Dan Alistarh is a Professor at the Institute of Science and Technology Austria (IST Austria) and leads the Deep Algorithms and Systems Lab (DASLab). His research focuses on efficient algorithms and systems for machine learning, including distributed optimization, sparse and quantized neural networks, and scalable training/inference techniques. He holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL) and has held positions at MIT, Microsoft Research, and ETH Zurich. Research interests: Optimization for data analysis, parallel and distributed optimization, efficient machine learning algorithms, and distributed systems. Collaborations include work on optimization under uncertainty with Immanuel Bomze, Radu Bot, and others. Publications span top venues like NeurIPS, ICML, and DISC, with notable contributions in model compression (e.g., GPTQ, SparseGPT), communication-efficient distributed training, and concurrency algorithms. Awards include ERC grants and best paper awards. He advises a team of PhD students and postdocs, and his lab's tools are widely used (e.g., GitHub repositories). His work has been adopted in industry (e.g., OpenAI, Neural Magic).
Sai Qian Zhang is an Assistant Professor at New York University, holding dual appointments in the Electrical and Computer Engineering Department at NYU Tandon School of Engineering and the Computer Science Department at the Courant Institute of Mathematical Sciences. He earned his Ph.D. from Harvard University in 2021 and completed his B.A.Sc and M.A.Sc at the University of Toronto. Previously, he worked as a Senior Research Scientist at Meta Reality Labs (2022–2024). His research focuses on algorithm and hardware co-design for efficient deep neural network implementation, with a particular emphasis on AR/VR computing. Key areas include neural network accelerator design, efficient AI algorithms, and privacy-preserving techniques for AR/VR systems. He leads the System & Artificial Intelligence (SAI) Lab, which explores intersections between deep learning and hardware systems to optimize AI performance and resource efficiency. Recent research highlights include papers on gaze-tracked foveated rendering, speculative decoding strategies, and parameter-efficient fine-tuning for large models. He has advised numerous students, including recipients of prestigious awards such as the DAC Young Fellow and ECE Myron M. Rosenthal Award. Zhang teaches courses like Efficient AI and Hardware Accelerator Design (Spring 2025), emphasizing model compression, quantization, and accelerator architectures. His work has been recognized through grants and collaborations with industry partners like Meta and Andes Technology.
Dr. Eviatar Bach is a Lecturer in Mathematics of Environmental Data Science at the University of Reading, affiliated with the Department of Mathematics and Statistics within the School of Mathematical, Physical and Computational Sciences. His research focuses on data assimilation, dynamical systems, and environmental applications, particularly in climate modeling, remote sensing, and machine learning integration. He contributes to the Data Assimilation Research Centre (DARC) and teaches courses like ST2PST Probability and Statistical Theory. His work bridges computational methods with real-world environmental challenges, emphasizing uncertainty quantification and predictive modeling. Research interests include ensemble Kalman methods, inverse problems, and the application of statistical techniques to climate and ecological systems. His recent articles highlight advancements in filtering algorithms, parameter estimation, and the use of remote sensing data to assess environmental impacts. Dr. Bach collaborates on projects analyzing monsoon dynamics, solar farm effects, and vegetation responses to climate change. His teaching and research reflect a commitment to advancing data-driven solutions for environmental science.
Jiarong Xing is an Assistant Professor of Computer Science at Rice University, starting Fall 2025. Currently a Postdoctoral Scholar at UC Berkeley's Sky Computing Lab under Prof. Ion Stoica, his research focuses on secure, efficient, and scalable networked systems for cloud data centers, ML infrastructure, and 5G networks. He earned his Ph.D. from Rice University in 2024 under Prof. Ang Chen. His expertise spans computer systems, networking, and security. Key contributions include Occam (EuroSys'24), Pipeleon (SIGCOMM'23), and FlexCore (NSDI'22), addressing challenges in network management, SmartNIC optimization, and runtime programmability. His work on NetWarden (USENIX Security'20) mitigates covert channels without performance loss. Education : Ph.D. in Computer Science (Rice University, 2024); B.Sc. in Software Engineering (Shandong University, 2017) Awards : USENIX Security Distinguished Paper (2023), Google PhD Fellowship (2022), Meta Fellowship Finalist (2022), Multiple Rice University Fellowships He has served on program committees for NSDI, EuroSys, ACM SIGCOMM, and USENIX Security, and reviewed for top journals like IEEE Transactions on Networking. His GitHub repositories showcase open-source contributions to systems research, including Occam, Pipeleon, and Ripple.
