{"id":173,"date":"2023-05-23T00:29:56","date_gmt":"2023-05-22T18:59:56","guid":{"rendered":"https:\/\/aiml.3it.in\/?page_id=173"},"modified":"2023-10-18T10:49:19","modified_gmt":"2023-10-18T05:19:19","slug":"keynote_details","status":"publish","type":"page","link":"https:\/\/www.aimlsystems.org\/2023\/keynote_details\/","title":{"rendered":"Keynote Speakers"},"content":{"rendered":"

[et_pb_section fb_built=”1″ admin_label=”Header” _builder_version=”4.19.5″ _module_preset=”default” background_color=”gcid-1bcf785a-50e1-437b-b09f-65567babc1de” background_image=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/grid-bg-2.png” background_size=”initial” background_position=”bottom_center” background_repeat=”repeat” custom_padding=”||0px|||” collapsed=”on” global_colors_info=”{%22gcid-1bcf785a-50e1-437b-b09f-65567babc1de%22:%91%22background_color%22%93}”][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.19.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”2_3″ _builder_version=”4.19.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”7f63b212-a10a-4d30-afa2-e478a747ca88″ custom_margin=”||10px||false|false” header_2_font_size_phone=”33px” global_colors_info=”{}”]<\/p>\n

Keynote Speakers<\/h2>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.19.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/ai-icon-03.png” title_text=”ai-icon-03″ align=”center” disabled_on=”on|on|off” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”6px|||||” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Features” module_id=”about” _builder_version=”4.21.0″ _module_preset=”default” background_color=”#dbdbdb” background_image=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/rm380-10.jpg” background_blend=”overlay” custom_padding=”3.9%||||false|false” use_background_color_gradient_phone=”on” background_color_gradient_stops_phone=”#001528 0%|rgba(255, 255, 255, 0) 10%|rgba(255,255,255,0) 70%|#0f0122 100%” collapsed=”on” global_colors_info=”{}”][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.21.0″ _module_preset=”default” custom_padding=”27px||43px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/christopher.jpg” title_text=”christopher” align=”center” _builder_version=”4.21.0″ _module_preset=”default” max_width=”200px” custom_margin=”||15px|||” filter_saturate=”0%” animation_style=”slide” border_radii=”on|115px|115px|115px|115px” border_color_all=”#FFFFFF” box_shadow_style=”preset2″ global_colors_info=”{}” transform_styles__hover_enabled=”on|hover” transform_scale__hover_enabled=”on|hover” transform_translate__hover_enabled=”on|hover” transform_rotate__hover_enabled=”on|hover” transform_skew__hover_enabled=”on|hover” transform_origin__hover_enabled=”on|hover” transform_scale__hover=”104%|104%” filter_saturate__hover_enabled=”on|hover” filter_saturate__hover=”100%” border_width_all__hover_enabled=”on|hover” border_width_all__hover=”1px” border_radii__hover_enabled=”on|hover” border_radii__hover=”on|115px|115px|115px|115px”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”25d2b0d8-2373-4ae8-9188-0ef4b1bb77f4″ text_text_color=”#212A4F” text_font_size=”14px” header_4_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ header_4_font_size=”20px” custom_margin=”||15px|||” global_colors_info=”{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22header_4_text_color%22%93}”]<\/p>\n

Christopher Matthew De Sa <\/a><\/h4>\n

Cornell University, USA<\/p>\n

[\/et_pb_text][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Title” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Quantization and Compression in ML Systems<\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Abstract” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Low-precision arithmetic has been popular in machine learning for both training and inference for almost a decade. This quantization helps reduce the time, memory, and energy needed to learn big models on big data. This talk will cover some of the history of quantization, and compression more generally, in machine learning, discussing how it interacts differently with training and inference. Lastly, I will present some recent results from my lab that use a principled theory of quantization error to do post-training quantization of large language models down to 2 bits per weight.<\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Bio” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Christopher Matthew De Sa is currently an Assistant Professor in the Computer Science department at Cornell University. research interests include algorithmic, software, and hardware techniques for high-performance machine learning, with a focus on relaxed-consistency variants of stochastic algorithms such as asynchronous and low-precision stochastic gradient descent (SGD) and Markov chain Monte Carlo. He has authored numerous papers in leading journals and conferences in Machine Learning. He holds BS and MS degrees, both in Electrical Engineering, from Stanford University, and completed his PhD from the same department at Stanford University. For more information about his work please see\u00a0<\/span>https:\/\/www.cs.cornell.edu\/~cdesa\/<\/a>.<\/span><\/p>\n

