{"id":7301,"date":"2025-09-14T23:26:06","date_gmt":"2025-09-14T17:56:06","guid":{"rendered":"https:\/\/www.aimlsystems.org\/2025\/?page_id=7301"},"modified":"2025-09-18T10:32:22","modified_gmt":"2025-09-18T05:02:22","slug":"tutorial-agentic-ai","status":"publish","type":"page","link":"https:\/\/www.aimlsystems.org\/2026\/tutorial-agentic-ai\/","title":{"rendered":"Tutorial-Agentic AI"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;Header&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;gcid-1bcf785a-50e1-437b-b09f-65567babc1de&#8221; background_image=&#8221;https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/grid-bg-2.png&#8221; background_size=&#8221;initial&#8221; background_position=&#8221;bottom_center&#8221; background_repeat=&#8221;repeat&#8221; custom_padding=&#8221;||0px|||&#8221; collapsed=&#8221;on&#8221; global_colors_info=&#8221;{%22gcid-1bcf785a-50e1-437b-b09f-65567babc1de%22:%91%22background_color%22%93}&#8221;][et_pb_row _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.19.2&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;7f63b212-a10a-4d30-afa2-e478a747ca88&#8243; header_2_font_size=&#8221;44px&#8221; custom_margin=&#8221;||10px||false|false&#8221; header_2_font_size_phone=&#8221;33px&#8221; custom_css_free_form=&#8221;selector h2{color:white}&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h2><strong>Tutorial &#8211; Agentic AI<\/strong><\/h2>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;Features&#8221; module_id=&#8221;about&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#dbdbdb&#8221; background_image=&#8221;https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/rm380-10.jpg&#8221; background_blend=&#8221;overlay&#8221; custom_padding=&#8221;3.9%||||false|false&#8221; use_background_color_gradient_phone=&#8221;on&#8221; background_color_gradient_stops_phone=&#8221;#001528 0%|rgba(255, 255, 255, 0) 10%|rgba(255,255,255,0) 70%|#0f0122 100%&#8221; collapsed=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row column_structure=&#8221;3_5,2_5&#8243; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;27px||43px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;3_5&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_button button_url=&#8221;https:\/\/in.explara.com\/e\/tutorial-aiml-systems-2025\/checkout&#8221; url_new_window=&#8221;on&#8221; button_text=&#8221;Register Now&#8221; button_alignment=&#8221;left&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_button=&#8221;on&#8221; button_text_size=&#8221;15px&#8221; button_text_color=&#8221;gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68&#8243; button_border_width=&#8221;1px&#8221; button_border_radius=&#8221;78px&#8221; button_font=&#8221;Poppins|500||on|||||&#8221; button_icon=&#8221;&#x24;||divi||400&#8243; animation_style=&#8221;fade&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22button_text_color%22%93}&#8221; button_bg_color__hover_enabled=&#8221;on|hover&#8221; button_bg_color__hover=&#8221;&#8221; button_bg_enable_color__hover=&#8221;off&#8221; button_bg_color_gradient_stops__hover=&#8221;#2b87da 0%|#0d1c63 100%&#8221; button_bg_use_color_gradient__hover=&#8221;on&#8221;][\/et_pb_button][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>&nbsp;<\/p>\n<h3 class=\"text-[#946f43] text-4xl text-center font-semibold font-comfortaa pt-[50px] mt-0 mb-8\">&#8220;No Code? No Problem. Build Your First AI Agent from Scratch!&#8221;<\/h3>\n<section class=\"bg-white\/70 backdrop-blur-sm rounded-lg shadow-lg p-8 mb-12\"><\/section>\n<p><span>Generative AI in the natural language space is showing tremendous potential in automating various routine jobs. Recent studies have also demonstrated that Generative AI can aid with creative content creation as well. At the center of these innovations are Large Language Models (LLMs) like GPT 4, Claude2, Llama 2, etc. Many of these LLMs are commercial, but there are numerous open-sourced options available that can help organizations innovate and unlock tremendous value. These sessions will explore practical ways to develop end-to-end applications using LLMs in a scalable and affordable way. The speaker would also discuss the software development life cycle for Generative AI solutions along with problem statement definition to help budding AI engineers, AI researchers, and product managers alike.<\/span><\/p>\n<p><span class=\"a_GcMg font-feature-liga-off font-feature-clig-off font-feature-calt-off text-decoration-none text-strikethrough-none\">\u201cThe Agentic AI Tutorial at AIML Systems 2025 introduces participants to generative AI and agent-based architectures through hands-on exercises in prompt engineering, automation, and no-code agent building. Attendees will gain skills in coding assistance, data analysis, literature review, and ethical AI, preparing them to innovate in intelligent systems.