Computational Intelligence and Operations Lab - CIOL

🎉 Huge Congratulations to the CIOL Team! 🎉

We are incredibly proud to announce that our researchers at the Computational Intelligence and Operations Laboratory (CIOL) have been awarded 2nd Place for the 2026 Health Systems Best Track Paper at the IISE Annual Conference & Expo 2026 in Arlington, TX! 🏆

Please join us in congratulating our outstanding team members on this massive achievement. The work was led by Azmine Toushik Wasi , with contributions from Mahfuz Ahmed Anik and MD Shafikul Islam Sohan , and guidance from Manjurul Ahsan! Congratulations to the entire team on this remarkable accomplishment.

Their award-winning paper, "Multimodal Vision-Language Models for Automated and Explainable Postoperative Complication Risk Stratification," introduces GROVE (GROunded Vision–languagE risk model for ICU).

About the Research:
Postoperative deterioration is a major challenge in intensive care, often hidden by alert fatigue and the cognitive burden of tracking diverse clinical data. The team's GROVE framework tackles this by acting as a multimodal vision-language risk model that bridges the gap between unstructured clinical notes and structured physiological measurements (like heart rate and oxygen saturation mapped as temporal plots).
By jointly modeling these quantitative and qualitative signals, GROVE generates interpretable, natural language assessments that achieved an AUROC of 0.92 for early detection of complications like sepsis and hemorrhage—with 94% of outputs rated as evidence-aligned by clinicians!
This is a massive step forward for human-centric clinical informatics and actionable decision support in high-stakes medical environments. We are incredibly proud of the dedication and innovation our members continue to bring to the field of biomedical AI! 🚀🏥

বাংলাদেশের তরুণ গবেষকদের আন্তর্জাতিক AI গবেষণায় আরেকটি উল্লেখযোগ্য সাফল্য!

Machine Learning এবং Artificial Intelligence গবেষণার অন্যতম শীর্ষ আন্তর্জাতিক সম্মেলন 𝐈𝐂𝐌𝐋 𝟐𝟎𝟐𝟔-এর main track-এ regular paper হিসেবে accepted হয়েছে Bangladesh-led student research collaboration-এর পেপার, “𝐓𝐢𝐦𝐞𝐒𝐩𝐨𝐭: 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 𝐆𝐞𝐨-𝐓𝐞𝐦𝐩𝐨𝐫𝐚𝐥 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐢𝐧 𝐕𝐢𝐬𝐢𝐨𝐧–𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐒𝐞𝐭𝐭𝐢𝐧𝐠𝐬”। ICML 2026 অনুষ্ঠিত হবে South Korea-এর Seoul-এ।

এই পেপারে co-lead হিসেবে আছেন 𝐀𝐳𝐦𝐢𝐧𝐞 𝐓𝐨𝐮𝐬𝐡𝐢𝐤 𝐖𝐚𝐬𝐢 (IPE, 𝐒𝐔𝐒𝐓) এবং 𝐒𝐡𝐚𝐡𝐫𝐢𝐲𝐚𝐫 𝐙𝐚𝐦𝐚𝐧 𝐑𝐢𝐝𝐨𝐲 (CSE, 𝐍𝐒𝐔)। Co-author হিসেবে আছেন 𝐊𝐨𝐮𝐬𝐡𝐢𝐤 𝐀𝐡𝐚𝐦𝐞𝐝 𝐓𝐨𝐧𝐦𝐨𝐲 (𝐍𝐒𝐔), 𝐊𝐢𝐧𝐠𝐚 𝐓𝐬𝐡𝐞𝐫𝐢𝐧𝐠 (𝐍𝐒𝐔), 𝐒. 𝐌. 𝐌𝐮𝐡𝐭𝐚𝐬𝐢𝐦𝐮𝐥 𝐇𝐚𝐬𝐚𝐧 (𝐍𝐒𝐔), এবং 𝐖𝐚𝐡𝐢𝐝 𝐅𝐚𝐢𝐬𝐚𝐥 (𝐒𝐔𝐒𝐓)। কাজটির supervisors ছিলেন 𝐃𝐫. 𝐓𝐚𝐬𝐧𝐢𝐦 𝐌𝐨𝐡𝐢𝐮𝐝𝐝𝐢𝐧 এবং 𝐃𝐫. 𝐌𝐝 𝐑𝐢𝐳𝐰𝐚𝐧 𝐏𝐚𝐫𝐯𝐞𝐳, দুজনেই 𝐐𝐚𝐭𝐚𝐫 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐞 (𝐐𝐂𝐑𝐈)-এর Research Scientist।

বর্তমান Vision–Language Models বা VLMs ছবি দেখে object, scene, landmark, এবং অনেক সময় location চিনতে বেশ ভালো করছে। তবে real-world intelligence শুধু “ছবিতে কী আছে” বোঝার মধ্যে সীমাবদ্ধ নয়। একটি intelligent multimodal system-এর বুঝতে পারা উচিত ছবিটি কোথায় তোলা হয়েছে, কখন তোলা হয়েছে, দিনের কোন সময়, আলো-ছায়া কী বলছে, এবং visual evidence কীভাবে physical world-এর সাথে connected।