Richard Hahn is an Associate Professor of Statistics at Arizona State University's School of Mathematical and Statistical Sciences. He joined ASU from the University of Chicago Booth School of Business. His research focuses on causal inference using regression tree models, Bayesian statistics, and quantitative social science. He teaches courses in Bayesian Statistics and Causal Inference, and advises PhD students in statistics and data science. Education: Ph.D., Statistical Science, Duke University, 2011 M.Sc., Mathematics, New Mexico Institute of Mining and Technology, 2007 B.A., Economics-Philosophy of Science, Columbia University, 2005 Research Interests: His work includes causal inference, computational statistics, nonlinear regression, Bayesian foundations, and applications in social sciences. Notable contributions include stochastic tree ensembles for heterogeneous treatment effects and Bayesian methods for survival analysis. Teaching: STP 505 Bayesian Statistics STP 598 Causal Inference Advanced graduate-level courses in statistics Advising: Current PhD students: Palak Jain, Nikolay Krantsevich, Xiangwei Peng, Maggie Wang, Judy Yun. Past students include Jingyu He, Drew Herren, and Chelsea Krantsevich. Professional Activities: Active in academic conferences, including the Atlantic Causal Inference Conference. Editor of 'Bayesian Modeling and Computation in Python' (2023). Collaborates on interdisciplinary projects like the national growth mindset experiment in education.
Zoltan Szabo is a Professor of Data Science at the Department of Statistics, London School of Economics and Political Science. His research focuses on statistical machine learning, particularly kernel methods, information theoretical estimators, and scalable computation, with applications spanning safety-critical learning, style transfer, hypothesis testing, distribution regression, econometrics, and gene analysis. Affiliation : Department of Statistics, LSE Academic Rank : Professor Key Expertise : Kernel Methods, Information Theoretical Estimators, Scalable Computation His work integrates theoretical rigor with practical applications, addressing challenges in safety-critical systems and developing robust nonparametric methods. Szabo has published extensively on topics like Nyström approximation, Stein discrepancy, and random Fourier features, contributing to advancements in hypothesis testing, distribution regression, and GPU-accelerated kernel techniques. He has served as an Area Chair for top conferences (ICML, NeurIPS, AISTATS), moderated arXiv's stat.ML, and contributed to editorial roles at JMLR and ACM Transactions on Probabilistic Machine Learning. His recent articles emphasize scalable kernel methods for high-dimensional data, with applications in climate science, finance, and neuroimaging. Scientific Awards : Best Paper Award, NeurIPS 2017 HDR (Habilitation à Diriger des Recherches) with distinction, 2019 Programme Director of MSc Data Science, LSE As an advisor, Szabo mentors PhD students and interns in machine learning and statistics. His work often involves interdisciplinary collaboration, including grants with institutions like the Turing Institute and European Research Council.
