Alumni & Student SocialJoin us on Thursday, November 6, from 5–7 PM in Odeum A/B for the Alumni & Student Social. This event brings together WPI alumni, students, and faculty for an evening of networking, conversation, and community building.Celebrate Business Week by strengthening connections, sharing experiences, and creating opportunities for future collaborations. Whether you’re a student eager to learn from alumni or an alum looking to give back and reconnect, this social is the perfect way to conclude the week.Don’t miss this chance to engage with the WPI Business School community!You can register by clicking this link.

Title:Algorithm–Hardware Co-Design for LiDAR Point Cloud Processing: Classic and Learning-Based Approaches Abstract:LiDAR perception is a cornerstone of modern autonomous systems, enabling reliable 3D scene understanding for self-driving, robotics, and intelligent infrastructure. As sensors shift from mechanical to solid-state designs and produce denser, higher-rate point clouds, computational demands escalate. Meeting real-time, low-power, and small-form-factor requirements on edge platforms remains challenging for conventional CPU/GPU pipelines.This dissertation presents a hardware-friendly LiDAR perception framework through algorithm–hardware co-design on FPGAs and introduces three key designs: (1) a stream-based ground-segmentation method with a parallel optimization path for solid-state LiDAR, enabling fully pipelined, low-latency processing; (2) a Fast Channel-Clustering (FCC) algorithm for real-time instance formation using line-buffered, local-search micro-architectures; and (3) a ReAct Binary PointPillar detector that binarizes network weights and replaces multiplications with XNOR–popcount, substantially reducing computational and memory requirement while preserving accuracy. All designs emphasize deep pipelining, resource efficiency, and deterministic execution to meet tight timing and energy budgets on edge platforms.In addition to standalone modules, this work integrates classical geometric methods with learning-based approaches to form a hybrid pipeline. It proposes a panoptic-segmentation flow that couples semantic segmentation with clustering-based instance formation, achieving state-of-the-art accuracy.The proposed designs are evaluated on KITTI/SemanticKITTI dataset and real-world collected LiDAR data, demonstrating strong accuracy, high throughput, and robust generalization across multiple types of LiDAR — pointing toward practical, deployable LiDAR intelligence at the edge. Research Advisor:Prof. Xinming HuangECE Department, WPI Committee Members:Prof. Shahin TajikECE Department, WPIProf. Tian Guo Computer Science Department, WPIProf. Honggang WangGraduate Department Chair, Computer Science & Engineering, Yeshiva University

Join ODIME on Fridays from 12:00-2:00pm ET in OASIS House for milk and freshly baked cookies! For more information or accommodations, please contact ODIME at diversity@wpi.edu. All are welcome!

Gain insight from some of The Business School's most recent interns!Join us on Friday, November 7, from 3 PM–4 PM online for the "Preparing for your Internship Virtual Panel." Students who are currently or recently on internship will share about their experiences.Hear about their internship, their first date, lessons learned and what they would have done differently.You can register for the Zoom at this link.

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