\u201d<\/span><\/p>\n<p><span class=\"a_GcMg font-feature-liga-off font-feature-clig-off font-feature-calt-off text-decoration-none text-strikethrough-none\"><\/span><\/p>\n<p><strong>Step inside interactive hands on session on prompt engineering, vibe coding and AI-agent development using no code platform.<\/strong><\/p>\n<ul><\/ul>\n<p>[\/et_pb_text][et_pb_code admin_label=&#8221;Code&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; link_option_url=&#8221;https:\/\/cmt3.research.microsoft.com\/AIMLSys2025\/&#8221; link_option_url_new_window=&#8221;on&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<!-- [et_pb_line_break_holder] --><html><!-- [et_pb_line_break_holder] -->  <\/p>\n<style><!-- [et_pb_line_break_holder] -->  #submit_button{color:black;border-color:black}<!-- [et_pb_line_break_holder] -->  <\/style>\n<p><!-- [et_pb_line_break_holder] --><\/html>[\/et_pb_code][et_pb_code admin_label=&#8221;Code&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; link_option_url=&#8221;https:\/\/cmt3.research.microsoft.com\/AIMLSys2025\/&#8221; link_option_url_new_window=&#8221;on&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<!-- [et_pb_line_break_holder] --><html><!-- [et_pb_line_break_holder] -->  <\/p>\n<style><!-- [et_pb_line_break_holder] -->  #submit_button{color:black;border-color:black}<!-- [et_pb_line_break_holder] -->  <\/style>\n<p><!-- [et_pb_line_break_holder] --><\/html>[\/et_pb_code][et_pb_code admin_label=&#8221;Code&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; link_option_url=&#8221;https:\/\/cmt3.research.microsoft.com\/AIMLSys2025\/&#8221; link_option_url_new_window=&#8221;on&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<!-- [et_pb_line_break_holder] --><html><!-- [et_pb_line_break_holder] -->  <\/p>\n<style><!-- [et_pb_line_break_holder] -->  #submit_button{color:black;border-color:black}<!-- [et_pb_line_break_holder] -->  <\/style>\n<p><!-- [et_pb_line_break_holder] --><\/html>[\/et_pb_code][\/et_pb_column][et_pb_column type=&#8221;2_5&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_tabs active_tab_background_color=&#8221;#1c1b3a&#8221; inactive_tab_background_color=&#8221;#0b91c6&#8243; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; tab_text_color=&#8221;#FFFFFF&#8221; background_color=&#8221;rgba(0,0,0,0)&#8221; border_radii=&#8221;on|11px|11px|11px|11px&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{%22gcid-f1f9244b-c8ab-43e1-95c3-c0bdf69ac7b5%22:%91%22active_tab_background_color%22,%22active_tab_background_color%22%93}&#8221;][et_pb_tab title=&#8221;Important Dates&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<div class=\"text-attention\">\n<ul>\n<li class=\"s10\"><strong><span class=\"s5\">Tutorial Date: <\/span><\/strong><span class=\"s5\">9th Oct, 2025<\/span><strong><span class=\"s5\"><\/span><\/strong><strong><span class=\"s5\"><\/span><\/strong><\/li>\n<li class=\"s10\"><strong><span class=\"s5\">Tutorial Timings: <\/span><\/strong><span class=\"s5\">3:00 PM &#8211; 4:30 PM<\/span><\/li>\n<li class=\"s10\"><strong><span class=\"s5\">Venue: <\/span><\/strong><span class=\"s5\"><span class=\"a_GcMg font-feature-liga-off font-feature-clig-off font-feature-calt-off text-decoration-none text-strikethrough-none\">The Chancery Pavilion, 135, Residency Road<\/span><\/span><\/li>\n<li class=\"s10\"><strong><span class=\"s5\"><span class=\"a_GcMg font-feature-liga-off font-feature-clig-off font-feature-calt-off text-decoration-none text-strikethrough-none\">Conference<\/span><\/span><\/strong><strong><span class=\"s5\">\u00a0Dates: <\/span><\/strong><span class=\"s5\">7th Oct &#8211; 11th Oct, 2025<\/span><\/li>\n<\/ul>\n<\/div>\n<p>[\/et_pb_tab][\/et_pb_tabs][et_pb_tabs active_tab_background_color=&#8221;#1c1b3a&#8221; inactive_tab_background_color=&#8221;#0b91c6&#8243; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; tab_text_color=&#8221;#FFFFFF&#8221; background_color=&#8221;rgba(0,0,0,0)&#8221; custom_padding=&#8221;||0px||false|false&#8221; link_option_url_new_window=&#8221;on&#8221; border_radii=&#8221;on|11px|11px|11px|11px&#8221; global_colors_info=&#8221;{%22gcid-f1f9244b-c8ab-43e1-95c3-c0bdf69ac7b5%22:%91%22active_tab_background_color%22,%22active_tab_background_color%22%93}&#8221;][et_pb_tab title=&#8221;Tutorial Chairs&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; body_line_height=&#8221;1.4em&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<ul>\n<li><a href=\"https:\/\/www.linkedin.com\/in\/anupamisb\/?originalSubdomain=in\" target=\"_blank\" rel=\"noopener\">Anupam Purwar<\/a>,<span> Sprinklr<\/span><\/li>\n<\/ul>\n<p>[\/et_pb_tab][\/et_pb_tabs][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;25px|||||&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Agenda&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span>Learn how to build AI agent from scratch using no code platform. We will use GitHub copilot and VSCode to help you create python code of an AI Agent capable of reading enterprise specific documents hosted in your own PC and deploy it as web app.