جست‌وجوی استاد و پژوهشگر

Debasmita Loharمشاهده پروفایل

پژوهشگر ارشد

• Program Analysis
• Approximate Computing
• Probabilistic Analysis
• +۵ مورد دیگر

Debasmita Lohar is a Postdoctoral Researcher at Karlsruhe Institute of Technology (KIT), working with Bernhard Beckert in the Application-oriented Formal Verification group at KASTEL — Institute of Information Security and Dependability. She is set to join the IT University of Copenhagen as an Assistant Professor starting December 2024. Her educational background includes: PhD from Saarland University in collaboration with the Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, advised by Eva Darulova Graduate studies at the Indian Institute of Technology (IIT) Kharagpur, advised by Soumyajit Dey Dr. Lohar's research focuses on the intersection of program analysis, approximate computing, and probabilistic analysis, with applications spanning embedded systems, scientific computing, and machine learning. Her work addresses critical challenges in numerical error analysis, particularly in the context of finite-precision arithmetic and its implications for system reliability and safety. She has made significant contributions to sound static program analysis techniques for neural networks and cyber-physical systems, developing novel approaches for mixed-precision tuning and quantization that balance computational efficiency with numerical accuracy guarantees. Her recent publications demonstrate a strong trajectory in formal methods for numerical programs, with increasing focus on neural networks and cyber-physical systems. The research shows a clear progression from fundamental numerical error analysis to practical applications in machine learning systems, reflecting the growing importance of formal verification in AI safety-critical domains. Her scientific recognition includes: Selected to participate in the 10th Heidelberg Laureate Forum, 2023 Best Presentation Award (iFM PhD Symposium), 2019 Dr. Lohar has extensive teaching experience across multiple institutions, having taught courses on Floating-Point Analysis, LLMs in Formal Verification, Neural Networks in Formal Verification at KIT, Advanced Program Analysis and Program Analysis at Saarland University, and Fault Tolerant Systems, Theory of Computation, and Computer Organization and Architecture Lab at IIT Kharagpur. Her research is supported through her position at KASTEL and likely through competitive research grants given her publication record in top-tier venues. She leads research in the Application-oriented Formal Verification group at KASTEL, collaborating with researchers like Bernhard Beckert, Eva Darulova, and others. Her work on the Aster project demonstrates her leadership in developing practical tools for sound mixed fixed-point quantization of neural networks.

Sian Jinمشاهده پروفایل

استادیار

• High-performance computing (HPC)
• Data reduction & lossy compression
• Parallel file system & I/O systems
• +۳ مورد دیگر

Sian Jin is an Assistant Professor in the Department of Computer & Information Sciences at Temple University. He holds a Ph.D. in Computer Engineering from Indiana University (2023) and a B.S. in Physics from Beijing Normal University (2018). His research focuses on high-performance computing (HPC), data reduction, and machine learning optimization, particularly leveraging lossy compression techniques to improve scientific data analytics and management. Key achievements include over 20 top-tier publications (SC, VLDB, EuroSys, etc.), development of frameworks like Foresight and DeepSZ, and recognition via awards such as the SC 2022 Student Grant and DGRP 2022. His work addresses challenges in parallel I/O acceleration, error-bounded compression for scientific simulations, and efficient DNN training through compression. Current research emphasizes in situ task scheduling for HPC applications and neural network-driven super-resolution techniques. He advises on Ph.D. opportunities in HPC, machine learning, and data reduction systems.

Tony Gengمشاهده پروفایل

استادیار

• Computer Architecture
• Machine Learning
• Generative AI
• +۶ مورد دیگر

Tony Geng is an Assistant Professor at the University of Rochester, jointly appointed in the Departments of Electrical and Computer Engineering and Computer Science. He directs the IntelliArch Lab and holds secondary affiliations with the Goergen Institute for Data Science. His research spans computer architecture, generative AI, graph intelligence, and high-performance computing, focusing on sustainable and efficient artificial general intelligence. His work integrates hardware-software co-design for accelerating machine learning workloads, with applications in scientific discovery, fintech, and social media. Recent projects explore dynamical systems for graph learning and diffusion models for scientific simulations. Notable recognitions include multiple DOE and NSF research awards, the Excellence in Graduate Teaching Award, and substantial GPU allocations from NERSC for generative AI research. He actively mentors PhD students and postdocs in sustainable AI development.

