Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Mikail Rubinov serves as Assistant Professor of Biomedical Engineering (primary appointment), Computer Science, Psychiatry, and Psychology at Vanderbilt University's School of Engineering. His interdisciplinary work bridges computational neuroscience, network science, and clinical applications. His research focuses on integrative statistical models of large-scale neural data , exploring brain network organization across species and scales. Key interests include evolutionary principles of brain networks, transcriptomic basis of neural individuality, information transfer in neural systems, and neuropsychiatric connectivity phenotypes. The Rubinov Lab develops computational frameworks for analyzing complex neural systems and integrates neuroscientific knowledge with multi-omics data. Recent publications reveal strong trends in network neuroscience methodology development (circular analysis frameworks, unbiased sampling techniques) and translational applications (epilepsy networks, autism spectrum connectomics, gut-brain axis interrogation). His work increasingly incorporates transcriptomic data with neuroimaging at biobank scale. NIH Grant Writing Workshop (June 2022) NIH Workshop Short Talks (April 2023) Rubinov actively mentors graduate and undergraduate students across Biomedical Engineering and Computer Science. His lab maintains collaborations with UCSF, HHMI Janelia Research Campus, Weizmann Institute, and international neuroscience consortia. Current projects include integrative models of large-scale neural data and transcriptomic basis of neural individuality. The Rubinov Lab operates within Vanderbilt's Department of Biomedical Engineering with extensive cross-school collaborations. Technical resources include GitHub repositories for constraint network models (cnm-code), volumetric segmentation (voluseg), and brain connectivity toolboxes.
Lingyang Chu is an Assistant Professor at McMaster University's Department of Computing and Software, previously serving as a postdoc fellow at Simon Fraser University under Jian Pei. He earned his Ph.D. in Computer Science from the University of Chinese Academy of Sciences. Research interests span data mining , machine learning , and statistics , with focus on trustworthy AI (privacy, interpretability, security, robustness, fairness), federated learning , and graph-based machine learning . His work includes scalable data mining on large graphs and deploying systems like personalized federated learning on Huawei Cloud's Harmony OS devices. Publications emphasize adversarial attacks, medical AI, graph robustness, and federated learning frameworks. His advising record includes 28 mentees across Ph.D., M.Sc., and internship levels. Scientific achievements include Best paper candidate at ICME'13 Best demo award at ICMR'13 Academic service roles include: Program Committee: NeurIPS, SIGKDD, CVPR, ICML, and 12+ other top-tier conferences Journal Reviewer: IEEE TKDE, ACM Transactions on KDD, and 8+ journals Editorial Board: ACM Transactions on KDD (Associate Editor) Grant Reviewer: Hong Kong RGC Labs/teams: Maintained open-source ALID algorithm (VLDB'15) for dominant cluster detection, demonstrating technical leadership in scalable graph mining
Benjamin C. Flores is a Professor of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP) , where he has built an internationally recognized career spanning advanced radar signal processing and large-scale STEM education initiatives. He directs the UT System Louis Stokes Alliance for Minority Participation (LSAMP) and the Bridge to the Doctorate Program , managing more than $40 million in funded projects aimed at increasing access and success for Hispanic and other under-represented students in STEM disciplines. Education: While specific degrees are not listed in the provided text, Dr. Flores’s faculty appointment and extensive technical expertise in radar and chaotic systems imply advanced training in electrical engineering. Research Interests: Radar & Signal Processing: high-resolution radar, inverse synthetic aperture radar (ISAR), range-Doppler processing, micro-Doppler analysis, bistatic radar, chaotic wideband signal design, neural-network-based classification of radar jamming signals. Antenna Engineering: fractal antennas, 3-D printed antenna prototyping, anechoic chamber measurements. STEM Education & Diversity: evidence-based retention strategies for non-traditional and Hispanic students, peer-led team learning, graduate mentoring, systemic change models for faculty diversity. Publication Trends: Dr. Flores’s recent articles (2021-2025) reveal two dominant thrusts—(1) cutting-edge radar/chaotic signal processing and joint radar-communication systems, and (2) rigorous, data-driven studies on broadening participation in STEM, with emphasis on mentoring, social networks, and program evaluation at Hispanic-Serving Institutions. Scientific Awards & Honors: Texas STAR Award – Texas Higher Education Coordinating Board (2005) ABET President’s Diversity Award (2006) Presidential Award for Excellence in Science, Mathematics, and Engineering Mentorship (2010) Grants & Leadership Roles: Principal Investigator & Project Director, Model Institutions for Excellence Initiative (1999-2007) Principal Investigator, UTEP PUENTES Program (US Dept. of Education, 2010-2015) Principal Investigator & Director, UT System LSAMP & Bridge to the Doctorate Program (since 2005) Laboratory & Facilities: Dr. Flores’s research group utilizes UTEP’s anechoic chamber and rapid-prototyping laboratories for antenna design and characterization, while also housing real-time radar test-beds and analog-computer platforms for chaotic oscillator experiments.