এই গবেষণায় introduce করা হয়েছে 𝐓𝐢𝐦𝐞𝐒𝐩𝐨𝐭, একটি benchmark for evaluating 𝐠𝐞𝐨-𝐭𝐞𝐦𝐩𝐨𝐫𝐚𝐥 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 in Vision–Language Models under real-world settings। Benchmark-টিতে রয়েছে 𝟏,𝟒𝟓𝟓 ground-level images from 𝟖𝟎 countries, যেখানে models-কে test করা হয় তারা visual evidence alone থেকে ছবিটি 𝐰𝐡𝐞𝐫𝐞 এবং 𝐰𝐡𝐞𝐧 captured হয়েছে তা infer করতে পারে কি না।

তাদের evaluation দেখায়, strong spatial recognition থাকা মানেই reliable temporal reasoning থাকা নয়। অনেক state-of-the-art open-source এবং closed-source VLM image-এর place বা visual context চিনতে পারলেও 𝐭𝐢𝐦𝐞-𝐨𝐟-𝐝𝐚𝐲 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧, 𝐠𝐞𝐨𝐝𝐞𝐬𝐢𝐜 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧, এবং 𝐠𝐞𝐨-𝐭𝐞𝐦𝐩𝐨𝐫𝐚𝐥 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲-তে গুরুত্বপূর্ণ limitation দেখায়।

এই কাজটি গুরুত্বপূর্ণ কারণ future AI systems যদি navigation, robotics, disaster response, climate monitoring, geospatial intelligence, বা real-world decision-making-এ ব্যবহার করতে হয়, তাহলে শুধু static image recognition যথেষ্ট হবে না। Models-কে পৃথিবীর জায়গা, সময়, আলো, environment, এবং physical constraints একসাথে বুঝতে হবে। 𝐓𝐢𝐦𝐞𝐒𝐩𝐨𝐭 সেই gap systematically measure করার জন্য একটি benchmark তৈরি করেছে।

এই অর্জন দেখায় যে বাংলাদেশের student researchers এবং young AI community future multimodal AI systems কীভাবে real world বুঝবে, reason করবে, এবং grounded intelligence অর্জন করবে—সেই দিকেও গুরুত্বপূর্ণ অবদান রাখছে।

🚨 A defining moment for Bangladesh in global AI 🇧🇩

Proud to see this milestone featured in The Daily Star, one of the country’s most widely read and respected news outlets.
🔗 https://www.thedailystar.net/campus/news/bangladeshi-team-earns-spotlight-icml-ai-governance-research-4165646

Our work, Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws, has been accepted as a Spotlight Position Paper at ICML 2026, marking a rare and historic achievement for Bangladesh in top-tier AI research.

Grateful to the CIOL team, collaborators, and mentors for making this possible. This recognition reflects not just a paper, but Bangladesh’s growing presence in shaping global AI governance discourse.

Bangladeshi team earns spotlight at ICML for AI governance research The paper, “Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws,” is scheduled to be presented on May 11 in South Korea's Seoul

🚨 Excited to share our work: 𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧: 𝐀𝐈 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐍𝐞𝐞𝐝𝐬 𝐈𝐒𝐎-𝐥𝐢𝐤𝐞 𝐈𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐋𝐚𝐰𝐬 has been accepted as a 𝐒𝐩𝐨𝐭𝐥𝐢𝐠𝐡𝐭 Position Paper at ICML2026!!

Huge congratulations to Azmine Toushik Wasi, Mst Rafia Islam, Mahfuz Ahmed Anik for their contributions and to Dr. Manjurul Ahsan and Prof. Dong-Kyu Chae for their guidance and support!

As AI systems become deeply embedded in global infrastructure, regulation is currently split across regions, with frameworks like the EU AI Act, US NIST guidelines, and China’s governance policies evolving independently. This fragmentation makes cross-border deployment complex and costly.
Our work argues that laws alone are not sufficient. Instead, we propose ISO-like interoperability protocols that allow AI systems to communicate risk and compliance in a standardized, machine-readable form across jurisdictions.
At the core of this idea are AI “nutrition labels”: structured, unified reports describing key properties such as bias, energy usage, data provenance, and safety characteristics. These enable consistent verification without repeated legal reinterpretation.
We further propose modular, versioned standards inspired by GDPR-era technical compliance (such as ISO 27001), designed to evolve alongside AI systems while maintaining global compatibility.
The goal is a shift from fragmented legal compliance to interoperable technical governance, enabling a shared global language for responsible AI deployment.

ICML is an A*-tier conference and widely regarded as one of the most prestigious venues in artificial intelligence and machine learning. ICML is also considered part of the AI/ML “grand slam” (the “big three”), alongside ICLR and NeurIPS! This paper marks only the second time a Bangladeshi contribution has received Spotlight recognition at ICML and the firstinstance in the Position Paper track!