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Himanshu Tiwari

dblp:227/5334 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Cellular and mobile networks
6g
2.022026
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
1.012026
NUA-FedBeam: Noisy-Uplink-Aware Federated Downlink Beamforming for Distributed MIMO · INFOCOM 2026
Cellular and mobile networks
integrated sensing and communication
1.012026
Digital Twin-Assisted Belief-State Reinforcement Learning for Latency-Robust ISAC in 6G Networks · INFOCOM 2026
Machine learning › Efficient and distributed learning
federated learning
0.312026
NUA-FedBeam: Noisy-Uplink-Aware Federated Downlink Beamforming for Distributed MIMO · INFOCOM 2026
Edge and fog computing
digital twin
0.312026
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
YearPublicationVenuePosition
2026 NUA-FedBeam: Noisy-Uplink-Aware Federated Downlink Beamforming for Distributed MIMO
Himanshu Tiwari, Binayak Kar, Priyanshu Tiwari
INFOCOM1
2026 Digital Twin-Assisted Belief-State Reinforcement Learning for Latency-Robust ISAC in 6G Networks
Himanshu Tiwari, Binayak Kar, Priyanshu Tiwari
INFOCOM1
2021 CLC+ Backbone: Set the Scene in Copernicus for the Coming Decade
abstract
With 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
IGARSS10
2018 Identifying the Relative Importance of Customer Issues on Product Ratings through Machine Learning
abstract
Millions 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
DocEng1