Steve Hailes is a Professor of Wireless Systems at the Department of Computer Science, University College London. He has served as Head of Department since 2019 and Deputy Head from 2005. His research spans wireless networks, computational trust, AI, and sensor systems for health, ecological, and environmental applications. Education: PhD and undergraduate degree from Cambridge University Appointment: Joined UCL in 1991 (postdoc), Lecturer in 1992 Research interests include: Trust and security in networked systems (co-founder of computational trust) Security of industrial control systems AI/ML applications in security and causal discovery Multi-agent reinforcement learning and moral behavior modeling Gas sensor fabrication and deployment for diverse applications Recent publications focus on feature selection for cybersecurity , moral alignment in LLM agents , trust-based consensus algorithms , and causal discovery using reinforcement learning . Collaborations include co-authors like Westphal, Musolesi, and Tennant. Applications span healthcare (dementia, JIA), ecology (endangered species in Botswana), and environmental monitoring (CO distribution, meth lab detection).
Westley Weimer is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan, College of Engineering. He teaches advanced courses such as EECS 590 (Advanced Programming Languages) and EECS 481 (Software Engineering), and has previously taught at the University of Virginia. His research integrates software engineering, programming languages, and cognitive science, focusing on automated program repair, program analysis, and the neuroscience of code comprehension. University: University of Michigan School: College of Engineering Department: Department of Electrical Engineering and Computer Science Academic Rank: Professor His research interests include automated program repair (e.g., GenProg), software quality, cognitive modeling of programming, neuroimaging studies of code review, and the application of medical imaging to software engineering. He explores deep questions at the intersection of consciousness, time, and computation, advocating for interdisciplinary approaches to understanding the mind through programming behavior. The most recent publications reflect a trend toward empirical and cognitive studies in software engineering, combining automated repair with human factors, neuroimaging (fMRI, TMS), and real-world software challenges. Themes include bias in code review, programming under cognitive influences, and the neurological basis of code comprehension. His work increasingly bridges computer science with psychology, neuroscience, and social science. Scientific awards include multiple Distinguished Paper Awards at ICSE, FSE, and ESEC/FSE, Best Paper and Runner-up awards, and several 10-Year Most Influential Paper Awards from ASE, GECCO, POPL, and ASPLOS, recognizing the lasting impact of his contributions to automated software repair and program analysis. He has advised numerous PhD and Master’s students, many of whom have gone on to faculty positions or industry research roles. He contributes to academic service through organizing diversity and inclusion initiatives, maintaining graduate career resources, and promoting ethical and inclusive practices in computing. He leads a vibrant research group focused on improving software quality through both technical and human-centered innovations, with ongoing projects in automated repair, cognitive modeling, and secure systems.
Ameet Talwalkar is an Associate Professor in the Machine Learning Department at Carnegie Mellon University and Chief Scientist at Datadog. He holds a PhD from the Courant Institute at NYU (2010) where he received the Janet Fabri Prize for Best Thesis. His professional achievements include co-founding Determined AI (acquired by HPE), creating MLlib in Apache Spark, co-authoring the textbook 'Foundations of Machine Learning,' and spearheading the MLSys conference. Talwalkar's research focuses on fundamental challenges in machine learning systems, including distributed ML, federated learning, neural architecture search, and human-AI interaction. His work bridges theoretical foundations with practical applications across domains like computational biology, PDE solving, and code generation. Current interests include AI for science, specialized model development, and agent-based systems. His publications demonstrate strong focus on ML systems optimization, foundation model evaluation, and interpretable AI. Recent works investigate specialized foundation models, PDE-solving frameworks, code generation tools, and human-AI interaction paradigms. The research consistently targets efficiency, scalability, and practical deployment challenges. Best Paper Award at EAAMO 2023 Best Student Paper at NYAS ML Symposium 2009 Runner-up for Best Real-world Application at Socal ML Symposium 2017 Janet Fabri Prize for Best PhD Thesis (2010) Talwalkar leads the CMU MLSys Lab focused on scalable ML systems and has served as Board President for the MLSys conference series. His educational contributions include developing courses like 'Machine Learning with Large Datasets' and creating the LEAF benchmark for federated learning and NAS-Bench-360 for neural architecture search.