• Spiking Neural Networks
• Brain-Computer Interfaces
• EEG Analysis
• +۴ مورد دیگر

Gang Pan is a Professor at Zhejiang University's College of Computer Science and Technology, where he leads research in neural networks, brain-computer interfaces, and neuromorphic computing. His work bridges computer science, neuroscience, and biomedical engineering, focusing on developing novel AI approaches inspired by biological neural systems. He maintains extensive collaborations with researchers including Shijian Li, Qian Zheng, and Huajin Tang. Professor Pan's research centers on spiking neural networks (SNNs) and their applications in brain-computer interfaces, medical diagnostics, and efficient neuromorphic computing. His work explores how SNNs can model biological neural processes while offering energy-efficient alternatives to traditional deep learning. Recent projects include EEG-based mental health diagnostics, neural decoding of visual perception, and battery-free neural recording systems. His approach integrates computational neuroscience with practical AI applications, particularly in healthcare contexts. Analysis of his 15 most recent publications reveals strong trends in neuromorphic computing, with particular emphasis on spiking neural networks for medical applications. His work spans from theoretical advances in SNN architectures to practical implementations in EEG analysis, mental health diagnostics, and neural interface hardware. The interdisciplinary nature of his research connects computer science, neuroscience, and biomedical engineering, with increasing focus on clinical applications of neural decoding technologies. Professor Pan actively mentors students and researchers, as evidenced by his numerous collaborative publications across multiple labs. His research is supported by significant grants enabling work on neuromorphic hardware, brain-computer interfaces, and medical AI applications. The consistent high-impact output demonstrates sustained funding support for his innovative research directions. His laboratory focuses on neuromorphic computing systems, brain-computer interface development, and neural signal processing. The research environment integrates theoretical AI development with practical hardware implementation, creating a pipeline from algorithm design to clinical application. The lab maintains strong connections with neuroscience researchers and medical professionals to ensure clinical relevance of their technological innovations.

Tahmina Zebinمشاهده پروفایل

مدرس ارشد

• Explainable AI
• Inclusive AI
• Human Activity Recognition
• +۷ مورد دیگر

Dr. Tahmina Zebin serves as a Senior Lecturer in Computer Science within the College of Engineering, Design and Physical Sciences at Brunel University London. Previously, she held academic positions at the University of East Anglia as a Lecturer leading the On-device and Explainable AI Research Group, and completed postdoctoral work at the University of Manchester on the EPSRC-funded Wearable Clinic project. Her academic foundation includes a PhD in Electrical and Electronic Engineering from the University of Manchester, where she was awarded the Presidents Doctoral Scholarship. Her educational background comprises: PhD in Electrical and Electronic Engineering, University of Manchester (2013-2016), funded by Presidents Doctoral Scholarship M.Sc in Digital Image and Signal Processing, University of Manchester (2012) MS in Applied Physics, Electronics, and Communication Engineering, University of Dhaka First degree in Applied Physics, Electronics, and Communication Engineering, University of Dhaka Dr. Zebin's research centers on Explainable and Inclusive AI with significant contributions to healthcare and agricultural technology. Key focus areas include: Advanced Video and Signal Processing for Human Activity Recognition using wearable sensors Risk Prediction Modeling from Longitudinal Electronic Health Records Medical Image Analysis for disease detection and progression tracking On-Device AI deployment methodologies for resource-constrained environments Multimodal Algorithm Development for equitable AI systems Her recent publications (2023-2024) reveal a strong trend toward practical AI deployment in precision agriculture and cybersecurity, featuring robust CNNs for agricultural spraying evaluation, interpretable quantized networks for edge devices, and explainable intrusion detection systems. This work demonstrates consistent emphasis on resource efficiency, model transparency, and real-world applicability. Notable recognition includes: Presidents Doctoral Scholarship (2013-2016) from the University of Manchester Dr. Zebin actively supervises PhD students in specialized domains including medical image analysis, physical activity monitoring for smart homes, on-device model deployment, and explainable AI applications in fintech/cybersecurity. Her research has been supported by: EPSRC Wearable Clinic project during postdoctoral work Presidents Doctoral Scholarship for doctoral research She contributes to the Intelligent Data Analytics Research Group at Brunel University London, focusing on developing cutting-edge AI methodologies for healthcare and agricultural applications with emphasis on ethical implementation and accessibility.

