Ruben Verborgh is a Professor of Decentralized Web Technology at the Ghent University – imec and a Visiting Fellow at the Oxford Martin School (University of Oxford). He leads the Internet Technology and Data Science Lab (IDLab) and co-founded the Solid platform with Tim Berners-Lee to re-decentralize the Web. His research focuses on Linked Data Fragments , a paradigm for Web-scale query execution, and explores decentralized data governance , user-controlled data ownership , and rule-based Web agents for policy enforcement. He has co-authored two books on Linked Data and contributed to over 250 publications. Recent articles highlight trends in decentralized data ecosystems , including ODRL policy interoperability , event notification systems , and personal data vaults . His work bridges Linked Data , hypermedia APIs , and privacy-preserving technologies . Verborgh collaborates with institutions like MIT, Oxford, and the European Commission, and advises companies through Inrupt . His labs ( IDLab , Solid Ecosystem ) focus on sustainable data-driven societies.
Fan Zhang is an Assistant Professor in the Department of Computer Science at Yale University. He holds a Ph.D. from Cornell University, advised by Ari Juels, and a B.S. from Tsinghua University. His research focuses on computer security, applied cryptography, decentralized systems, blockchains, and trusted execution environments (TEEs). He leads the Decentralized Systems Group at Yale and is affiliated with the IC3, CDCC, and CADMY centers. He teaches courses on blockchain and real-world cryptography. Key research interests include blockchain decentralization, privacy-preserving protocols, and secure distributed systems. Notable contributions include the Town Crier oracle system (acquired by Chainlink), the DECO TLS protocol, and foundational work on transaction order fairness and MEV mitigation. Awards include the Ethereum Foundation Academic Grant (2022) and the IBM PhD Fellowship (2018-2020). His work has been published in top venues like CCS, S&P, CRYPTO, and USENIX Security.
Max Alekseyev is an Associate Professor in the Mathematics Department and Computational Biology Institute. His research spans computational graph theory, enumerative combinatorics, computational/algorithmic biology, and comparative genomics. He focuses on interdisciplinary problems, blending mathematics with biological applications, particularly in genome assembly and analysis. His work includes advancements in genome scaffolding algorithms, combinatorial sequence analysis, and mathematical biology. Notable contributions involve genome assembly tools like CAMSA and studies on ancestral genome reconstruction. He also explores theoretical topics such as Bernoulli series generalizations and modular data classification. His research trends highlight a blend of pure mathematics (e.g., number theory, graph theory) and applied computational methods, addressing challenges in genomics and evolutionary biology. He secured an NSF Student Travel Grant in 2018 for computational molecular biology.
David A. Muller serves as the Samuel B. Eckert Professor of Engineering in the School of Applied and Engineering Physics at Cornell University and co-directs the Kavli Institute at Cornell for Nanoscale Science. His research group focuses on developing quantitative electron microscopy methods to understand materials properties at the atomic scale, with particular emphasis on sustainable energy applications and quantum materials. Muller's laboratory utilizes some of the world's highest resolution electron microscopes housed in specially designed, environmentally isolated rooms. Muller received his undergraduate education at the University of Sydney and earned his Ph.D. in Physics from Cornell University in 1996. Between 1997 and 2003, he was a member of the technical staff at Bell Laboratories, where he applied his expertise in imaging single atoms and atomic-scale spectroscopy to determine the physical limits of transistor miniaturization. In 2003, he returned to Cornell as a faculty member, where he has since established himself as a leader in advanced electron microscopy techniques. Muller's research spans multiple frontiers in materials science, with particular focus on understanding how electronic-structure changes at