J. Cole Smith is the Dean of the College of Engineering and Computer Science at Syracuse University and holds the academic rank of Professor. He joined Syracuse in 2019 with a focus on advancing undergraduate and graduate education, research initiatives, and diversity, equity, and inclusion efforts. Previously, he earned a PhD in Industrial and Systems Engineering from Virginia Tech (2000) and a BS in Mathematical Sciences from Clemson University (1996). His research focuses on mathematical optimization, particularly mixed-integer programming and combinatorial optimization, with applications in network interdiction, logistics, national security, healthcare, and sports. His work has been supported by agencies such as the National Science Foundation, Office of Naval Research, and Defense Advanced Research Projects Agency. Recent research trends emphasize network interdiction under uncertainty, bilevel optimization, and large-scale optimization challenges. His publications in top journals like Operations Research and Mathematical Programming reflect these themes. Awards include INFORMS Fellow (2023) and recognition from Virginia Tech's Grado Department of Industrial and Systems Engineering. While his dean duties reduce direct research involvement, he remains active through collaboration with PhD students. His team's work addresses real-world problems in security, logistics, and healthcare, leveraging advanced algorithmic techniques.
Ovijit Chaudhuri is an Associate Professor of Mechanical Engineering at Stanford University, with a courtesy appointment in Bioengineering. He leads research at the interface of mechanics and biology, focusing on how cellular and extracellular mechanical properties influence biological processes like cancer progression and tissue formation. His work employs advanced tools such as atomic force microscopy and 3D cell culture systems. Education: Ph.D., University of California, Berkeley/San Francisco (Bioengineering, 2009) B.S., University of California, Berkeley (Engineering Physics, 2003) Postdoctoral Fellow, Harvard University (Biomaterials, 2013) Research Interests: His lab explores molecular mechanisms behind cellular mechanics, extracellular matrix dynamics, and how mechanical cues regulate cell behavior. Key areas include cancer metastasis, mechanotransduction, and engineered biomaterials for 3D cell culture. Publications Trends: Recent work emphasizes viscoelastic hydrogels, matrix mechanics in cancer progression, and mechanistic insights into cell migration. Over 50 publications since 2015 highlight interdisciplinary approaches in biomaterials and mechanobiology. Awards: Not explicitly listed in provided materials. Advising & Labs: No specific advisee names listed, but his lab focuses on collaborative projects in mechano-biology. Active in developing biomaterial systems for drug discovery and tissue engineering applications. Labs/Teams: Leads the Chaudhuri Lab at Stanford, which integrates engineering principles with biological systems to address complex disease mechanisms and therapeutic strategies.
Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.
Rozenn Dahyot is a Professor of Computer Science at Maynooth University within the Faculty of Science & Engineering. She previously held roles as Assistant and Associate Professor in Statistics at Trinity College Dublin (2008-2021) and Lecturer in Computer Science (2005-2008). Her research interests bridge Digital Signal Processing, Computer Vision, Machine Learning, and Statistical Analysis. She organized the European Signal Processing Conference (EUSIPCO2021) in Dublin and served as President of the Irish Pattern Recognition and Classification Society (IPRCS) from 2014-2020. Her work spans topics like semantic scene understanding, CNN compression, and medical image segmentation. Key contributions include advancements in graph-based image analysis, reinforcement learning optimization, and AI-driven systems for disaster management. Dahyot is a member of IEEE, ACM, and EURASIP, contributing to both academic and industrial collaborations.
Paolo Tonella is a Full Professor and Director of the Software Institute at the Faculty of Informatics, Università della Svizzera italiana (USI) in Lugano, Switzerland. He also holds an Honorary Professorship at University College London (UK) and previously led the Software Engineering group at Fondazione Bruno Kessler (Italy). His research focuses on software testing, analysis, and AI-driven systems. He has authored over 200 peer-reviewed papers and 100 journal articles, with an H-index of 72. He teaches courses in Data and Software Engineering and Informatics, including Information Modeling, Probability & Statistics, and Knowledge Search. Key contributions include foundational work on web application testing (ICSE MIP award), evolutionary testing techniques (eToc/EvoSuite tools), and reverse engineering of object-oriented systems. He led the ERC-funded PRECRIME project on anticipatory testing. His recent work addresses AI dependability, autonomous systems testing, and deep learning fault analysis. Scientific awards include the ICSE MIP Award (2001) and ERC Advanced Grant (2018). He has served on editorial boards for major journals like IEEE Transactions on Software Engineering and ACM TOSEM. Current roles include leadership in the Software Institute and organizing the SIESTA summer school.
