Speakers

Dr. Ahmed K. Elmagarmid is the Executive Director of the Qatar Computing Research Institute, which he founded in 2010. Previously, he served as a chief scientist at Hewlett-Packard and Professor of Computer Science at Purdue University. A recipient of the NSF Presidential Young Investigator award from President Reagan, Dr. Elmagarmid is a Fellow of IEEE, ACM, and AAAS. He plays a crucial role in Qatar's technological advancement, serving on multiple national committees including the Qatar Genome Project and the Sidra External Scientific Advisory Council.

Shakir Mohamed works on technical and sociotechnical questions in AI research and development, working on problems in foundational AI, applied problems in healthcare, education and environment, and participation and responsibility. Shakir is a Research Director at Google DeepMind in London, an Associate Fellow at the Leverhulme Centre for the Future of Intelligence, and a Honorary Professor of University College London. Shakir is a founder and trustee of the Deep Learning Indaba, a grassroots charity whose work is to build pan-African capacity and leadership in AI. In 2023, Shakir was included in the TIME 100 most influential people in AI. He serves on several boards, including the oversight board of the Ada Lovelace Institute, as the Chair of the international scientific advisory committee for the pan-Canadian AI strategy, the Royal Society Diversity and Inclusion committee, and the AI field’s leading conferences (ICLR, ICML, NeurIPS). Shakir is from South Africa, he completed a postdoc at the University of British Columbia, received his PhD from St John's College in the University of Cambridge, and received his masters and undergraduate degrees in Electrical and Information engineering from the University of the Witwatersrand, Johannesburg. 

Sahra is a Research Scientist at Google DeepMind where she is working on private inference and contextual integrity of LLM-based systems. She wrote her doctoral thesis at the University of Oxford on tackling privacy and model misspecification. During her doctoral studies, she interned with Microsoft Research Cambridge and Google DeepMind. She was also involved in the organization of NeurIPS, NeurIPS workshops and AISTATS. 

Ehsanoddin Asgari is a scientist at the Qatar Computing Research Institute (QCRI), working on multilingual and multimodal language processing, with applications spanning the processing of world's low-resource natural languages to language modeling of biological sequences and chemical compounds. Ehsan completed his Ph.D. at the University of California, Berkeley, and his master's from the École Polytechnique Fédérale de Lausanne (EPFL). Before joining QCRI, he led NLP technical efforts at the Volkswagen Group at Data:Lab and conducted part-time postdoctoral research at the Helmholtz Research Center for Infection Research. His previous roles include research positions at MIT's CSAIL, MIT Brain and Cognitive Sciences, the NLP group at LMU Munich, ABB Research, and the University of Illinois at Urbana-Champaign's Singapore research center (ADSC). 

Ibrahim is a research scientist at Google DeepMind, working mostly on multimodal systems and responsible AI. Prior to joining Google DeepMind, he founded and led the Advanced Analytics team at Saudi Aramco and managed the company's Enterprise Analytics program. He holds an MS in Electrical Engineering from Stanford University and PhD in Computer Science from KAUST. Twitter: ibomohsin@.

Mehdi Bennani is a Research Engineer at Google DeepMind. He has worked on Project Astra, a real-time multimodal AI assistant, as well as Gemini Safety. Previously, he has been working on AI for drug discovery at Aqemia, among the first hires. He worked on designing generative algorithms and building a Machine Learning and Physics integrated platform to design small molecules. He holds a MEng from ENSAE ParisTech in Statistics and Economics.

Dr. Mohamed Eltabakh specializes in big data management and scalable analytics, with particular emphasis on distributed data mining and machine learning systems. At the intersection of AI and data management, his research addresses the challenges of modern data lifecycles, focusing on developing efficient systems for storing, indexing, and querying complex datasets at scale. Dr. Eltabakh's work is particularly relevant to organizations handling unprecedented data volumes while requiring advanced analytics capabilities. His expertise spans AI-driven data management, time series infrastructures, and query optimization, contributing to the advancement of database systems that can handle today's complex data processing demands.

