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Murali Krishna EmaniView profile

Researcher

• High Performance Computing
• Scalable Machine Learning
• Parallel programming models
• +3 more

Murali Krishna Emani is an Assistant Computer Scientist in the Data Science group at Argonne Leadership Computing Facility (ALCF) within Argonne National Laboratory. Previously, he served as a Postdoctoral Research Staff Member at Lawrence Livermore National Laboratory. His research spans High Performance Computing , Scalable Machine Learning , and Emerging HPC architectures . Key interests include parallel programming models, runtime systems, and online adaptation for scientific applications. At ALCF, he co-leads the AI Testbed initiative exploring AI accelerator performance for scientific machine learning, and chaired the MLPerf HPC group at MLCommons for benchmarking large-scale ML on HPC systems. His recent publications (2023-2025) reveal strong focus on LLM optimization (MoE inference, KV cache management), AI accelerator benchmarking , and scientific applications (climate modeling, protein design). The work demonstrates cross-cutting themes in hardware-software co-design and performance modeling for emerging architectures. ACM Gordon Bell Prize finalist for climate modeling (2025) Winner of ACM Gordon Bell Special Prize for HPC-based Covid-19 research (2022) Emani actively mentors PhD students and postdocs, with advisees now faculty at Binghamton University, California State University, and researchers at NVIDIA, Microsoft, and national labs. His service includes program committees for SC, IPDPS, and AAAI conferences. Current projects focus on performance modeling for ML/DL frameworks on supercomputers, co-design of hardware architectures for ML algorithms, and benchmarking ML/DL frameworks on HPC systems.

Kunle OlukotunView profile

Professor

• Computer Architecture
• Parallel Computing
• Multicore Processors
• +5 more

Kunle Olukotun is the Cadence Design Systems Professor of Electrical Engineering and Computer Science at Stanford University, where he has been a faculty member since 1991. He is a pioneer in multicore processor design, leading the Stanford Hydra CMP project and founding Afara Websystems (acquired by Sun Microsystems), which developed the Niagara processor. Currently, he co-leads SambaNova Systems as Chief Technologist and directs the Pervasive Parallelism Lab (PPL), focusing on domain-specific languages (DSLs) and machine learning infrastructure. Education: PhD in Computer Engineering from the University of Michigan (1991). Research interests include parallel computing architectures, transactional memory, and scalable systems. Awards include ACM Fellow, IEEE Fellow, and the Harry H. Goode Memorial Award. Key projects include the Hydra chip multiprocessor, Transactional Coherence and Consistency (TCC), and modern initiatives in dataflow architectures and AI acceleration. His work spans over 100 publications, emphasizing compiler design, hardware-software co-design, and high-performance computing. Current roles: Director of PPL and DAWN Lab, advisor to multiple students, and leader in industry collaborations like SambaNova’s dataflow accelerators. His research bridges academic innovation with commercial impact, addressing challenges in parallelism and scalable systems.

• Adversarial Machine Learning
• Anomaly Detection
• Graph Convolutional Network
• +7 more

Tianjin Huang is a University Researcher in the Mathematics and Computer Science school at Eindhoven University of Technology, focusing on deep learning, adversarial training, and sparsity. Their work spans traffic prediction, fairness in AI, and robustness evaluation. Specializes in Adversarial Machine Learning and Spatiotemporal Graph Modeling Develops novel frameworks for Traffic Prediction under real-world data challenges Explores Sparse Neural Networks for efficiency and performance Recent research trends include 4-bit training stability for LLMs, confusional spectral regularization for fairness, and principal eigenvalue methods for certified robustness. Their 2025 publications demonstrate cutting-edge work in adversarial training, graph neural networks, and low-bit optimization. Scientific recognition includes the Best Paper Award of LoG 2022 for collaborative work on sparse neural network training. They collaborate with institutions across Europe and Asia, focusing on trustworthy AI systems.

