جست‌وجوی استاد و پژوهشگر

Vassilis Christophidesمشاهده پروفایل

استاد

• Databases
• Web Information Systems
• Big Data Processing
• +۹ مورد دیگر

Vassilis Christophides is a Professor of Computer Science at the University of Crete and holds an advanced research position at Inria Paris, where he leads work in the MiMove team. His research spans databases, web information systems, big data processing, and IoT analytics, with a strong emphasis on entity resolution, data integration, and scalable data mining. He has supervised numerous research projects funded by the European Union and the Greek State, and has published over 130 articles in top-tier conferences and journals. Research Interests: His primary research areas include Databases, Web Information Systems, Big Data Processing and Analytics, and Information Systems for the Internet of Things. He also focuses on entity resolution, knowledge graphs, streaming data, and explainable AI, particularly in the context of anomaly detection and fairness-aware data systems. His recent work explores hybrid attention models for entity alignment and causal analysis in time series classification. Recent Research Trends: Analysis of his recent publications (2021–2025) reveals a strong focus on entity resolution with fairness constraints, explainable anomaly detection, and adaptive scheduling in IoT edge analytics. He also investigates deepfake detection, crop type mapping using satellite data, and structural bias in knowledge graphs, demonstrating a broad and impactful research portfolio at the intersection of data management and machine learning. Scientific Awards: 2004 SIGMOD Test of Time Award Best Paper Award, ISWC 2003 Best Paper Award, ISWC 2007 Advising and Grants: While specific student names are not listed in the provided texts, Christophides has co-authored numerous papers with researchers such as Vasilis Efthymiou, Ioannis Tsamardinos, and Nikolaos Myrtakis, suggesting active mentorship. He has been the scientific coordinator of multiple EU and national research projects, indicating substantial grant leadership and project management experience. Labs and Teams: He is affiliated with the MiMove team at Inria Paris, a research group focused on mobility and data-intensive systems. His work bridges academic and applied research, leveraging Inria’s infrastructure for large-scale data experimentation and innovation in IoT and edge computing environments.

Sergei Lebedevمشاهده پروفایل

استاد

• Seismology
• Geophysics
• Seismic Tomography
• +۵ مورد دیگر

Sergei Lebedev is a Professor of Geophysics at the University of Cambridge's Department of Earth Sciences and holds an adjunct professorship at Dublin Institute for Advanced Studies. His primary affiliation is with the Bullard Laboratories. He specializes in seismology, seismic tomography, and the structural evolution of the Earth's crust and upper mantle, with a focus on regions like the North Atlantic, Africa, and Tibet. Lebedev leads major projects such as SEA-SEIS and Ireland Array, combining research with public engagement. His work integrates seismic data with geodynamic models to understand tectonic processes, volcanic systems, and resource exploration. PhD: Princeton University (2000) MSc: Moscow Institute of Physics and Technology (1991) Research interests include seismic imaging methods, lithospheric dynamics, and the interplay between seismicity and tectonic evolution. He has contributed to studies on cratonic lithosphere structure, mantle plumes, and crustal deformation mechanisms.

• Machine Learning
• Domain Adaptation
• Transfer Learning
• +۳ مورد دیگر

Dr. Sinno Jialin Pan is a leading researcher in machine learning and artificial intelligence at Nanyang Technological University, Singapore. His work focuses on domain adaptation, sentiment analysis, and efficient neural network optimization. Key research areas: Machine Learning, Domain Adaptation, Reinforcement Learning, Sentiment Analysis Recent publications demonstrate expertise in time-series classification (2025) using hierarchical domain adaptation, LLM efficiency (2025) through expert pruning, and graph generation (2024) via spectral diffusion. His work spans both theoretical advancements and practical applications in neural architecture optimization and adversarial learning. Scientific contributions include: 2025: Virtual-label hierarchical domain adaptation 2024: Spectral diffusion for graph generation 2024: Multilingual jailbreak analysis in LLMs Current trends show increasing focus on large language model optimization and robust neural architectures , with applications in fault diagnosis, recommender systems, and misinformation detection.