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[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.21.0″ _module_preset=”default” custom_padding=”45px|||||” border_width_top=”1px” border_color_top=”#878787″ global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/milind_tambe.jpg” title_text=”milind_tambe” align=”center” _builder_version=”4.21.0″ _module_preset=”default” max_width=”200px” custom_margin=”||15px|||” filter_saturate=”0%” animation_style=”slide” border_radii=”on|115px|115px|115px|115px” border_color_all=”#FFFFFF” box_shadow_style=”preset2″ global_colors_info=”{}” transform_styles__hover_enabled=”on|hover” transform_scale__hover_enabled=”on|hover” transform_translate__hover_enabled=”on|hover” transform_rotate__hover_enabled=”on|hover” transform_skew__hover_enabled=”on|hover” transform_origin__hover_enabled=”on|hover” transform_scale__hover=”104%|104%” filter_saturate__hover_enabled=”on|hover” filter_saturate__hover=”100%” border_width_all__hover_enabled=”on|hover” border_width_all__hover=”1px” border_radii__hover_enabled=”on|hover” border_radii__hover=”on|115px|115px|115px|115px”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”25d2b0d8-2373-4ae8-9188-0ef4b1bb77f4″ text_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ text_font_size=”14px” header_4_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ header_4_font_size=”20px” custom_margin=”||15px|||” global_colors_info=”{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22text_text_color%22,%22header_4_text_color%22%93}”]<\/p>\n

Milind Tambe<\/a><\/h4>\n

Google Research, Harvard University, USA<\/p>\n

[\/et_pb_text][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” hover_enabled=”0″ border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}” sticky_enabled=”0″][et_pb_accordion_item title=”Title” open=”on” _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Integrating ML+Optimization: Driving Social Impact in public health and conservation<\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” hover_enabled=”0″ border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}” sticky_enabled=”0″][et_pb_accordion_item title=”Abstract” open=”on” _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

For more than 15 years, my team and I have been focused on AI for social impact, deploying end-to-end systems in areas of public health, conservation and public safety. In this talk, I will highlight the results from our deployments for social impact in public health and conservation, as well as required innovations in integrating machine learning and optimization. First in terms of public health, I will present recent results from our work in India with the world\u2019s two largest mobile health programs for maternal and child care that have served millions of beneficiaries. Additionally, I will highlight results from earlier projects on HIV prevention and others. In terms of conservation, I will highlight efforts for protecting endangered wildlife in national parks around the globe. To address challenges of ML+optimizaton common to all of these applications, we have advanced the state of the art in decision focused learning, restless multi-armed bandits, influence maximization in social networks and green security games. In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques<\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Bio” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

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Prof. Milind Tambe is Gordon McKay Professor of Computer Science and Director of Center for Research in Computation and Society at Harvard University; concurrently, he is also Principal Scientist and Director “AI for Social Good” at Google Research. He is recipient of the IJCAI (International Joint Conference on Artificial Intelligence) John McCarthy Award, AAAI (Association for Advancement of Artificial Intelligence) Feigenbaum Prize, AAAI Robert S. Engelmore Memorial Lecture Award, AAMAS ACM (Association for Computing Machinery) Autonomous Agents Research Award, INFORMS ( Institute for Operations Research and the Management Sciences) Wagner prize for excellence in Operations Research practice and Rist Prize from MORS (Military Operations Research Society). He is a fellow of AAAI and ACM. For his work on AI and public safety, he has received Columbus Fellowship Foundation Homeland security award and commendations and certificates of appreciation from the US Coast Guard, the Federal Air Marshals Service and airport police at the city of Los Angeles.<\/div>\n<\/div>\n<\/div>\n

