Biplob Debnath NEC Labs America

Biplob Debnath is a Senior Researcher in the Integrated Systems Department at NEC Laboratories America, where he leads global initiatives in generative AI, large language models, and multimodal analytics. He holds a PhD in Electrical and Computer Engineering from the University of Minnesota, an Executive MBA from the Quantic School of Business and Technology, and a Bachelor of Science in Computer Science & Engineering from the Bangladesh University of Engineering and Technology. Before joining NEC, Dr. Debnath worked at Microsoft Research, IBM Research, and DELL. He brings over 15 years of experience in AI infrastructure, scalable data platforms, deep learning systems, and product-driven innovation. At NEC, his research spans multimodal AI, remote sensing, video analytics, log analytics, data deduplication. He has been instrumental in developing NEC’s AI infrastructure stack, driving solutions across industries including telecommunications, finance, transportation, and smart cities. Dr. Debnath is a prolific inventor and researcher, holding over 40 U.S. patents and authoring more than 50 peer-reviewed papers in top-tier venues including ACL, CVPR, ICCV, KDD, SIGMOD, VLDB, CIKM, ICDCS, and USENIX. His work integrates real-time systems engineering with applied machine learning to deliver low-latency, cloud-scale AI solutions.

Posts

DataXe: A System for Application Self-optimization in Serverless Edge Computing Environments

/in /by

A key barrier to building performant, remotely managed and self-optimizing multi-sensor, distributed stream processing edge applications is high programming complexity. We recently proposed DataX [1], a novel platform that improves programmer productivity by enabling easy exchange, transformations, and fusion of data streams on virtualized edge computing infrastructure. This paper extends DataX to include (a) serverless computing that automatically scales stateful and stateless analytics units (AUs) on virtualized edge environments, (b) novel communication mechanisms that efficiently communicate data among analytics units, and (c) new techniques to promote automatic reuse and sharing of analytics processing across multiple applications in a lights out, serverless computing environment. Synthesizing these capabilities into a single platform has been substantially more transformative than any available stream processing system for the edge. We refer to this enhanced and efficient version of DataX as DataXe. To the best of our knowledge, this is the first serverless system for stream processing. For a real-world video analytics application, we observed that the performance of the DataXe implementation of the analytics application is about 3X faster than a standalone implementation of the analytics application with custom, handcrafted communication, multiprocessing and allocation of edge resources.

Read more

UAC: An Uncertainty-Aware Face Clustering Algorithm

/in /by

We investigate ways to leverage uncertainty in face images to improve the quality of the face clusters. We observe that popular clustering algorithms do not produce better quality clusters when clustering probabilistic face representations that implicitly model uncertainty – these algorithms predict up to 9.6X more clusters than the ground truth for the IJB-A benchmark. We empirically analyze the causes for this unexpected behavior and identify excessive false-positives and false-negatives (when comparing face-pairs) as the main reasons for poor quality clustering. Based on this insight, we propose an uncertainty-aware clustering algorithm, UAC, which explicitly leverages uncertainty information during clustering to decide when a pair of faces are similar or when a predicted cluster should be discarded. UAC considers (a) uncertainty of faces in face-pairs, (b) bins face-pairs into different categories based on an uncertainty threshold, (c) intelligently varies the similarity threshold during clustering to reduce false-negatives and false-positives, and (d) discards predicted clusters that exhibit a high measure of uncertainty. Extensive experimental results on several popular benchmarks and comparisons with state-of-the-art clustering methods show that UAC produces significantly better clusters by leveraging uncertainty in face images – predicted number of clusters is up to 0.18X more of the ground truth for the IJB-A benchmark.

Read more

F3S: Free Flow Fever Screening

/in /by

Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19. We present a novel fever-screening system, F 3 S, that uses edge machine learning techniques to accurately measure core body temperatures of multiple individuals in a free-flow setting. F 3 S performs real-time sensor fusion of visual camera with thermal camera data streams to detect elevated body temperature, and it has several unique features: (a) visual and thermal streams represent very different modalities, and we dynamically associate semantically-equivalent regions across visual and thermal frames by using a new, dynamic alignment technique that analyzes content and context in real-time, (b) we track people through occlusions, identify the eye (inner canthus), forehead, face and head regions where possible, and provide an accurate temperature reading by using a prioritized refinement algorithm, and (c) we robustly detect elevated body temperature even in the presence of personal protective equipment like masks, or sunglasses or hats, all of which can be affected by hot weather and lead to spurious temperature readings. F 3 S has been deployed at over a dozen large commercial establishments, providing contact-less, free-flow, real-time fever screening for thousands of employees and customers in indoors and outdoor settings.

Read more

Austere Flash Caching with Deduplication and Compression

/in /by

Modern storage systems leverage flash caching to boost I/O performance, and enhancing the space efficiency and endurance of flash caching remains a critical yet challenging issue in the face of ever-growing data-intensive workloads. Deduplication and compression are promising data reduction techniques for storage and I/O savings via the removal of duplicate content, yet they also incur substantial memory overhead for index management. We propose AustereCache, a new flash caching design that aims for memory-efficient indexing, while preserving the data reduction benefits of deduplication and compression. AustereCache emphasizes austere cache management and proposes different core techniques for efficient data organization and cache replacement, so as to eliminate as much indexing metadata as possible and make lightweight in-memory index structures viable. Trace-driven experiments show that our AustereCache prototype saves 69.9-97.0% of memory usage compared to the state-of-the-art flash caching design that supports deduplication and compression, while maintaining comparable read hit ratios and write reduction ratios and achieving high I/O throughput.

Read more

LogLens: A Real-time Log Analysis System

/in /by

Administrators of most user-facing systems depend on periodic log data to get an idea of the health and status of production applications. Logs report information, which is crucial to diagnose the root cause of complex problems. In this paper, we present a real-time log analysis system called LogLens that automates the process of anomaly detection from logs with no (or minimal) target system knowledge and user specification. In LogLens, we employ unsupervised machine learning based techniques to discover patterns in application logs, and then leverage these patterns along with the real-time log parsing for designing advanced log analytics applications. Compared to the existing systems which are primarily limited to log indexing and search capabilities, LogLens presents an extensible system for supporting both stateless and stateful log analysis applications. Currently, LogLens is running at the core of a commercial log analysis solution handling millions of logs generated from the large-scale industrial environments and reported up to 12096x man-hours reduction in troubleshooting operational problems compared to the manual approach.

Read more