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Jean F. Honorio CarrilloView profile

Adjunct Professor

• Machine Learning
• Artificial Intelligence
• Optimization Theory
• +5 more

Jean F. Honorio Carrillo is an Adjunct Professor at Purdue University's Department of Computer Science and a Senior Lecturer at the University of Melbourne's School of Computing and Information Systems. He specializes in machine learning theory, optimization, and their applications to combinatorial and non-convex problems. His research focuses on developing algorithms with theoretical guarantees for structured prediction, robustness, fairness, and federated learning. He has advised numerous students across multiple institutions and holds adjunct roles at Purdue's Statistics Department and MIT CSAIL. Roles: Senior Lecturer (Melbourne), Adjunct Professor (Purdue), Adjunct at MIT CSAIL Research Areas: ML Theory, Non-Convex Optimization, Fairness, Federated Learning Key contributions include breakthroughs in exact inference for structured prediction, optimization frameworks for NP-hard problems, and theoretical foundations for modern ML challenges. His work has been published in top venues like NeurIPS, ICML, and JMLR. He has secured grants from NSF and industry partners, including a 2021 NSF DMS grant for deep learning research. His students have gone on to postdoctoral roles at NUS and UChicago/CMU.

Thomas LudwigView profile

Professor

• High-Performance Computing (HPC)
• Data Storage Systems
• Energy Efficiency in Computing
• +3 more

Prof. Dr. Thomas Ludwig is the Director of the German Climate Computing Center (DKRZ) and a Professor at the Universität Hamburg. He holds a doctoral degree and habilitation from the Technische Universität München, with expertise in High-Performance Computing (HPC), energy efficiency, and data storage systems. His research focuses on optimizing parallel systems, storage technologies, and computational efficiency for climate science applications. He leads projects like AIMES and PeCoH, advancing HPC storage and energy-aware computing. Education: Doctoral degree and habilitation from TU München (1988–2001). Chair in Parallel Computing at Universität Heidelberg (2001–2009). Research Interests: HPC, data reduction techniques, energy-efficient systems, parallel I/O optimization, and climate modeling infrastructure. Recent Research Trends: His work emphasizes storage system efficiency, machine learning in HPC, and convergence between HPC and Big Data. Key contributions include frameworks for portability (Vecpar), automated performance tools, and energy-aware storage solutions. Awards: Some publications received recognition, e.g., a Best Paper award in 2014 for work on energy efficiency. However, no personal awards are explicitly listed. Advising & Grants: Supervised numerous theses in HPC, I/O optimization, and energy efficiency. Leads major projects funded by national and international initiatives. Labs/Teams: Heads the DKRZ team providing supercomputing and data management for climate research, collaborating with global institutions like the University of Hamburg and European research networks.

Paolo TrunfioView profile

Professor

• Computer Engineering
• Big Data Analysis
• Machine Learning
• +5 more

Paolo Trunfio is a Professor of Computer Engineering at the University of Calabria, Italy, and co-founder of DtoK Lab S.r.l., an academic spin-off focused on data analysis and distributed systems. He holds a Ph.D. and is affiliated with the DIMES Department, specializing in big data, cloud computing, and high-performance computing (HPC). His research emphasizes scalable data analysis frameworks, edge-cloud continuum solutions, and machine learning applications for social media and disaster monitoring. Trunfio serves as an Associate Editor for ACM Computing Surveys and Journal of Big Data , and is on the editorial boards of several journals including Future Generation Computer Systems . He has authored four influential books, including Programming Big Data Applications (2024) and Data Analysis in the Cloud (2015). His work spans distributed systems, IoT-based smart objects, and exascale computing. Notable projects include the EU-funded eFlows4HPC and ASPIDE initiatives, which focus on HPC workflows and exascale programming models. Trunfio’s publications (over 200 papers) address topics like social media analytics, energy-efficient P2P networks, and parallel data mining. He leads research in urgent computing for disaster response, edge-cloud integration for urban mobility, and AI-driven data analysis. His contributions to cloud frameworks (e.g., JS4Cloud, ParSoDA) and HPC libraries (e.g., DCEx) highlight his expertise in bridging theory and practice in distributed computing ecosystems.