• Household Finance
• Financial Economics
• Behavioral Economics
• +۲ مورد دیگر

Tarun Ramadorai is Professor of Financial Economics at Imperial College London, with a distinguished career spanning household finance, financial economics, behavioral economics, real estate, and international finance. He serves as Executive Editor of the Review of Financial Studies and holds prestigious fellowships including Research Fellow of the Centre for Economic Policy Research (CEPR), Senior Academic Fellow of the Asian Bureau of Finance and Economics Research (ABFER), and Nonresident Senior Fellow at the National Council of Applied Economic Research (NCAER). Education BA in Mathematics and Economics from Williams College MPhil in Economics from the University of Cambridge PhD in Business Economics from Harvard University Research Interests Professor Ramadorai's research spans household finance, financial economics, behavioral economics, real estate, and international finance. His work examines how households make financial decisions across different markets and countries, with particular focus on housing markets, investment behavior, and financial inclusion. He has established himself as a leading expert in international comparative household finance, having previously served as Principal Investigator on a transformational initiative financed by the Sloan Foundation to establish this sub-field of finance and economics. His recent research explores the intersection of technology and personal finance, housing market dynamics, and optimal tax policy. He has demonstrated how housing costs impact fertility decisions, how machine learning affects credit markets, and how privacy policies influence consumer data extraction. His work combines rigorous theoretical frameworks with innovative empirical approaches using large-scale datasets from diverse markets. Publication Trends Professor Ramadorai's recent publications reveal a strong focus on household decision-making in financial markets, with increasing attention to the digital transformation of finance. His work bridges theoretical insights with practical policy implications, particularly in emerging economies. There is a clear trend toward interdisciplinary research that combines finance, economics, and data science to address pressing questions about financial inclusion, housing affordability, and the impact of technology on traditional financial services. Scientific Awards Brattle prize for best paper in the Journal of Finance Jensen prize for the best paper in the Journal of Financial Economics Wharton School-WRDS Best Paper Award in Empirical Finance James A Lebenthal Excellence in Municipal Finance Research Prize FMA Napa Conference Best Paper Prize INQUIRE Europe third prize Viz Risk Management Best paper prize Policy Engagement and Advisory Roles Professor Ramadorai has made significant contributions to policy discussions worldwide. He served as Chairman of the Inter-Regulatory Committee on Household Finance constituted by the Reserve Bank of India, which produced the influential "Indian Household Finance" report. He has advised numerous institutions including the Economic Advisory Council to the Prime Minister of India, the European Securities and Markets Authority, and the Norwegian Sovereign Wealth Fund. Currently, he co-chairs the Fintech workstream of the India-UK Financial Partnership, helping to shape the future of financial technology across borders. Research Initiatives Professor Ramadorai previously led the Initiative on International Comparative Household Finance, funded by the Sloan Foundation, which established household finance as a distinct sub-field of research. He is in the process of setting up a new initiative at Imperial College Business School to further advance this area of study. His work has influenced both academic research and practical policy interventions in financial markets around the world.

• Biosecurity
• Synthetic Biology
• Conservation Biology
• +۷ مورد دیگر

Stephen Turner is an Associate Professor of Data Science and Assistant Dean for Research at the University of Virginia School of Data Science . His work bridges genomics, data science, and national security , focusing on biosecurity, synthetic biology, conservation, and bioinformatics applications in human health . Previously, he was a faculty member in the UVA School of Medicine’s Department of Public Health Sciences (2011–2019) and directed the UVA Bioinformatics Core . Ph.D., Human Genetics, Vanderbilt University M.S., Applied Statistics, Vanderbilt University B.S., Biology, James Madison University Turner’s research spans computational approaches to biosecurity, biodiversity conservation, and human health . Recent publications highlight tools like the qqman and kgp R packages, PLANES for epidemiological modeling, and biorecap for bioRxiv preprint summarization. His work integrates large-scale sequencing, genome editing, and machine learning in conservation biotechnology and public health forecasting. Scientific contributions include applications in infectious disease forecasting , forensic genomics , and maternal-fetal biology . He has mentored interdisciplinary students and collaborated on NIH-funded research , while advising biotech startups at the intersection of academia, industry, government, and policy .

