{"id":5745,"date":"2024-09-12T18:57:04","date_gmt":"2024-09-12T13:27:04","guid":{"rendered":"https:\/\/www.aimlsystems.org\/2024\/?page_id=5745"},"modified":"2025-10-09T10:19:02","modified_gmt":"2025-10-09T04:49:02","slug":"invited-speakers","status":"publish","type":"page","link":"https:\/\/www.aimlsystems.org\/2025\/invited-speakers\/","title":{"rendered":"Invited Speakers"},"content":{"rendered":"

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Invited Speakers<\/h2>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.19.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/ai-icon-03.png” title_text=”ai-icon-03″ align=”center” disabled_on=”on|on|off” _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”6px|||||” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Features” module_id=”about” _builder_version=”4.21.0″ _module_preset=”default” background_color=”#dbdbdb” background_image=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/rm380-10.jpg” background_blend=”overlay” custom_padding=”3.9%||||false|false” use_background_color_gradient_phone=”on” background_color_gradient_stops_phone=”#001528 0%|rgba(255, 255, 255, 0) 10%|rgba(255,255,255,0) 70%|#0f0122 100%” collapsed=”on” global_colors_info=”{}”][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.19.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”2_3″ _builder_version=”4.19.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.25.1″ _module_preset=”7f63b212-a10a-4d30-afa2-e478a747ca88″ custom_margin=”||10px||false|false” header_2_font_size_phone=”33px” custom_css_free_form=”selector h2{color:white}” global_colors_info=”{}”]<\/p>\n

Invited Speakers for Research Track<\/span><\/h2>\n

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Invited Speakers<\/h2>\n

[\/et_pb_text][et_pb_image src=”https:\/\/www.aimlsystems.org\/2025\/wp-content\/uploads\/2025\/07\/Supratik.webp” title_text=”Supratik” align=”center” _builder_version=”4.25.1″ _module_preset=”default” max_width=”200px” custom_margin=”||15px|||” filter_saturate=”0%” animation_style=”slide” border_radii=”on|115px|115px|115px|115px” border_color_all=”#FFFFFF” box_shadow_style=”preset2″ global_colors_info=”{}” transform_styles__hover_enabled=”on|hover” transform_scale__hover_enabled=”on|hover” transform_translate__hover_enabled=”on|hover” transform_rotate__hover_enabled=”on|hover” transform_skew__hover_enabled=”on|hover” transform_origin__hover_enabled=”on|hover” transform_scale__hover=”104%|104%” filter_saturate__hover_enabled=”on|hover” filter_saturate__hover=”100%” border_width_all__hover_enabled=”on|hover” border_width_all__hover=”1px” border_radii__hover_enabled=”on|hover” border_radii__hover=”on|115px|115px|115px|115px”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.25.1″ _module_preset=”25d2b0d8-2373-4ae8-9188-0ef4b1bb77f4″ text_text_color=”#212A4F” header_4_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ header_4_font_size=”20px” custom_margin=”||15px|||” global_colors_info=”{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22header_4_text_color%22%93}”]<\/p>\n

Supratik Mukhopadhyay<\/a><\/h4>\n

Louisiana State University<\/p>\n

[\/et_pb_text][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” disabled_on=”off|off|off” _builder_version=”4.25.1″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Title” open=”on” _builder_version=”4.25.1″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

AI for Science: Revolutionizing Biology, Healthcare, and Environment<\/span><\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” disabled_on=”off|off|off” _builder_version=”4.25.1″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Abstract” open=”on” _builder_version=”4.25.1″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Despite advances in vaccines and antibiotics, millions of people continue to suffer from diseases without effective cures. The rise of antibiotic resistance, fueled by widespread and prolonged use, has outpaced the development of new antibacterial agents, which has slowed to historically low levels. This crisis underscores the urgent need for innovative drug discovery strategies. Traditional methods are slow, costly, and inefficient. To address these challenges, we have developed DeepDrug, an AI-powered pipeline that leverages large-scale biological and chemical datasets to design and identify novel therapeutics. By accelerating discovery and reducing costs, DeepDrug illustrates how AI can transform biology with an impact comparable to the Newtonian revolution in physics.<\/u><\/u><\/span><\/p>\n

In parallel, humanity faces existential threats from climate change and environmental degradation\u2014manifesting as extreme weather, biodiversity loss, pollution, declining food security, and heightened risks of pandemics. These complex, interconnected challenges demand scalable, adaptive solutions. We demonstrate how AI can serve as a powerful tool for environmental resilience by enabling data-driven sensing, forecasting, decision-making, and feedback. Our recent work spans carbon mapping, hypoxia prediction, climate-smart agriculture, biodiversity preservation, wildfire detection, and energy-efficient design of the built environment.<\/u><\/u><\/span><\/p>\n

Together, these efforts highlight a unifying vision: AI as a scientific force multiplier, accelerating discovery and decision-making to advance both human health and planetary sustainability.<\/span><\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” disabled_on=”off|off|off” _builder_version=”4.25.1″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Bio” open=”on” _builder_version=”4.25.1″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Supratik Mukhopadhyay is a Professor in the Department of Computer Science and Engineering at Louisiana State University (LSU). His research spans multiple areas including Artificial Intelligence, Machine Learning, Formal Methods, and Computational Biology. He has made significant contributions to applying AI and formal verification in diverse domains such as cybersecurity, healthcare, smart infrastructure, and environmental monitoring.<\/p>\n