• Information Retrieval
• Federated Learning
• Edge Computing
• +4 more

Nicola Tonellotto is a researcher at the University of Pisa, Department of Information Engineering, focusing on Information Retrieval , Federated Learning , and Edge Computing . He has collaborated extensively with institutions like CNR, City University of London, and Sapienza University of Rome. Key research areas: Query Processing , Conversational Search , Dense Retrieval , Distributed Learning His recent work addresses challenges in Sequential Recommender Systems , Cross-Modal Query Suggestion , and Energy-Aware Resource Management for FaaS platforms. Notable contributions include FedCMD for emotion recognition and ColBERT-PRF for semantic relevance feedback. As a coauthor, he has advanced GPU-based parallelization for tree ensembles and SIMD extensions to accelerate document scoring. He also explores reproducibility in IR research, emphasizing scientific integrity.

• Green Software Engineering
• AI Energy Efficiency
• Federated Learning
• +3 more

Dr. Ana Oprescu is a Visiting Professor at the Informatics Institute of the University of Amsterdam. Her research focuses on the intersection of software engineering, AI, energy efficiency, and data privacy, with particular emphasis on sustainable computing practices. University of Amsterdam, Faculty of Science Key Research Areas: Green software engineering for AI systems, energy-efficient code generation using Large Language Models, privacy-preserving machine learning techniques, and sustainable data processing methods. She actively explores trade-offs between energy consumption, data privacy, and algorithmic accuracy. Her recent publications demonstrate a strong focus on environmentally sustainable computing, with articles covering quantisation effects on AI energy consumption, k-anonymisation impacts on machine learning, and dynamic federated learning approaches. She has also contributed to educational initiatives in green software practices. Scientific Recognition: Recipient of VENI-2014 research grant Dr. Oprescu works at the intersection of software optimization, security, and sustainability, with a particular interest in microservice architectures, model-based testing, and energy-aware system design. She has published extensively on topics like energy-driven software engineering, code clone refactoring, and distributed tracing.

Corentin DumeryView profile

Researcher

• Computer Vision
• Computer Graphics
• 3D Reconstruction
• +4 more

Corentin Dumery is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Computer Vision Laboratory (CVLAB) and Machine Learning and Optimization Laboratory (MLO) within the School of Computer and Communication Sciences . His research bridges Computer Vision and Computer Graphics , focusing on 3D scene reconstruction, garment modeling, and neural rendering techniques. Previously, he interned at Meta Redmond , worked at CEA Paris-Saclay on polycube mapping, and was a visiting researcher at ETH Zurich under Prof. Olga Sorkine-Hornung . Corentin holds dual MSc degrees in Computer Science from National University of Singapore (NUS) and Télécom Paris . His work emphasizes 3D content creation for AR/VR Diffusion models for garment reconstruction Neural radiance field optimization Polycube mapping for hexahedral meshing His recent publications (2022–2025) span top venues like SIGGRAPH , ICCV , and CVPR , addressing challenges in 3D Gaussian splatting, view-consistent NeRF training, and single-view garment recovery. He also contributes to academic service as an Outstanding Reviewer at CVPR25 and co-organizes workshops like OpenSUN3D . At EPFL, he serves as Head Teaching Assistant for courses CS433 Machine Learning (2023–2024) and CS442 Computer Vision (2023–2024). Additionally, he is the VP/Treasurer of EPIC , EPFL's computer science PhD association.

• Intelligence Partitioning
• IoT
• Deep Learning
• +5 more

Irida Shallari is a Lecturer in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, specializing in Intelligence Partitioning for IoT systems. Her research spans Deep Learning , Embedded Systems , and Data Fusion , with applications in Edge Computing , 6G , and Smart Agriculture . Education: Doctoral Thesis (2021): "Intelligence Partitioning for IoT : Design Space Exploration for a Data Intensive IoT Node" (Mid Sweden University) Licentiate Thesis (2019): "Intelligence Partitioning for IoT : Communication and Processing Inter-Effects for Smart Camera Implementation" (Mid Sweden University) Her research focuses on optimizing IoT node architectures, balancing Communication vs. Computation trade-offs, and deploying Deep Learning in resource-constrained environments. Recent work includes Time-dependent Clustering for neural network quantization, ArUco Marker Calibration , and Generative AI in Education ethics. The 15 most recent publications (2025-2024) highlight trends in Distributed CNN Optimization , Model Evaluation for industrial IoT, and cross-disciplinary applications in Environmental Monitoring and Academic Integrity . No scientific awards are explicitly mentioned in the provided text.

