1const publications = [ 2 { 3 year: "2026", 4 title: "TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning", 5 authors: "Priyanshu Karmakar, Vijay Sai Borru, Shubhojit Mallick, Abhik Jana, Shreya Ghosh, Manish Gupta", 6 venue: "EMNLP 2026 Findings", 7 links: [{label:"Paper",url:"assets/papers/TripPulse.pdf"}] 8 }, 9 { 10 year: "2026", 11 title: "UTP-Bench: Uncertainty-aware Travel Planning Benchmark", 12 authors: "Revanth Rao Etcharla, Priyanshu Karmakar, Shubhojit Mallick, Manish Gupta, Shreya Ghosh, Abhik Jana", 13 venue: "EMNLP 2026 Findings", 14 links: [{label:"Paper",url:"assets/papers/UTP-Bench.pdf"}] 15 }, 16 { 17 year: "2026", 18 title: "GeoAgent: Evaluating VLM Geolocalization Through Embodied Navigation", 19 authors: "Arka Mukherjee, Soham Roy, Kartikeya Trivedi, Shreya Ghosh", 20 venue: "EMNLP 2026 Findings", 21 links: [{label:"Paper",url:"https://openreview.net/pdf/3bb3cdcde590c5f392701535c3dfd291175d54e9.pdf"}] 22 }, 23 { 24 year: "2026", 25 title: "IntelliCode: A Multi-Agent LLM Tutoring System with Centralized Learner Modeling", 26 authors: "Jones David, Shreya Ghosh", 27 venue: "EACL 2026 System Demonstrations", 28 links: [{label:"Paper",url:"https://aclanthology.org/2026.eacl-demo.10/"}, {label:"Live demo",url:"https://intellicode.redomic.in"}] 29 }, 30 { 31 year: "2025", 32 title: "mmJEE-Eval: A Bilingual Multimodal Benchmark for Evaluating Scientific Reasoning in Vision-Language Models", 33 authors: "Arka Mukherjee, Shreya Ghosh", 34 venue: "IJCNLP-AACL 2025 Findings", 35 links: [{label:"Paper",url:"https://aclanthology.org/2025.findings-ijcnlp.140/"}, {label:"Project",url:"https://mmjee-eval.github.io/"}] 36 }, 37 { 38 year: "2024", 39 title: "Clock Against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation", 40 authors: "Shreya Ghosh, Prasenjit Mitra, Preslav Nakov", 41 venue: "ICWSM 2024", 42 links: [{label:"Paper",url:"https://ojs.aaai.org/index.php/ICWSM/article/view/31327"}] 43 } 44]; 45 46const teachingDetails = { 47 ai: { 48 eyebrow:"IIT BHUBANESWAR", 49 title:"Artificial Intelligence", 50 intro:"My AI courses combine classical foundations with modern AI systems. Alongside search, CSP, planning, uncertainty and reinforcement learning, I use current examples from generative AI, LLMs and agentic systems to connect core ideas to contemporary practice.", 51 topics:["Search & heuristic methods", "Constraint satisfaction", "Adversarial search", "Planning", "Uncertainty", "Reinforcement learning", "Generative AI & LLMs", "Explainable AI"], 52 courses:[ 53 {term:"2026", name:"Artificial Intelligence", url:"https://sites.google.com/view/ai2026-iitbbsr/home?read_current=1"}, 54 {term:"2024", name:"Artificial Intelligence", url:"https://sites.google.com/view/ai-aut2024/home?read_current=1"} 55 ] 56 }, 57 daa: { 58 eyebrow:"IIT BHUBANESWAR", 59 title:"Design & Analysis of Algorithms", 60 intro:"The emphasis is on learning how to formulate a problem, choose an appropriate design paradigm, reason about correctness and complexity, and develop the habit of comparing alternative algorithmic solutions.", 61 topics:["Asymptotic analysis", "Divide & conquer", "Greedy methods", "Dynamic programming", "Graph algorithms", "Optimization", "Correctness", "Problem-solving practice"], 62 courses:[ 63 {term:"2026", name:"Design & Analysis of Algorithms", url:"https://sites.google.com/view/daa2026/home?read_current=1"}, 64 {term:"2025", name:"Design & Analysis of Algorithms", url:"https://sites.google.com/view/daa-2025/home"} 65 ] 66 }, 67 professional: { 68 eyebrow:"PROFESSIONAL TEACHING · PHYSICS WALLAH (PW)", 69 title:"Generative & Agentic AI", 70 intro:"I also teach working professionals through Physics Wallah (PW), with sessions