Hee Lee is an Assistant Professor at the University of Pittsburgh's Department of Electrical and Computer Engineering. His research focuses on energy harvesting, low-power integrated circuits, miniature system design, and battery management. He holds a Ph.D. from the University of Michigan (2014) and M.S./B.S. degrees from Yonsei University (2007/2005). Research Interests: Design of ultra-low-power analog/mixed-signal circuits for IoT and biomedical applications Energy-autonomous systems using photovoltaic, piezoelectric, and biofuel-based harvesting Miniaturized sensor nodes with embedded neural network accelerators Recent work emphasizes hybrid timestamping circuits (2025), subthreshold voltage references (2024-2025), and mm-scale neural accelerators (2023). His publications span IEEE journals and conferences like ISSCC, JSSC, and VLSI Symposia. Patents include innovations in voltage references and energy harvesting interfaces. He has developed systems like the mSAIL platform for monarch butterfly tracking and implantable IOP monitors. Lab activities involve collaboration with Michigan's Blaauw/Sylvester group on projects like System-on-Mud oceanic sensors and millimeter-scale biofuel-powered devices. Current research explores high-temperature IoT systems and neuromorphic hardware accelerators.
Furqan Aziz is a Lecturer in the School of Computing and Mathematical Sciences at the University of Leicester since 2022. Previously, he served as a Research Fellow at the Institute of Cancer and Genomic Sciences, University of Birmingham. He holds a Ph.D. in Computer Science from the University of York, UK, focusing on interdisciplinary research. His research interests include Spectral Graph Theory, Complex Networks, Machine Learning, and Bioinformatics. He applies these techniques in healthcare informatics, network analysis, and computational biology. Notably, his work explores disease phenotype modeling, multimorbidity prediction, and drug response analysis using machine learning. Recent publications highlight trends in network science applications, including link prediction, graph characterization, and predictive modeling in healthcare. His bioinformatics research bridges computational methods with medical data analysis, addressing challenges in personalized medicine and public health surveillance. No awards or grants are explicitly listed in his profile. He currently advises students in computational science and mathematical modeling, though specific advisee names are not provided. His work spans collaborations in academia and industry, emphasizing interdisciplinary problem-solving.
Jorge Fernández-Berni is an Associate Professor at the University of Seville, affiliated with the Institute of Microelectronics of Seville (IMSE-CNM), a joint research center of CSIC and the university. He holds a PhD in Microelectronics (2011, with honors) and has been a faculty member since 2018, following a Juan de la Cierva Research Fellowship (2016–2017). Research Interests: Smart CMOS image sensors Vision chips Embedded systems and hardware-software co-design Distributed smart sensors Deep learning and edge AI Nature and biodiversity monitoring His recent publications reflect a strong trend toward integrating AI with low-power embedded vision systems for ecological applications, particularly in wildlife and environmental monitoring. The work emphasizes efficient, real-time processing on edge devices, focal-plane computation, and system-level optimization for IoT deployment. Topics include high dynamic range imaging, Gaussian pyramid extraction, thermal throttling impact, and performance prediction for deep learning models on embedded platforms. Scientific Awards: Best Paper Award, SPIE Electronic Imaging 2014 Third Prize, Student Paper Award, IEEE CNNA 2010 Winner, 'De Idea a Producto' contest 2022 HiPEAC 2023 Technology Transfer Award Winner, Startup Olé Marbella 2024 Student Pitch Competition Advising and Grants: Dr. Fernández-Berni has served as Principal Investigator (PI) in numerous research and tech transfer projects, including ULTIMATE (smart embedded platform for nature monitoring), SEMIoTICS (intelligent IoT components), and collaborations with SEO/BirdLife on automated bird identification. He has participated in approximately 25 research projects, with funding from national agencies (Ministerio de Ciencia e Innovación, Junta de Andalucía), the European Union (H2020), and the U.S. Office of Naval Research. He has also led the spin-off BiodAIverse (now biotfy), which has received recognition and acceleration support. Labs and Teams: He is a core researcher at the Institute of Microelectronics of Seville (IMSE-CNM), where he contributes to advanced sensor development and participates in the Doctoral Program on Physical Sciences and Technologies. He has collaborated extensively with the research group led by Á. Rodríguez-Vázquez and R. Carmona-Galán, focusing on smart vision systems and embedded processing.
