Michael Pyrcz is a Professor in the Hildebrand Department of Petroleum and Geosystems Engineering and holds the rank of Associate Professor in the Jackson School of Geosciences at the University of Texas at Austin. He is the recipient of the B. J. Lancaster Professorship in Petroleum Engineering and the George H. Fancher Centennial Teaching Fellowship in Petroleum Engineering. His research focuses on subsurface data analytics, geostatistics, and machine learning applications in energy systems and CO2 sequestration. Pyrcz teaches widely, including through online lectures and GitHub workflows, and has authored over 50 peer-reviewed publications and a textbook on spatial data analytics. His work integrates machine learning with geoscience challenges, such as uncertainty quantification in reservoir modeling and CO2 storage site evaluation. He leads initiatives in energy data analytics through the Freshman Research Initiative and collaborates with industry on workflow development. Key research areas include generative AI for subsurface models, stochastic methods for fracture networks, and anomaly detection in geologic monitoring. Education: Background in petroleum engineering and geosciences (details not explicitly provided). Grants/Advising: Extensive industry collaboration and mentorship roles at Chevron prior to UT Austin. Labs/Teams: Maintains active GitHub repositories (GeostatsGuy), YouTube lecture series (GeostatsGuyLectures), and social media outreach (X/GeostatsGuy).
Alan Sussman is a Professor and Associate Chair of Undergraduate Education in the Computer Science department at the University of Maryland. His research focuses on databases, high-performance computing, parallel systems, and educational curriculum development for computing disciplines. He holds a Ph.D. from Carnegie Mellon University (1991) and a B.S.E. from Princeton University (1982). His educational contributions include integrating parallel and distributed computing concepts into early undergraduate courses, supported by NSF-funded initiatives like the CyberTraining program. He has advised students such as Harshit Soora (Master's) and Xiaolong Tian (PhD). His research spans compiler optimizations for parallel programs, distributed data management systems, and scientific workflow frameworks like DYFLOW. He collaborates with UMIACS and contributes to interdisciplinary projects like the TASCS center. Key innovations include VeloxDFS for distributed dataset streaming, compiler techniques for irregular memory access in PGAS programs, and NetCDFaster for geospatial data optimization. His work emphasizes productivity improvements for high-performance applications and curriculum modernization to address emerging computational challenges. Awards: No individual awards explicitly listed; however, collaborator Jik-Soo Kim received a best paper award in 2006. Grants: NSF CyberTraining, TCPP Curriculum Initiative, and Center for Technology for Advanced Scientific Component Software (TASCS). Labs/Teams: Active in UMIACS and interdisciplinary collaborations, including the TASCS center and InterComm framework development.
Professor Guy-Vincent Jourdan is affiliated with the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Ph.D. from Université de Rennes/INRIA (France, 1995) focusing on distributed systems analysis. Prior to academia, he served as CTO and CEO of Decision Academic Graphics, an Ottawa-based firm. His research interests span software security, cybersecurity (including cybercrime prevention), distributed systems modeling, formal methods, mobile applications, and rich internet applications. Specific technical emphases include phishing detection systems, blockchain fraud analysis, and adversarial machine learning. Professor Jourdan has pioneered tools like D-ForenRIA for reconstructing user interactions in Rich Internet Applications and contributed to cybersecurity frameworks such as HEART for log anomaly detection. His work integrates machine learning techniques with domain-specific challenges in network security and software verification. His publications (2023-2025) reflect advancements in AI-driven vulnerability analysis, blockchain fraud detection, and automated phishing detection systems. Notable projects include SV-TrustEval-C for source code vulnerability analysis and Intellitweet for social media threat detection. While no scientific awards are explicitly listed, his prolific publication record and industry-academia transition highlight sustained contributions to computer science and cybersecurity domains.
