{"id":6132,"date":"2025-03-30T20:23:24","date_gmt":"2025-03-30T14:53:24","guid":{"rendered":"https:\/\/www.aimlsystems.org\/2025\/?page_id=6132"},"modified":"2025-10-08T00:54:10","modified_gmt":"2025-10-07T19:24:10","slug":"workshop-agentic-ai","status":"publish","type":"page","link":"https:\/\/www.aimlsystems.org\/2026\/workshop-agentic-ai\/","title":{"rendered":"Workshop-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; 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20 \" stroke-width = 3 stroke = \"white\" fill = \"none\"><\/polyline><!-- [et_pb_line_break_holder] --><\/svg><!-- [et_pb_line_break_holder] --><input placeholder = \"Search for info about AIMLS 2024 Conference\"  onkeyup = \"searchReq()\" id  = \"search\" type = \"search\" \/><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><\/p>\n<div style = \"position:relative\"> <!-- [et_pb_line_break_holder] -->    <!-- [et_pb_line_break_holder] --><\/p>\n<div id = \"options\"><\/div>\n<p><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><\/div>\n<p><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><\/html><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->[\/et_pb_code][\/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;1.9%||||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 _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; min_height=&#8221;474.9px&#8221; custom_padding=&#8221;45px|||||&#8221; locked=&#8221;off&#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 _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; min_height=&#8221;42px&#8221; custom_margin=&#8221;-26px||0px|||&#8221; custom_padding=&#8221;||9px|||&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h2 style=\"text-align: center;\">Schedule<\/h2>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||10px|||&#8221; custom_padding=&#8221;0px||0px|||&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h4 style=\"text-align: center;\">DATE: October 8, 2025<\/h4>\n<h4 style=\"text-align: center;\">Venue: AIML Systems 2025<\/h4>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;||0px|||&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<div class=\"WordSection1\">\n<p>&nbsp;<\/p>\n<table class=\"MsoNormalTable\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" style=\"border-collapse: collapse; width: 100%; height: 672px;\">\n<tbody>\n<tr style=\"height: 24px;\">\n<td style=\"border: 1pt solid black; background: #6d9eeb; text-align: center; padding: 6px; width: 100.938px; height: 24px;\"><b><span style=\"font-size: 10pt; font-family: 'Arial',sans-serif; color: black;\">Time<\/span><\/b><\/td>\n<td style=\"border: 1pt solid black; background: #6d9eeb; text-align: center; padding: 6px; width: 403.796px; height: 24px;\"><b><span style=\"font-size: 10pt; font-family: 'Arial',sans-serif; color: black;\">Event<\/span><\/b><\/td>\n<\/tr>\n<tr style=\"height: 24px;\">\n<td style=\"border: 1pt solid black; background: #ffefe7; text-align: center; width: 100.938px; height: 24px;\"><strong>9:30 AM \u2013 9:45 AM<\/strong><\/td>\n<td style=\"border: 1pt solid black; background: #ffefe7; width: 403.796px; text-align: left; height: 24px;\"><strong>\u00a0Welcome<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td style=\"border: 1pt solid black; background: #ffefe7; text-align: center; width: 100.938px; height: 48px;\"><strong>9:45 AM \u2013 11:00 AM<\/strong><\/td>\n<td style=\"border: 1pt solid black; background: #ffefe7; width: 403.796px; text-align: left; height: 48px;\"><strong>\u00a0Keynote Speaker \u2013 <\/strong><b>Venkatesan Chakaravarthy (IBM Research)<\/b><br \/><i>\u00a0\u201cAgentic frameworks for enterprise-scale software comprehension\u201d<\/i><\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td colspan=\"2\" style=\"border: 1pt solid black; background: #cccccc; text-align: center; width: 504.734px; height: 48px;\"><b>Tea \/ Coffee Break<\/b><br \/>11:00 AM \u2013 11:30 AM<\/td>\n<\/tr>\n<tr style=\"height: 72px;\">\n<td style=\"border: 1pt solid black; background: #ffefe7; text-align: center; width: 100.938px; height: 72px;\"><strong>11:30 AM\u2013 12:15 PM<\/strong><\/td>\n<td style=\"border: 1pt solid black; background: #ffefe7; width: 403.796px; height: 72px;\"><b>\u00a0Technical Paper Presentations<\/b><br \/>1. Directed Network Modeling with Generative AI for Driver Decision Support<br \/>2. AutoRAG-LoRA: Hallucination-Triggered Knowledge Retuning via Lightweight Adapters<\/td>\n<\/tr>\n<tr style=\"height: 144px;\">\n<td style=\"border: 1pt solid black; background: #ffefe7; text-align: center; width: 100.938px; height: 144px;\"><strong>12:15 PM \u2013 1:00 PM<\/strong><\/td>\n<td style=\"border: 1pt solid black; background: #ffefe7; width: 403.796px; text-align: left; height: 144px;\">\n<p><strong><strong>\u00a0<\/strong><\/strong>Tutorial + Hands-on session <strong>\u201cZero to Production: Building Secure, Scalable<\/strong><strong style=\"font-family: inherit; font-size: inherit;\">MCP Servers and AI\u00a0 \u00a0 \u00a0Agents with\u00a0 Open-Source Templates\u201d<\/strong><\/p>\n<p><a href=\"https:\/\/www.linkedin.com\/in\/tuhinsharma121\/\" target=\"_blank\" rel=\"noopener\">Tuhin Sharma, Red Hat<\/a><br \/><a href=\"https:\/\/www.linkedin.com\/in\/karankraina\/\" target=\"_blank\" rel=\"noopener\">Karan Raina, Red Hat<\/a><a href=\"https:\/\/www.linkedin.com\/in\/karankraina\/\" target=\"_blank\" rel=\"noopener\"><br \/><\/a><a href=\"https:\/\/www.linkedin.com\/in\/karankraina\/\" target=\"_blank\" rel=\"noopener\" style=\"font-size: 14px;\">Soham Dutta, Red Hat<\/a><\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td colspan=\"2\" style=\"border: 1pt solid black; background: #cccccc; text-align: center; width: 504.734px; height: 48px;\"><b>Lunch Break<\/b><br \/>1:00 PM \u2013 2:00 PM<\/td>\n<\/tr>\n<tr style=\"height: 24px;\">\n<td style=\"border: 1pt solid black; background: #ffefe7; text-align: center; width: 100.938px; height: 24px;\"><strong>2:00 PM \u2013 3:00 PM<\/strong><\/td>\n<td style=\"border: 1pt solid black; background: #ffefe7; width: 403.796px; text-align: left; height: 24px;\">\n<p><strong>\u00a0Keynote Speaker\u2014<\/strong>Kuldeep\u00a0Yadav, SHL<\/p>\n<p><strong>\u00a0\u201cFrom Vision to Value: Design Patterns for Agentic AI in Production\u201d<\/strong><\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 120px;\">\n<td style=\"border: 1pt solid black; background: #ffefe7; text-align: center; width: 100.938px; height: 120px;\"><strong>3:00 PM \u2013 3:20 PM<\/strong><\/td>\n<td style=\"border: 1pt solid black; background: #ffefe7; width: 403.796px; height: 120px;\"><b>\u00a0Technical Paper Presentations<\/b><br \/>2. Agentic Summarization of Large COBOL Programs Beyond LLM Context Limits Using Call Graph-Based Grouping<\/td>\n<\/tr>\n<tr style=\"height: 120px;\">\n<td style=\"border: 1pt solid black; background: #ffefe7; text-align: center; width: 100.938px; height: 120px;\"><strong>3:20 PM \u2013 4:00 PM<\/strong><\/td>\n<td style=\"border: 1pt solid black; background: #ffefe7; width: 403.796px; height: 120px;\"><b>Invited speaker &#8211; <\/b>Vijay Aski, Partner Director, AI Platform Microsoft<\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td colspan=\"2\" style=\"border: 1pt solid black; background: #cccccc; text-align: center; width: 504.734px; height: 48px;\"><b>Tea \/ Coffee Break <\/b><br \/>4:00 PM \u2013 4:30 PM<\/td>\n<\/tr>\n<tr style=\"height: 72px;\">\n<td style=\"border: 1pt solid black; background: #ffefe7; text-align: center; width: 100.938px; height: 72px;\"><strong>4:30 PM \u2013 5:30 PM<\/strong><\/td>\n<td style=\"border: 1pt solid black; background: #ffefe7; width: 403.796px; height: 72px;\">\n<p><b>\u00a0Panel Discussion \u2013 \u201cAgents in the real world\u201d<\/b><\/p>\n<ul>\n<li>Niyati Chhaya, Hyperbots<\/li>\n<li>Eshan Jain, Walmart<\/li>\n<li>Kuldeep Yadav, SHL<\/li>\n<li><span>Rekha Singhal, TCS Paceport<\/span><\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][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_text disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;|600|||||||&#8221; text_text_color=&#8221;#E02B20&#8243; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">Paper Submission Deadline: 7 August 2025, 11:59 pm AOE.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>The recent past has seen significant progress in LLM-based agents automating many software-engineering tasks such as code generation, debugging, test-case generation, code reviews, and program repair. While agents for software engineering have shown a great deal of promise, considerable work is still required\u2014particularly around reliability and correctness\u2014before they can be adopted at enterprise scale. Equally critical are the systems-level challenges for running these agents in production, where strict service-level objectives for end-to-end latency, sustainable throughput, and cost efficiency must be met. The workshop will address both functional quality and non-functional performance guarantees, paving the way for robust, cost-aware, and scalable deployment of agentic solutions. This workshop will focus on the use of software agents across the entire software-development lifecycle.