Anjo Vahldiek-Oberwagner is a Research Scientist at Intel Labs and an Adjunct Lecturer at TU Munich, where he contributes to both industrial R&D and academic education in systems and security. His work bridges hardware and software security, focusing on confidential computing, in-process isolation, and secure cloud deployments. PhD in Computer Science, Max Planck Institute for Software Systems & Saarland University, 2019 B.Sc. in Applied Computer Science, Cooperative University State University Baden-Wuertemberg, 2009 His research centers on system security, particularly techniques for protecting data confidentiality and integrity at rest, in-flight, and in-memory. He explores operating systems, distributed systems, and hardware-assisted security mechanisms such as Intel MPK and SGX. His work on ERIM, HFI, Endokernel, and Graphene has advanced secure in-process isolation and trusted execution environments. He has published extensively in top venues like USENIX Security, ASPLOS, and IEEE S&P. His recent publications reflect a strong trend toward practical, deployable security solutions for modern computing environments, including secure AI/ML deployments, efficient in-process isolation, and hardware-accelerated sandboxing. Themes include memory safety, performance optimization, and real-world applicability of security primitives. Scientific awards include: Distinguished Paper Award and Internet Defense Prize, USENIX Security 2019 (ERIM) Distinguished Paper Award, ASPLOS 2023 (HFI) IEEE Micro Top Picks 2024 (HFI) Intel Hardware Security Academic Award (Honorable Mention) DARPA Riser 2022 Intel Labs Gordy Award Honorable Mention He actively mentors and serves on program committees (EuroSys, USENIX Security, ASPLOS), chairs artifact evaluation (USENIX Security, EuroSys, SC), and is an Associate Editor for ACM TOPS. He has advised no formal students listed, but collaborates widely across Intel and academia. His work is supported by Intel and DARPA, and he holds multiple patents in secure computing and TEEs. He leads research on memory-safe architectures and secure cloud deployments at Intel Labs. He is involved in several research projects, including: Secure In-Process Memory Isolation, Shielding Applications in Untrusted Clouds via SGX, Memory-Safe Hardware and Software Architecture, and Research Artifacts and Evaluation. He is also a key contributor to the Graphene Library OS and works on validation and endorsement services for confidential computing.
Kanishkan Vadivel is a Researcher in the Electronic Systems group at Eindhoven University of Technology (TU/e), specializing in energy-efficient hardware architectures and compiler-based code-generation techniques. He holds a Master’s degree in Embedded Systems from TU/e (2017) and a Bachelor’s from Coimbatore Institute of Technology (India). Prior to academia, he worked in embedded systems at Tata Engineering and Arm Ltd. Research Interests : His work focuses on computation-in-memory architectures using resistive devices, optimal code generation for CGRA (Coarse-Grained Reconfigurable Architecture), and high-performance computing. Key projects include the MNEMOSENE initiative and development of the CIM-SIM simulator for computation-in-memory systems. Awards : HiPEAC collaboration grant (2019) Advising & Grants : His research is supported by grants focused on neuromorphic processors and edge-AI hardware. He collaborates on projects like NEUROKIT2E for embedded deep learning systems. Labs/Teams : Active member of TU/e’s Electronic Systems Center and Efficient Stream Processing Lab, contributing to neuromorphic and energy-efficient computing initiatives.
Dr. John Maclean is a Lecturer in Data Science and Statistics at the University of Adelaide, affiliated with the School of Computer and Mathematical Sciences within the Faculty of Sciences, Engineering and Technology. His research focuses on Data Assimilation (DA) and Numerical Multiscale Methods, with particular interests in coherent structure DA, projected DA, non-Gaussian measurement error modeling, and surrogate-based DA techniques. He also explores projective integration and patch dynamics for stiff systems and spatially heterogeneous problems. His work addresses challenges in combining uncertain model forecasts with data, accelerating simulations of complex systems, and designing efficient statistical surrogates. Notable contributions include methodologies for adaptive moving patches in multiscale simulations and theoretical insights into stochastic processes and uncertainty quantification. Dr. Maclean is actively involved in supervising postgraduate research students and is open to mentoring those interested in his research areas. His publications span topics from data assimilation algorithms to environmental and computational modeling, reflecting his interdisciplinary approach to applied mathematics and computational science.
