Professor Elena Pirogova is a faculty member of RMIT University’s School of Engineering, part of the STEM College. She holds the roles of Professor and Associate Dean of the Electrical & Biomedical Engineering Discipline. Her research focuses on biomedical engineering, biomaterials, therapeutic peptides, and biological effects of electromagnetic radiation. She earned her PhD in Biomedical Engineering from Monash University (2002) and a BEng (Hons) in Chemical Engineering from the National Technical University of Ukraine (1991). Academic Positions: Professor (2020–present), Associate Dean (2018–present), and roles in research leadership since 2002. Teaching: Coordinates courses in biomedical engineering design, project management, and professional engineering projects at both undergraduate and postgraduate levels. Research: Over 170 publications in areas like tissue engineering, microfluidics, and wearable technologies. Key projects include biofabrication of scaffolds, electromagnetic radiation effects on cells, and smart textile applications. Supervision: Advises on advanced projects involving biomaterials, medical devices, and interdisciplinary engineering solutions. Her work bridges engineering and healthcare, emphasizing translational research in regenerative medicine and medical technology. She is active in curriculum innovation, particularly post-pandemic educational strategies.
Dr. Thomas Brandstetter is a researcher and group leader at the University of Freiburg's Department of Microsystems Engineering (IMTEK), leading the Bioanalytical Surfaces group since 2007. He holds a Ph.D. in Biology from the University of Freiburg (2000) and has extensive postdoctoral experience in biochip technologies and clinical applications. His research focuses on developing innovative bioanalytical platforms for DNA/RNA detection, protein analysis, and biomedical applications such as tumor cell capture and biofilm prevention. He has pioneered surface-attached polymer networks and hydrogel coatings to enhance biochip sensitivity and reusability. Key research areas include biochip technology, microfluidics, surface chemistry, and materials science. His work integrates interdisciplinary approaches to address challenges in diagnostics, such as point-of-care testing and in vivo diagnostics. Notable projects include hydrogel-based sensor surfaces, PCR-compatible metallic coatings, and functionalized medical wires for capturing rare cells in blood. He oversees a team of PhD students and postdocs, contributing to advancements in analytical chemistry and biomedical engineering. Dr. Brandstetter has published extensively on topics like DNA microarray platforms, NASBA amplification, and antibacterial coatings. His lab collaborates with industry partners like Genescan Europe AG and contributes to translational research through patents on biochip fabrication and medical devices. He is actively involved in teaching and mentoring, fostering the next generation of researchers in bioanalytical technologies.
Susann Rohwedder is a Senior Economist at RAND and Professor of Economics at the RAND School of Public Policy. She is a leading scholar in the economics of aging, focusing on health economics, retirement, financial security, and survey methodology. Her research aims to improve the well-being of older populations through rigorous empirical analysis using large-scale longitudinal datasets such as the Health and Retirement Study (HRS) and the RAND American Life Panel. Ph.D. in Economics, University College London Master's in Economics, University of Warwick Master's in Economics, Sorbonne (University of Paris) Her research spans several key areas: Health Economics , particularly dementia, long-term care, and out-of-pocket medical expenditures; Life-Cycle Economics , including retirement spending, adequacy of retirement resources, and old-age poverty; Demography , with focus on life expectancy and differential survival; and Survey Research Methodology , especially measurement error and elicitation of subjective expectations. She has made significant contributions to understanding the retirement-consumption puzzle, cognitive aging, and financial decision-making in later life. The 15 most recent publications reflect a strong focus on cognitive health, retirement spending, life satisfaction, and health inequalities in aging populations. Her work frequently employs advanced econometric techniques and large representative datasets to produce policy-relevant insights. Trends include early detection of dementia, longitudinal analysis of life satisfaction, forecasting mortality inequalities, and evaluating the financial impacts of health shocks. Her scientific leadership includes: Research Fellow, Network for Studies on Pensions, Aging, and Retirement (NETSPAR), Netherlands Director for Strategic Planning, Michigan Disability and Retirement Research Center Associate Editor, Journal of the Economics of Ageing Member, Board of Directors, Western Economic Association International Rohwedder has advised on major national and international studies and has published in top journals such as the American Economic Review , Demography , and Journal of Health Economics . She leads data initiatives like the RAND HRS Longitudinal File and the Singapore Life Panel, demonstrating her commitment to high-quality data infrastructure. Her work bridges academic research and public policy, with implications for Social Security, pension reform, and long-term care financing. She is actively involved in research teams and collaborative projects across the U.S. and internationally, including studies on financial security over the lifespan and cross-national comparisons of retirement systems. Future work is likely to expand on cognitive screening, health equity in aging, and the economic impacts of demographic change.