Assoc. Prof. Dr. Sema Alaçam Doğan has been affiliated with Istanbul Technical University since 2014, serving as an Associate Professor in the Department of Architecture . She has held administrative roles including Deputy Head of Department and Erasmus Coordinator. Education : PhD in Informatics in Architectural Design (2008-2014), MS in Informatics in Architectural Design (2005-2008), and BS in Architecture (1999-2005) from Istanbul Technical University. Her research explores Computational Design , Artificial Intelligence in Architecture , and Sustainable Material Innovation . She investigates digital tools for heritage preservation, daylight optimization in BIM, and cognitive development in architecture students. Recent publications analyze AI-assisted design literacy , machine learning for Sinan mosques , and environmental comfort in Harran houses . Her work integrates algorithmic frameworks with sustainable practices. Scientific awards include multiple ITU Publication and Performance Awards (2021-2024), FABFEST Prizes , and the 2024 Artemis Educator Award from NASA. Active projects like "Physical Computation in Architectural Drawing" and "Robotic Fabrication with Recycled Wind Turbine Blades" demonstrate her leadership in computational and sustainable research.
Professor Grahame Holmes is an Honorary Professor in the School of Engineering at RMIT University, Australia. His expertise spans electrical energy conversion, smart energy systems, renewable energy integration, power electronics, and grid infrastructure. He focuses on advancing technologies for sustainable energy storage, grid stability, and high-efficiency power conversion. Research Interests : Electrical and Electronic Engineering, Communications Technologies, Power Electronics, Renewable Energy Systems, and Grid Integration Solutions. His work emphasizes practical applications such as hydrogen energy storage systems, grid-interactive inverters, and modular multilevel converters. Recent Contributions : Prof. Holmes has published extensively on topics like advanced PWM techniques, resonant current controllers, and DC transformer designs. His research bridges theoretical advancements with real-world implementations, addressing challenges in smart grid stability and high-frequency power conversion. Advising & Grants : Supervised projects include 'Hydrogen Energy Storage System for Nanogrid' (2015) and 'Synchronised Control of Grid-Interactive Inverters' (2015). While specific grant details are not listed, his work aligns with major themes in sustainable energy research. Labs & Collaborations : Engages in collaborative research through RMIT's facilities, focusing on hardware-software co-simulation frameworks and FPGA-based real-time systems.
Kyle C. Hale is an Associate Professor at Oregon State University's School of Electrical Engineering and Computer Science (College of Engineering). He holds a Ph.D. and M.S. from Northwestern University (2016, 2013) and a B.S. in Computer Science from UT Austin (2010). Prior to joining Oregon State in 2024, he served as an Associate Professor at Illinois Tech in Chicago. His research spans operating systems, high-performance computing (HPC), virtualization, computer architecture, and system security. Current work focuses on specialized system software stacks for emerging computing paradigms like memory disaggregation and parallelism optimization. He leads the HExSA Lab and collaborates with the HiPCastor group. Scientific Awards: NSF CAREER Award (2023-2028) Illinois Tech College of Computing Excellence in Research (2023) Illinois Tech College of Computing Excellence in Teaching (2021) Illinois Tech Department of Computer Science Teacher of the Year (2020) EuroSys '22 Best Artifact Award Recent Research Trends: His publications emphasize compiler techniques for memory-disaggregated systems, optimizing parallel runtimes through hardware-software integration, virtualization at fine granularities, and accelerating machine learning workloads via system-level innovations. Keywords include HPC, virtualization, parallelism, and secure execution contexts. Teaching: Courses taught include Computer Architecture (CS/ECE 472), System Security (CSP 544), Operating Systems (CS 450), and advanced topics in serverless/edge computing. He actively recruits PhD students to the HExSA Lab.