[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.21.0″ _module_preset=”default” custom_padding=”45px|||||” border_width_top=”1px” border_color_top=”#878787″ global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/06\/keynote3.jpg” title_text=”Professor Svetha Venkatesh” align=”center” _builder_version=”4.21.0″ _module_preset=”default” max_width=”200px” custom_margin=”||15px|||” filter_saturate=”0%” animation_style=”slide” border_radii=”on|115px|115px|115px|115px” border_color_all=”#FFFFFF” box_shadow_style=”preset2″ global_colors_info=”{}” transform_styles__hover_enabled=”on|hover” transform_scale__hover_enabled=”on|hover” transform_translate__hover_enabled=”on|hover” transform_rotate__hover_enabled=”on|hover” transform_skew__hover_enabled=”on|hover” transform_origin__hover_enabled=”on|hover” transform_scale__hover=”104%|104%” filter_saturate__hover_enabled=”on|hover” filter_saturate__hover=”100%” border_width_all__hover_enabled=”on|hover” border_width_all__hover=”1px” border_radii__hover_enabled=”on|hover” border_radii__hover=”on|115px|115px|115px|115px”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”25d2b0d8-2373-4ae8-9188-0ef4b1bb77f4″ text_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ text_font_size=”14px” header_4_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ header_4_font_size=”20px” custom_margin=”||15px|||” global_colors_info=”{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22text_text_color%22,%22header_4_text_color%22%93}”]<\/p>\n

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Svetha Venkatesh<\/a><\/h4>\n

Alfred Deakin Professor
Co-Director of Applied AI Institute, Deakin University, Australia.<\/p>\n

[\/et_pb_text][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Title” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Sample efficient AI with applications in health care and advanced manufacturing<\/p>\n

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[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Abstract” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

From Guglielmo Marconi who developed the radio telegraph to the Wright brothers who invented flying machines, curiosity driven experimentation has powered human innovation. Such experimental optimisation remains an integral part of the Scientific Method. This time-honoured method needs a step change to accelerate scientific innovation because this iterative method quickly hits limits.<\/p>\n

To speed-up innovation, it is imperative to expand the capability of experimental optimisation and improve its efficiency. This talk will demonstrate how sample efficient AI can be used to deliver this acceleration in experimental design. I will discuss how the methods can be applied widely, focusing on health and advanced manufacturing particularly in settings where data is scare and experimentation is expensive. In healthcare, I show how these methods can accelerate the design of clinical\/health trials to efficiently determine the optimal strategy. In advanced manufacturing, I will show how it can be applied broadly from inventing new materials and alloys to accelerating industrial processes.<\/p>\n

The second part of the talk will focus on the new machine learning innovations that have been formulated and solved to advance experimental design. These include incorporating experimental design constraints such as process constraints, transferring knowledge from pervious experiments or experimenter \u201chunches\u201d, and high dimensional Bayesian optimisation so that the number of experimental control variables can be increased.<\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Bio” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

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Svetha Venkatesh is an Alfred Deakin Professor and a Co- Director of Applied Artificial Intelligence Institute (A2I2) at Deakin University. She was elected a Fellow of the International Association of Pattern Recognition in 2004 for contributions to formulation and extraction of semantics in multimedia data, a Fellow of the Australian Academy of Technological Sciences and Engineering in 2006, and a Fellow of the Australian Academy of Science in 2021 for ground-breaking research and contributions that have had clear impact. In 2017, Professor Venkatesh was appointed an Australian Laureate Fellow, the highest individual award the Australian Research Council can bestow.<\/p>\n

Professor Venkatesh and her team have tackled a wide range of problems of societal significance, including the critical areas of autism, security and aged care. The outcomes have impacted the community and evolved into publications, patents, tools and spin-off companies.<\/p>\n<\/div>\n

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[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.21.0″ _module_preset=”default” custom_padding=”45px|||||” border_width_top=”1px” border_color_top=”#878787″ global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/08\/Sanghamitra-1-e1691477702321.jpg” title_text=”Sanghamitra” align=”center” _builder_version=”4.21.0″ _module_preset=”default” max_width=”200px” custom_margin=”||15px|||” filter_saturate=”0%” animation_style=”slide” border_radii=”on|115px|115px|115px|115px” border_color_all=”#FFFFFF” box_shadow_style=”preset2″ global_colors_info=”{}” transform_styles__hover_enabled=”on|hover” transform_scale__hover_enabled=”on|hover” transform_translate__hover_enabled=”on|hover” transform_rotate__hover_enabled=”on|hover” transform_skew__hover_enabled=”on|hover” transform_origin__hover_enabled=”on|hover” transform_scale__hover=”104%|104%” filter_saturate__hover_enabled=”on|hover” filter_saturate__hover=”100%” border_width_all__hover_enabled=”on|hover” border_width_all__hover=”1px” border_radii__hover_enabled=”on|hover” border_radii__hover=”on|115px|115px|115px|115px”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”25d2b0d8-2373-4ae8-9188-0ef4b1bb77f4″ text_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ text_font_size=”14px” header_4_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ header_4_font_size=”20px” custom_margin=”||15px|||” global_colors_info=”{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22text_text_color%22,%22header_4_text_color%22%93}”]<\/p>\n

