Yannis Chronis is an incoming Assistant Professor at the Department of Computer Science , ETH Zurich (starting August 2025), where he will join the ETH Systems Group . Prior to this, he spent 3 years as a Systems Researcher at Google's Systems Research Group in Sunnyvale, USA. He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison (advised by Prof. Jignesh Patel) and a Bachelor's/Master's from the University of Athens, Greece (advised by Prof. Yannis Ioannidis). His research focuses on optimizing databases and data processing for modern hardware through software-hardware co-design. Key areas include database efficiency in memory-centric architectures, learned query optimizers, and cloud resource management. His work is supported by a Facebook Fellowship and has been recognized with the EDBT 2016 Medal for best paper. Teaching: Taught CS 564 - Database Management Systems at UW-Madison (Spring 2022). Service: Served on program committees for SIGMOD, VLDB, CIDR, and others, and chairs the CIDR Proceedings Committee. Key Publications: His recent work includes studies on memory-centric computing for databases, cardinality estimation benchmarks, and adaptive query processing techniques.
Peter Pietzuch is a Professor in the Department of Computing at Imperial College London, where he leads the Large-Scale Data & Systems (LSDS) group. He also serves as the Director of Research and is a Visiting Researcher at Microsoft Research Cambridge. Pietzuch holds a Ph.D. from the University of Cambridge and a B.A. from Girton College. His research spans distributed systems, cloud computing, big data processing, and systems security. Key interests include: Scalable architectures for cloud-native applications Efficient stream processing and machine learning systems Trusted execution environments and secure cloud infrastructure Optimization of serverless computing and distributed databases His recent publications focus on adaptive machine learning frameworks, secure cloud resource management, and high-performance stream processing systems. Trends show strong emphasis on hardware-software co-design, confidential computing, and fault-tolerant architectures. Awards include: Best Paper Award at Middleware'03 He actively advises PhD students and secures grants for projects like Faasm (serverless computing) and Teechain (blockchain security). His LSDS group collaborates with industry partners including Microsoft Research. Pietzuch teaches undergraduate and graduate courses including Scalable Systems for the Cloud and Operating Systems . He co-founded the ACM DEBS conference and serves on steering committees for EuroSys and Middleware.
Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.
Rita Cucchiara is a Full Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. She leads the AImageLab research laboratory, part of the Artificial Intelligence Research and Innovation Center (AIRI) in Modena. Her research focuses on Computer Vision, Pattern Recognition, Machine Learning, and Multimedia, with applications in video surveillance, medical imaging, human-centered AI, and generative models. She is actively involved in interdisciplinary projects like ELIAS (European Lighthouse for AI Sustainability) and ELSA (European Lighthouse on Secure AI). Her recent roles include being elected Rector of the University of Modena and Reggio Emilia in 2025. She has organized and participated in major AI events, including workshops at NeurIPS, CVPR, and ECCV, and has contributed to advancements in multimodal models, deepfake detection, and trustworthy AI. Education details are not explicitly provided, but her extensive academic and research experience at the University of Modena underscores her expertise. She collaborates with institutions like NVIDIA, CINECA, and industry partners such as Digital Design and NVIDIA's AI Technology Center. AImageLab's projects include developing systems for medical imaging, ethical AI, and generative adversarial networks (GANs) for design surfaces. She co-organizes initiatives like the ELLIS Summer School on Large-Scale AI and contributes to policy discussions on AI ethics and societal impact. Her work spans from foundational research (e.g., vision transformers, continual learning) to applied projects (e.g., DDGan system for surface printing). Key grants and collaborations include the FAIR project and PNRR-M4C2 initiatives. She advises students and researchers in AI, with 7 PhD positions funded under national programs. Her leadership roles in AIRI and AImageLab highlight her commitment to bridging academia and industry, fostering innovation in AI-driven solutions for sustainability and healthcare.
Constantinos Daskalakis is the Armen Avanessians (1982) Professor in the MIT Schwarzman College of Computing and the Department of Electrical Engineering and Computer Science (EECS). He joined MIT in 2009 and is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). His research focuses on theoretical computer science, with emphasis on game theory, machine learning, and high-dimensional statistics. Education: Ph.D. in Computer Science (not explicitly stated, but implied by tenure and awards). Research interests include computational complexity of Nash equilibria, multi-item auctions, machine learning algorithms, and causal inference. His work bridges game theory, economics, probability, and statistics, with applications in AI and healthcare. Key contributions include resolving long-standing problems in computational game theory and developing efficient methods for statistical hypothesis testing. He has been recognized with the 2018 Nevanlinna Prize, ACM Grace Murray Hopper Award, and the Kalai Game Theory Prize. Affiliations: CSAIL, LIDS, ORC, and the Foundations of Data Science Institute. Active in multi-agent learning, bias mitigation in data, and generative models.
Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science, University of Waterloo. Her research focuses on data integration, data management, and open data systems. She holds a PhD in Computer Science from the University of Wisconsin-Madison and bachelor’s degrees in Mathematics and Cognitive Science from MIT. Her work addresses challenges in data preparation, integration, and curation, aiming to reduce the burden on data scientists. She co-authored foundational papers on data exchange and schema mapping, earning the ICDT Test-of-Time Award (2013) and the Alonzo Church Award (2020). Miller has led major initiatives like the NSERC Business Intelligence Network and the International Very Large Data Base Foundation. Her grants include NSERC Accelerator Awards and funding from IBM, SAP, and Microsoft. Notable students include Ariel Fuxman (SIGMOD Dissertation Award winner) and Oktie Hassanzadeh (IBM PhD Fellow). Her research group, the Miller Lab, develops tools like Clio for schema mapping and systems for data lake exploration (RONIN, JOSIE).