• Brain-inspired Circuit Design
• Artificial Intelligence Acceleration
• Spiking Neural Networks
• +3 more

Jason Eshraghian is an Assistant Professor at the Department of Electrical and Computer Engineering, University of California, Santa Cruz. He leads the UCSC Neuromorphic Computing Group, focusing on brain-inspired circuits for AI acceleration and spiking neural networks. His work bridges biological principles with hardware implementation to solve computational challenges in AI efficiency. Assistant Professor, Electrical and Computer Engineering UCSC Neuromorphic Computing Group leader Research intersections: Neuromorphic engineering, AI hardware, spiking networks Research Interests: Dr. Eshraghian's work centers on neuromorphic computing and spiking neural networks for AI acceleration. His lab explores Hardware-software co-design for ultra-low-power systems Memristor-based neural architectures Event-driven medical diagnostics Spiking language models (e.g., SpikeGPT) Closed-loop neurostimulation Article Trends: Recent publications emphasize Scalable neuromorphic architectures for AI (2025: Ising machines, FPGA implementation) Medical applications including cytometry and brain-computer interfaces Energy-efficient designs for language models and tracking systems Hybrid attention mechanisms and temporal learning frameworks

• Information Visualization
• Machine Learning
• Data Mining
• +5 more

Gilles Venturini is a Professor in Computer Science at the University of Tours, France, affiliated with PolytechTours and the Computer Science Lab. He has held this position since 1998 following his appointment as Assistant Professor from 1994-1998. His academic credentials include a Research Supervisor Degree (HdR) from the University of Tours (1997), a PhD in Computer Science from the University of the South of Paris (1994) supervised by Yves Kodratoff, and dual qualifications in Computer Science (MSc, 1990) and Electrical Engineering (ESIEE, 1990). Professor Venturini's research concentrates on information visualization, machine learning, and data mining with emphasis on genetic algorithms, neural networks, and computer vision. His work develops practical tools for visual data exploration, automatic dashboard generation, and analysis of large datasets, particularly addressing usability challenges for novice users in open data contexts. His 2023-2025 publications reveal sustained innovation in ternarization of vision-language models for edge devices, few-shot object detection, and backpropagation alternatives for binary neural networks. These contributions bridge theoretical machine learning advancements with applications in cultural heritage visualization and human resources analytics. No scientific awards were documented in the source materials. While specific grant details and student supervision records are absent from the provided text, his professorial role implies active involvement in research funding acquisition and academic mentoring. His work frequently intersects with interdisciplinary teams, particularly in digital humanities projects. He remains operationally active within the University of Tours' Computer Science Lab, driving research in visualization techniques and machine learning applications through this primary institutional affiliation.

Mustansar Ali GhazanfarView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Dr Mustansar Ali Ghazanfar is an Associate Professor at the Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London. He leads the MSc Artificial Intelligence program, which has attracted over 50 students in two years. Research Focus: Artificial Intelligence, Machine Learning, Recommender Systems, Big Data, Cyber Security Key Contributions: 70+ international publications, h-index of 28, 2500+ citations Media Engagement: Industry 4.0 expert featured on GB News London and Faculti, Kay2Tv Pakistan His research spans Artificial Intelligence and Data Science , emphasizing machine learning , deep learning , and predictive modeling . Recent publications highlight reinforcement learning in recommender systems, sentiment-driven cryptocurrency prediction , and security applications of generative AI. Scientific contributions include: Developing scalable context-aware recommender systems using kernel methods Advancing time series forecasting for stock markets via neural networks Innovating masked facial recognition algorithms during the pandemic Pioneering dimensionality reduction techniques for big data analytics