Lisa Baiمشاهده پروفایل

پژوهشگر ارشد

• Resource recovery from waste and wastewater
• Bioenergy production
• Biodegradable plastics (PHA)
• +۷ مورد دیگر

Dr. Lisa Bai is a Research Fellow at the Australian Centre for Water and Environmental Biotechnology (ACWEB) within the School of Chemical Engineering at the University of Queensland. She also maintains affiliations with the ARC Training Centre for Bioplastics and Biocomposites. Her research focuses on sustainable waste and wastewater management with expertise in resource recovery technologies and bioenergy production from organic waste streams. Dr. Bai earned her PhD from the University of Queensland and has a background in Bioengineering and Environmental Engineering. Prior to her academic career, she spent four years working at an environmental consultancy in North Queensland, where she specialized in aquaculture water bioremediation using macroalgae technologies and waste stream utilization for biomass production. Her research program spans four interconnected themes: 1) Data-Based Waste Identification and Characterisation for both core and non-core organic wastes; 2) Tailored Organic Waste and Wastewater Treatment Technology focusing on anaerobic digestion and nutrient recovery; 3) Biotechnology Assisted Process Optimisation for co-digestion modeling; and 4) Biotechnology Based Process Enhancement investigating microbial ecology mechanisms. Her work bridges molecular biology with engineering applications for practical waste management solutions. Analysis of her publication record reveals a consistent research trajectory in anaerobic digestion technologies and resource recovery, with recent work (2023-2025) focusing on enhancing digestion of challenging waste streams like paunch waste from meat processing. Her research demonstrates progression from fundamental laboratory studies to pilot-scale implementation with industry partners. Dr. Bai is actively involved in research funding, currently leading the 'Pilot-scale Validation of Anaerobic Co-digestion at Large Municipal STP' project (2024-2025) funded by Urban Utilities. Previously, she contributed to the 'Waste-to-Energy' project (2020-2024) under the Fight Food Waste CRC program. She serves as an Associate Advisor for PhD students working on biotechnologies for PHA production. Her research team collaborates closely with industry partners including Central SEQ Distributor-Retailer Authority and Red-Meat-Processing facilities to translate laboratory findings into commercial applications for sustainable waste management, demonstrating strong industry engagement and practical implementation focus.

• Networked Epidemic Modeling
• Graph Signal Processing
• Control Systems
• +۲ مورد دیگر

Victor M. Preciado is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research focuses on network science , control theory , and graph signal processing . Research Interests: Modeling and controlling spreading processes on complex networks Optimization algorithms for time-varying systems Applications in public health and cyber-physical security Selected Publications: Recent work includes machine learning for operator inference (2022), hybrid systems stability analysis (2021), and pandemic modeling frameworks (2021). Earlier contributions focus on spectral analysis of epidemics (2009-2016) and geometric optimization (2014).

Silvia Bonomiمشاهده پروفایل

دانشیار

• Cybersecurity
• Distributed Systems
• Blockchain
• +۸ مورد دیگر

Dr. Silvia Bonomi serves as an Associate Professor in the Department of Computer, Control and Management Engineering at Sapienza University of Rome, where she has held academic positions since 2006. Her career progression includes Research Fellow (2010-2011), Tenure Track Assistant Professor (2016-2019), and current Associate Professor appointment since 2019. She maintains her office in Room B114 and is actively engaged in research and teaching within the university's engineering faculty. Her educational background includes: PhD in Computer Engineering, Sapienza University of Rome and Institut de Formation Supérieure en Informatique et Communication (IFSIC/IRISA), Rennes, France (completed under advisors Prof. Roberto Baldoni and Prof. Michel Raynal) Dr. Bonomi's research critically examines dynamic distributed systems where entities autonomously join and leave networks, with applications spanning VANETs, airborne networks, social networks, and distributed cloud services. She pioneers work in Byzantine fault tolerance for mobile environments, blockchain security with emphasis on smart contract vulnerability analysis, and resilient cybersecurity frameworks integrating human factors. Her investigations into publish-subscribe systems focus on quality-of-service enhancements, while her peer-to-peer systems research addresses fundamental connectivity challenges in large-scale decentralized environments. This interdisciplinary approach bridges theoretical distributed computing with practical cybersecurity applications. Analysis of her 15 most recent publications (2021-2024) reveals dominant trends in blockchain security (particularly smart contract vulnerability taxonomies and analysis tool efficacy) and fault-tolerant distributed systems (reliable communication under Byzantine faults in dynamic networks). Her work increasingly integrates human factors into cybersecurity models and develops visual analytics for business-centric risk assessment. Publications span top venues including IEEE CSR, OPODIS, SAFECOMP, and journals like Computers & Security, demonstrating consistent contributions to both theoretical foundations and practical cybersecurity implementations.