Professor Alan Heavens is a cosmologist and astrophysicist at Imperial College London's Department of Physics, part of the Faculty of Natural Sciences. His primary research focuses on dark matter, dark energy, gravitational lensing, and cosmological models. He has contributed to projects like the Euclid mission and co-led the CosmoVerse initiative addressing observational tensions in cosmology. He has also engaged in interdisciplinary work, including studies on medical treatments for severe cases of SARS-CoV-2 infections through the RECOVERY trials. Heavens' cosmological research explores fundamental physics questions, such as testing Einstein's gravity theory and analyzing the Standard Cosmological Model's limitations. His techniques include Bayesian inference, data compression, and machine learning applied to cosmic surveys. Notable collaborations include work on weak lensing, cosmic shear, and parameter inference from galaxy surveys. His publications span cosmology, astrophysics, and medical research, reflecting his dual engagement in theoretical physics and applied clinical studies. He has advised on large-scale observational projects and contributed to international initiatives like the Snowmass 2021 process. No specific awards or grants are detailed in the provided texts, though his active research profile suggests significant contributions to the field.
Muhammad Shahbaz is an Assistant Professor of Computer Science and Engineering at the University of Michigan , where he leads the NextGArch Lab . His research spans computer systems, networks, and architecture , focusing on domain-specific abstractions and programmable infrastructure for emerging workloads like machine learning and 5G networks. Postdoc, Electrical Engineering, Stanford University (2020) Ph.D. & M.A., Computer Science, Princeton University (2018) B.E., Computer Engineering, National University of Sciences and Technology (NUST) His work includes designing reconfigurable architectures for line-rate machine learning and next-generation networks . Recent publications explore in-network acceleration , DDoS detection , and SmartNIC optimization , reflecting his expertise in networking systems and domain-specific compilers . Notable honors include the NSF CAREER Award (2024) , Google Research Scholar Award (2024) , and SRC JUMP 2.0 Best Paper Award (2024) . He mentors students in systems research and collaborates on projects like the NSF Convergence Accelerator Phase 1 Award for Track G.
Benoit Miramond is a Full Professor at Université Côte d’Azur, affiliated with the Laboratory of Electronics, Antennas and Telecommunications (LEAT) and Polytech Nice Sophia. He serves as Director of Research at the 3IA Côte d'Azur – Interdisciplinary Institute for Artificial Intelligence, and leads the eBRAIN Research Group focused on neuromorphic engineering. His research interests lie at the intersection of embedded systems, artificial intelligence, and neuroscience. He specializes in bio-inspired AI , spiking neural networks , event-based processing , and neuromorphic hardware architectures . His work emphasizes energy efficiency, real-time performance, and hardware-aware design for AI at the edge. The recent publications highlight a strong trend in embedded AI , neuromorphic computing , and energy-efficient sensor networks . His team explores hardware-aware neural architecture search, distillation for spiking networks, and FPGA-based implementations, demonstrating a consistent focus on deploying intelligent systems in resource-constrained environments. He is actively involved in national and interdisciplinary initiatives: Member of PEPR IA (EMERGENCES project) Scientific advisor at AICO Technology (startup) Member of the Scientific Committee of the NeuroMod Institute Member of GDR BioComp He advises several researchers and students in the fields of embedded AI and neuromorphic systems. His work is supported by institutional affiliations and collaborative grants, particularly through 3IA Côte d’Azur and PEPR IA. He contributes to advancing the next generation of edge intelligence through interdisciplinary research bridging hardware, algorithms, and cognitive principles. His research is conducted within the LEAT laboratory, a CNRS UMR7248 unit, where the eBRAIN group fosters innovation in bio-inspired computing and adaptive hardware systems.