<\/span><\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;]<\/p>\n<p><span>We can share the online teams link with people who are not from BLR.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3>Accepted Papers<\/h3>\n<p>[\/et_pb_text][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;1. Directed Network Modeling with Generative AI for Driver Decision Support&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p class=\"p1\">Yogesh Dhumal (MNR University, Hyderabad), Jaswanth Nidamanuri (MNR University, Hyderabad)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;2. AutoRAG-LoRA: Hallucination-Triggered Knowledge Retuning via Lightweight Adapters&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p class=\"p1\">Kaushik Dwivedi (Birla Institute of Science Pilani, Pilani)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;3. Unified AI-Driven Log Anomaly Detection and Adaptive Response System Integrating Threat Intelligence and Reinforcement Learning&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p class=\"p1\">Vijay kumar (BMS Institute of Technology and Management)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;4. Agentic Summarization of Large COBOL Programs Beyond LLM Context Limits Using Call Graph- Based Grouping&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p class=\"p1\">Sourav Bhattacharyya (IBM India Pvt Ltd), Vasudev Chatterjee (IBM India Pvt Ltd),\u00a0William Alexander ( IBM USA ),\u00a0 Ranjan Kumar ( IBM India Pvt Ltd )<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_text disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;15px|||||&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3>Keynote Speakers<\/h3>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=&#8221;1_4,3_4&#8243; disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;27px||43px|||&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;1_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/www.aimlsystems.org\/2024\/wp-content\/uploads\/2024\/10\/culotta-photo.jpg&#8221; title_text=&#8221;culotta-photo&#8221; align=&#8221;center&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; max_width=&#8221;200px&#8221; custom_margin=&#8221;||15px|||&#8221; filter_saturate=&#8221;0%&#8221; animation_style=&#8221;slide&#8221; border_radii=&#8221;on|115px|115px|115px|115px&#8221; border_color_all=&#8221;#FFFFFF&#8221; box_shadow_style=&#8221;preset2&#8243; global_colors_info=&#8221;{}&#8221; transform_styles__hover_enabled=&#8221;on|hover&#8221; transform_scale__hover_enabled=&#8221;on|hover&#8221; transform_translate__hover_enabled=&#8221;on|hover&#8221; transform_rotate__hover_enabled=&#8221;on|hover&#8221; transform_skew__hover_enabled=&#8221;on|hover&#8221; transform_origin__hover_enabled=&#8221;on|hover&#8221; transform_scale__hover=&#8221;104%|104%&#8221; filter_saturate__hover_enabled=&#8221;on|hover&#8221; filter_saturate__hover=&#8221;100%&#8221; border_width_all__hover_enabled=&#8221;on|hover&#8221; border_width_all__hover=&#8221;1px&#8221; border_radii__hover_enabled=&#8221;on|hover&#8221; border_radii__hover=&#8221;on|115px|115px|115px|115px&#8221;][\/et_pb_image][\/et_pb_column][et_pb_column type=&#8221;3_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;25d2b0d8-2373-4ae8-9188-0ef4b1bb77f4&#8243; text_text_color=&#8221;#212A4F&#8221; header_4_text_color=&#8221;gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68&#8243; header_4_font_size=&#8221;20px&#8221; custom_margin=&#8221;||15px|||&#8221; global_colors_info=&#8221;{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22header_4_text_color%22%93}&#8221;]<\/p>\n<h4><a href=\"https:\/\/www.cs.tulane.edu\/\/~aculotta\/\" target=\"_blank\" rel=\"noopener\">Dr. Aron Culotta<\/a><\/h4>\n<p>Tulane University, USA<\/p>\n<p>[\/et_pb_text][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Title&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>Co-creating Civic AI: Partnering Academia with Local Communities<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Abstract&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>I&#8217;ll present ongoing work at Tulane&#8217;s Center for Community-Engaged AI partnering with local non-profits to build AI tools for transparency and accountability in criminal court and city government. I will first discuss our work with Eye on Surveillance, who have developed a retrieval-augmented generation tool over New Orleans city council transcripts (<a href=\"https:\/\/www.sawt.us\/\" target=\"_blank\" rel=\"noopener\">sawt.us<\/a>). We have collaborated to use automated LLM evaluations and to engage with community users to improve the trustworthiness of the system. Second, I will discuss our work with Court Watch NOLA to build AI models for monitoring the equity in criminal court, including methods to estimating causal effects from text data. I will conclude with an overview of future directions in how generative AI can be used for a number of civic applications.