• Computer Architecture
• Reconfigurable Computing
• Network-on-Chip (NoC)
• +۷ مورد دیگر

Ioannis Sourdis is a Full Professor at the Department of Computer Engineering, Chalmers University of Technology, Sweden. His research focuses on computer architecture, reconfigurable computing, network-on-chip (NoC) design, memory systems, and fault-tolerant embedded systems, with applications in biomedical informatics and hardware security. Current projects include EUMMSS (Efficient Uncore Mechanisms for Multicore Space Systems, funded by the Swedish National Space Board) and eProcessor (European Processor Ecosystem, funded by the European Commission). Past initiatives include the DeSyRe project (on-demand system reliability), ECOSCALE (exascale reconfigurable computing), and SHARCS (secure hardware-software architectures). His work spans NoC router design (e.g., FastTrackNoC, DDRNoC), memory compression (MemSZ, L2C), and biomedical security applications (heartbeat-based protocols). He has published extensively in venues like DATE, ICS, PACT, and IEEE Transactions on Networking. Key research areas: Chiplet-based systems , hybrid memory architectures , FPGA acceleration , and real-time stream aggregation .

George Churchمشاهده پروفایل

استاد

• Genomics
• Synthetic Biology
• Genome Engineering
• +۱۲ مورد دیگر

George M. Church is a Professor of Genetics at Harvard Medical School and affiliated with MIT, where he directs PersonalGenomes.org, providing open-access genomic, environmental and trait data. His laboratory focuses on transformative technologies for reading and writing 3D/4D biological structures with attention to ethics, safety, and equitable access. Church has co-initiated major scientific initiatives including the BRAIN Initiative (2011) and multiple Genome Projects (GP-Read-1984, GP-Write-2016, PGP-2005). Church's research spans multiple cutting-edge domains including genome engineering, synthetic biology, aging reversal, and space genetics. His lab pioneered foundational methods for direct genome sequencing, molecular multiplexing and barcoding in 1984, leading to the first genome sequence in 1994. His innovations contributed to nearly all next-generation DNA sequencing methods and companies. Current research directions include machine learning for protein engineering, tissue reprogramming, organoids, gene therapy, and in situ 3D DNA/RNA/protein imaging. His work bridges fundamental biology with therapeutic applications across diverse fields from Alzheimer's disease to de-extinction biology. Church's recent publications reveal a remarkable breadth of scientific inquiry, spanning from fundamental genome editing techniques to applications in aging research, neuroscience, and space biology. His work increasingly integrates artificial intelligence with biological systems, as seen in papers on machine-guided cell-fate engineering and automation of systematic reviews with large language models. His research maintains a strong translational focus, with numerous papers addressing therapeutic applications in cancer immunotherapy, gene therapy, and diagnostics. The consistent theme across his diverse publications is the development and application of transformative technologies to address fundamental biological questions and medical challenges. National Academy of Sciences (NAS) membership National Academy of Engineering (NAE) membership Franklin Bower Laureate for Achievement in Science Co-initiator of the BRAIN Initiative (2011) Director of multiple NIH Centers for Excellence in Genomic Science (2004-2020) Church directs numerous research centers including the NIH-CEGS, Personal Genome Project (PGP), Lipper Center for Computational Genetics, and Wyss Institute Synthetic Biology center. His laboratory has trained PhD students across multiple Harvard and MIT programs including Biophysics, BBS, Biomedical Informatics, ChemBio, Chemistry, SSQB, MCO, Virology, HST, EE/CS, Physics and Applied Math. His commercial impact is extensive through companies spanning medical diagnostics (Knome/PierianDx, Alacris, Nebula, Veritas) and synthetic biology/therapeutics (AbVitro/Juno, Gen9/enEvolv/Zymergen/Warpdrive/Gingko, Editas, Egenesis). Church also pioneered new privacy, biosafety, ELSI, environmental and biosecurity policies. The Church Lab operates across multiple research domains including molecular multiplexing, next-generation sequencing, nanopore technology, and genome engineering. The lab maintains strong connections with the Personal Genome Project, Wyss Institute, and multiple commercial ventures. Current research directions include the Spatial Atlas of Human Anatomy (SAHA), human skin rejuvenation via mRNA, and space genetics research through the Consortium for Space Genetics and BioAstra. The lab's mission focuses on transformative technologies for reading and writing 3D/4D structures at any scale, inspired by but not limited by biology.