designed around both conceptual foundations and hands-on system building for modern LLM applications.", 71 topics:["Prompting & effective GenAI", "RAG & vector databases", "Tool-augmented LLMs", "LangChain & LangGraph", "Multi-agent systems", "RLHF, DPO & preference learning", "LLM evaluation & benchmarking", "LLMOps"], 72 courses:[] 73 } 74}; 75 76const researchProjects = [ 77 { 78 id: "travel", 79 number: "01", 80 title: "Travel Planning with LLMs", 81 eyebrow: "TRAVEL INTELLIGENCE · PLANNING · ROBUSTNESS", 82 tagline: "From realistic itinerary generation to disruption-aware, review-grounded and uncertainty-aware travel agents.", 83 summary: "This work looks at how language models can produce useful travel plans while handling spatial constraints, individual preferences, disruptions, and real-world uncertainty.", 84 accent: "teal", 85 cover: "travel", 86 metrics: ["4 connected papers", "Real-world travel data", "Planning + evaluation + agents"], 87 heroImage: "assets/research/trippulse-architecture.png", 88 gallery: [ 89 {src:"assets/research/trippulse-architecture.png", caption:"TripPulse: review-grounded multi-agent planning architecture."}, 90 {src:"assets/research/utpbench-overview.png", caption:"UTP-Bench: uncertainty-aware travel planning and evaluation."} 91 ], 92 story: [ 93 {label:"Foundation", title:"TripCraft", text:"Moves travel planning beyond semi-synthetic settings with spatio-temporally coherent real-world data, personas, transit, events and continuous evaluation metrics."}, 94 {label:"Adaptation", title:"TripTide", text:"Tests whether LLMs can revise an existing itinerary after disruptions while preserving traveler intent, feasibility and overall plan structure."}, 95 {label:"Experience", title:"TripPulse", text:"Adds 100K+ real-world reviews and specialized agents so planners can reason about experiential signals such as safety, service, ambiance, crowding and hidden risks."}, 96 {label:"Uncertainty", title:"UTP-Bench", text:"Evaluates whether plans survive empirical transportation delays and crowd variability using uncertainty-aware metrics for buffers, timing and delay absorption."} 97 ], 98 highlights: [ 99 "Treats itinerary generation as a real spatio-temporal reasoning problem rather than a text-only recommendation task.", 100 "Progresses from static planning to adaptation, human experience, and stochastic robustness.", 101 "Combines benchmark design, evaluation methodology, LLM reasoning, multi-agent orchestration and deterministic scheduling.", 102 "Built with strong student leadership and industry collaboration, including Microsoft researchers on multiple travel-planning works." 103 ], 104 papers: [ 105 { 106 title:"TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning", 107 venue:"ACL 2025 · Main Track · Long Paper", 108 year:"2025", 109 authors:"Soumyabrata Chaudhuri, Pranav Purkar, Ritwik Raghav, Shubhojit Mallick, Manish Gupta, Abhik Jana, Shreya Ghosh", 110 note:"Real-world, persona-aware, spatio-temporally fine-grained itinerary generation with five continuous evaluation metrics.", 111 links:[ 112 {label:"Paper",url:"https://aclanthology.org/2025.acl-long.834/"}, 113 {label:"arXiv",url:"https://arxiv.org/abs/2502.20508"} 114 ] 115 }, 116 { 117 title:"TripTide: A Benchmark for Adaptive Travel Planning under Disruptions", 118 venue:"ACL 2026 · Findings", 119 year:"2026", 120 authors:"Priyanshu Karmakar, Soumyabrata Chaudhuri, Shubhojit Mallick, Manish Gupta, Abhik Jana, Shreya Ghosh", 121 note:"Evaluates itinerary revision under transit cancellations, weather closures and overbooked attractions using preservation, responsiveness and adaptability metrics.", 122 links:[ 123 {label:"Paper",url:"https://aclanthology.org/2026.findings-acl.2002/"}, 124 {label:"arXiv",url:"https://arxiv.org/abs/2510.21329"} 125 ] 126 }, 127 { 128 title:"TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning", 129 venue:"EMNLP 2026 · Findings", 130 year:"2026", 131 authors:"Priyanshu Karmakar, Vijay Sai Borru, Shubhojit Mallick, Abhik Jana, Shreya Ghosh, Manish Gupta", 132 note:"A review-grounded multi-agent planner with domain-specific agents, a global orchestrator and deterministic or LLM scheduling backends.", 133 links:[{label:"Paper",url:"assets/papers/TripPulse.pdf"}] 134 }, 135 { 136 title:"UTP-Bench: Uncertainty-aware Travel Planning Benchmark", 137 venue:"EMNLP 2026 · Findings", 138 year:"2026", 139 authors:"Revanth Rao Etcharla, Priyanshu Karmakar, Shubhojit Mallick, Manish Gupta, Shreya Ghosh, Abhik Jana", 140 note:"A benchmark spanning 504 Indian cities with empirical delay and crowd signals and new robustness metrics: BAS, CATS and TDAS.", 141 links:[{label:"Paper",url:"assets/papers/UTP-Bench.pdf"}] 142 } 143 ], 144 contributors: ["Soumyabrata Chaudhuri", "Priyanshu Karmakar", "Revanth Rao Etcharla", "Vijay Sai Borru", "Pranav Purkar", "Ritwik Raghav", "Abhik Jana", "Shubhojit Mallick · Microsoft", "Manish Gupta · Microsoft", "Shreya Ghosh"] 145 }, 146 { 147 id: "agents", 148 number: "02", 149 title: "LLM Agents for Real-World Reasoning", 150 eyebrow: "AGENTIC AI · MULTIMODAL · SAFETY", 151 tagline: "Agents that explore, gather evidence, enforce structure and know when their reasoning is uncertain.", 152 summary: "This line studies agentic AI in settings where one-shot generation is not enough: an agent must navigate an environment, maintain state, gather missing evidence, coordinate specialized roles and apply explicit safety checks.", 153 accent: "indigo", 154 cover: "agents", 155 metrics: ["Embodied VLMs", "Neuro-symbolic orchestration", "Uncertainty-aware safety"], 156 heroImage: "assets/research/healthcare-agent-architecture.png", 157 gallery: [{src:"assets/research/healthcare-agent-architecture.png", caption:"Healthcare agent: structured intake, multi-agent diagnosis and uncertainty-aware safety supervision."}], 158 story: [ 159 {label:"Perception + action", title:"GeoAgent", text:"Turns geolocalization into embodied navigation: VLM agents explore Street View, gather sequential observations and iteratively refine location hypotheses."}, 160 {label:"Safety + structure", title:"Healthcare Agent", text:"Uses a deterministic OLDCARTS state-tracking gate and semantic-entropy uncertainty gate to reduce premature diagnostic handoff and silent hallucination."} 161 ], 162 highlights: [
163 "Focuses on what changes when an LLM can act, observe and revise rather than answer once.", 164 "Bridges multimodal perception, sequential reasoning, agent orchestration and explicit verification.", 165 "Explores reliability through architectural safeguards rather than relying only on prompt instructions or LLM-as-a-judge routing.", 166 "Targets both open-world spatial reasoning and high-stakes structured decision workflows." 167 ], 168 papers: [ 169 { 170 title:"GeoAgent: Evaluating VLM Geolocalization Through Embodied Navigation", 171 venue:"EMNLP 2026 · Findings", 172 year:"2026", 173 authors:"Arka Mukherjee, Soham Roy, Kartikeya Trivedi, Shreya Ghosh", 174 note:"An agentic benchmark across 1,200 Street View locations in 100 cities, evaluating how frontier VLMs explore and self-correct during geolocalization.", 175 links:[{label:"Paper",url:"https://openreview.net/pdf/3bb3cdcde590c5f392701535c3dfd291175d54e9.pdf"}] 176 }, 177 { 178 title:"Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications", 179 venue:"arXiv preprint · 2026", 180 year:"2026", 181 