• Computational Drug Design
• Molecular Modeling
• Anticancer Agents
• +5 more

Dr. Vibhu Jha is an Assistant Professor at the Institute of Cancer Therapeutics , part of the School of Pharmacy & Medical Sciences at the University of Bradford . He holds a PhD from the University of Pisa (Italy) with a focus on computational drug discovery, followed by postdoctoral research at the University of Gothenburg (Sweden) and the University of Dundee (UK). His expertise lies in computational modeling techniques (e.g., molecular docking, machine learning) for anticancer drug design, targeting pathways like 1C metabolism and EGFR-driven resistance. Education: B.Pharm, Chhattisgarh Swami Vivekananda Technical University (2008–2012) M.S. Pharm (Medicinal Chemistry), National Institute of Pharmaceutical Education and Research (2013–2015) PhD, University of Pisa (2017–2021) Research Focus: Dr. Jha’s work integrates AI/ML with conventional computational methods to study protein interactions (e.g., protein-ligand, protein-DNA) and develop inhibitors against cancer targets. Key projects include: Targeting MTHFD2/SHMT2 in breast/colorectal cancers Designing dual EGFR/ACK1 inhibitors for lung cancer Developing topoisomerase-HDAC dual inhibitors His research also explores drug repurposing and collaborative projects with institutions in Italy, Sweden, Germany, and India. Awards: 8 awards (specific names not listed in available texts). Advising & Collaboration: Supervised master’s projects at the University of Gothenburg. Collaborates with Prof. Raj Kumar (Central University of Punjab), Prof. Leif Eriksson (University of Gothenburg), and Prof. Tiziano Tuccinardi (University of Pisa). Active in computational drug design, AI-driven predictive models, and structure-based drug design. Labs & Teams: Based at the Institute of Cancer Therapeutics, Bradford. Engaged in cross-disciplinary teams focused on computational oncology and translational drug discovery.

Lei FanView profile

Assistant Professor

• Power System Operations
• Quantum Computing
• Optimization Algorithms
• +3 more

Dr. Lei Fan is an Assistant Professor in the Department of Engineering Technology at the University of Houston, with a joint appointment in the Electrical and Computer Engineering (ECE) Department. His research focuses on power system operations, optimization algorithms, quantum computing, and energy storage systems. He holds a Ph.D. from the University of Florida and a B.S. from Hefei University of Technology. Education: Ph.D., University of Florida B.S., Hefei University of Technology Research Interests: Dr. Fan’s work bridges theoretical optimization and practical energy systems, including quantum algorithms for power grid management, battery storage planning, and distributed quantum computing architectures. His LORE (Learning & Operations Research & Energy) lab explores cutting-edge applications in teleoperation, satellite networks, and environmental monitoring. Publications: Recent work emphasizes quantum computing’s role in solving complex optimization problems, such as entanglement routing in satellite networks and distributed hydrogen-power systems. His research also integrates machine learning for methane plume detection and hyperspectral imaging. Labs/Teams: He leads the LORE lab, advancing interdisciplinary research in energy systems and quantum technologies.

Marc Sánchez ArtigasView profile

Associate Professor

• Distributed Computing
• Cloud Computing
• Big Data
• +6 more

Marc Sánchez Artigas is an Associate Professor at Rovira i Virgili University, Department of Computer Engineering and Mathematics. He holds a PhD from Pompeu Fabra University (2009) and conducted postdoctoral research at EPFL (Switzerland). His research focuses on distributed computing, cloud storage systems, and serverless architectures. He leads the CloudLab research group and coordinates major EU projects like Horizon Europe's CloudSkin and H2020's IOStack. Education: PhD in Computer Science (2009), Pompeu Fabra University MSc in Computer Engineering (2004), Universitat Rovira i Virgili BSc in Computer Engineering (2002), Universitat Rovira i Virgili Research Interests: Distributed systems, cloud computing, software-defined storage, serverless computing, and privacy-preserving storage solutions. His work emphasizes scalable architectures, data management in heterogeneous environments, and optimizing cloud storage efficiency through novel algorithms and frameworks. Awards: Best Paper (IEEE LCN 2007), Best Dataset (ACM IMC 2015), Serra-Hunter Excellence Professorship, and multiple grants from EU and Spanish funding bodies. Grants & Projects: Coordinated over €5 million in projects including H2020 CloudButton (serverless analytics), FP7 CloudSpaces (personal clouds), and national initiatives like Software-Defined Edge Clouds. Active in coordinating IPCEI-CIS for cloud infrastructure. Teaching: Courses on distributed systems, parallel architectures, and cloud computing. Taught at Universitat Rovira i Virgili and Universitat Oberta de Catalunya.