Laurie Williams serves as a Goodnight Distinguished University Professor in the Computer Science Department within the College of Engineering at North Carolina State University. She co-directs both the NCSU Secure Computing Institute and the NC State Science of Security Lablet, demonstrating deep institutional leadership in cybersecurity research. With over 260 refereed publications, her work establishes her as a prominent figure in software security academia. Her research spans critical areas including software security, agile development practices (particularly continuous deployment), software reliability, and software supply chain security. Williams focuses on practical security solutions addressing modern challenges like malicious dependencies in open-source ecosystems, AI-generated code vulnerabilities, and runtime protection mechanisms. Her work bridges theoretical security principles with industry-relevant applications. Recent publications reveal strong trends toward software supply chain security, with multiple 2024-2025 papers addressing vulnerability exploitability, malicious commit detection, and metrics-driven security control selection. Her research increasingly incorporates machine learning for threat detection while maintaining focus on human factors in secure development practices. IEEE Fellow (2018) National Science Foundation CAREER Award (2004) ACM SIGSOFT Influential Educator Award (2009) Multiple IBM Faculty Awards (2002-2012) NCSU Alumni Association Outstanding Research Award (2015-2016) Williams leads multiple major NSF-funded projects including the $5.7M SaTC Frontiers grant on secure software supply chains and the Science of Security Lablet with $3.6M in DoD funding. Her research emphasizes practical industry impact through collaborations with Cisco and Laboratory for Analytic Sciences. She actively mentors through the NCSU Research Leadership Academy and maintains significant educational outreach in software security. Her laboratory work centers on the Secure Computing Institute and Science of Security Lablet, where her team develops frameworks for vulnerability prediction, supply chain risk assessment, and secure development methodologies. Current projects focus on machine learning integrity, cognitive modeling for security decisions, and empirical analysis of build/deployment logs for anomaly detection.
Francis Y. Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), holding an affiliate appointment in Electrical & Computer Engineering within the Grainger College of Engineering. He leads the Illinois Networked Systems and AI (NSAI) research group, focusing on building intelligent networked systems that are safe, robust, and performance-optimized through practical machine learning integration. Prior to joining UIUC in January 2025, he served as a Senior Researcher at Microsoft Research Redmond under Victor Bahl. His educational background includes: Ph.D. in Computer Science from Stanford University (2020), advised by Keith Winstein and Philip Levis B.S. in Computer Science (Yao Class) and B.A. in Economics from Tsinghua University (2015) Additional undergraduate studies at MIT Yan's research adopts a holistic approach to practical machine learning for networked systems, emphasizing judicious application rather than indiscriminate use. He builds real-world systems and research platforms to lay ML foundations, devises deployable algorithms using domain insights, and validates performance through extensive empirical evidence. His work consistently addresses operator concerns regarding ML deployment—focusing on safety, robustness, generalization, and efficiency—while strategically combining ML with classical networking and systems techniques. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in resource allocation for microservices (DeDe, Autothrottle), real-time video optimization (Mowgli, GRACE), and LLM-driven network algorithm design. His work bridges theoretical advances with industrial deployment, evidenced by platforms like Puffer (400,000+ users) and OpenNetLab that have become community standards for validating congestion control algorithms. His research has been recognized with top honors: USENIX NSDI Outstanding Paper Award (2024) for Autothrottle APNet Best Paper Award (2022) IRTF Applied Networking Research Prize (2021) USENIX NSDI Community Award (2020) USENIX ATC Best Paper Award (2018) for Pantheon Yan actively recruits master's and undergraduate researchers for his NSAI group, prioritizing self-motivated students for projects in networked systems and AI. His research is supported by industry collaborations (notably Microsoft) and manifests in deployable platforms like Puffer—which has enabled award-winning research at NSDI and SIGCOMM—and OpenNetLab for real-time communications. His work directly impacts production systems including Microsoft Teams and Bing. He founded and directs the Illinois Networked Systems and AI (NSAI) research group, which operates critical infrastructure including Puffer (a live TV service and research platform) and OpenNetLab. These platforms facilitate community-wide validation of novel algorithms, with Puffer alone supporting multiple best-paper awards at top conferences. Current workstreams span cloud resource management (Teal, Autothrottle, DeDe), low-latency video (Puffer, Tambur, Mowgli), and LLM-augmented systems (Nada, Designing Network Algorithms via LLMs).