• Data Mining
• Large Language Models (LLMs)
• Knowledge Graphs
• +7 more

Jing Gao is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University , West Lafayette, Indiana. She serves as the Director of Graduate Admissions and focuses on data mining, machine learning, and their applications in healthcare, biology, and societal systems. Research Interests Data veracity analysis and multi-source data integration Knowledge graphs and large language models Fairness, interpretability, and efficiency in AI Federated learning and transfer learning Applications in healthcare, education, and criminal justice Recent Article Trends Her work emphasizes large language models (LLMs) with innovations in knowledge editing, federated learning, and fairness-aware systems. Key themes include: Improving LLM efficiency via sparse adaptation and quantization Advancing privacy-preserving federated learning architectures Developing fairness-aware algorithms through counterfactual reasoning Integrating knowledge graphs for factuality validation Optimizing multi-agent reasoning and self-reflective confidence Technical Areas Her research spans computer engineering domains including: Signal & Image Processing Communications & Networking AI/ML Model Efficiency

• Machine Learning
• Neural Networks
• FPGA Optimization
• +3 more

Vladimir Loncar is a researcher specializing in machine learning, FPGA optimization, and high-energy physics computing. His work focuses on accelerating neural networks and scientific algorithms using hardware-aware techniques. Notably, he contributes to the hls4ml framework for FPGA deployment of machine learning models, and has applied these methods to particle physics experiments like LHCb and the HL-LHC. His research spans symbolic regression, recurrent neural networks, and real-time data processing for large-scale physics detectors. Key projects include developing resource-efficient inference systems (e.g., Tailor for CNN optimization), benchmarking frameworks for GNN-based surrogate models, and latency-critical implementations for collider experiments. Loncar's work bridges theoretical physics and computational engineering, emphasizing practical applications in experimental particle physics, quantum simulations, and autonomous detector control. He collaborates extensively with institutions like CERN and the sPHENIX collaboration.

Hui GuanView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Programming Systems
• +3 more

Hui Guan is an Assistant Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. Currently on leave, she works at Amazon AWS developing LLM systems. She holds a PhD in Electrical Engineering from North Carolina State University (2020) and is a member of the PLASMA lab at UMass. Education: PhD in Electrical Engineering, North Carolina State University, 2020 Research Interests: Focuses on machine learning systems, optimizing deep multitask and graph learning. Aims to improve efficiency, scalability, and reliability of ML through system innovations. Leverages principles like composability and locality awareness to democratize ML applications. Awards: NSF CAREER Award (2024) Amazon Research Award (2022) NCSU ECE Distinguished Dissertation Award (2020) IBM PhD Fellowship (2015-2018) Grants and Projects: Includes NSF support for projects like Adaptive Deep Learning Systems and Memory-Driven Collaboration for Embedded Systems. Received grants from Adobe and Dolby. Students and Advising: Advises PhD students including Lijun Zhang, Kunjal Panchal, and Qizheng Yang. Collaborates with students from other groups like Sohaib Ahmad. Labs and Teams: Active member of the PLASMA lab, focusing on programming languages and systems research.

Dolly SapraView profile

Researcher

• Machine Learning
• Edge Computing
• Embedded Systems
• +8 more

Dolly Sapra is a researcher at the University of Amsterdam , affiliated with the Department of Computer Science under the Faculty of Science . Her work focuses on adaptive deep learning, secure neural inference, energy-efficient computing, and fault-aware systems. She received the IEEE/ACM CASES '24 Outstanding Reviewer Award . Her research spans Machine Learning , Edge Computing , and Embedded Systems , with a focus on Model elasticity for CNNs Privacy-preserving edge intelligence Power-efficient inference Transformer optimization Climate-aware computing . The 15 most recent articles highlight her leadership in adaptive neural architectures , secure multi-party inference , and sustainable computing , with applications in embedded systems and real-time environments .

Dan AlistarhView profile

Professor

• Machine Learning
• Algorithms
• Distributed Systems
• +3 more

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.