Fariba Karimi is a Professor of Social Data Science at Graz University of Technology and leads the Algorithmic Fairness research group at the Complexity Science Hub (CSH) in Vienna. Her work sits at the intersection of computational social science, network science, and AI ethics, with a strong focus on fairness, inequality, and bias in algorithmic systems. She holds a PhD in Physics and Computational Science from Umeå University, Sweden (2015), and was a Postdoctoral Researcher at GESIS – Leibniz Institute for Social Sciences, Germany. Her research combines large-scale data analysis, agent-based modeling, and network science to study how social structures and algorithms interact to shape disparities, particularly for underrepresented groups. Computational Social Science Algorithmic Fairness and Bias Network Science and Homophily Digital Humanism Urban Inequality and Implicit Bias Gender and Age Equity in Academia Her recent publications reveal a consistent focus on understanding and mitigating algorithmic and structural inequities. Themes include the visibility of minorities in networks, the impact of segregation on health and cognition, gender citation gaps in physics, and the development of fair ranking and recommendation systems. She employs interdisciplinary methods to analyze both digital and real-world social systems. Fariba Karimi has received several prestigious recognitions, including: Young Scientist Award from the German Physical Society (2023) ERC Starting Grant (2024) Nomination for the Hedy Lamarr Award (2021) She leads the 'Humanized Algorithms' project at CSH, funded by the ERC, and co-leads an EU Horizon project (MAMMOth) on multi-criteria fairness in AI. She advises PhD researchers such as Lisette Espín-Noboa and collaborates with institutions like the University of Mannheim and the Centre for Social Sciences. Her work contributes to both academic knowledge and practical solutions for fairer AI systems and more equitable societies.
Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.
Guanghan Meng is an Assistant Professor at the University of California, Berkeley , with dual appointments in the Herbert Wertheim School of Optometry and Vision Science and the Department of Electrical Engineering and Computer Science (EECS) . He leads the Visionary Optical Imaging Lab (VOILA) , focusing on interdisciplinary research combining optical physics and computational science to develop advanced microscopy technologies for eye and brain imaging. Education : PhD (2021, UC Berkeley), BE (2015, Shanghai Jiao Tong University) PhD Programs Affiliated With : Vision Science, Applied Science & Technology (AS&T), EECS His research integrates optical physics , computational biology , and artificial intelligence to create cutting-edge imaging tools. Recent work includes differentiable wave-optics libraries (Chromatix), super-resolution microscopy techniques, and high-speed neural imaging systems. Publications highlight applications in neuroscience (cerebral circulation, synaptic activity) and biomedical imaging (OCT, two-photon microscopy). VOILA is a highly interdisciplinary team spanning physics , engineering , and biology . In 2025, the lab will welcome 2 PhD students and 1 postdoc, though funding is currently at capacity for new members. Meng is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) and Berkeley Center for Computational Imaging (BCCI) .
