Literature



Creating and maintaining systematic reviews face many challenges:

·       It typically takes one to two years to produce one systematic review

·       Authors should sift through and screen 100’s to 1000’s of studies

·       Reviews usually gets outdated as new evidence emerges

·       Need effective collaboration and sharing to support peer-reviewing

·       Dealing with duplicates and other cases of similarity

·       Data extraction from the abstracts and full-texts

·       Assess the validity of the evidence and the risks of bias (RoB)

Description of Technology

An end-to-end collaborative platform (web and mobile) to expedite the creation of systematic reviews and other types of literature reviews, using state-of-the-art machine-learning and data analytics techniques. A systematic review is a literature review focused on a research question to identify, appraise, select, and synthesize relevant research evidence​​

Applications and Level of Development

With more than 10k users and a  weekly growth rate of 3%, the user-base is doubling every 6 months. Users hail from 90+ countries where UK, Sweden, Netherlands, Brazil, Japan and the United States are at the top of the list. An extensive  user survey shows several interesting observations on current strengths and potential improvements. Of particular importance, users reported an average of 50% time-saving compared to other existing technologies.  The system could potentially be extended to other types of data, such as financial reports, law cases, and patents, as well with other smart filtering and mapping of the data.

The system has been developed during 4 years. Production system in place for three years


Research/development team:

Dr. Mourad Ouzzani

Hossam Hammady

Dr. Ahmed Elmagarmid

Dr. Zbys Fedorowicz

Secure and reliable cloud storage



Cloud data services offer compelling benefits but are challenged by the problem of lack of trust. Clients lose control of their valuable data but are still required to trust the cloud service provider. This creates a real hurdle that prevents using the cloud for storing sensitive corporate and government data. With SafeDrive, the client does not need to trust anyone. SafeDrive offers an effective and secure solution that guarantees cloud data confidentiality, reliability, availability and high performance.

Application & Level of Development

The technology is currently at the level of a basic prototype with minimal features. The aim is to release a minimal viable product that will be tested with individuals before moving to the next level of testing with corporate users. It is worth mentioning that testing and future product release will not require any expensive infrastructure as the technology is leveraging existing cloud storage services.

Description of Technology

SafeDrive is an efficient, reliable, and secure multi-cloud file storage system that keeps your data safe and accessible even if you do not fully trust the service provider, whose systems can be compromised or become unavailable.  SafeDrive relies on distributed storage on multiple clouds and employs novel fast and optimally cost-effective techniques for tolerating multiple service outages. Data remain seamlessly accessible as long as m-out-of-n clouds are available. With SafeDrive, data and access credentials never exist in an unencrypted form outside the client’s machine.  A service provider learns nothing about the cloud-shared data, its structure, or its encryption keys. All of the above advantages are offered without any noticeable impact on performance. SafeDrive continues to offer the standard cloud benefits such as sharing, scalability, and any-where accessibility.


Qutaibah Malluhi

Naram Muhesen

Artificial Intelligence for Digital Response



Artificial Intelligence for Digital Response (AIDR) combines human-computation and machine learning techniques to process high volume social media posts that arrive at high-rate during natural or man-made disasters. AIDR is a hosted platform, which has been used by a number of humanitarian organizations like UN OCHA, UNICEF at time of major disasters (including Nepal Earthquake 2015, Typhoon Hagupit 2014, Super Typhoon Yolanda 2013). Among other two main uses of AIDR is to gain situational awareness and actionable insights from an on-going event. AIDR provides convenient ways to use state-of-the-art machine learning techniques to its non-technical users.

Problems being addressed by this technology

AIDR technology helps stakeholders (e.g. humanitarian organizations, NGOs, governments emergency departments) gain early insights from social media world in the early hours of a crisis situation when no other information sources (e.g. News media, Radio) are available.

Level of development

AIDR’s core strength is its capability to process textual content from social media using its natural language processing pipeline. AIDR is now being extended to process imagery content.

​Existing or potential opportunities

In the humanitarian space, a number of opportunities AIDR has been used for. For instance, real-time critical situation monitoring, extraction of reports of injured or dead people after a disaster hits, reports of critical infrastructure damage and so on. The list of potential opportunities of this technology will grow as the image processing pipeline becomes ready. For instance, one potential use would be to assess the severity of damage from the imagery data on social media during a disaster.

Research/development team

Muhammad Imran

Ferda Ofli

Firoj Alam

Dat Nguyen

eHealth with a focus on childhood obesity



QCRI have developed different technologies in local population affected by childhood obesity. The developed 360 Quantified Self technology can help to better understand health behaviors that affect childhood obesity. In particular, the focus is on health coaching that involved both sleep and physical activity, as those behaviors play a major role in obesity.


The focus is on childhood obesity which is a global pandemic, specially in emerging countries. There is strong interest in Qatar among many stakeholders of whom we have ongoing relationship.

Description of Technology

Visualization tools for wearables, predictive models for health behaviors, feasibility studies of different solutions, demo of health coaching app for children.


Visualization of Wearable Data and Biometrics for Analysis and Recommendations in Childhood Obesity

Implementing 360° Quantified Self for childhood obesity

Sleep Quality Prediction From Wearable Data Using Deep Learning

Slideshare –


  • Dr. Luis Fernandez-Luque as PI has 14 years of experience in digital health for patient empowerment acquired both as researcher and entrepreneur.
  • Dr. Michael Aupetit is expert in machine learning and visualization. Local partners have extensive experience in behavioral change, obesity and public health.