Scott Kerlin is a Senior Lecturer in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds an M.S. and B.S. in Computer Science from the University of North Dakota. Prior to academia, he worked at the Mayo Clinic on medical software systems and IBM as a build master for enterprise products. His career spans roles including network administrator, lab manager, and Undergraduate Director at UND. Dr. Kerlin's research focuses on bridging industry experience with academic curriculum, particularly in computer science education, project-based learning, and cybersecurity. He emphasizes practical applications of theoretical concepts, such as integrating 3D printing and scanning technologies with security systems for small satellites. His work also explores student efficacy in AI courses and scalable software project management methodologies. He has taught at multiple institutions, including the University of Minnesota and Augsburg University, and held roles at Michigan Tech as Senior Security Engineer. His 2024 Engineering+ Outstanding Teaching Award highlights his commitment to pedagogical innovation. Current projects include in-space 3D printing, solar energy systems, and cryptographic solutions for satellite communications. Key areas of contribution include: 3D printing/Scanning: Material characterization, key replication, and aerospace applications Cybersecurity: Intrusion detection, satellite communications security, and chaotic cryptosystems Educational Innovation: Active learning frameworks, PBL implementation, and student performance modeling His interdisciplinary approach connects computer science fundamentals with real-world engineering challenges, emphasizing sustainability and industry relevance in curricula.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
Ninette Pilegaard is a Professor and serves as Deputy Head of Division and Head of Section for Transport Policy at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). Her academic career spans multiple research domains with significant contributions to transportation policy and economics. Her research interests focus on transportation policy analysis with particular expertise in: Road charging systems and pricing mechanisms Bicycle infrastructure and safety analysis Commuting behavior and accessibility impacts Car ownership and usage patterns Relationship between transportation accessibility and labor market outcomes Dr. Pilegaard's scholarly work demonstrates a strong empirical approach combining transportation engineering with economic analysis. Her recent publications reveal a consistent focus on evidence-based policy evaluation, particularly in the Danish context. She frequently employs quasi-natural experimental methods to assess transportation policy impacts, with particular attention to road pricing, cycling infrastructure, and the economic implications of transportation systems. Her research has been supported by major funding bodies including Innovation Fund Denmark and Forskningsrådsfinansiering, and has resulted in publications in high-impact transportation journals such as Transportation Research Parts A and D, and Journal of Safety Research. As an academic supervisor, Dr. Pilegaard has served as Main Supervisor for PhD projects, including the 'Productivity and agglomeration' project. She has been actively involved in numerous research collaborations both within DTU and with external partners. Her laboratory work focuses on transportation data analysis, particularly utilizing Danish transportation datasets to examine policy impacts. She has developed expertise in analyzing hospital data for traffic safety research and has contributed to methodologies for assessing infrastructure effects on traffic accidents.
Chao Wang is an Associate Professor at the Department of Chemical and Biomolecular Engineering within the Whiting School of Engineering at Johns Hopkins University. He also serves as the Director of the Nano Energy Laboratory and the department’s Master’s Admissions Director. His research focuses on sustainable energy systems and nanomaterials for CO2 capture and conversion, electrocatalysis, thermocatalysis, and green chemical engineering. Education: Bachelor’s degree, University of Science and Technology of China (2004) Doctorate, Brown University (2009) Wang’s research targets efficient energy conversion and storage via nanomaterials with tailored atomic structures, emphasizing catalytic activity, selectivity, and stability. His group explores electrochemical and thermochemical processes for reduced carbon footprints, including CO2 and methane conversion, ammonia recovery, and phosphorus/nitrogen nutrient recycling using zeolite-based systems. Recent publications (2021–2024) highlight his work in high-entropy alloys, solid-state battery materials, CO2 electroreduction, and biomedical nanotechnologies. Collaborative efforts span catalysis, nanoparticle dynamics, and environmental applications. Grants include a $1M DOE award for multi-university research, a $625K DOE grant for electrified transportation systems, and $3M in startup funding for carbon-removal technology commercialization. Alumni under his mentorship include Ph.D. graduates like Michael J. Manto (2018) and Master’s students like Mitchell Keller (2018), with notable achievements in catalyst development for ammonia/phosphorus recovery and industry placements at Grace & Co. and GEA Engineering.
