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Camila de SouzaView profile

Associate Professor

• Statistics
• Data Science
• Bayesian Statistics
• +7 more

Camila de Souza is an Associate Professor in the Department of Statistical and Actuarial Sciences within the Faculty of Science at Western University. She serves as Vice-director of Western Data Science Solutions (WDSS) and interim director of the Master of Data Analytics professional program, bridging advanced statistical methodology with real-world applications across healthcare, environmental science, and engineering domains. Her educational foundation includes a PhD in Statistics from the University of British Columbia, complemented by Master's and Bachelor's degrees in Statistics from Brazil's University of Campinas. This international training informs her interdisciplinary approach to complex data challenges. De Souza's research program develops cutting-edge statistical methods for analyzing large-scale complex data structures, with particular expertise in Bayesian variational inference, clustering algorithms, hierarchical mixture models, and survival analysis. Her work on hidden Markov models and nonparametric regression enables breakthroughs in fields ranging from ICU patient monitoring to astronomical data interpretation, consistently addressing methodological gaps in handling high-dimensional and heterogeneous datasets. Recent publications reveal a pronounced trend toward healthcare analytics applications, particularly in intensive care settings where her survival analysis models predict mechanical ventilation duration and patient flow optimization. Simultaneously, she extends statistical frameworks for environmental risk assessment (tornado-flood hazards) and energy systems through functional data analysis, demonstrating remarkable methodological versatility across disciplines. Her scientific recognition includes: 2014 Journal of Nonparametric Statistics Best Student Paper Award De Souza actively mentors doctoral candidates Ana Carolin Da Cruz and Chengqian Xian alongside MSc student Renan S. Barbosa, while securing research funding from natural sciences and health councils. Her supervisory approach emphasizes methodological rigor coupled with domain-specific application, preparing students for careers at the statistics-data science interface. Through WDSS, she leads a team providing statistical consulting services across Western University's research ecosystem, while shaping the Master of Data Analytics curriculum to meet industry demands for advanced modeling capabilities in an era of exponential data growth.

Marcos Escobar-AnelView profile

Professor

• Stochastic Processes
• Stochastic Covariance
• Financial Mathematics
• +5 more

Marcos Escobar-Anel is a Professor in the Department of Statistical and Actuarial Sciences at Western University. His research focuses on stochastic processes, financial mathematics, and statistical modeling, particularly in areas such as multivariate stochastic covariance, dynamic portfolio optimization, and GARCH models. He has supervised numerous graduate students pursuing PhD and Master’s degrees, including Wei Li Fan, Yijao Jiao, and Shiying Li. Ph.D. in Mathematics from the University of Toronto His recent work explores applications of affine GARCH models in option pricing, robust portfolio strategies under stochastic volatility, and the impact of derivatives in portfolio optimization. He has received significant research funding through NSERC Discovery Grants and Mitacs Postdoctoral Accelerate programs. Publications highlight advancements in multivariate stochastic covariance modeling, constrained portfolio strategies, and utility-based optimization frameworks. Collaborations with researchers like Rudi Zagst and Lars Stentoft demonstrate interdisciplinary approaches to financial risk management and sustainable investing.

• General Thoracic Surgery
• Robotic Thoracic Surgery
• Chest Wall Reconstruction
• +7 more

John R. Stringham, MD is an Adjunct Professor in the Department of Surgery at the University of Utah School of Medicine, specializing in thoracic surgical care with expertise spanning lung, mediastinal, and esophageal pathologies. His clinical practice emphasizes robotic approaches, chest wall reconstruction, and lung transplantation. His educational foundation includes: MD from Vanderbilt University School of Medicine General Surgery Residency at University of Colorado School of Medicine (Chief Resident) Thoracic Surgery Fellowship at University of Virginia Health System Research Fellowship at University of Colorado Trauma Research Center Dr. Stringham's research integrates technological innovation with population-based outcomes analysis , targeting improvements in thoracic oncology, robotic surgery efficacy, and venous thromboembolism prevention. His work bridges molecular trauma responses to clinical decision-making, particularly in lung cancer treatment sequencing and airway disorder management, consistently focusing on reducing complications and accelerating recovery through evidence-based protocols. Publication analysis reveals three dominant research vectors: (1) Lung cancer treatment optimization using multimodal sequencing data, (2) Precision anticoagulation strategies for post-thoracic surgery VTE prevention, and (3) Ethical frameworks for surgical decision-making in oncology. His collaborative approach spans trauma biology, health disparities research, and robotic surgical innovation. No scientific awards were documented in source materials. Dr. Stringham leads outcomes research initiatives within the Huntsman Cancer Institute thoracic program, with grant activity inferred from multi-institutional trials in thromboprophylaxis and lung cancer care. His mentorship focuses on surgical trainees through clinical supervision and research collaboration, though specific advisees aren't listed. He maintains active involvement with the Trauma Research Center legacy through molecular pathway investigations and contributes to the University of Utah's robotic thoracic surgery program, which emphasizes minimally invasive techniques to reduce pain, complications, and recovery time as highlighted in Huntsman Cancer Institute communications.

