Demo Data from EDBT 2026 / reviewers in the wild / expert
Himanshu Tiwari
dblp:227/5334
· DBLP profile ↗
4ranked-venue papers
3as first author
3since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 2 first-author · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
2 papers |
Cellular and mobile networks · 70% Physical-layer communications · 23% Edge and fog computing · 7% | |
| Artificial intelligence
2 papers |
Reinforcement learning · 77% Efficient and distributed learning · 23% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cellular and mobile networks
6g |
2.0 | 2 | 2026 | Digital Twin-Assisted Belief-State Reinforcement Learning for Latency-Robust ISAC in 6G Networks · INFOCOM 2026 NUA-FedBeam: Noisy-Uplink-Aware Federated Downlink Beamforming for Distributed MIMO · INFOCOM 2026 |
Physical-layer communications › MIMO
distributed MIMO |
1.0 | 1 | 2026 | NUA-FedBeam: Noisy-Uplink-Aware Federated Downlink Beamforming for Distributed MIMO · INFOCOM 2026 |
Cellular and mobile networks
integrated sensing and communication |
1.0 | 1 | 2026 | Digital Twin-Assisted Belief-State Reinforcement Learning for Latency-Robust ISAC in 6G Networks · INFOCOM 2026 |
Machine learning › Efficient and distributed learning
federated learning |
0.3 | 1 | 2026 | NUA-FedBeam: Noisy-Uplink-Aware Federated Downlink Beamforming for Distributed MIMO · INFOCOM 2026 |
Edge and fog computing
digital twin |
0.3 | 1 | 2026 | Digital Twin-Assisted Belief-State Reinforcement Learning for Latency-Robust ISAC in 6G Networks · INFOCOM 2026 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 2.0federated learning · 2.0digital twin · 2.0beamforming optimization · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | NUA-FedBeam: Noisy-Uplink-Aware Federated Downlink Beamforming for Distributed MIMO
Himanshu Tiwari, Binayak Kar, Priyanshu Tiwari |
INFOCOM | 1 |
| 2026 | Digital Twin-Assisted Belief-State Reinforcement Learning for Latency-Robust ISAC in 6G Networks
Himanshu Tiwari, Binayak Kar, Priyanshu Tiwari |
INFOCOM | 1 |
| 2021 | CLC+ Backbone: Set the Scene in Copernicus for the Coming DecadeabstractWith the CLC+ product suite as part of the Copernicus Land Monitoring Service (CLMS), the European Environment Agency (EEA) has initiated a true paradigm change in European land cover/land use monitoring, building on the 30-years-long rich legacy of the European CORINE Land Cover (CLC) flagship product. The CLC+ Backbone, as first component of the upcoming CLC+ era, will feature an object-oriented wall-to-wall high-resolution inventory of European land cover and its characteristics in unprecedented quality and detail, for the reference year 2018. It will comprise a pan-European combined “hardbone” and “soft-bone” segmentation of vector-based stable landscape objects and a raster-based classification of 11 EAGLE compliant land cover classes at 10m spatial resolution. To this end, a combination of image segmentation and Deep Learning approaches are implemented within a cloud-based infrastructure for a fully integrated analysis of optical/radar time series of Sentinel-1/-2 satellite imagery and auxiliary data. Vector and raster datasets will be fused into a fully attributed, 18 land cover class, vector product with 0.5 ha minimum mapping unit (MMU), additionally incorporating a multitude of further information layers derived from satellite data and various other Copernicus products. Markus Probeck, Inés Ruíz, Gernot Ramminger, Christoff Fourie, Pirmin Maier, Martin Ickerott, Cornelia Storch, Anna Homolka, Sybrand Jacobus Muller, Himanshu Tiwari, André Stumpf, Sooyeon Chun, Cristina Mattos, Amelie Lindmayer, Fahad Jahangir, Pilar Endara, Fabian Berndt, Mario Dohr, Wolfgang Kapferer, Christian Schleicher, Stefan Ralser, Florian Innerbichler, Michael Riffler, Martin Siklar, Dora Aifantopoulou, Sideris Paralykidis, Camille Pinet, Gabriel Jaffrain, Annalaura di Federico, Marco Corsi, Tobias Langanke, Hans Dufourmont |
IGARSS | 10 |
| 2018 | Identifying the Relative Importance of Customer Issues on Product Ratings through Machine LearningabstractMillions of customer reviews for products are available online across hundreds of different websites. These reviews have a tremendous influence on the purchase decision of new customers and in creating a positive brand image. Understanding which of the product issues are critical in determining the product ratings is crucial for marketing teams. We have developed a solution which can derive deep insights from customer reviews which goes significantly beyond keyword based analysis. Our solution can identify key customer issues voiced in the reviews and the impact of each of these on the final rating that a customer gives the product. This insight is very actionable as it helps identify which customer concerns are responsible for bad ratings of products. Himanshu Tiwari, Shameed Sait, Md Imbesat Hassan Rizvi, Niranjan Damera-Venkata |
DocEng | 1 |