جست‌وجوی استاد و پژوهشگر

• Antenna Design
• 5G Communications
• mmWave Technology
• +۴ مورد دیگر

Dr. Md Hasanuzzaman Sagor is a Lecturer at the School of Electronic Engineering and Computer Science , Queen Mary University of London. His research focuses on advanced antenna systems for emerging communication technologies, including transparent/flexible 5G antennas, wearable device antennas, and metamaterial-loaded RF components. Teaching: Digital Circuit Design, Microprocessor Systems Design, Networks and Protocols (all for BUPT joint programme) Research Highlights: Pioneering work in mmWave antenna design, energy harvesting rectennas, and graphene-based THz antennas Key Project: Co-PI and Head of Ground Station Design Team for BRAC Onnesha, Bangladesh's first satellite under the BIRDS project Contact: m.h.sagor@qmul.ac.uk His recent work explores material innovations in antenna design, showing a clear trajectory from wearable communication systems to education technology applications. Notably, Sagor's research team successfully deployed space-bound technology through their satellite project collaboration. Research Trends: Analysis of publications reveals sustained focus on mmWave antenna geometries (2014-2021), with recent expansion into educational technology systems (2024-2025). Key technical contributions include novel metamaterial integration methods and wearable device optimization.

Natasha Jaquesمشاهده پروفایل

استادیار

• Reinforcement Learning
• Multi-Agent Learning
• Human-AI Interaction
• +۵ مورد دیگر

Natasha Jaques is an Assistant Professor at the University of Washington Paul G. Allen School of Computer Science & Engineering and a Senior Research Scientist at Google DeepMind. She leads the Social RL Lab , focusing on algorithms that integrate social learning with multi-agent reinforcement learning (RL) to enhance AI agents' learning, generalization, coordination, and human-AI interaction. PhD from MIT Media Lab (2019) MSc in Computer Science from University of British Columbia (2014) BSc/BA in Computer Science and Psychology from University of Regina (2012) Her research spans three core areas: Social Reinforcement Learning - Developing frameworks like PAIRED for emergent complexity and zero-shot transfer through adversarial environment generation, and Social Influence for intrinsic motivation in multi-agent coordination. Human-AI Interaction - Innovating offline RL techniques for dialog systems using implicit signals like sentiment, and affective computing approaches for physiological data analysis. Machine Learning for Wellbeing - Pioneering personalized multi-task models to predict mood, stress, and health outcomes using hierarchical Bayes and domain adaptation. Her scientific awards include: Best Paper, AAAI Representation Learning for Responsible Human-Centric AI workshop (2023) Outstanding PhD Dissertation, Association for the Advancement of Affective Computing (2021) Best of Collection, IEEE Transactions on Affective Computing (2021) Best Paper, NeurIPS Workshop on Cooperative AI (2020) Best Paper Honourable Mention, ICML (2019) Rising Stars in EECS Pitch Competition Winner (2019) Jaques has made significant contributions to open-source tools like the EDA Explorer for wearable data analysis and PAIRED environment generation. Her work appears in top venues including NeurIPS , ICML , EMNLP , and CVPR , with press coverage in Science Magazine , MIT Technology Review , and CBC Radio .

Naveen Vermaمشاهده پروفایل

دانشیار

• Advanced Sensing Systems
• Machine Learning
• Statistical Signal Processing
• +۳ مورد دیگر

Naveen Verma is an Associate Professor at Princeton University's Department of Electrical Engineering, specializing in advanced sensing systems. His research bridges algorithm development with novel sensing and computing technologies. Education: B.A.Sc. from UBC (2003), M.S./Ph.D. from MIT (2005/2009) Key Focus: Large-area flexible sensors, energy-efficient architectures, machine-learning algorithms His work explores how systems for learning and inference can be enhanced through new hardware paradigms, particularly in thin-film electronics and analog computing. Recent publications highlight applications in imaging systems, biomedical sensors, and error-resilient machine learning. His research outputs include: Embedded thin-film classifiers for image sensing Compressive-sensing circuits using flexible electronics Error-adaptive boosting algorithms for hardware resilience Energy-scalable processor designs Scientific awards received: NSF CAREER Award (2013) AFOSR Young Investigator Award (2014) IEEE Best Paper Awards Teaching excellence awards Industry recognition from Intel and IEEE societies