authors:"Divyansh Srivastava, Shreya Ghosh, Anshul Verma, Rajkumar Buyya", 182 note:"A multi-agent clinical reasoning framework with deterministic symptom-completeness checks, semantic-entropy uncertainty quantification and recursive safety supervision.", 183 links:[ 184 {label:"Paper",url:"assets/papers/Agentic-Healthcare.pdf"}, 185 {label:"arXiv",url:"https://arxiv.org/abs/2606.18068"} 186 ] 187 } 188 ], 189 contributors:["Arka Mukherjee", "Soham Roy", "Kartikeya Trivedi", "Divyansh Srivastava", "Anshul Verma", "Rajkumar Buyya", "Shreya Ghosh"] 190 }, 191 { 192 id: "education", 193 number: "03", 194 title: "AI for Education & Scientific Reasoning", 195 eyebrow: "EDTECH · TUTORING AGENTS · MULTIMODAL REASONING", 196 tagline: "AI systems that do more than give answers: they model learners, diagnose reasoning gaps and support deeper scientific thinking.", 197 summary: "In education, I am interested in both learning systems and their evaluation: from persistent tutoring agents to bilingual multimodal benchmarks for scientific reasoning.", 198 accent: "gold", 199 cover: "education", 200 metrics:["Persistent learner state", "English + Hindi reasoning", "Future: CineLearn"], 201 story:[ 202 {label:"Tutoring systems", title:"IntelliCode", text:"A multi-agent DSA tutor with a centralized, versioned learner state tracking mastery, misconceptions, engagement and personalized review schedules."}, 203 {label:"Scientific reasoning", title:"mmJEE-Eval", text:"A bilingual multimodal benchmark of 1,460 JEE Advanced questions across Physics, Chemistry and Mathematics for evaluating reasoning beyond pattern matching."}, 204 {label:"Next direction", title:"CineLearn", text:"Ongoing work exploring cinema as a multimodal resource for learning, explanation, and reasoning."} 205 ], 206 highlights:[ 207 "Moves beyond stateless chatbots toward longitudinal, auditable learner models.", 208 "Evaluates frontier and open VLMs on complex scientific problems in both English and Hindi.", 209 "Connects pedagogical personalization with rigorous model evaluation and multimodal reasoning.", 210 "CineLearn is a future research direction and is intentionally presented as ongoing work, not a published result." 211 ], 212 papers:[ 213 { 214 title:"IntelliCode: A Multi-Agent LLM Tutoring System with Centralized Learner Modeling", 215 venue:"EACL 2026 · System Demonstrations", 216 year:"2026", 217 authors:"Jones David, Shreya Ghosh", 218 note:"Six specialized agents coordinate around a centralized learner state to support mastery-aware hints, curriculum adaptation and spaced repetition.", 219 links:[ 220 {label:"Paper",url:"https://aclanthology.org/2026.eacl-demo.10/"}, 221 {label:"Live demo",url:"https://intellicode.redomic.in"}, 222 {label:"arXiv",url:"https://arxiv.org/abs/2512.18669"} 223 ] 224 }, 225 { 226 title:"mmJEE-Eval: A Bilingual Multimodal Benchmark for Evaluating Scientific Reasoning in Vision-Language Models", 227 venue:"IJCNLP-AACL 2025 · Findings", 228 year:"2025", 229 authors:"Arka Mukherjee, Shreya Ghosh", 230 note:"Evaluates 17 VLMs on JEE Advanced questions, exposing a large frontier/open-model gap and weaknesses under deeper metacognitive reasoning.", 231 links:[ 232 {label:"Paper",url:"https://aclanthology.org/2025.findings-ijcnlp.140/"}, 233 {label:"Project",url:"https://mmjee-eval.github.io/"}, 234 {label:"arXiv",url:"https://arxiv.org/abs/2511.09339"} 235 ] 236 } 237 ], 238 contributors:["Jones David", "Arka Mukherjee", "Shreya Ghosh"] 239 }, 240 { 241 id:"culture", 242 number:"04", 243 title:"Culturally Aware Multimodal AI", 244 eyebrow:"RESPONSIBLE AI · VISION-LANGUAGE MODELS · CULTURE", 245 tagline:"Evaluating whether multimodal models adapt meaningfully