<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Bio&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>Dr. Culotta is an Associate Professor of Computer Science at Tulane University and Director of the <a href=\"https:\/\/sse.tulane.edu\/cs\/ceai\" target=\"_blank\" rel=\"noopener\">Tulane Center for Community-Engaged Artificial Intelligence<\/a>. He received his PhD in computer science from the University of Massachusetts-Amherst in 2008, and his research focuses on machine learning, natural language processing, and social network analysis, and their implications on society. His NSF-funded research has developed AI methods for several interdisciplinary projects in public health, marketing, political science, and emergency response. He has published over seventy academic articles on AI, serves on the steering committee of the International Conference on Web and Social Media, and has received best paper awards from the ACM Conference on Computer-Supported Cooperative Work and Social Computing and from the AAAI Conference on Artificial Intelligence.<\/p>\n<p><span><\/span><\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=&#8221;1_4,3_4&#8243; disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;27px||43px|||&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;1_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/www.aimlsystems.org\/2024\/wp-content\/uploads\/2024\/10\/Hamm_jihun_002.jpg&#8221; title_text=&#8221;Hamm_jihun_002&#8243; align=&#8221;center&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; max_width=&#8221;200px&#8221; custom_margin=&#8221;||15px|||&#8221; filter_saturate=&#8221;0%&#8221; animation_style=&#8221;slide&#8221; border_radii=&#8221;on|115px|115px|115px|115px&#8221; border_color_all=&#8221;#FFFFFF&#8221; box_shadow_style=&#8221;preset2&#8243; global_colors_info=&#8221;{}&#8221; transform_styles__hover_enabled=&#8221;on|hover&#8221; transform_scale__hover_enabled=&#8221;on|hover&#8221; transform_translate__hover_enabled=&#8221;on|hover&#8221; transform_rotate__hover_enabled=&#8221;on|hover&#8221; transform_skew__hover_enabled=&#8221;on|hover&#8221; transform_origin__hover_enabled=&#8221;on|hover&#8221; transform_scale__hover=&#8221;104%|104%&#8221; filter_saturate__hover_enabled=&#8221;on|hover&#8221; filter_saturate__hover=&#8221;100%&#8221; border_width_all__hover_enabled=&#8221;on|hover&#8221; border_width_all__hover=&#8221;1px&#8221; border_radii__hover_enabled=&#8221;on|hover&#8221; border_radii__hover=&#8221;on|115px|115px|115px|115px&#8221;][\/et_pb_image][\/et_pb_column][et_pb_column type=&#8221;3_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;25d2b0d8-2373-4ae8-9188-0ef4b1bb77f4&#8243; text_text_color=&#8221;#212A4F&#8221; header_4_text_color=&#8221;gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68&#8243; header_4_font_size=&#8221;20px&#8221; custom_margin=&#8221;||15px|||&#8221; global_colors_info=&#8221;{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22header_4_text_color%22%93}&#8221;]<\/p>\n<h4><a href=\"https:\/\/www.cs.tulane.edu\/~jhamm3\/\" target=\"_blank\" rel=\"noopener\">Dr. Jihun Hamm<\/a><\/h4>\n<p>Tulane University, USA<\/p>\n<p>[\/et_pb_text][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Title&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>Can synthetic images be better than real images?<br \/>A study of utility and privacy of synthetic images in dermatology<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Abstract&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>Advances in generative models such as GAN, VAE, and more recently, Diffusion models have revolutionized the field of image generation by enabling the generation of photorealistic synthetic images for many potential applications. Along with the advances, the question whether synthetic data can replace real data has become increasingly relevant. Synthetic data has been demonstrated to improve classification and to overcome the data scarcity issues such as data imbalance, robustness, and biases. This is done by generating synthetic data distributions with improved balance and other properties. Furthermore, there is an increasing demand for privacy-preserving synthetic image generation across various domains such as healthcare, finance, and social media. However, achieving the optimal tradeoff between utility and privacy remains a significant technical challenge, and various privacy-preserving techniques are being studied. In this talk, I will first discuss how generative AI can help in skin disease diagnosis problem, and also present benchmarking results on utility-privacy of recent image synthesis methods.