Christian Freund is Professor of Protein Biochemistry at the Institute for Chemistry & Biochemistry, Freie Universität Berlin, holding this W2 professorship since 2011. He serves as Coordinator of the FU Berlin-UCSF Collaborative Initiative and Founding Member/Vice-chair of the DFG Collaborative Research Centre SFB/TRR 186 on Molecular Switches in Cellular Signal Transmission, leading interdisciplinary research across Berlin and Heidelberg institutions. His academic foundation includes Chemistry studies at Heinrich-Heine-Universität Düsseldorf (1983-1986) and Ludwig-Maximilians-Universität München (1986-1989), followed by a PhD in Structural Biology at the Max-Planck-Institute of Biochemistry (1994) and Habilitation in Biochemistry at Freie Universität Berlin (2005). Freund's research integrates structural biology, biophysics, and immunology to investigate molecular mechanisms of antigen presentation and cellular signaling. His work centers on MHC class II dynamics, protein conformational switches, and nanoscale organization of signaling complexes, employing NMR spectroscopy, quantitative proteomics, and molecular engineering to dissect immune recognition pathways and neuronal signaling mechanisms. Analysis of his 2010-2019 publications reveals consistent focus on MHC-mediated antigen presentation (60% of works), with significant contributions to understanding peptide exchange dynamics and HLA-DM editing functions. Secondary research streams explore synaptic protein networks (25%) and T cell signaling machinery (15%), demonstrating methodological breadth across structural biology, proteomics, and cell biological approaches. His scientific recognition includes: Biofuture award from the German Ministry of Education and Research (1999) Swiss National Funds Post-doctoral Scholarship (1997) Innovationswettbewerb Medizintechnik grant (2009) As research group leader at Leibniz-Institute of Molecular Pharmacology (2000-2011) and current FU Berlin professor, Freund has secured major collaborative funding through DFG SFB/TRR 186 and the UCSF partnership. His mentorship spans postdoctoral fellows at Harvard/Dana-Farber and Leibniz-Institute, with current supervision of graduate students in the Berlin biochemistry program. Freund directs a research group within FU Berlin's Institute for Chemistry & Biochemistry, operating as core component of SFB/TRR 186. His laboratory maintains active collaborations with UCSF's QBI (Nevan Krogan) and Heidelberg-based structural biology teams, utilizing advanced NMR, cryo-EM, and single-molecule imaging facilities across the Berlin-Heidelberg research alliance.
Dr. Marios Georgakis is a Clinician-Scientist and Junior Group Leader at the Institute for Stroke and Dementia Research (ISD) at Ludwig-Maximilians-Universität München (LMU Munich). He also serves as a Visiting Scientist at the Broad Institute of MIT and Harvard and is completing his clinical residency in Neurology at LMU University Hospital. As Principal Investigator of the Georgakis Lab, he leads a research team focused on developing precision medicine approaches for cerebrovascular diseases. Education: Medical studies (M.D.): Medical School, National and Kapodistrian University of Athens, Greece (2009-2015) Master studies (M.Sc.): Molecular Physiology (Neurosciences), National and Kapodistrian University of Athens, Greece (2015-2017) Doctoral studies (D.Sc.) in Epidemiology, National and Kapodistrian University of Athens, Greece (2015-2019) Doctoral studies (Ph.D.) in Graduate School of Systemic Neurosciences (GSN), LMU Munich, Germany (2017-2020) Dr. Georgakis' research focuses on leveraging big data from epidemiological studies and human biobanks to develop precise and personalized preventive and therapeutic strategies for cerebrovascular diseases. His work spans biomedical neuroscience with particular emphasis on cerebrovascular disease, stroke, atherosclerosis, cerebral small vessel disease, multi-omics, data science, epidemiology, and population genetics. He employs innovative bioinformatic tools including genome-wide association studies, Mendelian randomization, multi-omics integration, single-cell transcriptomics, spatial transcriptomics, and machine learning to discover causal mechanisms, identify therapeutic targets, develop risk stratification tools, and create accurate biomarkers for cerebrovascular diseases. His laboratory has established the AtherOMICS biobank for human atherosclerotic plaque samples and developed computational pipelines for big data analyses. Recent publication trends show a strong focus on genetic architecture of stroke, inflammatory pathways