to cultural identity rather than merely generating fluent text.", 246 summary:"This work studies how cultural cues in images and prompts shape stories generated by vision-language models, and how well automatic measures reflect human judgments of cultural alignment.", 247 accent:"rose", 248 cover:"culture", 249 metrics:["42 cultural identities", "2,940 prompt-image pairs", "5 VLMs"], 250 story:[ 251 {label:"Cultural grounding", title:"Multimodal story generation", text:"The study varies cultural identity in both visual and textual context and examines changes in vocabulary, semantics, and visual-cultural alignment."}, 252 {label:"Evaluation", title:"Where current models fall short", text:"The results show substantial variation across model families, including cases of inverse cultural alignment and disagreement between automatic metrics and human assessment."} 253 ], 254 highlights:[ 255 "First large-scale multimodal cultural competence evaluation framed as a downstream generative task.", 256 "Studies cultural adaptation across names, familial terms, geographic markers and cross-modal similarity.", 257 "Highlights why culturally responsible AI needs human-centered evaluation in addition to automated metrics.", 258 "Released code and data to support reproducible follow-up research." 259 ], 260 papers:[ 261 { 262 title:"Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation", 263 venue:"ICCV 2025 Workshops", 264 year:"2025", 265 authors:"Arka Mukherjee, Shreya Ghosh", 266 note:"A multimodal framework for evaluating VLM cultural competence through story generation across culturally diverse image-prompt pairs.", 267 links:[ 268 {label:"Paper",url:"https://openaccess.thecvf.com/content/ICCV2025W/ASI/html/Mukherjee_Toward_Socially_Aware_Vision-Language_Models_Evaluating_Cultural_Competence_Through_Multimodal_ICCVW_2025_paper.html"}, 269 {label:"arXiv",url:"https://arxiv.org/abs/2508.16762"}, 270 {label:"Code",url:"https://github.com/ArkaMukherjee0/mmCultural"} 271 ] 272 } 273 ], 274 contributors:["Arka Mukherjee", "Shreya Ghosh"] 275 }, 276 { 277 id:"misinformation", 278 number:"05", 279 title:"Misinformation Detection & Intervention", 280 eyebrow:"SOCIAL MEDIA · TEMPORAL GRAPHS · TRUSTWORTHY NLP", 281 tagline:"Detecting misinformation early, understanding who propagates it, and designing interventions before harmful narratives fully spread.", 282 summary:"My work on misinformation spans early detection, user and network modeling, vaccine-related discourse, and time-aware intervention on social media.", 283 accent:"red", 284 cover:"misinformation", 285 metrics:["Early detection", "User + network modeling", "Temporal intervention"], 286 story:[ 287 {label:"Early detection", title:"Before the story goes viral", text:"Models user profiles, propagation paths and network characteristics to identify false information while signals are still sparse."}, 288 {label:"Domain analysis", title:"Vaccination misinformation", text:"Studies vaccine dissent and misinformation discourse, including knowledge extraction and user-level behavior."},
289 {label:"Intervention", title:"Clock Against Chaos", text:"Moves beyond classification to dynamic misinformation scoring and temporal intervention using TGN/RNN modeling, active learning and an embargo strategy."} 290 ], 291 highlights:[ 292 "Frames misinformation as a dynamic propagation problem rather than a static text-classification problem.", 293 "Combines linguistic signals with user attributes, network structure and temporal dynamics.", 294 "Includes both general misinformation benchmarks and public-health misinformation case studies.", 295 "Extends detection toward actionable intervention and continuous model updating." 