Associate Professor Aditi Dey holds a conjoint appointment at the Sydney Medical School within the University of Sydney , and serves as Manager of Surveillance at the National Centre for Immunisation Research and Surveillance (NCIRS) . Her work focuses on immunization program evaluation, vaccine coverage analysis, and surveillance of vaccine-preventable diseases and adverse events. She has extensive experience in public health research, spanning roles in Thailand and India prior to her current positions in Australia. Dr Dey completed an MBBS, followed by postgraduate qualifications including a DTM&H (Tropical Medicine & Hygiene), Grad Dip Applied Science (Health Information Management), MPH, and a PhD from the University of Sydney. Her research integrates epidemiological analysis with public health policy implementation, particularly addressing health disparities in vaccination access and outcomes among Indigenous populations and other underserved groups. Her research interests prominently feature vaccine safety , epidemiological trends of infectious diseases, and the impact of vaccination policies such as Australia’s “No Jab, No Pay” initiative. She also investigates the effectiveness of vaccination programs for diseases like rotavirus, HPV, and varicella-zoster virus, often analyzing large-scale national health datasets. Notable contributions include evaluations of Australia’s HPV vaccination program, analysis of rotavirus vaccine efficacy, and assessments of adverse events following immunization. Her work emphasizes improving immunization coverage through better data systems and healthcare provider education, particularly for at-risk populations. Dr Dey actively contributes to public health efforts through teaching and course coordination at the University of Sydney, and collaborates with institutions like the Sydney Infectious Diseases Institute . Her expertise bridges clinical practice, research, and policy, driving evidence-based improvements in Australia’s immunization landscape.
Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.
Garvesh Raskutti is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds joint affiliations with the Departments of Computer Science, Electrical and Computer Engineering, and the Wisconsin Institute of Discovery Optimization Group. His research focuses on statistical machine learning, optimization, graphical/network modeling, and information theory, with applications to systems biology and neuroscience. Education: MEng from the University of Melbourne (2008), PhD from UC Berkeley (2012, advised by Martin Wainwright and Bin Yu), and postdoctoral work at SAMSI. Teaching awards include Honored Instructor (2014, 2017) and Madison Teaching and Learning Excellence Fellow (2015). He advises multiple PhD and undergraduate students, including Yuan Li, Hyebin Song, and Lili Zheng. His grants include NGA HM0476-17-1-2003 (Co-PI), ARO W911NF-17-1-0357 (Co-PI), and NSF-DMS 1407028 (Sole PI). Research interests span large-scale statistical inference, computational-statistical trade-offs, and applications in systems biology and neuroscience. His work bridges optimization (e.g., gradient descent, convex regularization), high-dimensional regression, and network analysis. Recent trends in publications emphasize methods for non-convex optimization, sparse models, and network structure learning. Awards include teaching recognition and grants in statistical methodology. Advising spans theoretical and applied projects, with former students like Gunwoong Park (now at University of Korea). Collaborations include interdisciplinary teams in machine learning and signal processing.