<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Bio&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>Dr. Jihun Hamm has been an Associate Professor of Computer Science at Tulane University since 2019. He received his PhD degree from the University of Pennsylvania in 2008 supervised by Dr. Daniel Lee. Dr. Hamm&#8217;s research interest is in machine learning, from theory to applications. He has worked on the theory and practice of robust and adversarial machine learning, privacy and security and optimization. Dr. Hamm also has worked on medical data analysis. His work in machine learning has been published in top venues such as ICML, NeurIPS, CVPR, JMLR, and IEEE-TPAMI. His work has also been published in medical research venues such as MICCAI, MedIA, and IEEE-TMI. Among other awards, he has earned the Best Paper Award from MedIA, Finalist for MICCAI Young Scientist Publication Impact Award, and Google Faculty Research Award.<\/p>\n<p><span><\/span><\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;4px|||||&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text content_tablet=&#8221;<\/p>\n<h2><b>Keynote Talks<\/b><\/h2>\n<p data-ogsb=%22white%22><span data-ogsc=%22rgb(34, 34, 34)%22><strong>Title:<\/strong> My Experiments with Large Language Models<\/span><\/p>\n<p data-ogsb=%22white%22><span data-ogsc=%22rgb(34, 34, 34)%22><strong>Speaker: <a href=%22https:\/\/www.cse.iitd.ac.in\/~mausam\/%22><span style=%22font-weight: 400;%22>Prof. Mausam, IITD.<\/span><\/a><\/strong><\/span><span data-ogsc=%22rgb(34, 34, 34)%22><strong><span style=%22font-weight: 400;%22><\/span><\/strong><\/span><\/p>\n<p data-ogsb=%22white%22><span data-ogsc=%22rgb(34, 34, 34)%22><strong><span style=%22font-weight: 400;%22><img src=%22https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/10\/mausam-head_cropped-300x300.jpg%22 width=%22300%22 height=%22300%22 alt=%22%22 class=%22wp-image-4051 alignnone size-medium%22 \/><\/span><\/strong><\/span><\/p>\n<p data-ogsb=%22white%22><span data-ogsc=%22rgb(34, 34, 34)%22><strong>Abstract:<\/strong> The development of large language models, leading up to OpenAI\u2019s GPT4 has caused another AI revolution. These models are being envisaged as foundation models \u2013 i.e., they are a strong starting point for all aspects of AI, including language, knowledge, reasoning and decision making. However, the strongest models are only available through an API, so the standard fine-tuning paradigm is not applicable to them. In this talk, I describe our initial experiments that assess the extent to which the current best LLMs hold promise to be foundation models. I also explore supervised settings, and find that workflows that can use LLMs along with trained models obtain best performance. Finally, I argue that workflows which include LLMs as components will be quite useful, necessitating optimization approaches for obtaining strong cost-quality tradeoffs.<\/span><\/p>\n<p><span style=%22font-weight: 400;%22><strong>Title:<span> <\/span><\/strong><span>Towards transforming the landscape of Indian language technology<\/span><strong><\/strong><br aria-hidden=%22true%22 \/><\/span><\/p>\n<p><span style=%22font-weight: 400;%22><strong>Speaker:<\/strong> <a href=%22https:\/\/www.cse.iitm.ac.in\/~miteshk\/%22>Prof. <span>Mitesh Khapra, IITM<\/span><\/a><\/span><\/p>\n<p><span style=%22font-weight: 400;%22><span><img src=%22https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/10\/mitesh-300x300.jpg%22 width=%22300%22 height=%22300%22 alt=%22%22 class=%22wp-image-4052 alignnone size-medium%22 \/><\/span><\/span><\/p>\n<p><strong>Abstract:<\/strong><span> In this talk, I will reflect on our journey towards transforming the landscape of Indian language technology. I will delve on our engineering-heavy approach in addressing the initial scarcity of data for Indian languages, while gradually establishing the necessary human resources to gather high-quality data on a larger scale through Bhashini. The objective is to share our insights into developing high quality open-source technology for Indian languages. This involves curating extensive data from the internet, constructing multilingual models for transfer learning, and crafting high-quality datasets for fine-tuning and evaluation. I will then transition into how our experiences can benefit the broader AI community, particularly as India aspires to create Language Model Models (LLMs) for Indic languages.