296 ], 297 papers:[ 298 { 299 title:"Clock Against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation", 300 venue:"ICWSM 2024 · Full Paper", 301 year:"2024", 302 authors:"Shreya Ghosh, Prasenjit Mitra, Preslav Nakov", 303 note:"Dynamic misinformation scoring with Temporal Graph Networks and RNNs, active learning, dual-model grading and temporal embargo-based intervention.", 304 links:[{label:"Paper",url:"https://ojs.aaai.org/index.php/ICWSM/article/view/31327"}] 305 }, 306 { 307 title:"How Early Can We Detect? Detecting Misinformation on Social Media Using User Profiling and Network Characteristics", 308 venue:"ECML PKDD 2023", 309 year:"2023", 310 authors:"Shreya Ghosh, Prasenjit Mitra", 311 note:"Early misinformation detection through propagation-path modeling, causal user-attribute inference and auxiliary prediction tasks.", 312 links:[{label:"DOI",url:"https://doi.org/10.1007/978-3-031-43427-3_11"}] 313 }, 314 { 315 title:"Catching Lies in the Act: A Framework for Early Misinformation Detection on Social Media",
316 venue:"ACM Hypertext 2023", 317 year:"2023", 318 authors:"Shreya Ghosh, Prasenjit Mitra", 319 note:"A framework combining propagation-path classification, linguistic patterns and user-level modeling for early-stage misinformation detection.", 320 links:[{label:"DOI",url:"https://doi.org/10.1145/3603163.3609057"}] 321 }, 322 { 323 title:"Tweeted Fact vs Fiction: Identifying Vaccine Misinformation and Analyzing Dissent", 324 venue:"ASONAM 2023", 325 year:"2023", 326 authors:"Shreya Ghosh, Prasenjit Mitra", 327 note:"Knowledge extraction and discourse analysis for COVID-19 vaccine misinformation and dissent on Twitter.", 328 links:[{label:"DOI",url:"https://doi.org/10.1145/3625007.3627307"}] 329 } 330 ], 331 contributors:["Prasenjit Mitra", "Preslav Nakov", "Shreya Ghosh"] 332 }, 333 { 334 id:"mobility", 335 number:"06", 336 title:"Mobility & Spatio-Temporal Intelligence", 337 eyebrow:"MOBILITY ANALYTICS · SPATIO-TEMPORAL DATA · EDGE INTELLIGENCE", 338 tagline:"Understanding movement from trajectories, mobility semantics and city-scale spatio-temporal patterns â from human behavior to cloud-edge systems.", 339 summary:"My mobility work spans semantic trajectory analysis, mobility knowledge transfer, cloud-edge intelligence, and city-scale spatio-temporal data mining. Across these projects, the common goal is to move from raw movement traces to useful representations of behavior, context and urban dynamics.", 340 accent:"blue", 341 cover:"mobility", 342 metrics:["Semantic mobility", "Spatio-temporal data mining", "Cloud · fog · edge"], 343 story:[ 344 {label:"Movement semantics", title:"From traces to behavior", text:"Early work studied how temporal and spatial patterns in GPS trajectories can reveal activities, movement profiles and higher-level behavioral signals."}, 345 {label:"Knowledge transfer", title:"MoveInsight / Mobilytics", text:"Mobility knowledge graphs, multi-task learning and transfer learning are used to infer trip purpose and POI semantics and to transfer mobility knowledge across regions."}, 346 {label:"Urban dynamics", title:"MARIO", text:"Taxi trajectories are mined on Google Cloud to model mobility association rules and predict time-varying travel demand across urban regions."}, 347 {label:"Mobility-aware systems", title:"Mobi-IoST", text:"Mobility prediction is integrated into a collaborative cloud-fog-edge-IoT architecture for time-critical applications involving moving agents."