Dan Alistarhمشاهده پروفایل

استاد

• Machine Learning
• Algorithms
• Distributed Systems
• +۳ مورد دیگر

Dan Alistarh is a Professor at the Institute of Science and Technology Austria (ISTA) and ML Research Lead at Neural Magic, Inc. His research focuses on efficient algorithms and systems for machine learning, spanning theoretical foundations to practical implementations. He holds a PhD from EPFL and has held postdoctoral positions at MIT CSAIL and researcher roles at ETH Zurich and Microsoft Research. Education: PhD in Computer Science from EPFL (advisor: Prof. Rachid Guerraoui), Postdoctoral Associate at MIT CSAIL (advisor: Prof. Nir Shavit). Research interests include distributed systems, optimization algorithms, neural network compression (quantization, pruning), and parallel computing. His work bridges theory and practice, with impactful contributions to LLM efficiency, such as GPTQ, Marlin, and SparseGPT. He leads a lab at ISTA with active collaborations and visiting researchers from top institutions. Key scientific awards include the Best Paper Award at DISC 2021 (for work on leader election algorithms) and a Distinguished Paper Award at SPAA 2023. His research is supported by grants from FWF BILAI, ERC, NVIDIA, Google, and Amazon. Advising and grants: Supervises PhD students and postdocs focusing on ML efficiency. Open positions exist for interns, PhD candidates, and postdocs with backgrounds in CS/Math. Recent lab achievements include 4 NeurIPS 2024 acceptances, including an oral presentation for PV-Tuning. Labs/Teams: IST-DASLab, known for open-source contributions (GitHub: IST-DASLab) and innovations in LLM compression. Collaborations span academia and industry, with alumni progressing to roles at OpenAI, Neural Magic, and top universities.

Matthieu Martelمشاهده پروفایل

استاد

• Green & Frugal Computing
• Precision Tuning
• Scientific Data Compression
• +۵ مورد دیگر

Matthieu Martel is a Professor in Computer Science at Université de Perpignan Via Domitia and serves as Vice-President for International & Cross-Border Relations. He leads the Laboratoire de Mathématiques et de Physique (LAMPS) and is a co-founder and scientific advisor of Numalis, a startup focused on reliable numerical computation. His research spans precision tuning, scientific data compression, numerical accuracy, and safety-critical systems. He has advised numerous PhD students and collaborates on projects like Linguatec IA and Numalis . Research Interests: Green computing, precision tuning, neural network validation, embedded systems safety, and abstract interpretation. Recent work includes error-bounded compressed array computations and formal verification of neural networks. Professional Activities: Organizes EJCP 2024 and serves on conference committees (CODIT, CoDaC, ICSRS). Awards include the Best Paper Award at DRBSD 2023 and SC '23 Workshops. Labs & Teams: Active in LAMPS lab and collaborates with Numalis on software tools for reliable numerical computation.

Ravi Ramachandranمشاهده پروفایل

استاد

• Digital Speech Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Ravi Ramachandran is a Full Professor in the Department of Electrical & Computer Engineering at Rowan University, affiliated with the Henry M. Rowan College of Engineering. He holds a Ph.D. from McGill University (1990), following M.E. (1986) and B.E. (1984) degrees from McGill and Concordia University, respectively. His research focuses on digital speech processing, machine learning, signal processing, and educational technology. Professional experience includes roles at AT&T Bell Laboratories, Rutgers University, and T-NETIX/SpeakEZ Inc. He teaches courses ranging from introductory clinics to graduate-level digital speech processing. Active in IEEE and ASEE, he leads NSF-funded projects integrating biometrics and machine learning across undergraduate curricula. Research emphasizes applications of AI in speech processing, including speaker recognition, speech coding, and health technologies. Notable grants include a 2011-2015 NSF TUES award for vertical integration of biometrics in ECE education. Over 160 publications span journals like IEEE Transactions and conferences such as ISCAS.