<\/span><br aria-hidden=%22true%22 \/><br aria-hidden=%22true%22 \/><strong>Bio<\/strong><span>: Mitesh M. Khapra is an Associate Professor in the Department of Computer Science and Engineering at IIT Madras. He heads the AI4Bharat Research Lab at IIT Madras which focuses on building datasets, tools, models and applications for Indian languages. His research work has been published in several top conferences and journals including TACL, ACL, NeurIPS, TALLIP, EMNLP, EACL, AAAI, etc. He has also served as Area Chair or Senior PC member in top conferences such as ICLR and AAAI. Prior to IIT Madras, he was a Researcher at IBM Research India for four and a half years, where he worked on several interesting problems in the areas of Statistical Machine Translation, Cross Language Learning, Multimodal Learning, Argument Mining and Deep Learning. Prior to IBM, he completed his PhD and M.Tech from IIT Bombay in Jan 2012 and July 2008 respectively. His PhD thesis dealt with the important problem of reusing resources for multilingual computation. During his PhD he was a recipient of the IBM PhD Fellowship (2011) and the Microsoft Rising Star Award (2011). He is also a recipient of the Google Faculty Research Award (2018), the IITM Young Faculty Recognition Award (2019), the Prof. B. Yegnanarayana Award for Excellence in Research and Teaching (2020) and the Srimathi Marti Annapurna Gurunath Award for Excellence in Teaching (2022).<\/span><\/p>\n<\/p>\n<p>&#8221; content_phone=&#8221;<\/p>\n<h2><b>Keynote Talks<\/b><\/h2>\n<p data-ogsb=%22white%22><span data-ogsc=%22rgb(34, 34, 34)%22><strong>Title:<\/strong> My Experiments with Large Language Models<\/span><\/p>\n<p data-ogsb=%22white%22><span data-ogsc=%22rgb(34, 34, 34)%22><strong>Speaker: <a href=%22https:\/\/www.cse.iitd.ac.in\/~mausam\/%22><span style=%22font-weight: 400;%22>Prof. Mausam, IITD.<\/span><\/a><\/strong><\/span><span data-ogsc=%22rgb(34, 34, 34)%22><strong><span style=%22font-weight: 400;%22><\/span><\/strong><\/span><\/p>\n<p data-ogsb=%22white%22><span data-ogsc=%22rgb(34, 34, 34)%22><strong><span style=%22font-weight: 400;%22><img src=%22https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/10\/mausam-head_cropped-300x300.jpg%22 width=%22300%22 height=%22300%22 alt=%22%22 class=%22wp-image-4051 alignnone size-medium%22 \/><\/span><\/strong><\/span><\/p>\n<p data-ogsb=%22white%22><span data-ogsc=%22rgb(34, 34, 34)%22><strong>Abstract:<\/strong> The development of large language models, leading up to OpenAI\u2019s GPT4 has caused another AI revolution. These models are being envisaged as foundation models \u2013 i.e., they are a strong starting point for all aspects of AI, including language, knowledge, reasoning and decision making. However, the strongest models are only available through an API, so the standard fine-tuning paradigm is not applicable to them. In this talk, I describe our initial experiments that assess the extent to which the current best LLMs hold promise to be foundation models. I also explore supervised settings, and find that workflows that can use LLMs along with trained models obtain best performance. Finally, I argue that workflows which include LLMs as components will be quite useful, necessitating optimization approaches for obtaining strong cost-quality tradeoffs.<\/span><\/p>\n<p><span style=%22font-weight: 400;%22><strong>Title:<span> <\/span><\/strong><span>Towards transforming the landscape of Indian language technology<\/span><strong><\/strong><br aria-hidden=%22true%22 \/><\/span><\/p>\n<p><span style=%22font-weight: 400;%22><strong>Speaker:<\/strong> <a href=%22https:\/\/www.cse.iitm.ac.in\/~miteshk\/%22>Prof. <span>Mitesh Khapra, IITM<\/span><\/a><\/span><\/p>\n<p><span style=%22font-weight: 400;%22><span><img src=%22https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/10\/mitesh-300x300.jpg%22 width=%22300%22 height=%22300%22 alt=%22%22 class=%22wp-image-4052 alignnone size-medium%22 \/><\/span><\/span><\/p>\n<p><strong>Abstract:<\/strong><span> In this talk, I will reflect on our journey towards transforming the landscape of Indian language technology. I will delve on our engineering-heavy approach in addressing the initial scarcity of data for Indian languages, while gradually establishing the necessary human resources to gather high-quality data on a larger scale through Bhashini. The objective is to share our insights into developing high quality open-source technology for Indian languages. This involves curating extensive data from the internet, constructing multilingual models for transfer learning, and crafting high-quality datasets for fine-tuning and evaluation. I will then transition into how our experiences can benefit the broader AI community, particularly as India aspires to create Language Model Models (LLMs) for Indic languages.