Prior to joining LSU, Dr. Mukhopadhyay held research and faculty positions at prestigious institutions and has collaborated extensively with national laboratories and industry. He has published over 100 peer-reviewed articles in top-tier journals and conferences and has received several federal research grants from agencies including NSF, DOE, and NASA.<\/p>\n

Supratik received his Ph.D. in Computer Science from the Indian Statistical Institute. He is actively involved in interdisciplinary research and innovation and serves on editorial boards and program committees of leading conferences in AI and systems.<\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.21.0″ _module_preset=”default” custom_padding=”27px||43px|||” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.aimlsystems.org\/2025\/wp-content\/uploads\/2025\/07\/suparna.jpg” title_text=”suparna” align=”center” _builder_version=”4.25.1″ _module_preset=”default” max_width=”200px” custom_margin=”||15px|||” filter_saturate=”0%” animation_style=”slide” border_radii=”on|115px|115px|115px|115px” border_color_all=”#FFFFFF” box_shadow_style=”preset2″ global_colors_info=”{}” transform_styles__hover_enabled=”on|hover” transform_scale__hover_enabled=”on|hover” transform_translate__hover_enabled=”on|hover” transform_rotate__hover_enabled=”on|hover” transform_skew__hover_enabled=”on|hover” transform_origin__hover_enabled=”on|hover” transform_scale__hover=”104%|104%” filter_saturate__hover_enabled=”on|hover” filter_saturate__hover=”100%” border_width_all__hover_enabled=”on|hover” border_width_all__hover=”1px” border_radii__hover_enabled=”on|hover” border_radii__hover=”on|115px|115px|115px|115px”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.25.1″ _module_preset=”25d2b0d8-2373-4ae8-9188-0ef4b1bb77f4″ text_text_color=”#212A4F” header_4_text_color=”gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68″ header_4_font_size=”20px” custom_margin=”||15px|||” global_colors_info=”{%22gcid-5fa2e3a6-d98c-4022-811a-b5fb6fa40d68%22:%91%22header_4_text_color%22%93}”]<\/p>\n

Suparna Bhattacharya<\/a><\/h4>\n

HPE Fellow at AI Research Lab, Hewlett Packard Labs<\/p>\n

[\/et_pb_text][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” disabled_on=”off|off|off” _builder_version=”4.25.1″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” hover_enabled=”0″ border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}” sticky_enabled=”0″][et_pb_accordion_item title=”Title” open=”on” _builder_version=”4.25.1″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

The Foundation Model OS: A Self-Evolving Layer for Agentic AI<\/b><\/p>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” disabled_on=”off|off|off” _builder_version=”4.25.1″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” hover_enabled=”0″ border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}” sticky_enabled=”0″][et_pb_accordion_item title=”Abstract” open=”on” _builder_version=”4.25.1″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Generative AI-based Foundation Models (FMs), such as large language models (LLMs), large reasoning models, and diffusion models are profoundly transforming the paradigm of how AI is applied across a broad spectrum of use cases. FMs provide a powerful substrate for agentic AI in enterprise applications and scientific discovery. However, to fully harness their potential, these models must be continuously augmented with accurate knowledge, their reasoning verified to ensure compliance with context specific expectations for safety and reliability, and their execution optimized to meet resource constraints specific to the desired use case.<\/div>\n
In this talk, we explore the potential of mechanisms inspired by operating systems to address these challenges and construct a dynamic, evolving “virtual” FM substrate. By drawing on parallels with concepts such as virtualization and adaptability in OS design, we demonstrate how these techniques can enhance the flexibility and capabilities of FMs\u2014much like the human brain employs different modes and speeds of thinking to adapt to new situations and information. Our aim is to initiate deeper dialogue between the machine learning and systems communities, guiding how such capabilities can shape the future design and evolution of FMs.<\/div>\n

[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=”#f7f7f7″ icon_color=”#0C71C3″ use_icon_font_size=”on” disabled_on=”off|off|off” _builder_version=”4.25.1″ _module_preset=”default” custom_margin=”||14px|||” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”18%” border_radii=”on|30px|30px|30px|30px” global_colors_info=”{}”][et_pb_accordion_item title=”Bio” open=”on” _builder_version=”4.25.1″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Suparna Bhattacharya is an HPE Fellow in the AI research lab at Hewlett Packard Labs where she currently focuses on data centric trustworthy AI. She has 30+ years of experience in systems software development and research (10 years at HPE preceded by 21 years at IBM) including several enjoyable years of open source contributions to the Linux kernel. Over the last decade Suparna has developed a passion for blending insights from diverse technical domains in innovations that span technology boundaries e.g. rethinking systems software stacks in the era of AI and IoT. For example, during her stint in HPE Storage, she advanced the use of analytics aware optimization and persistent memory optimizations in storage and hyper-converged systems for containers, artificial intelligence\/machine learning and IoT edge to core data services. Suparna is an IEEE Fellow, a Fellow of the India National Academy of Engineering, an ACM India eminent speaker and has served on program committees for ASPLOS, OOPSLA, MASCOTS, ECOOP, HotStorage and USENIX FAST. She co-authored a book on Resource Proportional Software Design for Emerging Systems, (with Doug Voigt and Prof K. Gopinath) published by CRC Press (2020). Suparna holds a B.Tech from IIT Kharagpur (1993) and a (late-in-life) PhD with a best thesis award from the Indian Institute of Science (2013).<\/p>\n