Gaofeng JiaView profile

Associate Professor

• Structural Engineering
• Risk Assessment
• Infrastructure Resilience
• +4 more

Gaofeng Jia is Associate Professor in Civil and Environmental Engineering at Colorado State University's Walter Scott, Jr. College of Engineering. He specializes in natural hazard risk assessment, infrastructure resilience, and uncertainty quantification. Research integrates simulation-based approaches with high-performance computing for assessing complex engineering systems. Core areas include infrastructure deterioration modeling, wave energy converter optimization, tsunami evacuation planning, and surrogate modeling techniques. Recent work emphasizes physics-informed machine learning for engineering applications. Publications demonstrate consistent methodological innovation in uncertainty quantification across domains including seismic engineering, coastal hazards, renewable energy systems, and structural reliability. Articles employ advanced computational statistics, Bayesian methods, and multi-fidelity modeling. Young Researcher Best Paper Award Best Student Paper Award

Giulia TotiView profile

Assistant Professor

• Computer Science Education
• Ethics in Data Science
• Machine Learning
• +3 more

Giulia Toti is an Assistant Professor of Teaching in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. Her work focuses on computer science education, equity in curriculum design, and fostering inclusive learning environments. She teaches courses such as Applied Machine Learning (CPSC 330), Fairness, Accountability, Transparency, and Ethics (FATE) in Data Science (DSCI 430), and Computers and Society (CPSC 430). Her research explores diversity initiatives in CS education, mastery learning frameworks, and equitable grading practices. Notable contributions include studies on pandemic-era remote teaching impacts and the development of Agora, a tool for enhancing large-classroom engagement. Toti has received UBC Faculty Teaching Awards for her pedagogical innovations. Her interdisciplinary work spans machine learning applications in industry and healthcare, including semantic search systems for clinical data (SemEHR) and predictive analytics for energy production. She is affiliated with the ACE Lab and actively contributes to curriculum reforms addressing DEI (Diversity, Equity, Inclusion) challenges in STEM education. Grants/Awards: Faculty Teaching Awards Labs/Teams: ACE Lab (Advancing Computing Education) Advising: No explicit advisee listings found, but contributes to pedagogical research impacting teaching practices.

Rahat MasoodView profile

Lecturer

• Cybersecurity
• Privacy Technologies
• Authentication & Authorization
• +7 more

Dr. Rahat Masood is a Lecturer at the School of Computer Science & Engineering (CSE), UNSW Sydney. Her research focuses on cybersecurity, including privacy-preserving technologies, authentication mechanisms, critical infrastructure protection, and network security analysis. She holds a PhD in Information Security and Privacy from UNSW (Data61-CSIRO, Australia), an MS in Computer and Communication Security from NUST, Pakistan, and a B.Sc. in Software Engineering from the University of Engineering & Technology, Pakistan. Her academic contributions span theoretical and applied cybersecurity domains. Dr. Masood’s educational background includes: PhD: Information Security and Privacy (UNSW, Data61-CSIRO, Australia) MS: Computer and Communication Security (NUST, Pakistan) B.Sc.: Software Engineering (University of Engineering & Technology, Pakistan) Her research interests emphasize privacy technologies, authentication systems, and securing distributed energy resources. Recent work includes developing frameworks for quantifying privacy risks and analyzing social media manipulations. She employs data-driven methodologies and machine learning to address challenges such as WiFi device tracking and federated learning security. In her publications, she highlights trends in privacy controls usability, satirical news detection using multilingual models, and threat modeling for critical infrastructure. These studies underscore her commitment to bridging cybersecurity theory with real-world applications. No scientific awards are mentioned in the provided texts. Her teaching and supervision roles at UNSW are active, though specific student advisees or grant details are not listed. She is affiliated with Data61-CSIRO through her PhD and contributes to interdisciplinary cybersecurity efforts within CSE.