Dr. Chutima Boonthum-Denecke is a Professor in the Department of Computer Science at Hampton University's School of Science. She joined Hampton University in 2006 as an Assistant Professor and now serves as Director of the Information Assurance and Cyber Security Center (IAC@HU). She leads the NSF CyberCorps Scholarship for Service program and has contributed to NSF initiatives like ARTSI and STARS Alliances. Her educational background includes a Ph.D. in Computer Science from Old Dominion University (2007), an MS in Applied Computer Science from Illinois State University (2000), and a BS in Computer Science from Srinakharinwirot University (1997). Dr. Boonthum-Denecke's research integrates artificial intelligence, natural language processing, and cybersecurity. Key interests include: Developing intelligent tutoring systems and educational games Secure coding practices for software engineering NLP applications in information retrieval and assessment tools Cyber-physical security for IoT and robotics Her recent publications (2016-2021) focus on machine learning applications in cybersecurity, including sentiment analysis for threat detection, blockchain-enhanced IoT security, and vulnerability assessments of emerging technologies. Collaborative work with students frequently addresses privacy ethics in AI assistants, RFID implants, and cloud systems. She mentors students through the IAC@HU lab, resulting in award-winning conference presentations on cybersecurity topics. As Principal Investigator of NSF CyberCorps, she oversees scholarship programs that bridge academic research with national security needs.
Professor Dahlia Malkhi is a leading academic and researcher in distributed systems and blockchain technology. She currently holds a faculty position at the University of California, Santa Barbara (UCSB), where she heads the Foundations of Financial Technology (FfTech) research lab. Her work focuses on reliability, security, and consensus mechanisms in distributed systems, with a recent emphasis on blockchain innovations like HotStuff, which underpins Diem, Aptos, and other blockchains. She has held influential roles at industry leaders such as Chainlink Labs, Diem Association, VMware, and Microsoft Research. Education: Ph.D. in Computer Science from The Hebrew University of Jerusalem. Past roles include CTO of Diem Association (2019–2022), Principal Researcher at VMware (2014–2019), and Partner Principal Researcher at Microsoft Research (2004–2014). Research Interests: Blockchain consensus algorithms (e.g., HotStuff, Flexible Paxos), Byzantine Fault Tolerance (BFT), secure multi-party computation (FairPlay), and distributed database systems (CorfuDB). Her work bridges academic theory with industrial applications, emphasizing practical scalability and security. Awards: ACM Fellow (2011), IEEE TCDP Outstanding Technical Achievement Award (2021), IBM Faculty Award (2003/2004). She has also held leadership roles in conferences like Usenix ATC and program chairs for multiple distributed systems events. Advising & Grants: Advises projects at Space Computer, Lyquor Labs, and Chainlink Labs. Her research labs and collaborations include work on BBCA-Chain, Lumiere, and BFTBrain, advancing consensus mechanisms in decentralized systems. Labs/Teams: Leads UCSB’s FfTech lab, co-founded VMware Research, and contributed to foundational blockchain projects like DiemBFT and Espresso Systems. Her work impacts technologies such as NSX-T control planes and distributed financial infrastructure.
Peter Alvaro is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. He joined the faculty in 2015 after earning his PhD from UC Berkeley under Professor Joe Hellerstein. His research lies at the intersection of databases, distributed systems, and programming languages , with a strong emphasis on data-centric approaches to building robust, scalable, and predictable distributed systems. He is the creator of the Dedalus language and co-creator of the Bloom language, both designed to simplify reasoning about distributed computation. Peter's recent work focuses on non-volatile memory (NVM) , computational storage , and data-centric operating systems , as seen in the Twizzler OS project. His publications span top venues such as USENIX ATC, HotNets, and Communications of the ACM, showing trends toward system resilience, efficient data management, and novel abstractions for modern hardware. Best Presentation award at USENIX ATC 2020 Peter advises graduate students, including Daniel Bittman, and is a key contributor to the Storage Systems Research Center (SSRC), now succeeded by the Center for Research in Storage Systems (CRSS). His work is supported by ongoing collaborations with researchers at UC Santa Cruz and beyond, particularly in the areas of storage, operating systems, and distributed computing.