Ahmed Eldawy is an Associate Professor in the Department of Computer Science at the University of California, Riverside. He leads groundbreaking research in databases, big data management, and spatial data processing, with a focus on scalable exploratory analytics through systems like Beast , UCR-Star , Raptor , and Spider . His work spans geospatial data infrastructure, distributed computing, and computational geometry. Research Interests : Databases, big data management, spatial data processing, geospatial analytics, distributed systems, computational geometry. Awards : NSF CAREER award (2021), 10-year Influential Paper Award (ICDE 2025), Best Demo award (SIGSPATIAL 2020). Grants : NSF (IIS-2046236, IIS-1954644, CNS-1924694), USDA (USDA NIFA 2020-69012-31914), UC Office of the President (M21PL3368). Labs & Centers : RAISE@UCR, Data Science Center, Center for Robotics and Intelligent Systems (CRIS), Affiliate of Center for Geospatial Sciences and Winston Chung Global Energy Center. His recent publications focus on LLM-driven geospatial visualization (LASEK), distributed raster analytics (RDPro), learned spatial query optimization, and scalable spatiotemporal systems. He advises numerous PhD and Master’s students, many of whom now work at top tech companies like Amazon, Microsoft, and Meta.
Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He previously served as an Assistant Professor at Georgia Tech (2013-2017) and holds the Halicioğlu Chair in Computer Architecture . As founder/director of the Alternative Computing Technologies (ACT) Laboratory and associate director of UCSD's Center for Machine Integrated Computing and Security (MICS) , his research drives cross-stack solutions for next-generation computer systems. Early tenure recipient at UCSD Coined the term "dark silicon" in computer architecture Developed Tabla/DnnWeaver open-source frameworks Research Interests span: Approximate Computing Neural Acceleration FPGA/ASIC Hardware Design Machine Learning Systems Dark Silicon Challenges Security/Privacy in Accelerated Systems Scientific Recognition : 4 CACM Research Highlights 4 IEEE Micro Top Picks Distinguished Paper Award (HPCA 2016) Inducted to ISCA Hall of Fame (2018) Teaching : Developed courses on accelerator design (CSE 240D) and alternative computing (CS 8803 ACT) Advocates for hands-on FPGA-based learning in Processor Design with FPGAs courses
Zhijian Liu is a Research Scientist at NVIDIA with a PhD from MIT, advised by Song Han. His work focuses on efficient machine learning and systems through sparse computation and hardware-aware neural network design. Rising Star in Data Science (UChicago/UCSD) Rising Star in ML and Systems (MLCommons) Qualcomm Innovation Fellowship awardee Research highlights include: SPVCNN++ for LiDAR segmentation HAQ framework in Intel OpenVINO TorchSparse framework for 3D CNN efficiency His recent publications explore: Efficient visual language models (NVILA) Contextual sparsity in LLM fine-tuning (SparseLoRA) Training-free acceleration of diffusion LLMs Query-aware visual sparsity mechanisms Contact: zhijian@mit.edu
Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.
Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).
Leslie Ann Goldberg is a Senior Research Fellow at St Edmund Hall and Professor of Computer Science at the University of Oxford. She currently serves as Head of the Department of Computer Science (on sabbatical 2025-26) and focuses on foundational problems in Algorithms and Complexity Theory , particularly randomised algorithms for network communication, machine learning, and statistical physics models. Her research includes solving Aldous' 1987 conjecture on backoff protocol instability (with John Lapinskas), developing rigorous mathematical analysis frameworks for algorithmic efficiency, and advancing approximate counting techniques via Markov Chain Monte Carlo methods (with Andreas Galanis and collaborators). Key projects involve graph homomorphisms , Moran process dynamics , and #BIS complexity class analysis. Recent publications (2023-2024) span topics like Sybil defense mechanisms, low-temperature sampling on random graphs, and parameterised subgraph counting modulo 2. Her work demonstrates cross-disciplinary impact in computational biology, statistical physics, and database theory. Scientific Awards include Best Paper Prizes at ICALP 2016, ICALP 2010, and IPEC 2017. She supervises PhD student Paulina Smolarova and collaborates extensively with researchers in Oxford and beyond.
Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA
Daniel Grier is an Assistant Professor jointly appointed in the Computer Science and Engineering and Mathematics departments at the University of California, San Diego (UCSD). His research focuses on quantum complexity theory , particularly exploring near-term quantum computing paradigms and proving quantum advantage over classical systems. He holds a Ph.D. from MIT and was previously a postdoctoral fellow at the University of Waterloo’s Institute for Quantum Computing. Education: Ph.D. in Computer Science, MIT B.S. in Computer Science and Mathematics, University of South Carolina Research Interests: Grier’s work bridges theoretical computer science and quantum computing, emphasizing algorithm design, complexity class separations, and foundational questions about quantum supremacy. He studies how low-depth quantum circuits, boson sampling, and other near-term technologies can achieve computational tasks classically deemed intractable. Recent Article Trends: His publications explore efficient quantum state learning (e.g., classical shadows), hardness results for quantum sampling problems (e.g., bipartite Gaussian boson sampling), and circuit lower bounds (e.g., depth-2 QAC circuits). These contributions highlight his focus on rigorously defining quantum computational advantages. Awards: None explicitly listed in the text. Advising & Grants: Advises at least one student, Jackson Morris. His research is supported by grants exploring quantum complexity and algorithm design. Teaches advanced courses on quantum complexity theory, computability, discrete mathematics, and quantum computing fundamentals. Labs/Teams: Maintains an active lab focused on quantum complexity theory, collaborating with colleagues on topics like interactive protocols and shallow quantum circuits.
Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.