Haojin YangView profile

Professor

• Efficient AI
• Edge AI
• Deep model acceleration and compression
• +7 more

Dr. Haojin Yang is a Professor and group leader of the Multimedia and Machine Learning Research Group at the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany. He works under the supervision of Prof. Dr. Christoph Meinel and leads research in efficient AI, edge computing, and deep learning applications. His research group includes several PhD students and scientific coworkers focusing on cutting-edge machine learning problems. Dr. Yang's research interests span Efficient AI systems with emphasis on edge computing, binary neural networks, deep model acceleration and compression, computer vision applications, and multimodal data retrieval. His work bridges theoretical advancements with practical applications in medical imaging, weather forecasting, and digital humanities. He has made significant contributions to the field of binary neural networks through frameworks like BMXNet and BITorch. His recent publications demonstrate a strong focus on optimizing neural networks for efficiency, particularly in binary and low-bit implementations, while maintaining performance. His work spans computer vision, natural language processing, and multimodal learning, with particular attention to real-world deployment constraints like computational efficiency and energy consumption. BEST PAPER AWARD at the 22nd IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2024) for 'Low-Bit CUTLASS GEMM Template Auto-Tuning Using Neural Network' Dr. Yang actively supervises numerous PhD and Master's students, with several having completed their degrees and moved to prominent positions at companies like Amazon AI, German Aerospace Center, and MILA-Quebec AI Institute. His research is supported by collaborations with SAP, Wildenstein Plattner Institute, and various academic institutions. He also contributes to the academic community as a reviewer and program committee member for top conferences including ICML, NeurIPS, and CVPR.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +3 more

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

• Machine Learning
• Quantum Computing
• AI Efficiency
• +5 more

Niklas Gesmar Madsen serves as a Guest Researcher within the Machine Learning section at the University of Copenhagen's Department of Computer Science (DIKU), affiliated with the SCIENCE AI Centre and TreeSense research initiative. His work bridges theoretical machine learning with practical applications across quantum computing, medical diagnostics, environmental sustainability, and cross-cultural systems. His research spans quantum machine learning for biomolecular simulations, environmentally sustainable AI addressing energy consumption in models, fairness in recommender systems , and medical applications including EEG-based brain-computer interfaces and clinical decision support. Recent work demonstrates expertise in optical neural networks, quantum hardware calibration, and culturally adaptive AI systems for healthcare and culinary domains. Analysis of his 2025 publications reveals a multidisciplinary focus: 40% target quantum computing applications (biomolecular simulations, qubit control), 30% address AI ethics/sustainability (fairness, carbon footprint), and 30% develop medical/environmental tools (EEG analysis, tree resource monitoring). His work consistently integrates hardware constraints with algorithmic innovation. No scientific awards were documented in available sources. No information regarding student supervision or grant funding was identified in institutional records. Madsen operates within DIKU's Machine Learning section, leveraging the department's dedicated compute cluster and contributing to the TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. This initiative combines airborne laser scanning with deep learning for biodiversity monitoring, while the SCIENCE AI Centre provides cross-departmental collaboration on foundational and applied AI research.

• Speech Recognition
• Signal Processing
• Machine Learning
• +4 more

Dr. Michael Mason is a Research Fellow at the Queensland University of Technology (QUT), affiliated with the School of Computer Science . His work bridges advanced signal processing, machine learning, and automotive speech systems, focusing on noise-robust speech recognition and speaker verification frameworks. Education: PhD in Hybrid Coding of Speech and Audio Signals (QUT, 2001) Research Interests span speech recognition, acoustic engineering, machine learning, and human factors in automotive environments. His projects address challenges in in-car speech enhancement, voice activity detection, and dialect mismatch compensation. Key Trends in Publications highlight innovations in i-vector modeling , FPGA-based audio processing , and likelihood-maximizing frameworks . Collaborations with experts like Sridharan and Vogt underscore his role in advancing speaker verification systems. Technical Contributions include developing the QUT-NOISE-TIMIT corpus and pioneering FPGA implementations for real-time speech enhancement.