Chicheng Zhang is an Assistant Professor in the Computer Science Department at the University of Arizona, where he conducts research in the theory and applications of interactive machine learning. He earned his Ph.D. in Computer Science from the University of California, San Diego (UCSD) in 2017 under the supervision of Professor Kamalika Chaudhuri, and was previously an undergraduate student at Peking University working with Professor Liwei Wang. From 2017 to 2019, he was a postdoctoral researcher at the Machine Learning Group at Microsoft Research NYC. His research lies at the intersection of learning theory and practical algorithm design, focusing on interactive machine learning paradigms such as reinforcement learning, contextual bandits, active learning, and imitation learning. He aims to develop algorithms that are data-efficient, computationally tractable, and robust, with applications in healthcare, wireless communication, and fair AI systems. His work emphasizes principled algorithm design with theoretical guarantees and empirical validation. The most recent publications reflect a strong trend in developing efficient, theoretically grounded methods for sequential decision-making and interactive learning. Key themes include sample efficiency, robustness to noise, fairness in algorithmic decisions, and application-driven research in domains like oral cancer detection and mmWave network optimization. His work frequently bridges theoretical analysis with real-world deployment considerations. While no scientific awards are mentioned in the provided text, Dr. Zhang actively mentors prospective PhD students and encourages collaboration. He has contributed to interdisciplinary projects involving fairness-aware bandit algorithms for network coexistence, interpretable classifiers for cancer detection, and LLM-based initialization for reinforcement learning. His lab focuses on developing intelligent agents that actively learn from environments and human experts. He can be reached at chichengz@arizona.edu .
Dominik Roeser is a Professor and Associate Dean of Research Forests & Community Outreach at the University of British Columbia's Faculty of Forestry, Department of Forest Resources Management. With over 21 years of experience in forest research and innovation, he has built a comprehensive forest operations research program since joining UBC in 2018, following his tenure as Senior Director at FPInnovations where he managed multidisciplinary teams focused on improving forest sector competitiveness and wildfire management solutions in Western Canada. Professor Roeser's research interests center on sustainable forest management and the bioeconomy, with specific expertise in forest bioproduction, supply chain design, steep slope harvesting, and biomass operations. His work through the Forest Action Lab applies diverse research methods including productivity studies, field trials, and modeling to address sustainability challenges across different operational environments. His research portfolio spans sustainable forest biomass utilization, harvesting in difficult terrain, innovative forest planning tools, operational productivity, carbon management, and community sustainability impacts from reforestation. His publication record shows a strong focus on practical applications of forest science, with recent work emphasizing wildfire management, remote sensing technologies for precision forestry, biomass energy systems, and the socio-ecological dimensions of forest management. His research increasingly integrates advanced technologies like LiDAR and drone-based systems with traditional forest operations to address contemporary challenges in sustainable forest management. Roeser has received the Recognition Award from the Canadian Forest Service (2017) for his contributions to forest science and innovation. His work demonstrates significant impact on both academic understanding and practical implementation of sustainable forest operations across North America and Europe. As an educator, Professor Roeser teaches several key courses including FOPR 264 Introduction to Forest Operations, FOPR 362 Harvesting systems and forest access, FOPR 464 Operational planning and management, and FRST 452 Coastal field school. He considers educating the next generation of forestry professionals one of his passions, bridging theoretical knowledge with practical industry applications. The Forest Action Lab, led by Professor Roeser, represents a multidisciplinary research hub applying diverse methodologies to address forestry stakeholders' needs across British Columbia, Canada, and globally. The lab's work connects academic research with industry implementation, focusing on practical solutions for sustainable forest utilization in varied operational environments.
Paulo Blikstein is an Associate Professor of Communication, Media, and Learning Technologies Design at Teachers College, Columbia University. He holds affiliations with the Mathematics, Science & Technology department and the Communication, Media, and Learning Technologies Design program. His expertise spans curriculum design, digital innovation, science education, and educational technology. Dr. Blikstein earned a Ph.D. in Learning Sciences from Northwestern University (2009), M.Sc. in Media Arts & Sciences from MIT Media Lab (2002), and degrees in Engineering from the University of São Paulo (Brazil). His research focuses on leveraging technology to enhance learning through computational modeling, maker education, and tangible interfaces. He leads the Transformative Learning Technologies Lab and the FabLearn Program, which develop innovative tools like MoDa and PlayData, integrating computational thinking with real-world science experiments. His work emphasizes equitable access to technology-driven education, particularly in the Global South. Recent projects include deploying cloud labs for biology education, analyzing disinformation dynamics via agent-based models, and exploring how social media influences political radicalization. He critiques commercial education technology discourse through a critical pedagogy lens, advocating for culturally responsive, hands-on learning. Blikstein’s research bridges theory and practice, addressing systemic challenges in science education through participatory design with teachers and communities. His labs create sustainable educational technologies, such as DIY liquid handling robots and haptic feedback systems, to democratize STEM access. He also investigates computational identity formation in K-12 students and the role of making in fostering gender equity in STEM.