Scott A. HaleView profile

Associate Professor

• Human-Computer Interaction (HCI)
• Machine Learning
• Natural Language Processing (NLP)
• +7 more

Dr Scott A. Hale is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute (OII), University of Oxford, and a Fellow of the Alan Turing Institute. His work bridges computer science and social sciences, focusing on equitable information access, multilingual online dynamics, and misinformation mitigation. He holds degrees in Computer Science, Mathematics, and Spanish from Eckerd College, followed by a DPhil (PhD) in Social Data Science from the OII. Hale’s research has been supported by grants from UK Research and Innovation, the US National Science Foundation, and organizations like the Omidyar Network and the Alan Turing Institute. Key Roles: Programme on AI, Government & Policy; Director of Research at Meedan; Co-Director of the Social Data Science MSc Research Focus: Misinformation, multilingual systems, social media impact, and AI ethics Education: Eckerd College (BS), OII (MSc, DPhil). His DPhil explored social media design’s role in cross-language information sharing. Recent projects include the Digital Good Network and AI alignment studies. Articles highlight trends in multilingual misinformation detection, LLM cultural biases, and hate speech dynamics. Hale’s work bridges technical innovation with social science rigor to address global digital challenges. Awards: Alan Turing Institute Fellowship, recognition in Oxford’s Teaching Excellence Awards. Grants: Over 20 funding sources including DSO National Laboratories and Meta.

• Machine Learning
• Reinforcement Learning
• Sample-efficient Generalization
• +5 more

Dr. Yangchen Pan is a Departmental Lecturer in Machine Learning at the University of Oxford's Department of Engineering Science. His research focuses on achieving sample-efficient generalization in machine learning, particularly in settings involving distribution shifts (e.g., adversarial learning, domain adaptation) and adaptive capabilities like offline/online reinforcement learning and continual learning. He has contributed to foundational work in reinforcement learning, robustness, and risk-averse optimization. Education and Academic Background: While specific degree details are not explicitly listed, his academic trajectory includes roles at leading institutions such as the University of Alberta (PhD, 2017-2020), where he collaborated with prominent researchers like Martha White and Amir-massoud Farahmand. Additional affiliations include teaching roles at the University of Waterloo, University of Toronto, and Indiana University. Research Interests: Pan's work spans machine learning theory and applications, with emphasis on scalable algorithms for complex decision-making systems. Key areas include adversarial robustness, offline reinforcement learning, and mitigating distribution shifts in real-world deployments. His recent contributions explore risk-aware policy optimization and novel approaches to sample efficiency. Professional Service: Pan serves on the program committees of top conferences like NeurIPS, ICML, and ICLR. He has reviewed for journals including the Journal of Machine Learning Research and Transactions on Machine Learning Research. Teaching: Pan teaches advanced courses in optimization, machine learning, and AI at the University of Oxford, including C25 Optimization and AI/ML with Python. He has also taught at the University of Alberta and Indiana University. Labs/Teams: While no specific lab name is mentioned, his research is closely tied to Oxford's Engineering Science department and collaborative projects with institutions like the ZERO Institute (as seen in event leadership at IMAD2025).

Tianhao WangView profile

Assistant Professor

• Differential Privacy
• Machine Learning Privacy
• Data Security
• +3 more

Tianhao Wang is an Assistant Professor in the Department of Computer Science at the University of Virginia School of Engineering and Applied Science. His work focuses on advancing differential privacy and machine learning privacy, with particular expertise in privacy-preserving technologies for data synthesis, adversarial machine learning, and secure AI systems. His research interests span differential privacy mechanisms, secure data sharing, and mitigating privacy risks in modern AI systems. He explores how to protect sensitive information in machine learning models, synthetic data generation, and network analysis while maintaining utility. Recent work highlights include developing benchmarks for private image synthesis (DPImageBench), safeguarding text data from misuse (ExpShield), and analyzing privacy threats in pre-trained language models. His publications reflect a strong emphasis on both theoretical foundations and practical applications of privacy-preserving techniques. Dr. Wang's contributions address cutting-edge challenges in AI ethics, secure machine learning, and privacy engineering, with implications for healthcare, cybersecurity, and data-driven decision-making systems.