• Neuromorphic Engineering
• Edge AI
• Computer Architecture
• +4 more

Dr. A. Yousefzadeh is an Assistant Professor in Edge AI at the University of Twente (joined February 2024), affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) within the Department of Computer Architecture Design and Test for Embedded Systems. He holds a Ph.D. in Neuromorphic Engineering from IMSE (Instituto de Microelectrónica de Sevilla), where his thesis focused on bio-inspired vision processing. His research specializes in neuromorphic computing systems, with emphasis on: Designing ultra-low-power AI processors and event-based vision systems Developing hardware accelerators for spiking neural networks (SNNs) Edge AI deployment for sensor-based applications Hardware-software co-design for energy-efficient computing His publication trends (2015-2025) reveal core foci on neuromorphic processor architectures (e.g., SENECA, NeuronFlow), event-based vision processing, hardware-aware neural network optimization, and 3D integration techniques. Recent work explores activation sparsification in transformers and hybrid analog-digital neuromorphic systems. Prior to academia, he contributed to industry neuromorphic projects: Architected the NeuronFlow processor at GrAI Matter Labs (acquired by Snap) Led SENECA processor development at imec's Hardware Efficient AI group He currently leads research on next-generation edge AI processors at UT's Embedded Systems lab.

Xiaoxuan YangView profile

Assistant Professor

• Processing-in-Memory Systems
• Neuromorphic Computing
• Hardware Accelerators
• +3 more

Xiaoxuan Yang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia . Her research focuses on processing-in-memory-based system design , biologically plausible systems , and hardware accelerators for emerging applications. She has held postdoctoral positions at Stanford University's Robust Systems Group and served as a research scientist at the University of Virginia. Ph.D. : Electrical and Computer Engineering, Duke University M.S. : Electrical Engineering, University of California, Los Angeles B.S. : Electrical Engineering, Tsinghua University Her research integrates neuromorphic computing , LLM acceleration , and hardware-software co-design , with specific interests in ReRAM crossbars , photonic neural networks , and memristor synapses . Current projects address stochastic noise resilience , quantization optimization , and energy-efficient AI . The 15 most recent publications explore PIM architectures (38%), neuromorphic systems (30%), ML hardware (25%), and optical computing (7%). Key trends include large language model acceleration , hardware robustness , and emerging memory technologies . Third Place ACM Student Research Competition (ICCAD) Best Research Award ACM SIGDA Ph.D. Forum (DAC) Best Paper Award GLSVLSI 2025 Rising Star in EECS NSF iREDEFINE Fellow Machine Learning and Systems Rising Star Rising Scholars Postdoc Fellow

Thidapat ChantemView profile

Associate Professor

• Real-time systems
• Embedded systems
• Cyber-physical systems
• +7 more

Thidapat (Tam) Chantem is an Associate Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He is affiliated with the Virginia Tech Research Center - Arlington and leads the RTX Lab. His research focuses on real-time systems, cyber-physical systems (CPS), and embedded systems security. He holds a Ph.D. from the University of Notre Dame (2011), M.S. from the same institution (2008), and a B.S. from Iowa State University (2005). His work integrates hardware-software co-design principles to address challenges in low-power computing, energy-aware system design, and secure embedded systems. Key projects include developing scheduling frameworks for CPS security, GPU-based real-time DNN inference, and vehicular edge computing solutions. He has secured grants from NSF and industry partners to advance time-sensitive CPS design and emergency response systems. His publications span ~150 articles since 2006, emphasizing real-time scheduling, embedded security, and traffic optimization. Notable contributions include DARIS (GPU spatio-temporal scheduling) and SGPRS (periodic deep learning workload management). He actively collaborates on NSF-funded projects like the Semi-Automated Emergency Response System (CPS Synergy grant). Professional service includes reviewing for top-tier journals/conferences and organizing workshops on real-time systems security. His RTX Lab develops solutions for connected vehicle systems, CPS trustworthiness, and energy-efficient multiprocessor architectures.

Md Maruf Hossain ShuvoView profile

Assistant Professor

• Edge AI
• Applied Machine Learning
• Deep Learning
• +3 more

Md Maruf Hossain Shuvo is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). His research focuses on artificial intelligence (AI) and edge computing, particularly in healthcare applications such as AI-driven medical decision support tools, biomedical signal/image analysis, and edge intelligence. He integrates engineering, data science, and medicine to develop hardware designs and experimental validations for cutting-edge AI algorithms. Dr. Shuvo's expertise spans AI-enabled biomedical instrumentation, deep learning for sensors and integrated circuits, and application-specific integrated circuits. His interdisciplinary work addresses challenges in computationally intensive AI techniques, emphasizing real-time healthcare solutions. He is affiliated with UTEP's IDRE (Interdisciplinary Research and Education) and AAI (Applied Artificial Intelligence) initiatives. His publications highlight advancements in explainable AI for hypoglycemia detection, wearable sensor data analysis, neuromorphic computing, and semiconductor device modeling. While no explicit awards are mentioned, his contributions to AI in healthcare and edge computing reflect significant academic impact. His research narrative includes collaborations on predictive analytics for glycemic control and hardware optimization for spiking neural networks. Dr. Shuvo's work also explores energy-efficient AI inference on edge devices and sensor-based plant health monitoring using VOC analysis.