<\/span><br aria-hidden=%22true%22 \/><br aria-hidden=%22true%22 \/><strong>Bio<\/strong><span>: Mitesh M. Khapra is an Associate Professor in the Department of Computer Science and Engineering at IIT Madras. He heads the AI4Bharat Research Lab at IIT Madras which focuses on building datasets, tools, models and applications for Indian languages. His research work has been published in several top conferences and journals including TACL, ACL, NeurIPS, TALLIP, EMNLP, EACL, AAAI, etc. He has also served as Area Chair or Senior PC member in top conferences such as ICLR and AAAI. Prior to IIT Madras, he was a Researcher at IBM Research India for four and a half years, where he worked on several interesting problems in the areas of Statistical Machine Translation, Cross Language Learning, Multimodal Learning, Argument Mining and Deep Learning. Prior to IBM, he completed his PhD and M.Tech from IIT Bombay in Jan 2012 and July 2008 respectively. His PhD thesis dealt with the important problem of reusing resources for multilingual computation. During his PhD he was a recipient of the IBM PhD Fellowship (2011) and the Microsoft Rising Star Award (2011). He is also a recipient of the Google Faculty Research Award (2018), the IITM Young Faculty Recognition Award (2019), the Prof. B. Yegnanarayana Award for Excellence in Research and Teaching (2020) and the Srimathi Marti Annapurna Gurunath Award for Excellence in Teaching (2022).<\/span><\/p>\n<\/p>\n<p>&#8221; content_last_edited=&#8221;on|desktop&#8221; disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h2><\/h2>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_text][et_pb_accordion icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; background_enable_color=&#8221;off&#8221; custom_margin=&#8221;||14px|||&#8221; animation_direction=&#8221;bottom&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; disabled=&#8221;on&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Important note to authors about the new ACM open access publishing model&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#f7f7f7&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>ACM has introduced a new open access publishing model for the International Conference Proceedings Series (ICPS). Authors based at institutions that are not yet part of the <a href=\"https:\/\/libraries.acm.org\/acmopen\/open-participants\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/libraries.acm.org\/acmopen\/open-participants&amp;source=gmail&amp;ust=1721965018718000&amp;usg=AOvVaw08K2raXgm5uGBK4NAjqzgG\" rel=\"noopener\">ACM Open program<\/a> and do not qualify for a waiver will be required to pay an article processing charge (APC) to publish their ICPS article in the ACM Digital Library. To determine whether or not an APC will be applicable to your article, please follow the detailed guidance here: <a href=\"https:\/\/www.acm.org\/publications\/icps\/author-guidance\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.acm.org\/publications\/icps\/author-guidance&amp;source=gmail&amp;ust=1721965018718000&amp;usg=AOvVaw3yW8py6g90M47RyskouKNT\" rel=\"noopener\">https:\/\/www.acm.org\/<wbr \/>publications\/icps\/author-<wbr \/>guidance<\/a>.<\/p>\n<p>Further information may be found on the ACM website, as follows:<\/p>\n<p>Full details of the new ICPS publishing model: <a href=\"https:\/\/www.acm.org\/publications\/icps\/faq\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.acm.org\/publications\/icps\/faq&amp;source=gmail&amp;ust=1721965018718000&amp;usg=AOvVaw1HKKXkd4ki_HfyAVLEGg8c\" rel=\"noopener\">https:\/\/www.acm.org\/<wbr \/>publications\/icps\/faq<\/a><br \/>Full details of the ACM Open program: <a href=\"https:\/\/www.acm.org\/publications\/openaccess\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.acm.org\/publications\/openaccess&amp;source=gmail&amp;ust=1721965018718000&amp;usg=AOvVaw2yL9XalOCin6I5BV91zRH-\" rel=\"noopener\">https:\/\/www.acm.org\/<wbr \/>publications\/openaccess<\/a><\/p>\n<p>Please direct all questions about the new model to <a href=\"mailto:icps-info@acm.org\" target=\"_blank\" rel=\"noopener\">icps-info@acm.org<\/a>.