Alan KaptanogluView profile

Professor

• Applied Mathematics
• Scientific Machine Learning
• Nuclear Fusion
• +3 more

Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.

Stephen LeeView profile

Assistant Professor

• Computer Systems
• Cyber-Physical Systems
• Data Mining and Data Science
• +3 more

Stephen Lee is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with Pitt Cyber. His research focuses on distributed systems, cyber-physical systems, and sustainability, emphasizing energy efficiency and cost optimization. Dr. Lee holds a PhD from the University of Massachusetts Amherst, a Master’s from Chennai Mathematical Institute, and a Bachelor’s from St. Stephen’s College, Delhi. He actively seeks students for his research group. Education: PhD, Computer Science, University of Massachusetts Amherst Master’s, Chennai Mathematical Institute Bachelor’s, St. Stephen’s College, Delhi Research Interests: Dr. Lee’s work integrates distributed systems, machine learning, and optimization to enhance sustainability. Key areas include IoT-enabled energy systems, emission-aware computing, and privacy-preserving frameworks. He leads projects like GreenWhisk (serverless emission reduction) and Sat2map (3D building modeling from satellite imagery). Recent Achievements: Best Paper Award in IEEE TPS 2024 DOE-funded Cyber Energy Center (2024) MCSI Seed Grant for Pitt building sustainability (2024) NSF Grant on sustainable distributed infrastructures (2023) Grants & Advising: Secured over $2M in grants, including NSF and DOE funding. Advises on energy-efficient systems and IoT security. Teaches CS 2510 (Operating Systems) and CS 1699 (Systems & Sustainability). Labs & Teams: Directs the Sustainable Systems Research Group, focusing on decarbonizing IT and optimizing renewable energy systems. Collaborates with industry partners on smart grid solutions and edge-cloud systems.

Marcus BotacinView profile

Assistant Professor

• Malware Analysis
• Hardware-Assisted Security
• Antivirus Technology
• +4 more

Marcus Botacin is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University (TAMU), USA. Previously, he held positions as Visiting Assistant Professor (2022-2024) and Lecturer at the Federal University of Paraná (Brazil). His research focuses on malware analysis, hardware-assisted security, and antivirus technology. He earned his Ph.D. in Computer Science from UFPR (2021), M.Sc. from UNICAMP (2017), and B.Sc. in Computer Engineering from UNICAMP (2015). Education: Ph.D., Computer Science, Federal University of Paraná, Brazil (2021) M.Sc., Computer Science, University of Campinas, Brazil (2017) B.Sc., Computer Engineering, University of Campinas, Brazil (2015) Research Interests: Botacin's work addresses challenges in malware detection (static/dynamic methods, sandboxing), hardware-enhanced security mechanisms (e.g., branch monitoring), and antivirus internals. He emphasizes practical solutions and ethical considerations in cybersecurity, advocating for culturally aware threat models (e.g., Brazilian financial malware studies). Publications & Trends: His recent work explores adversarial ML attacks on malware detectors, HPC-based detection frameworks, and automated malware generation risks. He frequently publishes in top venues like ACM CCS, USENIX Security, and IEEE TDSC. Awards: Top-3 Best Ph.D. Thesis in Computer Security (Brazilian Computer Society, 2022) Best PhD Thesis Award from UFPR (2022) MLSec Evasion Challenge 1st place (2021/2020) Advising & Grants: Supervises 15+ students at TAMU and leads NSF-funded projects (e.g., $523K grant for HPC malware detection frameworks). Serves on 21+ conference program committees and reviews for over 50 journals. Lab & Projects: Develops Corvus (public malware analysis sandbox) and explores hardware-accelerated AV solutions like Terminator and HEAVEN.