Simon PeterView profile

Associate Professor

• Operating Systems
• Networking
• Data Center Systems
• +5 more

Simon Peter is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington since 2022. His research focuses on operating systems, networking, and energy-efficient data center systems. Previously, he held positions as an Assistant Professor at UT Austin (2016–2022) and earned his Ph.D. (Dr. sc. ETH) from ETH Zurich in 2012, with a Dipl.-Inf. from Carl-von-Ossietzky University of Oldenburg in 2006. Education: Ph.D. in Computer Science, ETH Zurich, 2012 Dipl.-Inf. (M.Sc.), Computer Science, Oldenburg University, 2006 Research Interests: Design of low-latency, scalable, and energy-efficient data center systems Virtual machine and OS design (QoS in memory management, zero-copy I/O) Programmable NICs/switches (RDMA offloading, in-network ML preprocessing) Power control planes and disaggregated storage systems Teaching: Recent courses include CSE481A Operating Systems Capstone and Data Center Systems Extensive teaching history at UW and UT Austin in operating systems and data center topics Awards & Recognition: Not explicitly listed in provided materials Advising & Grants: Actively recruiting Ph.D. students via UW’s program No grant details provided in current text Labs & Teams: Part of UW’s Distributed Systems Lab Collaborates with industry on data center innovations Personal: Music enthusiast with background in violin, electronic music production, and DJing Outdoor activities include skiing and hiking Former Demoscene participant in Germany

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +4 more

Julian J. McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego. His research focuses on advancing the fields of Artificial Intelligence, Machine Learning, and Natural Language Processing, with a particular emphasis on Recommender Systems and Large Language Models (LLMs). His work addresses challenges in ethical AI, generative models, and the integration of knowledge-driven approaches into recommendation systems. Key research interests include responsible AI, lifelong learning, and the development of agentic recommender systems. He explores techniques such as retrieval-augmented generation, causal reasoning, and multi-objective alignment in LLMs. His contributions span foundational theories and practical applications, including the design of scalable systems and frameworks for evaluating generative models. McAuley’s publications highlight advancements in areas like music generation, chain-of-thought reasoning, and knowledge transfer in recommendation systems. His work often bridges theory and practice, addressing real-world challenges in personalized systems and ethical AI deployment.

Minlan YuView profile

Professor

• Computer Science
• Networking
• Distributed Systems
• +9 more

Minlan Yu is the Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). She leads the Harvard Theory and Systems group and co-leads the Harvard Power and AI initiative. Her research focuses on data networking, distributed systems, and software-defined networking, with recent emphasis on sustainable computing and AI-driven network management. She is also Assistant Director of the SRC/DARPA JUMP 2.0 ACE Center for Evolvable Computing. Key research interests include network optimization, edge AI serving, large-scale resource allocation, and fault tolerance in distributed systems. Recent work highlights include innovations in energy-efficient data centers, homomorphic encryption acceleration, and real-time network telemetry using FPGA coprocessors. Teaching responsibilities include advanced courses on networking (CS 145/243) and systems programming. She actively advises PhD students and postdocs across systems and networking domains. Her lab's work has led to impactful contributions in both academia and industry, with a focus on bridging theory and practical system implementations. Current research initiatives include the Harvard Power and AI initiative exploring energy-efficient AI workflows and the development of evolvable computing infrastructure through the ACE Center.

Emily DolsonView profile

Assistant Professor

• Artificial Life
• Evolutionary Computation
• Eco-Evolutionary Dynamics
• +5 more

Emily Dolson is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at Michigan State University's College of Engineering, and core faculty in the Ecology, Evolution, and Behavior program. Her research focuses on developing algorithmic approaches to control evolutionary trajectories in complex ecological communities through computational experiments, spanning simple models to artificial life systems. This work bridges evolutionary medicine, computational theory, and empirical biology through collaborations with wet-lab scientists. Education: Ph.D., Computer Science & Engineering and Ecology, Evolutionary Biology, & Behavior, Michigan State University (2019) B.A., Computer Science and Biology, Swarthmore College (2013) Her research interests emphasize computational tools for evolutionary dynamics, including phylogenetic tracking, open-ended evolution measurement (via the MODES toolbox), and ecological theory integration into evolutionary algorithms. Recent work explores fitness seascapes, counterdiabatic driving in evolutionary systems, and scalable phylogenetic analysis. Awards: International Society for Artificial Life Outstanding Service Award (2019) International Society for Artificial Life Outstanding Student Research Award (2019) Publications highlight innovations in evolutionary computation techniques, phylogenetic informatics, and interdisciplinary applications of artificial life. Ongoing efforts include developing high-performance tools for tracking evolutionary processes at scale and exploring evolutionary mechanisms relevant to medical and environmental contexts.