<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_text disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; disabled=&#8221;on&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><b>Submission Guidelines<\/b><\/p>\n<p><span style=\"font-weight: 400;\">We invite authors to submit original and unpublished research papers (up to 4 pages excluding references). All submissions will undergo a rigorous peer-review process by the program committee. The authors are requested to follow the ACM sigconf template (see <\/span><a href=\"https:\/\/www.overleaf.com\/gallery\/tagged\/acm-official\"><span style=\"font-weight: 400;\">https:\/\/www.overleaf.com\/gallery\/tagged\/acm-official<\/span><\/a><span style=\"font-weight: 400;\">). All accepted papers will be published in the proceedings of AIMLSys 2024.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Submission link: (please select the GenerateAI Workshop AI track).<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><b>Important Dates<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Paper Submission Deadline: 04 Aug 2024<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Notification of Acceptance: 24 Aug 2024<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Camera-Ready Deadline: 21 Sept 2024 (AoE)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Workshop Date: 8 Oct 2024<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Conference Dates: October 8-11, 2024<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Workshop Organization<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The workshop will feature keynote speeches, technical paper and poster sessions, tutorials and possibly panel discussions. A detailed outline of the program would be available on the website shortly. The workshop would also provide ample opportunities for attendees to network with leading experts and well gain hands-on experience on Generative AI from tutorial sessions.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><b>Registration<\/b><\/p>\n<p><span style=\"font-weight: 400;\">At least one author of an accepted paper will need to register for the conference and in case of multiple papers with the same author, co-authors need to register (1 unique registration by one of the authors per paper is required).<\/span>\u00a0<\/p>\n<p>&nbsp;<\/p>\n<p><b>Workshop Venue<\/b><\/p>\n<p>Energy, Coast and Environment Building<b><\/b><\/p>\n<p><span style=\"font-weight: 400;\">Louisiana State University, Baton Rouge, USA<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><b>Contact Information<\/b><\/p>\n<p><span style=\"font-weight: 400;\">For any inquiries regarding the workshop, please feel free to contact the workshop organizers.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><span><\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tutorial &#8211; Agentic AI&nbsp; &#8220;No Code? No Problem. Build Your First AI Agent from Scratch!&#8221; Generative AI in the natural language space is showing tremendous potential in automating various routine jobs. Recent studies have also demonstrated that Generative AI can aid with creative content creation as well. At the center of these innovations are Large [&hellip;]<\/p>\n","protected":false},"author":10,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"<!-- wp:paragraph -->\n<p>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<!-- \/wp:paragraph -->\n\n<!-- wp:quote -->\n<blockquote class=\"wp-block-quote\"><!-- wp:paragraph -->\n<p>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<!-- \/wp:paragraph --><\/blockquote>\n<!-- \/wp:quote -->\n\n<!-- wp:paragraph -->\n<p>...or something like this:<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:quote -->\n<blockquote class=\"wp-block-quote\"><!-- wp:paragraph -->\n<p>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<!-- \/wp:paragraph --><\/blockquote>\n<!-- \/wp:quote -->\n\n<!-- wp:paragraph -->\n<p>As a new WordPress user, you should go to <a href=\"https:\/\/www.aimlsystems.org\/2023\/wp-admin\/\">your dashboard<\/a> to delete this page and create new pages for your content. Have fun!<\/p>\n<!-- \/wp:paragraph -->","_et_gb_content_width":"","footnotes":""},"class_list":["post-7301","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.aimlsystems.org\/2026\/wp-json\/wp\/v2\/pages\/7301","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.aimlsystems.org\/2026\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.aimlsystems.org\/2026\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.aimlsystems.org\/2026\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aimlsystems.org\/2026\/wp-json\/wp\/v2\/comments?post=7301"}],"version-history":[{"count":7,"href":"https:\/\/www.aimlsystems.org\/2026\/wp-json\/wp\/v2\/pages\/7301\/revisions"}],"predecessor-version":[{"id":7354,"href":"https:\/\/www.aimlsystems.org\/2026\/wp-json\/wp\/v2\/pages\/7301\/revisions\/7354"}],"wp:attachment":[{"href":"https:\/\/www.aimlsystems.org\/2026\/wp-json\/wp\/v2\/media?parent=7301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}