Dr. Lijun Chang is an Associate Professor in the School of Computer Science at the University of Sydney. He holds an ARC Future Fellowship (2019–2022) and an ARC DECRA Fellowship (2015–2017). Previously, he was at the University of New South Wales. His research focuses on graph analytics, mining, algorithms, and network science. He teaches courses like INFO5011 (Competitive Programming), COMP5313 (Large Scale Networks), and COMP9120 (Database Management Systems), and coaches the USYD Programming Competition Teams. Education: B.Eng. in Computer Science & Technology from Renmin University of China; PhD from the Chinese University of Hong Kong. Research highlights include scalable graph processing systems (e.g., ScaleG), densest subgraph detection, and graph similarity search. He leads projects funded by ARC grants such as 'Advanced Search of Cohesive Subgraphs in Big Graphs' (2018) and 'Directionality-Aware Cohesive Subgraph Search' (2022). His work emphasizes efficient algorithms for large-scale networks and graph databases. Awards : ARC Future Fellow, ARC DECRA Fellow Students : Yu KONG, Rashmika MATHTHAKA GAMAGE, Mouyi XU Labs/Teams : Focuses on graph algorithms and systems research, contributing to open-source tools and large-scale network analysis.
Ton Dieker is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. He is a DSI Member and affiliated with the Center for Financial and Business Analytics. His research focuses on stochastic models, simulation techniques, and high-dimensional stochastic analysis. Dieker holds a Master’s in Operations Research from Vrije Universiteit Amsterdam (2002) and a PhD in Mathematics from the University of Amsterdam (2006). Dieker’s research explores stochastic processes, queueing theory, and rare-event simulation. He has contributed to methodologies like QPLEX for stochastic systems and advanced techniques in sequential analysis and exact simulation. His work bridges theoretical foundations with computational applications, addressing challenges in large-scale networks and high-dimensional problems. Education: PhD in Mathematics, University of Amsterdam, 2006 Master’s in Operations Research, Vrije Universiteit Amsterdam, 2002 Dieker’s articles emphasize computational modeling, stochastic calculus, and optimization, reflecting his focus on bridging theory and practice. His work often addresses efficiency in simulation, exactness in algorithms, and scalability in complex systems. Awards: Goldstine Fellowship (IBM Research) NSF CAREER Award Erlang Prize (INFORMS) Fouts Family Early Career Professorship (Georgia Tech) He serves on editorial boards for Operations Research and Mathematics of Operations Research . His research also addresses capacity management in stochastic networks and applications in cloud computing and commodity sourcing.
Dr Alasdair B R Stewart is a Lecturer in Social and Public Policy at the University of Glasgow's School of Social and Political Sciences. He holds dual roles as a Research Fellow and Data Lead at the GCRF Centre for Sustainable, Healthy and Learning Cities and Neighbourhoods (SHLC), and Co-Investigator for a Health Foundation-funded project examining welfare conditionality's impact on mental health. His research focuses on how large-scale social processes shape inequality, with particular attention to homelessness, welfare systems, and open-source research tools like PythiaQDA. Affiliations: GCRF SHLC, University of Glasgow's School of Social and Political Sciences Education: Not explicitly stated in provided text Stewart’s work bridges post-philosophical sociology with empirical research, critiquing proprietary qualitative analysis software and advocating for open science. His projects frequently address the intersection of welfare policies, mental health, and social security. He co-developed PythiaQDA, an open-source QDA tool designed to democratize qualitative research. His recent articles analyze Universal Credit's impact on mental health claimants, punitive welfare sanctions, and EU migrant welfare rights. Key findings emphasize systemic invalidation of mental health struggles within welfare systems and the need for policy reforms. Grants & Funding: Health Foundation, GCRF, Joseph Rowntree Foundation Supervision: Advises student Faith Ougham Stewart is actively involved in the Theory Reading Group at the University of Glasgow, exploring themes like 'The Body and Emotions' through transdisciplinary lenses. He also maintains blogs ( Constellations ) for open academic discourse.