<\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\"><span><\/span><\/span><\/p>\n<p style=\"font-weight: 400;\"><strong>Topics of Interest:<\/strong><\/p>\n<p style=\"font-weight: 400;\">We solicit submissions describing original and unpublished results focussed on leveraging software agents for software engineering tasks. Topics of interest include but are not limited to:<\/p>\n<p style=\"font-weight: 400;\">1.\u2060<span>\u00a0<\/span>\u2060Novel Applications of software agents in different stages of the software lifecycle.<\/p>\n<p style=\"font-weight: 400;\">2.\u2060<span>\u00a0<\/span>\u2060Frameworks for building software agents.<\/p>\n<p style=\"font-weight: 400;\">3.\u2060<span>\u00a0<\/span>\u2060Validation of code generated by software agents.<\/p>\n<p style=\"font-weight: 400;\">4.\u2060<span>\u00a0<\/span>\u2060Novel metrics for benchmarking software agents.<\/p>\n<p style=\"font-weight: 400;\">5.\u2060<span>\u00a0<\/span>\u2060Case studies of applying software agents for software development and maintenance.<\/p>\n<p style=\"font-weight: 400;\">6.\u2060<span>\u00a0<\/span>\u2060Tools and techniques for tailoring software agents for specific code bases.<\/p>\n<p style=\"font-weight: 400;\">7.\u2060<span>\u00a0<\/span>\u2060Productivity gains from software agents.<\/p>\n<p style=\"font-weight: 400;\">8.\u2060<span>\u00a0<\/span>\u2060Security and Performance characteristics of code generated by software agents.<\/p>\n<p style=\"font-weight: 400;\">9.\u2060<span>\u00a0<\/span>\u2060(Post) Training models for<span>\u00a0<\/span><span>agentic<\/span><span>\u00a0<\/span>tasks.<\/p>\n<p style=\"font-weight: 400;\">10. Architectures and optimizations for low-latency, high-throughput agentic pipelines.<\/p>\n<p style=\"font-weight: 400;\">11. Cost-aware scheduling, resource management, and scaling strategies for LLM-powered development agents.<\/p>\n<ul><\/ul>\n<p>[\/et_pb_text][\/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<div class=\"s10\"><strong><span class=\"s5\">\u2022 Paper Submission Deadline: 7th Aug 2025<\/span><\/strong><\/div>\n<div class=\"s10\"><strong><span class=\"s5\">\u2022 Notification of Acceptance: 7th Sept 2025<\/span><\/strong><\/div>\n<div class=\"s10\"><strong><span class=\"s5\">\u2022 Camera-Ready Deadline: 15 Sept 2025 (AoE)<\/span><\/strong><\/div>\n<div class=\"s10\"><strong><span class=\"s5\" style=\"font-size: 14px;\">\u2022 <\/span><span style=\"font-size: 14px;\">Conference dates: October <\/span><span style=\"font-size: 14px;\">8<\/span><span style=\"font-size: 14px;\">-1<\/span><span style=\"font-size: 14px;\">1<\/span><span style=\"font-size: 14px;\">, 202<\/span><span style=\"font-size: 14px;\">4<\/span><\/strong><\/div>\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;Workshop 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\/palani-kodeswaran-3b55153\/\" target=\"_blank\" rel=\"noopener\">Palanivel Kodeswaran <\/a>,<span> Walmart<\/span><\/li>\n<li><span><a href=\"https:\/\/www.tcs.com\/insights\/authors\/shruti-kunde\" target=\"_blank\" rel=\"noopener\">Shruti Kunde<\/a>, TCS Research<\/span><\/li>\n<\/ul>\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;Technical Program Committee&#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\/palani-kodeswaran-3b55153\/\" target=\"_blank\" rel=\"noopener\"><\/a><span><a href=\"https:\/\/profiles.uts.edu.au\/meeralakshmi.radhakrishnan\" target=\"_blank\" rel=\"noopener\">Meera Radhakrishnan<\/a>, University of Technology, Sydney<\/span><\/li>\n<li><span><a href=\"https:\/\/www.linkedin.com\/in\/anshuvedajain\/\" target=\"_blank\" rel=\"noopener\"> Anshu Veda,<\/a> Poshmark, USA<\/span><\/li>\n<li><span><a href=\"https:\/\/scholar.google.com\/citations?user=V-DXftkAAAAJ&amp;hl=en\" target=\"_blank\" rel=\"noopener\">Madhu Kumar<\/a>, NIT Calicut<\/span><\/li>\n<li><span><a