Prof. Sanghamitra Bandyopadhyay<\/a><\/h4>\n

Indian Statistical Institute, India<\/p>\n

[\/et_pb_text][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Title” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Artificial Intelligence Techniques for Making Biological Discoveries: Some Case Studies<\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Abstract” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Artificial Intelligence techniques are finding increasingly innovative applications in life sciences for making novel discoveries and for gaining deeper insights into various processes of life. In this talk we will first present an overview of molecular biology and bioinformatics. We will then discuss case studies in three areas, namely single cell RNA-seq data analytics, multiobjective optimization in drug design and graph based approaches in drug interaction studies.<\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Bio” open=”on” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

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Prof. Sanghamitra Bandyopadhyay did her B Tech, M Tech and Ph.D. in Computer Science from Calcutta University, IIT Kharagpur and Indian Statistical Institute respectively. She then joined the Indian Statistical Institute as a faculty member, and became the Director in 2015. Since 2020 she is continuing in her second tenure as the Director of the Institute. Her research interests include computational biology, soft and evolutionary computation, artificial intelligence and machine learning. She is the recipient of several awards including the Shanti Swarup Bhatnagar Prize in Engineering Science, TWAS Prize, Infosys Prize, JC Bose Fellowship, Swarnajayanti fellowship, INAE Silver Jubilee award, INAE Woman Engineer of the Year award (academia), IIT Kharagpur Distinguished Alumni Award, Humboldt Fellowship from Germany, Senior Associateship of ICTP, Italy, young engineer\/scientist awards from INSA, INAE and ISCA, and Dr. Shanker Dayal Sharma Gold Medal and Institute Silver from IIT, Kharagpur, India. She is a Fellow of the Indian National Science Academy (INSA), National Academy of Sciences, India (NASI), Indian National Academy of Engineers (INAE), Indian Academy of Sciences (IASc), Institute of Electrical and Electronic Engineers (IEEE), The World Academy of Sciences (TWAS), International Association for Pattern Recognition (IAPR) and West Bengal Academy of Science and Technology. She serves as a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India (PM-STIAC). In 2022, she received the Padma Shri award, the fourth highest civilian award of the Government of India.<\/div>\n
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[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

Keynote SpeakersChristopher Matthew De Sa Cornell University, USAQuantization and Compression in ML SystemsLow-precision arithmetic has been popular in machine learning for both training and inference for almost a decade. This quantization helps reduce the time, memory, and energy needed to learn big models on big data. This talk will cover some of the history of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"\n

This is an example page. It's different from a blog post because it will stay in one place and will show up in your site navigation (in most themes). Most people start with an About page that introduces them to potential site visitors. It might say something like this:<\/p>\n\n\n\n

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Hi there! I'm a bike messenger by day, aspiring actor by night, and this is my website. I live in Los Angeles, have a great dog named Jack, and I like pi\u00f1a coladas. (And gettin' caught in the rain.)<\/p>\n<\/blockquote>\n\n\n\n

...or something like this:<\/p>\n\n\n\n

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The XYZ Doohickey Company was founded in 1971, and has been providing quality doohickeys to the public ever since. Located in Gotham City, XYZ employs over 2,000 people and does all kinds of awesome things for the Gotham community.<\/p>\n<\/blockquote>\n\n\n\n

As a new WordPress user, you should go to your dashboard<\/a> to delete this page and create new pages for your content. Have fun!<\/p>\n","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"class_list":["post-173","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.aimlsystems.org\/2023\/wp-json\/wp\/v2\/pages\/173","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.aimlsystems.org\/2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.aimlsystems.org\/2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.aimlsystems.org\/2023\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aimlsystems.org\/2023\/wp-json\/wp\/v2\/comments?post=173"}],"version-history":[{"count":37,"href":"https:\/\/www.aimlsystems.org\/2023\/wp-json\/wp\/v2\/pages\/173\/revisions"}],"predecessor-version":[{"id":3925,"href":"https:\/\/www.aimlsystems.org\/2023\/wp-json\/wp\/v2\/pages\/173\/revisions\/3925"}],"wp:attachment":[{"href":"https:\/\/www.aimlsystems.org\/2023\/wp-json\/wp\/v2\/media?parent=173"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}