Shahriar Afkhami is a Researcher in the Department of Mechanical Engineering at LUT School of Energy Systems, LUT University. His research focuses on advanced materials science, additive manufacturing processes, and mechanical properties of high-strength steels and dissimilar joints. He specializes in fatigue analysis, welding technologies, and the optimization of structural components for industrial applications. His work integrates experimental methods with computational modeling to address challenges in material behavior under extreme conditions. Key research areas include: Welding of ultra-high strength steels and dissimilar materials Mechanical performance of additively manufactured components Fatigue life assessment of welded joints and cut edges Thermomechanical behavior of heat-affected zones Material characterization of laser powder bed fusion (LPBF) steels Publications highlight trends in additive manufacturing for industrial applications, particularly in optimizing 3D-printed metal structures and analyzing their mechanical integrity. His work on notch-load interactions and fatigue strength has advanced methodologies for predicting component failure under complex loading conditions. Notable contributions include the VERKOTA project exploring 3D printing networks for enhanced industrial adoption. No scientific awards are listed in the provided information. Afkhami's research has been supported by collaborative projects such as the VERKOTA initiative, though specific grants are not detailed here. He maintains an active presence on professional networks including LinkedIn and Google Scholar.
Prof Apostolos Antonacopoulos is a Professor of Pattern Recognition at the University of Salford, leading the PRImA research Lab (Pattern Recognition and Image Analysis). He holds a PhD from UMIST (1995) and has held academic roles at the University of Liverpool and Salford. His expertise spans Document Analysis, Computer Vision, and AI applications in Cultural Heritage. Education: PhD in Computer Science, University of Manchester Institute of Science and Technology (UMIST), UK (1995) Research Interests: Digitisation of historical documents and large-scale data Image Analysis and Pattern Recognition AI-driven solutions for cultural heritage preservation Performance evaluation frameworks for OCR systems Recent Projects: Leading a £750K ONS-funded project (2020–2025) digitising UK census reports Europeana Newspapers (€4M EU project, 2012–2015) for European Digital Library SUCCEED (€1.8M EU project, 2013–2015) for digitisation competencies Awards and Roles: IAPR/ICDAR Young Investigator Award (2005) Former President of International Association for Pattern Recognition (IAPR) Editorial roles in IJDAR and IEEE Transactions on Multimedia Labs/Teams: Director of PRImA Lab, collaborating with institutions like British Library and Wellcome Library. Active in industry partnerships for digitisation solutions.
Vikram Deshpande is a Professor in the Department of Engineering at the University of Cambridge, UK, where he has been employed since 2010. He also maintains significant international connections, having served as a Visiting Professor at the Technical University of Eindhoven (2009-2017) and previously holding positions at the University of California, Santa Barbara and Brown University. His research spans multiple disciplines within solid mechanics and materials science, focusing on fundamental mechanisms that govern material behavior across different scales. His research interests encompass Mechanobiology , where he explores cellular organization mechanisms; Solid mechanics with applications to impact and failure; Data-driven mechanics approaches; Microarchitectured solids including mechanical metamaterials; Fluid-structure interaction in impact scenarios; Chemo-mechanics of battery materials; and Dislocation mechanics for understanding material deformation. His work uniquely bridges fundamental physics with practical engineering applications, particularly in developing materials with tailored mechanical properties. The analysis of his recent publications reveals a strong focus on mechanical metamaterials, cellular mechanics, and electro-chemo-mechanical phenomena in energy storage systems. His research demonstrates a consistent pattern of addressing fundamental scientific questions while maintaining strong connections to practical engineering applications, particularly in materials design, protective systems, and energy technologies. His publications frequently combine experimental approaches with sophisticated modeling techniques across multiple scales. 2024 Zdeněk P. Bažant Medal for Failure and Damage Prevention 2023 Fellow, Royal Academy of Engineering and International Member US National Academy of Engineering 2022 Warner T. Koiter Medal and William Prager Medal 2022 European Research Council (ERC) Advanced Grant 2021 Gili Agostinelli Prize and IIT Bombay Distinguished Alumnus Award 2020 Fellow, Royal Society of London and Rodney Hill Prize Professor Deshpande has served on numerous editorial boards including the Journal of the Mechanics and Physics of Solids (current Associate Editor), Modelling and Simulation in Materials Science and Engineering, and Proceedings of the Royal Society A. He chairs the Royal Society Sectional Committee 4 and serves on the Advisory Board of the European Mechanics Society EUROMECH. His leadership extends to directing the International Conference on Fracture and chairing the EUROMECH Mechanics of Materials Conference committee. His research group at Cambridge, accessible through cambridgesolidmechanics.co.uk, focuses on developing fundamental understanding of material behavior to enable the design of next-generation engineering materials.