Shuangqing Weiمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• Network Security
• +۴ مورد دیگر

Shuangqing Wei is the Michel B. Voorhies Distinguished Professor and Division Chairman in the Division of Electrical & Computer Engineering at Louisiana State University (LSU). He holds a Ph.D. (2003) from the University of Massachusetts and is affiliated with the School of Electrical Engineering and Computer Science. His research focuses on wireless communication systems, including OFDMA networks, multiple antenna systems, and privacy-enhanced communication protocols. Key research interests include: Multi-agent reinforcement learning and cooperative strategies Privacy-preserving techniques in federated learning and data analysis Covert communication over AWGN channels Optimization algorithms for distributed systems Cognitive radio networks and spectrum management Recent work emphasizes communication efficiency in distributed optimization (e.g., ADMM with Gaussian process regression) and trade-offs between privacy and utility in machine learning systems. His publications span over two decades, addressing topics from information theory fundamentals to applied security in vehicular networks and social media systems. He maintains an active research group at LSU, exploring interdisciplinary challenges at the intersection of signal processing, machine learning, and network security. Labs/Teams : Active involvement in LSU's wireless communication and machine learning research initiatives, including collaborative projects on federated learning frameworks and secure multi-agent systems.

Stefan Heimمشاهده پروفایل

استاد

• Neuroanatomy
• Language Processing
• Cognitive Neuroscience
• +۱ مورد دیگر

Prof. Dr. Stefan Heim leads the Neuroanatomy of Language working group at Research Center Jülich's Institute of Neuroscience and Medicine (INM-1). His research bridges cognitive neuroscience and computational approaches to language processing. Research Focus His work investigates the structural and functional organization of language networks in the brain, combining neuroanatomical approaches with advanced computational methods. Current projects explore machine learning applications in neuroscience and computational linguistics. Publication Trends Recent publications focus on machine learning innovations including large language models, efficient training techniques, and applications in scientific domains like plasma physics and renewable energy.

Yiren Zhaoمشاهده پروفایل

استادیار

• Large-scale GenAI Systems
• Hardware-Algorithm Co-design
• AI Security
• +۴ مورد دیگر

Dr. Yiren Zhao is an Assistant Professor (Lecturer) in Computer Engineering at Imperial College London, part of the Faculty of Engineering. He leads the DeepWok Research Lab and holds affiliations with the UKRI Artificial Intelligence for Engineering Biology Consortium and the University of Cambridge as a Visiting Researcher. His work focuses on Large-scale GenAI Systems, emphasizing hardware-algorithm co-design, efficiency, and security. He has secured over £5M in funding since 2022 and reviews for top conferences like ICLR, ICML, and NeurIPS. Dr. Zhao earned a BEng in Electrical and Electronic Engineering from Imperial College London (2016), followed by an MPhil and PhD in Computer Science from the University of Cambridge (2017–2022). His research spans GenAI acceleration, unstructured data processing (e.g., graphs), and system-level AI safety. Recent projects include MASE (unified ML system exploration) and ImpNet (imperceptible backdoor attacks). Awards: Apple Scholar in AI/ML (2020), Microsoft Research Award (2023), and Junior Research Fellowship at St John’s College (2021). Labs/Teams: DeepWok Lab (20+ members), involved in Imperial-X and AI-4-EB initiatives. Grants: Industry/government-funded projects totaling over £5M since 2022. His work bridges computer systems and AI, with notable contributions to quantization, adversarial attacks, and hardware-aware optimization. Collaborations include the Center for Spatial Computational Learning and CaRAML group.