Mohammad Raza is a researcher in AI and formal reasoning techniques. He attained his PhD from Imperial College London and spent many years working at Microsoft Research as well as various product divisions at Microsoft. He has led the research and productization of symbolic reasoning and program synthesis techniques for task automation in a variety of domains including data science, business intelligence and code editing. This has led to the development of intelligent features shipped in mass-market products such as Microsoft Power BI, Visual Studio Code and Excel. His recent focus at QCRI is in the combinations of formal reasoning approaches with large language models.

Pranali is a software engineer at Google DeepMind, working mostly on using LLMs and building embeddings for scientific tasks. She has been at Google/ DeepMind for 8 years, and finished her bachelors in Computer Science & Engineering from IIT Madras in India prior to that. Fun fact, Pranali contributed her voice for text to speech translation in Marathi language across all Google platforms, which is the third most spoken language in India by ~84M people. 

Dr. Dena Al-Thani is currently an Associate Professor, the co-founder of the A-sense Centre of Excellence at the College of Science and Engineering at Hamad Bin Khalifa University. Dr. Al-Thani was awarded her MSC in Software Engineering and Ph.D. in Computer Science from the Queen Mary University of London. Her research areas of interest include accessibility, inclusive design, Human-AI interaction and eHealth. Dr. Al-Thani is now actively publishing her work in high-ranked journals and presenting her work at international conferences. She is keen to see the impact on the world of her research on inclusion, and, to that end, she is a member of the World Health Organization (WHO) technical advisory group on assistive technology and the Arab ICT Accessibility Expert Group, an initiative by Mada.

Dr. Khaled Harras, is the Senior Associate Dean for Faculty, and Professor of Computer Science at Carnegie Melon University in Qatar. He is also the Director of the Hamad Bin Jassim Center for CS Education, and the founder/director of the Networking Systems Lab (NSL). Academically, over the past two decades, he has been working on various systems domains in the areas of challenged and opportunistic networks, sensor networks, unmanned air vehicles (UAVs), ubiquitous and pervasive systems, Edge/Fog computing, and federated learning. He also has expertise in the domains of wireless and mobile networks measurement, building real systems and testbeds, as well as designing and implementing frameworks based on real data. Dr. Harras has more than 150 refereed publications in numerous international prestigious journals, conferences, and workshops, as well as 4 US patents. Along with his research group in the past few years, he has won the national best computing research award twice, received two best paper awards, and his work has been featured online various venues like MIT Tech Review and Tech the Future. To date, he has been involved in or managing research grants that amount to more than 5 Million USD, and has supervised over 45 different personnel including undergraduate and graduate students, postdoctoral researchers, and research engineers. He is a senior member of the ACM and IEEE. Outside of Academia, Dr. Harras has worked in the technology industry at Citrix Systems, and collaborated closely on joint projects with Intel, Lockheed Martin, and NASA’s Jet Propulsion Lab (JPL). He also worked as an advisor to the Minister of ICT, as well as a Senior Systems Scientist at the Qatar Computing and Research Institute (QCRI).

Morteza is an entrepreneur, AI researcher, and university lecturer with extensive experience in Silicon Valley. He is the co-founder and CEO of qeen.ai, an innovative AI startup transforming e-commerce sales and marketing for SMEs. Before launching qeen.ai, Morteza built and led multiple AI-focused teams and startups, notably serving as a founding engineer at Urban Engines—later acquired by Google—and co-founding a healthcare intervention venture. He also served as the tech lead for Lyft’s dynamic pricing team until its IPO.

Morteza’s breadth of experience includes several roles at Google, where he contributed to YouTube, Ads, and Geo teams, culminating in his position as tech lead and manager of the Research Engineering team of DeepMind Research in California. His research centers on reinforcement learning and uncertainty estimation in deep learning, recently focusing on applying these techniques to building goal-oriented, self-learning AI agents. He has also taught reinforcement learning at Stanford University.

Masoud is Co-Founder and Partner at Phaze Ventures, and the Founder and CEO of PhazeRo, an AI consultancy for the enterprise. He is also a founding member of the UK-Oman Digital Hub, an advisory board member of German University of Technology in Oman & Sultan Qaboos University.