the atomic scale control macroscopic behavior in diverse systems like turbine blades, fuel cells, and transistors. His current work emphasizes the physics of renewable energy materials, atomic-scale control of materials to create electronic phases that cannot exist in bulk, and developing hardware and algorithms for 'big data' acquisition from high-bandwidth pixelated electron microscope detectors. His group's work bridges theoretical physics and experimental techniques, requiring researchers who can think in both real and reciprocal space while considering both fundamental principles and practical applications. Analysis of Muller's recent publications reveals a strong trend toward advancing electron ptychography and 4D-STEM techniques for atomic-scale imaging. His group has pioneered methods for 3D atomic-scale metrology, strain mapping, and imaging of radiation-sensitive materials. The research spans applications from semiconductor technology to quantum materials and energy storage systems, demonstrating the versatility of his microscopy approaches across multiple scientific domains. Top 100 Young Innovator by Tech Review Magazine (2003) Burton Medal from Microscopy Society of America (2006) Ernst Ruska Prize of German Society for Electron Microscopy (2021) John Cowley Medal from International Federation of Societies for Microscopy (2023) Fellow of American Physical Society Fellow of American Association for the Advancement of Science Fellow of Microscopy Society of America Muller has mentored an extensive group of students and postdocs who have gone on to successful careers in academia and industry. His former students hold faculty positions at institutions including Rice University, University of Southern California, Seoul National University, Colorado School of Mines, and the University of Michigan, among others. His research has been supported by substantial grants, including a $22.5M NSF grant that accelerates materials discovery. The Muller lab maintains close collaborations with the Kavli Institute at Cornell and PARADIM (Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials). The Muller lab operates at the forefront of electron microscopy, housing specialized instrumentation including high-resolution transmission electron microscopes in environmentally isolated rooms. The group collaborates extensively with other research teams at Cornell and worldwide, focusing on understanding materials atom by atom. Current research directions include applying machine learning to electron microscopy data analysis, developing cryogenic techniques for studying low-melting-point materials, and exploring quantum phenomena in engineered materials systems.
Rasmus Kyng is an Assistant Professor in the Department of Computer Science at ETH Zurich, where he has been since 2019. His research focuses on fast algorithms for graph problems, convex optimization, and their applications in machine learning. He has received grants from the Swiss National Science Foundation, including project grants and a starting grant. Education: B.A. in Computer Science from the University of Cambridge (2011), PhD in Computer Science from Yale University (2017), advised by Daniel A. Spielman. Postdoctoral positions included Harvard University (2018–2019) and a research fellowship at the Simons Institute, UC Berkeley (2017). Research Interests: Development of nearly linear-time algorithms for fundamental graph problems (e.g., maximum flow, minimum-cost flow), dynamic graph algorithms, discrepancy theory, and fine-grained complexity. His work bridges numerical linear algebra and combinatorial optimization, emphasizing practical implementations such as the Laplacians.jl package. Awards: FOCS Best Paper Award (2022), Inaugural ICBS Frontiers of Science Award (2022), Machtey Award (Best Student Paper, FOCS 2017). Teaching: Advanced Graph Algorithms and Optimization (ETH Zurich, 2020–2023), Algorithms, Probability, and Computing (ETH Zurich, 2020–2022). Supervised numerous PhD students and mentored postdocs in theoretical computer science. Labs/Teams: Co-leads a research group with Maximilian Probst Gutenberg, focusing on dynamic graph algorithms and optimization. Collaborations include work on sparsification, spectral graph theory, and machine learning applications.