href=\"https:\/\/www.linkedin.com\/in\/sayandeep-sen-7752111b\/?original_referer=https%3A%2F%2Fin%2Esearch%2Eyahoo%2Ecom%2F&amp;originalSubdomain=in\" target=\"_blank\" rel=\"noopener\">Sayandeep Sen<\/a>, IBM Research<\/span><\/li>\n<li><span><a href=\"https:\/\/www.linkedin.com\/in\/atrimandal\/?originalSubdomain=in\" target=\"_blank\" rel=\"noopener\">Atri Mandal<\/a>, Oracle<br \/><\/span><\/li>\n<li><span><a href=\"https:\/\/dl.acm.org\/profile\/99661362405\" target=\"_blank\" rel=\"noopener\">Venkat Chakravarthy<\/a>, IBM Research<\/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;Submission Instructions&#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>Papers should be at most 4 pages, including title, abstract, figures and results, but excluding references, and not published or under review elsewhere. Papers should be prepared as per IEEE conference proceedings format. Please submit your papers through Microsoft CMT.<br \/><strong><span style=\"color: #ff0000;\">All accepted workshop full papers will be included in the IEEE proceedings<\/span><\/strong>. At least one author of each accepted paper must register for the conference and present the paper. In addition, no-shows of accepted papers at the workshop will result in those papers NOT being included in the proceedings.<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/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;27px||0px|||&#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 disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;15px|||||&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3>Keynote Speakers<\/h3>\n<hr>\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;off|off|off&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; min_height=&#8221;735.8px&#8221; custom_padding=&#8221;27px||27px|||&#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\/2025\/wp-content\/uploads\/2025\/09\/KuldeepYadav.jpg&#8221; title_text=&#8221;KuldeepYadav&#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 class=\"top-card-layout__title font-sans text-lg papabear:text-xl font-bold leading-open text-color-text mb-0\"><span><a href=\"https:\/\/www.linkedin.com\/in\/kyadav \" target=\"_blank\" rel=\"noopener\">Kuldeep Yadav<\/a><\/span><\/h4>\n<p><span>SHL, India<\/span><\/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>From Vision to Value: Design Patterns for Agentic AI in Production<\/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;on|on|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; disabled=&#8221;on&#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 data-start=\"78\" data-end=\"430\"><strong data-start=\"78\" data-end=\"101\">Kuldeep Yadav<\/strong> has extensive work experience across various roles and companies. He started his career as Senior Vice President of AI &amp; Labs at SHL in 2020, where he focused on fair and equitable hiring through AI-powered assessments and platforms. Prior to this, he worked as the Director of SHL Labs and Director of AI at the same company.<\/p>\n<p data-start=\"432\" data-end=\"801\">Before joining SHL, he served as the Chief Technology Officer (CTO) and Founding Member &amp; Head of AI at VideoKen from 2017 to 2020. At VideoKen, he utilized AI\/ML techniques to enhance the consumption and engagement of informational videos. He also played a key role in the development of VideoKen&#8217;s patented technology and received the Nasscom AI Game Changer Award.<\/p>\n<p data-start=\"803\" data-end=\"1284\">Throughout his career, he gained valuable experience through research internships at Nokia Research Center and Microsoft Research India. He also participated in the Microsoft Research India Summer School. His educational background includes a <strong data-start=\"1046\" data-end=\"1092\">B.Tech in Computer Science and Engineering<\/strong> from Sikkim Manipal Institute of Technology (2005\u20132009) and a <strong data-start=\"1155\" data-end=\"1201\">PhD in Computer Science (Mobile Computing)<\/strong> from Indraprastha Institute of Information Technology (IIIT), Delhi (2009\u20132013).