Past Events

Announcing: MS Thesis Defense Name: Matthew Wallace Title: Model Predictive Planning Date: 3/29/2024 Time: 10:00:00 AM Location: Room: HS 204.  Link: Teams Committee Members: Prof. Laurent Lessard (Advisor) Prof. Michael Everett Prof. Derya Aksaray Abstract: This thesis presents Model Predictive Planning (MPP), a trajectory planner for low-agility vehicles such as a fixed-wing aircraft to navigate obstacle-laden environments.  MPP consists of (1) a multi-path planning procedure that identifies candidate paths, (2) a raytracing procedure that generates linear constraints around these paths…

Announcing: PhD Proposal Review Name: Reza Vafaee Title: Efficient Algorithms for Sparse Sensor Scheduling in Large-Scale Dynamical Systems with Performance Guarantees Date: 4/1/2024 Time: 10:30:00 AM Location: Zoom Committee Members: Prof. Milad Siami (Advisor) Prof. Eduardo Sontag Prof. Laurent Lessard Prof. Alex Olshevsky (Boston University) Abstract: This research proposal introduces innovative frameworks for sparse sensor scheduling in large-scale dynamical networks. The first framework addresses sensor scheduling in discrete-time linear time-invariant dynamical networks, presenting a novel learning-based rounding method to convert…

Announcing: PhD Dissertation Defense Name: Batool Salehihikouei Title: Leveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins Date: 4/3/2024 Time: 11:00:00 AM Location: EXP-601A Committee Members: Prof. Kaushik Chowdhury (Advisor) Prof. Hanumant Singh Prof. Josep Jornet Dr. Mark Eisen Abstract: With the widespread Internet of Things (IoT) devices, a wide variety of sensors are now present in different environments. For example, self-driving vehicles and automated warehouses depend on sensor information for navigation and…

Announcing: PhD Dissertation Defense Name: Kaustubh Shivdikar Title: Enabling Accelerators for Graph Computing Date: 4/3/2024 Time: 3:30 PM Location: Zoom Committee Members: Prof. David Kaeli (Advisor) Prof. Devesh Tiwari Prof. Ajay Joshi (Boston University) Prof. John Kim (KAIST) Prof. José Luis Abellán (University of Murcia) Abstract: The advent of Graph Neural Networks (GNNs) has revolutionized the field of machine learning, offering a novel paradigm for learning on graph-structured data. Unlike traditional neural networks, GNNs are capable of capturing complex relationships…

Announcing: PhD Dissertation Defense Name: Anu Jagannath Title: Deep Learning at the Edge for Future G Networks: RF Signal Intelligence for Comprehensive Spectrum Awareness Date: 4/4/2024 Time: 1:00:00 PM Committee Members: Prof. Tommaso Melodia (Advisor) Prof. Kaushik Chowdhury Prof. Yanzhi Wang Abstract: Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant…

Announcing: PhD Proposal Review Name: Nicolas Bohm Agostini Title: Hardware/Software Codesign and Compiler Techniques for Efficient Hardware Acceleration of Dense Linear Algebra Kernels and Machine Learning Applications Date: 4/4/2024 Time: 3:30:00 PM Location: Zoom Committee Members: Prof. David Kaeli (Advisor) Prof. Gunar Schirner Prof. José Luis Abellán (University of Murcia) Antonino Tumeo (PNNL) Abstract: Today's linear algebra and machine learning applications (ML) continue to grow in size and complexity, placing rapidly increasing demands on the underlying hardware and software systems.…

Announcing: PhD Dissertation Defense Name: Jinkun Zhang Title: Low-latency Forwarding, Caching and Computation Placement in Data-centric Networks Date: 4/8/2024 Time: 3:30:00 PM Location: EXP-459, Committee Members: Prof. Edmund Yeh (Advisor) Prof. Stratis Ioannidis Prof. Kaushik Chowdhury Abstract: With the exponential growth of data- and computation-intensive network applications, such as real-time augmented reality/virtual reality rendering and large-scale language model training, traditional cloud computing frameworks exhibit inherent limitations. To address these challenges, dispersed computing has emerged as a promising next-generation networking paradigm.…