Masoud was also a general partner of the SparkLabs Energy Accelerator, and prior to launching his entrepreneurial and investment career, Masoud gained 10+ years’ experience in information technology at corporates.

Dr. Mohamed Abdallah is currently a Professor and Associate Dean of Undergraduate Studies and Quality Assurance at the College of Science and Engineering, Hamad Bin Khalifa University (HBKU). He received his M.Sc. and Ph.D. degrees from the University of Maryland at College Park, USA in 2001 and 2006, respectively.

His research interests lie in AI for communications, with a focus on 6G wireless networks, wireless security, electric vehicles, and smart grids. He has authored more than 220 journal and conference papers, contributed to four book chapters, and co-invented four patents.

Dr. Abdallah has received several prestigious awards, including the Research Fellow Excellence Award at Texas A&M University at Qatar in 2016, and Best Paper Awards at multiple IEEE conferences, such as IEEE BlackSeaCom 2019 and the IEEE First Workshop on Smart Grid and Renewable Energy in 2015. Additionally, he was a recipient of the Nortel Networks Industrial Fellowship for five consecutive years (1999–2003).

Safa’s research is centered around safe AI,generative models (energy-based models) and reinforcement learning and motivated by applications in computer vision and health intelligence. Safa obtained her PhD in Electrical and Computer Engineering (ECE) from the University of Illinois at Urbana-Champaign working with Prof. Alexander Schwing. She graduated with Bachelor and Master degrees in Electrical Engineering and Information Technology from the Technical University of Munich. She obtained a Master degree in Computer Engineering from Virginia Tech advised by Prof. Sandeep Shukla. She was a visiting scholar at UC-Berkeley, where she worked on her Master thesis under the supervision of Prof. Alberto Sangiovanni Vincentelli and Prof. Andreas Herkersdorf.

Andrew El-Kadi is a Research Engineer at Google DeepMind, working on the Weather effort. He completed his MEng in Mathematics and Computer Science at Imperial College London in 2023, where he focused on Quantum Algorithms for Satisfiability Problems. His current work focuses on scaling and operationalising ML weather models, recently working on GenCast: diffusion-based ensemble forecasting for medium-range weather. 

Amin Sadeghi is a Senior Scientist at QCRI. He led the development of the image generation modality of Fanar (the Qatari chatbot). Previously he served at EECS department at University of Tehran. He completed his PhD from the department of computer science at UIUC focusing on Machine Learning and Computer Vision. He is a recipient of CVPR best student paper award and the Bronze medal at the IOI.

Badih Ghazi is a Research Scientist at Google Research, where he leads the Algorithmic Privacy team. His work at Google has focused on privacy-preserving machine learning and analytics, with applications in computational advertising and large language models. Before joining Google, he completed his undergraduate studies at the American University of Beirut, and his M.S. and Ph.D. in Computer Science at MIT.

Alex is a software engineer at Google DeepMind, working on safety evaluations for frontier models. His background is in offensive cybersecurity: simulating cyber attacks against organisations, researching vulnerabilities, and writing associated tooling.

Nasem is a Technical Program Manager at Google DeepMind, working mostly on security to enable secure and privacy-preserving AI systems. Prior to Google DeepMind, he worked at a global investment bank and FinTech and helped manage their information security program. He studied Computer Engineering and holds an MS in Electronic Engineering from Manchester Met University. 

Dr. Sanjay Chawla is the research director of the Data Analytics department at the Qatar Computing Research Institute (QCRI), under Hamad Bin Khalifa University (HBKU). His research is in data mining and machine learning with a specialization in spatio-temporal data mining, outlier detection, class imbalanced classification, and adversarial learning. Prior to joining QCRI, Dr. Chawla was a professor in the Faculty of Engineering and IT at the University of Sydney. From 2008-2011, he served as the head of the School of Information Technologies. He was an academic visitor at Yahoo! Research in 2012.

Mina Khan is a Senior Research Scientist at Google DeepMind, where she works on Reinforcement Learning for Large Language Models in Gemini. Mina did her PhD at the Massachusetts Institute of Technology (MIT), where she researched the intersection of Artificial Intelligence and Human-Computer Interaction. Her Bachelors is in Mathematics, Physics, and Computer Science. She hopes and strives to use AI to solve real-world problems.