Laxman Dhulipalaمشاهده پروفایل

استادیار

• Parallel Algorithms
• Graph Processing
• Parallel Graph Algorithms
• +۷ مورد دیگر

Laxman Dhulipala serves as an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, while also working as a research scientist at Google Research with the Graph Mining team. Dr. Dhulipala earned his Ph.D. from Carnegie Mellon University under Guy Blelloch's supervision and completed a postdoctoral fellowship at MIT with Julian Shun. His research centers on efficient parallel algorithms, particularly for parallel clustering and graph processing, along with developing computational models for emerging hardware technologies. His scholarly output demonstrates significant expertise across parallel computing domains, with particular emphasis on scalable graph algorithms, dynamic data structures, and computational geometry. Dr. Dhulipala's work bridges theoretical computer science with practical systems implementation, producing algorithms that achieve both theoretical optimality and real-world performance. His research group has made substantial contributions to benchmarking frameworks including the Graph Based Benchmark Suite (GBBS) and ParClusterers Benchmark Suite, establishing standardized evaluation methods for graph processing systems. The collective work shows progression from theoretical foundations to practical implementations that handle massive-scale datasets. Best Paper Award at SPAA 2022 Best Paper Runner Up at VLDB 2022 Distinguished Paper Award at PLDI 2019 Memorable Paper Award Finalist at NVMW'20 CMU's SCS Dissertation Award Honorable Mention As an educator, Dr. Dhulipala mentors numerous graduate students while teaching advanced courses in algorithm design and parallel computing. His research collaborations span multiple institutions including Carnegie Mellon University, MIT, and Google Research, reflecting his position at the intersection of academia and industry research.

• Information Hiding
• Steganography
• Steganalysis
• +۴ مورد دیگر

Andrew D. Ker is Professor and Associate Professor of Computer Science at the University of Oxford's Department of Computer Science, and Tutorial Fellow in Computer Science at University College, Oxford since 2009. He received his BA in Mathematics & Computer Science (1994-1997) and DPhil in Computer Science (1997-2000) from Oxford, followed by academic appointments including Junior Research Fellow (2000-2003), Special Supernumerary Fellow (2003-2009), and Royal Society University Research Fellow (2003-2011). His research focuses on information hiding, particularly steganography (covert communication in digital media) and steganalysis (detection of hidden data), with additional interests in digital media forensics and programming language semantics. His foundational work includes the mathematical formalization of the 'square root law of steganographic capacity'. Recent publications explore practical implementations in social media platforms, GPU-accelerated steganalysis, and linguistic steganography techniques. He has received multiple scientific awards including Best Paper Awards at ACM Workshops on Information Hiding & Multimedia Security, SPIE conferences, and the International Workshop on Digital Watermarking. As Associate Editor for IEEE Transactions on Information Forensics and Security, he maintains active involvement in the academic community. Professor Ker has supervised numerous graduate students in computer security and steganography research, including doctoral candidates and master's students. He leads research within Cyber Security Oxford and has developed four major lecture series: Lambda Calculus and Types, Discrete Mathematics, Computer Security, and Advanced Security: Information Hiding. He remains active in teaching despite administrative responsibilities as Tutorial Fellow.

Shie Mannorمشاهده پروفایل

استاد

• Machine Learning
• Reinforcement Learning
• Game Theory
• +۸ مورد دیگر

Shie Mannor is a Professor at the Technion - Israel Institute of Technology in the Department of Electrical Engineering. He also holds a visiting professorship at Cornell-Tech in New York City and is affiliated with the Technion Machine Learning Center and the Grand Technion Energy Program . Key Research Interests: Machine Learning: Theory, algorithms, and applications to high-dimensional data and dynamics modeling. Reinforcement Learning and Markov Decision Processes: Adaptive control in large stochastic systems. Learning and control under uncertainty: Robust/stochastic optimization frameworks. Game Theory: Stochastic, dynamic, and network games applied to power markets and resource allocation. Multi-agent systems: Online learning and designing economic systems with optimal equilibria. Power Grid: Data-driven reliability, pricing, and decision-making in smart grids (e.g., EU-funded GARPUR project). Applications: Communication network optimization, mobile health, LDPC codes, and large-scale optimization problems. He actively seeks postdocs, graduate, and undergraduate students with strong mathematical or programming skills for projects in mobile phone programming and complex system optimization. Contact: shie.mannor@ee.technion.ac.il | Phone: ++972-4-829-3284

Amin Mesmoudiمشاهده پروفایل

دانشیار

• Data Engineering
• Machine Learning
• Anomaly Detection
• +۵ مورد دیگر

Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.

Gabriele Bavotaمشاهده پروفایل

دانشیار

• Software Quality
• Empirical Software Engineering
• Mining Software Repositories
• +۳ مورد دیگر

Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.