Yoav’s mission and passion is ‘Bringing R&D to life’ – maturing ideas from basic research to final products people love. He currently promotes this as GM - Industry & International Research Collaborations at TAD (Tel Aviv University’s AI/DS Research Center). Within this capacity, Yoav has helped shape TAD strategically and developed a new framework for Academia & Industry collaboration via TAD, called ‘Research Partner’.

In addition, Yoav is acting as Israel’s head of delegation and expert to the AI international Standardization committee (ISO&IECs - SC42) since 2018. Within this capacity Yoav also promotes awareness to Societal & Ethical concerns emanating from the development and deployment of AI technologies.

Yoav is a former Teaching-Fellow re Data & Business Strategy at Coller school of Management, Tel-Aviv University and regularly advises Industry as per Research, Product, Standardization and Regulation of AI.
 

Dror Bin is the CEO of the Israel Innovation Authority, an independent public entity that operates for the benefit of the Israeli innovation ecosystem and Israeli economy as a whole. Its role is to nurture and develop Israeli innovation resources, while creating and strengthening the infrastructure and framework needed to support the entire knowledge industry.

Prior to his role at the Authority, Dror served as President and CEO of RAD Data Communications, a leading global telecom network solutions company with hundreds of employees at the company's headquarters in Tel Aviv, a manufacturing center in Jerusalem and development center in Beer Sheva as well as dozens of corporate branches around the world.

Dror also served in a series of positions for close to a decade at Comverse Technology, the last one as a member of the management team and VP of Global Sales. Following this, Dror served as a venture partner at Carmel Ventures and a chairman at several of its portfolio companies. In addition, Dror served as a partner at Shaldor, a leading management consulting firm in Israel, where he led the development and implementation of business and marketing strategies for major organizations in the financial, consumer, retail, high-tech, banking and other industries. 

Dror holds two bachelor’s degrees from the Technion – Israel Institute of Technology: one in systems information engineering and the other in industrial management, as well as an MBA from Tel Aviv University.

Diffusion models are considered to be the state-of-the-art tool today for image generation guided by text. In this work we demonstrate how this tool can be used for 3D shape generation and for creating high-res textures for existing shapes. In addition, we show how diffusion models can be used for image reconstruction and editing .

Raja Giryes is an associate professor in the school of electrical engineering at Tel Aviv University. His research interests lie at the intersection between signal and image processing and machine learning, and in particular, in deep learning, inverse problems, sparse representations, computational photography, and signal and image modeling. Raja received the EURASIP best P.hD. award, the ERC-StG grant, Maof prize for excellent young faculty (2016-2019), VATAT scholarship for excellent postdoctoral fellows (2014-2015), Intel Research and Excellence Award (2005, 2013), the Excellence in Signal Processing Award (ESPA) from Texas Instruments (2008) and was part of the Azrieli Fellows program (2010-2013). He is an associate editor in IEEE Transactions on Image Processing and Elsevier Pattern Recognition and has organized workshops and tutorials on deep learning theory in various conferences including ICML, CVPR, and ICCV. He serves as a consultant in various high-tech companies including Innoviz technologies and developed a technology that was used as the basis for the MultiVu technologies startup. 

Recent advances in visual generation are almost unimaginable. This leap forward in capabilities has happened thanks to several factors, including the adoption of the diffusion approach, elaborate data collection, and more. I argue that one of the main forces to drive visual generation advancements is Language: recent Large Language models encapsulate astounding amounts of knowledge, since data collection is simpler for text compared to images or video. In this talk, I scan five of my recent publications, spanning generative capabilities from facial and sketch images, to general images and 3D human motion. Through these publications, I portray how Large Language Models (and especially CLIP) become more and more significant for controlling and improving visual generation.

I am a senior lecturer (assistant professor) at the Blavatnik School of Computer Science in Tel-Aviv University. My research focuses on Computer Graphics, Computer Vision, and Computational Fabrication (3D printing). I use Machine Learning (of course...) and Geometry Processing to advance and connect these fields. Previously, I was a postdoctoral Researcher at the Princeton Graphics Group, hosted by Professor Szymon Rusinkiewicz and Professor Thomas Funkhouser. Before that, I was a postdoctoral researcher at Disney Research Zurich. I have conducted my Ph.D. studies at ETH Zurich under the supervision of Prof. Dr. Markus Gross, in collaboration with the computational materials group of Disney Research Zurich. My Masters and Bachelors degrees were obtained at The Technion - Israel Institute of Technology under the supervision of Prof. Craig Gotsman.

Wouldn't we prefer segmentation over detection? a pixel level detection is preferable. scale sensitive, no anchors, no NMS. Let's see how this can be achieved.

Seasoned team leader, with over a decade of experience, solving complex industry problems, ranging from physical modeling to data science & deep-learning.

Ofir will talk about how Lightricks implemented generative AI and the latest developments in "image transformation."

Ofir Bibi is the VP of Research at Lightricks. Bringing the latest and greatest in ML to the core of consumer products, creating efficient research pipelines, methodologies and growing great researchers. His research interests are in the fields of efficient machine learning, statistical signal processing, computational photography and computer graphics. He has taken leading research positions in building systems for estimation and prediction, market optimization and recommendations, but his true passion is in solving challenges by a precise mix of engineering and research. 

Computer vision now heavily relies on "foundation models", text-and-image representations trained with massive data in a task-agnostic way, and then used for downstream tasks. But how can we make them useful for our own tasks? You may want to search for a photo of your own pet in your photo album, or generate an image of your friend in a funny situation.   I will discuss our work on personalizing text-vision models, by teaching models new words and concepts. I'll show applications to personalized ranking, segmentation and image generation.  

Gal Chechik is a Professor of computer science at Bar-Ilan University and a director of AI at NVIDIA. His research spans learning in brains and machines,  focusing mainly on deep machine learning for perception and reasoning.
In 2018 Gal founded the NVIDIA research group in Israel, and has been leading it since. Prior to that, he was a staff research scientist at Google working on machine perception and search.  Gal is the author of ~120 refereed publications, and 50 patents, including publications in Nature Biotechnology, Cell and PNAS. His research was awarded best papers in ICML and NeurIPS. 
 

Explainability is an ever-growing field of research, aiming to demystify the black box that is deep learning. In recent years, the naturally interpretable attention mechanism has become one of the most common building blocks of neural networks, allowing us to produce explanations intuitively and easily.
However, the applications of such explanations beyond the scope of accountability and interpretability remain limited.
In this talk, I will present my latest research on leveraging attention to significantly improve the accuracy and robustness of state-of-the-art large neural networks with limited resources. This is achieved by directly manipulating the attention maps based on intuitive objectives and can be applied to a variety of tasks ranging from object classification to image generation.
 

Hila is a PhD student and lecturer at Tel-Aviv University, advised by Prof. Lior Wolf. Additionally, Hila is currently an intern at Google Research in Tel-Aviv. Her research focuses on constructing faithful explainable AI algorithms, and leveraging explanations to promote model accuracy and robustness. Hila has recently received a 3-year scholarship for distinguished PhD candidates, awarded by the Israeli Council for Higher Education.