Associate Professor Colin Jackson is affiliated with the Research School of Chemistry at the Australian National University College of Physical & Mathematical Sciences . His research spans enzyme engineering, synthetic biology, and protein evolution, with a focus on directed evolution approaches for biocatalysis and molecular biophysics. Former CSIRO and Weizmann Institute researcher Key projects: plastic degradation enzymes, viral protease inhibitors, noncanonical amino acid incorporation His work leverages ancestral sequence reconstruction and machine learning to explore protein sequence spaces, with notable outputs in fitness landscape analysis and biocatalytic applications . Recent publications highlight advancements in: Plastic biodegradation enzyme engineering Antiviral peptide design targeting SARS-CoV-2 Fluorinated noncanonical amino acids for protein studies Marine bacterial transport proteins Organophosphate resistance mechanisms While no formal awards are listed in this data, his research portfolio demonstrates strong industry and biomedical applications through: ANU Researcher Portal publications Collaborative projects with international institutions 50+ funded projects including gene therapy platforms and food waste solutions
Jeffrey Guasto , Associate Professor at Tufts University, holds joint appointments in the School of Engineering (Mechanical Engineering) and School of Arts and Sciences (Physics & Astronomy). His work bridges engineering, physics, and biology to study transport properties in complex systems. Ph.D., Engineering (2009), Brown University Sc.M., Engineering (2004), Brown University Dual B.S. in Physics and Mechanical Engineering (2003), Lehigh University Research Interests focus on: Biophysics : Flagellar mechanics, chemotaxis, cell-fluid interactions Soft Matter : Active suspensions, colloids, viscoelastic materials Microfluidics : Device design for cell motility studies and gradient generation Environmental Transport : Microbial ecology in porous systems Scientific Trends from his 77+ publications show emphasis on microscale fluid dynamics, bacterial transport mechanisms, and viscoelastic flow instabilities. His 2024 Nature Microbiology work reveals phage-infected bacteria driving marine chemotaxis, while 2023 PNAS research explores stress topology in viscoelastic flows. Scientific Awards : NSF CAREER Award (2016) for cell dispersal mechanisms Collaborative NSF grants (2015-2023) Advising includes mentoring 15+ students and postdocs. His grants portfolio features 9+ awards, notably NSF grants for viral-microbe interactions (2018) and flagellar mechanics (2020). Labs & Teams : Leads the Guasto Laboratory at Tufts, integrating microfluidics and high-speed imaging for studying microbial transport, while collaborating with MIT, Harvard, and international institutions.
Eleanor O'Rourke is an Associate Professor at Northwestern University with joint appointments in the Department of Computer Science and the Learning Sciences, part of the McCormick School of Engineering. She co-directs the Delta Lab, focusing on interdisciplinary research in Human-Computer Interaction, Artificial Intelligence, and Learning Sciences. Her work examines how learning environments can foster motivation and effective practices in computer science education, supported by grants from NSF and Google. Educated at the University of Washington (PhD, MS in Computer Science & Engineering) and Colby College (BS in Computer Science and Spanish), her research employs mixed methods, including design-based research and grounded theory, to study student motivation, affective responses during programming, and AI-driven interventions. Notable contributions include tools like Ply and Isopleth , which support novice web developers, and studies on student self-assessment biases and growth mindset incentives. Her work has been recognized with multiple Best Paper Awards at ACM conferences, including ICER 2024 and SIGCSE 2022. She teaches courses such as Transformative AI and the Learning Sciences and Design of Learning Environments , and advises a diverse cohort of PhD students and undergraduates. The Delta Lab’s collaborative approach emphasizes innovation in educational technology and human-centered design.
Crytal Lee is an Assistant Professor in Computational Media and Design at MIT, with a joint appointment in the Schwarzman College of Computing and Comparative Media Studies/Writing. She is also a Faculty Associate at Harvard's Berkman Klein Center for Internet & Society, co-leading the Ethical Tech Working Group, and a Senior Fellow at Mozilla's Responsible Computing Challenge. Her research focuses on data visualization, disability studies, and ethical technology, emphasizing the 'life-cycle of data representations.' Education: PhD in History, Anthropology, Science, Technology, and Society from MIT (2022); MA and BA (High Honors) in History of Science from Stanford University (2016 and 2015). Research Interests : Crystal examines how data is curated, visualized, and contested, with a particular lens on disability justice and accessible design. Her work bridges STS, HCI, and critical data studies, addressing issues like misinformation, algorithmic bias, and inclusive technology. Awards & Grants : Honorable Mentions at EuroVis 2022 and CHI 2021; NSF Dissertation Improvement Grant; SSRC Social Data Fellowship. Advising & Mentorship : Advised projects on accessible visualization, participatory AI, and disability inclusion. Current book project: Crip Computation . Labs & Teams : Co-leads Ethical Tech Working Group at Berkman Klein; involved in MIT's Data + Feminism Lab and Accessible Interactions projects.