Michele Magnoمشاهده پروفایل

مدرس ارشد

• Smart Sensing
• Low-Power Machine Learning
• Wireless Sensor Networks
• +۴ مورد دیگر

Michele Magno is a Senior Lecturer and Privatdozent at ETH Zürich's Department of Information Technology and Electrical Engineering (D-ITET), leading the D-ITET Center for Project-based Learning (pbl.ee.ethz.ch). He holds a PhD in Electronic Engineering from the University of Bologna (2010) and has held visiting roles at institutions like the University of Nice and Mid Sweden University. His research focuses on low-power systems, wearable devices, energy harvesting, and IoT applications. Magno has authored over 350 peer-reviewed papers, with a Google H-index of 49. Notable awards include the 2024 Best Paper Award at ECCV and multiple best poster/demo recognitions at IEEE conferences. His industrial collaborations include projects with STMicroelectronics, Texas Instruments, and Logitech. Teaching contributions include courses on embedded systems, FPGA programming, and machine learning on microcontrollers. Magno's innovations span smart sensors for wind turbines, bio-medical monitoring, and autonomous racing systems, with patents in touch communication and energy-neutral devices. Recent work emphasizes ultra-low-power solutions for AI-integrated wearables, energy-efficient IoT nodes, and real-time embedded vision systems. His labs and teams pioneer technologies like TinyssimoRadar for in-ear gesture recognition and WakeMod for ultra-low-power IoT connectivity.

• Video Coding
• Energy Efficiency
• HEVC
• +۴ مورد دیگر

Christian Herglotz is a researcher affiliated with the University of Erlangen-Nuremberg , Germany. His work focuses on energy efficiency in video coding and decoding systems, with a particular emphasis on HEVC and VVC standards. He has published extensively in IEEE journals and conferences like ICIP, ICASSP, and QoMEX, often collaborating with André Kaup and Matthias Kränzler. Key research themes: energy-aware video compression, decoding power optimization, rate-energy-distortion modeling. Co-edited special sections on deep learning-based video coding. Recent Publications (2022-2025): Explored power reduction in HDR video encoding, motion prediction for 360-degree video, and heterogeneous quantization for DNN accelerators. His studies integrate machine learning with traditional codec design to improve energy efficiency. Technical Contributions: Developed models for decoding energy estimation, analyzed carbon impact of streaming devices, and proposed methods for viewport-adaptive motion compensation. Collaborative work spans thermal imaging for power analysis and reliability-aware DNN hardware optimization.

Eli De Poorterمشاهده پروفایل

استاد

• IoT
• Wireless Network Protocols
• Wireless Testbeds
• +۴ مورد دیگر

Eli De Poorter is a Professor at the Faculty of Engineering and Architecture , Ghent University , and affiliated with IMEC since 2017. His work bridges experimental wireless research with machine learning techniques, focusing on IoT , UWB localization , and self-learning networks . Received his PhD in 2011 from Ghent University’s Department of Information Technology FWO Postdoctoral Research Grant recipient Co-author of over 100 international publications Active in program committees of multiple conferences His research spans wireless protocol optimization , interference detection , and system configuration dependencies . Notable projects include UWB-based healthcare monitoring , industrial IoT networks , and animal wearables for agriculture . Develops energy-aware tinyML models for battery-less devices Explores Transformer networks and self-supervised learning for wireless systems Investigates multi-modal industrial IoT and Wi-Fi/UWB hybrid synchronization Scientific Awards FWO Postdoctoral Research Grant Labs & Teams Internet Technology and Data Science Lab (IDLab) IMEC research institute

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Shwai He is a PhD student and Affiliate Assistant Professor at the University of Maryland, advised by Ang Li. Their research focuses on advancing AI systems through innovative approaches in large language models (LLMs), fairness in machine learning, and efficient neural network architectures. Key areas include multi-agent systems, causal modeling for bias mitigation, and optimization techniques for mixture-of-experts models. Research interests span artificial intelligence, machine learning, and natural language processing with emphasis on practical applications like healthcare diagnostics and social pairing systems. Notable work includes developing GNWT-based multi-agent digital twins for social platforms and improving LLM transparency through token analysis. Publications highlight contributions to counterfactual fairness, dynamic-depth transformers, and parameter-efficient methods. Current efforts explore computational efficiency in vision-language models and bio-inspired antibody prediction systems. No scientific awards have been mentioned. Advising and grants: Currently a PhD student under Ang Li's supervision. Research involves collaborations across computer science and bioinformatics domains.