<\/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;off|off|off&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; min_height=&#8221;460.6px&#8221; custom_padding=&#8221;13px||43px|||&#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\/2025\/wp-content\/uploads\/2025\/09\/Venkatesan.jpg&#8221; title_text=&#8221;Venkatesan&#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 class=\"top-card-layout__title font-sans text-lg papabear:text-xl font-bold leading-open text-color-text mb-0\"><a href=\"https:\/\/in.linkedin.com\/in\/venkatesan-chakaravarthy-01024b4\" target=\"_blank\" rel=\"noopener\"><span>Venkatesan Chakaravarthy<\/span><\/a><\/h4>\n<p><span>Researcher at IBM India Research Lab<\/span><\/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>Agentic frameworks for enterprise-scale software comprehension<\/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><strong data-start=\"90\" data-end=\"121\">Venkatesan Chakaravarthy<\/strong> received his PhD in Computer Science from the University of Wisconsin\u2013Madison. He is a researcher at IBM Research\u2014India, with prior studies at Anna University and IIT Chennai. His current research focuses on high-performance algorithms for tensor analysis and graph-theoretic problems, with broader interests in approximation algorithms and computational complexity theory.<\/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;off|off|off&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; min_height=&#8221;460.6px&#8221; custom_padding=&#8221;13px||43px|||&#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\/2025\/wp-content\/uploads\/2025\/10\/Vijay-aski.jpg&#8221; title_text=&#8221;Vijay aski&#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 class=\"top-card-layout__title font-sans text-lg papabear:text-xl font-bold leading-open text-color-text mb-0\"><a href=\"https:\/\/scholar.google.com\/citations?user=aPQMUEAAAAAJ&amp;hl=en\" target=\"_blank\" rel=\"noopener\"><span>Vijay Aski<\/span><\/a><\/h4>\n<p>Microsoft, 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><span>Agentic AI at Scale: Unlocking Enterprise Innovation<\/span><\/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><span><strong>Vijay Aski<\/strong> is Partner Director of Engineering at Microsoft, where he leads the AI Platform that powers Azure\u2019s generative AI capabilities. He oversees engineering for the GenAI Model Training and Fine-Tuning Platform, Model Customization for AOAI and OSS Models, Foundry Model Catalog, and Speech and AI Services, delivering enterprise-ready solutions built on Azure OpenAI and open-source models.<\/span><\/p>\n<p><span>Over his 20+ years at Microsoft, Vijay has built and scaled distributed systems that support multi-billion-dollar businesses. As Principal Group Engineering Manager, he drove core Bing Ads engineering, advancing campaign management, fraud detection, authentication, and disaster recovery systems for a $6B platform processing billions of daily transactions. Earlier, he played a key role in developing Microsoft\u2019s System Center products, shipping multiple enterprise solutions.<\/span><\/p>\n<p><span>With deep expertise in AI platforms, large-scale distributed systems, and enterprise cloud services, Vijay continues to shape Microsoft\u2019s journey in defining the future of AI innovation for global customers.<\/span><\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/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;26px||43px|||&#8221; locked=&#8221;off&#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 _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;15px|||||&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3>Panel Discussion<\/h3>\n<hr \/>\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; hover_enabled=&#8221;0&#8243; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;][et_pb_accordion_item title=&#8221;Title : Agents in the real world&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;]<\/p>\n<p><strong>Coordinators:<\/strong><\/p>\n<ul>\n<li>Niyati Chhaya (Hyperbots)<\/li>\n<li>Eshan Jain (Walmart)<\/li>\n<li>Kuldeep Yadav (SHL)<\/li>\n<li><span>Rekha Singhal (TCS)<\/span><\/li>\n<\/ul>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/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; 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<hr>\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;off|off|off&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;15px|||||&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_text][\/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>Agentic AI for Software Engineering\u00a0<\/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. 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