Professor Vishnu Pareek is the John Curtin Distinguished Professor at Curtin University, leading the Western Australian School of Mines (WASM) within the Faculty of Science and Engineering. He has held academic roles including Dean of Engineering, Head of School, and various professorships since 2002. His research focuses on multiphase flow modeling, computational fluid dynamics, and reactor engineering, with applications in energy and chemical processes. He holds a BE (Hons) from MNIT, MTech from IIT Delhi, and a PhD from UNSW. Key research interests include LNG process modeling, erosion modeling, and granular flow dynamics. He has authored over 200 peer-reviewed publications, with recent work emphasizing structured packing design, biomass gasification, and additive manufacturing for process intensification. Notable projects include CFD-ANN hybrid models for fluidized beds and experimental studies on 3D-printed structured packings. His expertise spans industrial collaborations in LNG safety, fluid catalytic cracking, and biofuel production. Teaching areas include chemical engineering fundamentals and process systems engineering. He advises on energy policy and leads research teams in multiphase flow and reactor design.
Professor Charlotte Deane is a leading academic in structural bioinformatics, holding the position of Professor at the University of Oxford's Department of Statistics and Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC). She leads the Oxford Protein Informatics Group (OPIG), focusing on protein structure prediction, immunoinformatics, and AI-driven drug discovery. Her research integrates computational methods with biological insights, developing tools widely used in academia and industry. Prior roles include Head of the Department of Statistics, Deputy Head of the Mathematical, Physical and Life Sciences (MPLS) Division at Oxford, and Chief Scientist of Biologics AI at Exscientia. During the COVID-19 pandemic, she served on SAGE and as UKRI's COVID-19 Response Director. In 2022, she was awarded an MBE for her contributions to pandemic research. Her research group's work spans antibody design, T-cell receptor analysis, and small molecule discovery, with a focus on open-source software development. Current projects include advancing AI methods for protein structure prediction and therapeutic antibody engineering. Recent publications highlight innovations in computational drug design, antibody developability, and machine learning applications in structural biology.
Kevin Mackie is Professor and Chair of the Department of Civil, Environmental and Construction Engineering (CECE) at the University of Central Florida’s College of Engineering. He has been a faculty member since 2006, advancing from assistant to full professor, and previously served as associate chair and interim department chair. His leadership includes spearheading departmental improvements in culture, workload policy, and digital accessibility. Education: Ph.D. in Civil Engineering, University of California, Berkeley (2004) M.S. in Civil Engineering, University of California, Berkeley (2000) B.E. in Engineering, Cooper Union, New York (1998) His research focuses on structural engineering , particularly in bridge engineering , performance-based seismic design , nonlinear analysis , and advanced materials for infrastructure repair . He integrates analytical, numerical, and experimental methods to assess and improve the resilience of civil infrastructure under extreme loads. His work addresses critical challenges in soil-structure interaction, seismic retrofitting, and the use of composites and smart materials. The 15 most recent publications reflect a strong emphasis on nonlinear modeling , seismic performance , and computational structural analysis of bridges and tall buildings. Key themes include fiber-based modeling, contact-friction problems, soil-structure interaction, and probabilistic assessment, demonstrating a consistent trajectory toward resilient and sustainable infrastructure systems. Scientific Awards: Faculty advisor of the year (2022, 2021, 2019) – ASCE East Central Branch and Florida Section Technical Contribution Leader Award Winner (2021) – ASCE East Central Branch Distinguished faculty member at UCF (2018) – Department of Housing and Residence Life Arthur N.L. Chiu Award for excellence as faculty advisor (2018) – Chi Epsilon Honor Society Mackie has been a dedicated mentor, supervising 11 doctoral , 18 master’s , and over 30 undergraduate students . His research has been funded by the National Science Foundation , U.S. Department of Transportation , Caltrans , FDOT , and industry partners. He has published nearly 200 papers and been cited over 7,000 times. He led the reaccreditation of CECE’s undergraduate programs and established the accelerated bachelor’s-to-master’s pathway. He leads the Structures Laboratory at UCF and collaborates with multidisciplinary teams on infrastructure resilience. His vision includes expanding graduate programs, strengthening industry partnerships, and diversifying the Senior Design curriculum to reflect real-world engineering challenges.
Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.
Steven Swanson is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego, within the Jacobs School of Engineering. He is the Director of the Non-Volatile Systems Laboratory (NVSL), where he leads cutting-edge research in non-volatile memory, storage systems, and hardware-software co-design. His work bridges computer architecture, systems, and software to develop efficient, reliable, and secure computing platforms. Ph.D., University of Washington, 2006 B.S., University of Puget Sound, 1999 Dr. Swanson's research centers on non-volatile and persistent memory systems , exploring how next-generation storage technologies can transform computing. His lab develops full-stack solutions including file systems like NOVA and Orion , programming models such as NV-Heaps , and hardware prototypes like Moneta and Onyx . The team also works on low-power co-processors (e.g., GreenDroid ) and tools for debugging and verifying persistent memory programs. Research spans system reliability, security, energy efficiency, and performance optimization. His recent publications reveal a strong focus on persistent memory safety , zero-copy I/O , RDMA-based distributed file systems , and real-world characterization of Intel Optane . These works appear in top venues including ASPLOS, FAST, MICRO, and USENIX ATC, demonstrating sustained innovation in storage and systems research. Scientific honors include: NSF CAREER Award Google Faculty Award Facebook Faculty Award NetApp Faculty Fellow Dr. Swanson has advised 15 PhD students and 4 postdocs , many now faculty or senior engineers at Google, Microsoft, Intel, and other leading tech firms. He has secured significant research funding and leads major community initiatives such as the annual Non-Volatile Memories Workshop and Persistent Programming In Real Life (PIRL) . His educational efforts include innovative courses on robotic system design, quadcopter building, and modern storage systems, emphasizing hands-on learning and real-world implementation. The Non-Volatile Systems Laboratory (NVSL) under his leadership fosters a collaborative, international research environment, hosting visitors and postdocs from around the world. The lab is recognized globally as a pioneer in storage systems research and a key contributor to the adoption of persistent memory technologies in industry.
Trey Porto is an Adjunct Professor at the University of Maryland, affiliated with the Joint Quantum Institute (JQI) and NIST. His research focuses on ultra-cold atoms, quantum optics, and quantum information science. He leads projects on Rydberg atoms, optical lattices, and quantum networking, leveraging cold atom systems to explore novel quantum phenomena and control strategies. Research areas include ultra-cold Rb/Yb mixtures for studying Bose-Einstein condensates and engineered dissipation, as well as photon-photon interactions using Rydberg-dressed polaritons. His work bridges quantum simulation, quantum computing, and precision measurement, with applications in quantum networking and many-body physics. Key achievements include the 2023 UMD Quantum Invention of the Year Award for developing photon-counting methods that preserve quantum states. Porto collaborates with groups such as RQS and JQI, contributing to advancements in subwavelength optical potentials and Floquet-engineered systems. He mentors graduate students in experimental and theoretical aspects of cold atoms and quantum technologies. Publications highlight breakthroughs in Rydberg blockade enhancement, prethermal Bose-Einstein condensation, and compact auto-alignment systems for experimental setups. His lab is based in the Physical Sciences Complex on the UMD campus, with ongoing projects exploring quantum dissipation and photon-atom hybrid systems.