Dr. FARKAS Dávid is an Assistant Professor in the Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). His office is located in Building K, basement level, room 12/7, and he holds weekly consultation hours on Mondays from 1–3 pm. Educational contributions include teaching core courses in hydrogeology and groundwater engineering: Groundwater (BMEEOVVMV63) Hydrogeology (BMEEOGMMG62) Infrastructural Design Project (BMEEODHAI41) Research activities concentrate on quantitative hydrogeology, with a special emphasis on karstic groundwater systems, seepage hydraulics, and the safety of flood-protection levees. He couples field investigations in iconic Hungarian cave systems (Buda Castle Cave, Molnár János Cave) with laboratory sandbox experiments and numerical modelling to advance understanding of flow and transport processes in fractured carbonates. Over the past decade his work has evolved from fundamental hydrogeological mapping toward the design and deployment of automated monitoring networks that integrate classical hydrological instruments with modern sensor technologies. This progression is evident in his most recent publications (2024-2025) that document the establishment of high-resolution cave monitoring systems capable of capturing rapid responses to precipitation events. Scientific awards currently listed: none. Advising and grants: no specific student names, grant numbers, or funded project titles are provided in the supplied text. Laboratories and teams: while no dedicated laboratory name is stated, his affiliation with the Department of Hydraulic and Water Resources Engineering implies access to the faculty’s hydraulics and hydro-environmental laboratories, including sandbox and seepage modelling facilities.
SangHyung Ahn is a Lecturer at the School of Civil Engineering , University of Queensland (UQ), since 2017. He joined UQ as a postdoctoral research fellow in 2015 after earning his PhD in Civil Engineering (Construction Engineering and Management) from Purdue University, USA. Prior to his academic career, he worked as an assistant manager at Hyundai Engineering and Construction Co., Ltd. (2003-2007) and holds an MBA in international business from Hanyang University and a B.Sc in Civil Engineering from Korea University. Research Focus: Construction process modelling with virtual reality, decision support systems for construction, automation of data-driven simulation modelling, sensor-based operations analysis, and integration of Building Information Modelling (BIM). Teaching: Coordinates undergraduate courses Introduction to Project Management (CIVL3510) and Construction Engineering Management (CIVL4522) . Research Trends: His recent publications highlight interdisciplinary work in transportation engineering, structural design, and AI-driven simulation tools. Key themes include application of machine learning to car-following models, drone-based vehicle identification, and optimization of public transport systems using agent-based simulations. Supervision: Available for supervision, with completed supervision of PhD and Master’s theses on topics such as BIM-LCA integration, pedestrian trajectory analysis, and AI-driven driving behavior models.
Chang Hyun Park is an Assistant Professor at the Department of Information Technology, Uppsala University, where he is part of the Uppsala Architecture Research Team. His research focuses on computer architecture with emphasis on memory systems, virtualization, and system software optimization. Dr. Park completed his doctoral studies at KAIST (Korea Advanced Institute of Science and Technology) in South Korea, where he was advised by Professor Jaehyuk Huh. Prior to his current position, he served as a post-doctoral researcher at Uppsala University working with Professor David Black-Schaffer. Dr. Park's research spans several critical areas in computer architecture and systems: Virtual memory systems and address translation mechanisms Cache hierarchy optimization and memory systems design Support for non-volatile memory and heterogeneous memory systems Virtualization technology and optimizations for cloud environments High-speed I/O device integration and accelerator support His publication record demonstrates a consistent focus on improving memory system performance, particularly in virtualized environments. Over the past decade, his work has evolved from fundamental virtual memory optimizations to addressing challenges in emerging memory technologies and large-scale system architectures. Recent publications show increasing emphasis on heterogeneous memory systems, graph processing workloads, and hardware-software co-design approaches. Dr. Park actively collaborates with researchers at Uppsala University, particularly with Professor David Black-Schaffer, and maintains connections with his alma mater KAIST. His work appears regularly in top-tier computer architecture conferences including ISCA, MICRO, ASPLOS, and MEMSYS.
Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.