Yasin has been a research scientist at Google DeepMind since early 2020. Before that, he was with Queensland University of Technology (Brisbane), Adobe Research (San Jose, CA), and VinaAI (Hanoi). He obtained his PhD in 2012 and has since been studying various research questions in sequential decision problems and online learning. Currently, he is exploring uncertainty quantification, hallucination detection, and reasoning in large language models (LLMs). His research focuses on developing an understanding of generative models with predictive power.

Chris has spent 8 years at Google and Google DeepMind developing AI-powered solutions for environmental challenges, working with decision-makers from regional scales up to the UN. He has led the development of products like DynamicWorld, Cloud Score+, and DeepMind's foundational work with geospatial understanding. Chris holds a MS in Computer Engineering from Villanova University.

Avishkar is a Staff Research Engineer at Google DeepMind where he has worked for 8 years. He currently works on AI for Education, a team with a mission to build an AI tutor for every learner. His research interests include RL*F and multi-agent methods. Avishkar is also a long-standing organiser and executive board member of the Deep Learning Indaba, a movement to strengthen AI in Africa. 

David Barrett is a research scientist at Google DeepMind in Dublin working on AI applications in healthcare. His academic background is in theoretical physics, with an undergraduate degree from Trinity College Dublin and a PhD in machine learning and computational neuroscience from the Gatsby Unit at University College London. He held research positions at École normale supérieure and Cambridge University before joining Google DeepMind in 2016. 

Alan is a clinician and Research Scientist working on Foundation Models for health, most recently including Med-PaLM, Med-PaLM-2, Med-PaLM-Multimodal and AMIE. Prior to this his work at DeepMind and Google explored applications of AI in radiology, ophthalmology, dermatology and electronic health records, resulting in papers published in Nature and Nature Medicine. He is an honorary Lecturer in Vascular Surgery at Imperial College in London. He completed his MA in Neuroscience and Medical Degree (MBBChir) at the University of Cambridge before specialist training in surgery in the London Deanery, where he completed his Membership of the Royal College of Surgeons (MRCS), PhD in Vascular Surgery and was appointed as a NIHR Clinical Lecturer. In 2017 he joined DeepMind's health research team and in 2019 joined Google Health. Prior to joining Google he had published over 150 peer-reviewed articles including first-author studies in the New England Journal of Medicine and The Lancet.

Alhussein Fawzi is co-founder and CEO of a stealth startup, and until recently was a research scientist at Google DeepMind. He was awarded the MIT Innovators Under 35 award in 2023 for his work on discovering new algorithms with machine learning. He has worked on AI for Science, with a particular interest in using Machine Learning to unlock new results in Computer Science, and Algorithms. He recently published two papers in Nature magazine, where he used Machine Learning and Large Language Models to discover new algorithms for fundamental computational tasks, and new mathematical results. He has published more than 30 papers in major scientific journals and conferences.  

 Alex Graves is a senior staff researcher at InstaDeep, currently focused on applications of generative AI to Biology. He was previously chief scientist at NNAISENSE and a senior researcher at DeepMind. Prior to that he earned a BSc in Theoretical Physics from the University of Edinburgh and a PhD in artificial intelligence under Jürgen Schmidhuber at IDSIA. He was also a postdoc under Geoffrey Hinton at the University of Toronto. His contributions include the Connectionist Temporal Classification / RNN transducer algorithms for sequence labelling (both widely used for commercial speech and handwriting recognition), stochastic gradient variational inference, the Neural Turing Machine / Differentiable Neural Computer architectures, and most recently Bayesian Flow Networks.

 Natasha Latysheva is a Senior Research Engineer in the Genomics Initiative at Google DeepMind in London. Her work focuses on integrating deep learning with molecular biology and genomics, particularly in understanding regulatory DNA and predicting variant effects. She holds a PhD in computational biology from Cambridge University and a Biochemistry BSc from St Andrews University. Before joining DeepMind, she worked in data science and machine learning roles in gaming and natural language processing.