Fabrício Benevenuto is an Associate Professor in the Computer Science Department at Federal University of Minas Gerais (UFMG), where he conducts interdisciplinary research at the intersection of social media analysis, data science, and computational journalism. His work spans complex networks, machine learning, and natural language processing with strong societal impact. His research focuses on social media dynamics, particularly in Brazilian contexts, with major contributions to hate speech detection, fake news analysis, and political discourse monitoring. He leads large-scale projects against misinformation, including development of systems like WhatsApp Monitor, Media Bias Monitor, and Purple Feed. His work combines technical innovation with real-world applications for election transparency and public discourse integrity. Benevenuto's recent publications demonstrate strong trends in multilingual NLP for social media analysis, with emphasis on Brazilian Portuguese contexts. His team produces both theoretical contributions and practical systems addressing hate speech, misinformation, and media bias. Notable methodological approaches include combining network analysis with linguistic features, developing culturally-aware detection systems, and creating large annotated datasets for understudied languages. CAPES award for best Brazilian computer science thesis (2010) Humboldt Foundation scholarship recipient (2017-2018) Member of TikTok Safety Advisory Council WWW'20 Best Paper Nominee & CNIL-INRIA Privacy Protection Prize winner Multiple best paper awards at CEAS, WBC, and ICWSM conferences Test-of-Time Award at ICWSM'20 Benevenuto actively mentors PhD and MSc students, with numerous advisees securing academic positions at Brazilian universities and research roles at institutions like Max Planck Institute. His projects often receive funding supporting interdisciplinary collaborations across computer science and social sciences. Current work includes large-scale analysis of Telegram political groups, real-time election monitoring systems, and developing culturally-aware NLP tools for Portuguese. He leads research teams working on social media analysis systems with societal impact, particularly focused on Brazilian digital ecosystems. Projects involve cross-institutional collaborations with researchers from MPI-SWS, Max Planck Institute, and various Brazilian universities, emphasizing practical applications for public discourse integrity.
Prof. Enkelejda Miho is a Professor of Digital Life Sciences at the School of Life Sciences, FHNW, leading the aiHealthLab. Her work bridges computer science/AI with life sciences, focusing on drug discovery, personalized medicine, and immunology. She holds roles as Team Leader at aiHealthLab and Group Leader at the Swiss Bioinformatics Institute. Research Interests : She applies machine learning to analyze immune repertoires, antibody engineering, and autoimmunity diagnostics. Her lab develops computational tools like the RWD-Cockpit for real-world data analysis and synthetic antibody-antigen models (Absolut!) to advance biotherapeutics. Her work on dengue immunity and monoclonal gammopathies highlights translational applications. Key Projects : The aiHealthLab focuses on AI-driven diagnostics and therapeutics. Her contributions include AI frameworks for antibody specificity prediction, age-related immune repertoire changes, and large-scale network analysis of antibody repertoires. Labs/Teams : Leads aiHealthLab and collaborates with the Swiss Bioinformatics Institute, integrating computational and experimental immunology.