} 348 ], 349 highlights:[ 350 "Covers both mobility understanding and the computing systems needed to support mobility-aware applications.", 351 "Combines trajectory mining, knowledge graphs, deep learning, transfer learning, association-rule mining and distributed cloud-edge computing.", 352 "The SDM 2024 work uses mobility knowledge graphs and multi-task learning for semantic knowledge transfer across regions.", 353 "This body of work provides the spatio-temporal foundation for current research in travel intelligence and geospatial AI." 354 ], 355 papers:[ 356 { 357 title:"Bridging Semantics: Mobility Analytics Framework for Knowledge Transfer", 358 venue:"SIAM International Conference on Data Mining (SDM)", 359 year:"2024", 360 authors:"Shreya Ghosh, Prasenjit Mitra", 361 note:"MoveInsight uses a mobility knowledge graph with multi-task and transfer learning to infer trip purposes, annotate POIs and transfer semantic mobility knowledge across regions.", 362 links:[{label:"Paper",url:"https://epubs.siam.org/doi/10.1137/1.9781611978032.70"},{label:"DBLP",url:"https://dblp.org/rec/conf/sdm/0002M24"}] 363 }, 364 { 365 title:"Mobilytics: Mobility Analytics Framework for Transferring Semantic Knowledge", 366 venue:"IEEE Transactions on Mobile Computing", 367 year:"2024", 368 authors:"Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra", 369 note:"An end-to-end mobility analytics framework for trip-purpose extraction, POI annotation and semantic knowledge transfer across geographically different regions.", 370 links:[{label:"DOI",url:"https://doi.org/10.1109/TMC.2024.3413589"}] 371 }, 372 { 373 title:"MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories", 374 venue:"Journal of Network and Computer Applications", 375 year:"2020", 376 authors:"Shreya Ghosh, Soumya K. Ghosh, Rajkumar Buyya", 377 note:"A Google Cloud-based framework for mining mobility association rules and modelling travel-demand dynamics from real taxi trajectories.", 378 links:[{label:"DOI",url:"https://doi.org/10.1016/j.jnca.2020.102692"},{label:"Scholar",url:"https://scholar.google.com/citations?view_op=view_citation&hl=en&user=a5OKo7wAAAAJ&citation_for_view=a5OKo7wAAAAJ:r0BpntZqJG4C"}] 379 }, 380 { 381 title:"Mobi-IoST: Mobility-aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications", 382 venue:"IEEE Transactions on Network Science and Engineering", 383 year:"2020", 384 authors:"Shreya Ghosh, Anwesha Mukherjee, Soumya K. Ghosh, Rajkumar Buyya", 385 note:"A mobility-driven cloud-fog-edge-IoT framework that predicts moving-agent locations and uses mobility context to supp
385ort time-critical applications.", 386 links:[{label:"DOI",url:"https://doi.org/10.1109/TNSE.2019.2941754"},{label:"PDF",url:"https://www.buyya.com/papers/Mobi-IoST.pdf"}] 387 }, 388 { 389 title:"MANTRA: Semantic Mobility Knowledge Analytics Framework for Trajectory Annotation", 390 venue:"IEEE INFOCOM Workshops", 391 year:"2022", 392 authors:"Shreya Ghosh, Soumya K. Ghosh", 393 note:"A semantic mobility knowledge analytics framework for annotating trajectory traces with higher-level context.", 394 links:[{label:"Scholar",url:"https://scholar.google.com/scholar?q=MANTRA+Semantic+Mobility+Knowledge+Analytics+Framework+for+Trajectory+Annotation"}] 395 }, 396 { 397 title:"Activity-Based Mobility Profiling: A Purely Temporal Modeling Approach", 398 venue:"The Web Conference (WWW) Companion", 399 year:"2018", 400 authors:"Shreya Ghosh, Soumya K. Ghosh, Rahul Deb Das, Stephan Winter", 401 note:"A temporal approach to modeling individual activity patterns and characterizing mobility-profile uniqueness.", 402 links:[{label:"DOI",url:"https://doi.org/10.1145/3184558.3186356"}] 403 } 404 ], 405 contributors:["Prasenjit Mitra", "Soumya K. Ghosh", "Sajal K. Das", "Rajkumar Buyya", "Anwesha Mukherjee", "Rahul Deb Das", "Stephan Winter", "Shreya Ghosh"] 406 }];
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.