in cerebrovascular disease, and development of polygenic risk scores for clinical application. Scientific Awards: Emmy Noether Independent Group Leader Award, German Research Foundation (DFG), 2023 Early Career Achievement Award, CHARGE Consortium, 2023 Fellow of the Hertie Network of Excellence in Clinical Neuroscience, 2023 Clinician-Scientist Fellow of the Excellence Munich Cluster for Systems Neurology (SyNergy), 2023 Walter-Benjamin Fellowship for postdoctoral research by German Research Foundation (DFG), 2021-2022 Dr. Georgakis actively mentors a diverse team of 12 current students and postdocs including PhD students, MD students, and clinician scientists, with several alumni who have completed their training in his lab. His research is supported by multiple grants including the Emmy Noether program from the German Research Foundation, focusing on multi-omics characterization of immune mechanisms driving human atheroprogression, dissecting cerebrovascular atherosclerosis with population genetics, and developing personalized biomarkers using deep learning. The Georgakis Lab operates two main research platforms: the AtherOMICS Biobank for human atherosclerotic plaque samples and the Big Data Lab for computational analyses. These platforms enable his team to conduct deep phenotyping of human atherosclerosis, develop in vivo diagnostics, discover therapeutic targets, and create personalized diagnostic and risk prediction tools for cardiovascular diseases.
Lori Graham-Brady is a Professor in the Department of Civil and Systems Engineering at Johns Hopkins University's Whiting School of Engineering. She serves as Vice Dean for Faculty and directs the Center on AI for Materials in Extreme Environments (CAIMEE), while also holding secondary appointments in Mechanical Engineering and Materials Science and Engineering. Her research focuses on stochastic mechanics, multiscale modeling, and machine learning applications for understanding material variability under extreme conditions. Research areas include probabilistic mechanics, AI-driven materials design, and fragmentation modeling. Leadership roles: Director of CAIMEE, former Director of Center for Materials in Extreme Dynamic Environments, founding Director of HT-MAX, and founding Associate Director of HEMI (2012-2024). Education: PhD in Civil Engineering and Operations Research from Princeton University. Her recent work emphasizes AI for multiscale mechanics, error propagation in material characterization, and digital microstructure generation. Publications highlight stochastic modeling of ceramics, composites, and metals under impact and high-strain-rate loading. Scientific awards include the Presidential Early Career Award, Huber Civil Engineering Research Prize, and Fellowships in ASCE EMI and USACM. She led NSF IGERT programs and serves as Associate Editor for the ASCE Journal of Engineering Mechanics.
Fabrizio Riguzzi is a Full Professor at the Department of Mathematics and Computer Science of the University of Ferrara, Italy. His academic career spans over two decades at the same institution, having served as Associate Professor (2014-2020) and Assistant Professor/Ricercatore (1999-2014). He is an active researcher in the fields of Logic Programming and Statistical Relational Artificial Intelligence with numerous publications and leadership roles in international conferences. His educational background includes: PhD in Electronic and Computer Engineering from the University of Bologna (1999) Laurea in Computer Engineering from the University of Bologna (1995) Riguzzi's research focuses on probabilistic approaches to artificial intelligence, particularly probabilistic logic programming and statistical relational AI. His work bridges symbolic reasoning with probabilistic methods, developing frameworks for uncertain knowledge representation and reasoning. He has made significant contributions to probabilistic answer set programming, neuro-symbolic integration, and applications in areas like network intrusion detection and knowledge graph completion. His research demonstrates how logical formalisms can be enhanced with probabilistic reasoning to tackle real-world problems with uncertainty. An analysis of his recent publications reveals a strong trend toward integrating neural and symbolic approaches in AI, with significant work on probabilistic answer set programming frameworks. His research spans theoretical foundations of probabilistic logic programming, practical implementations, and applications in cybersecurity, knowledge graphs, and decision-making under uncertainty. The interdisciplinary nature of his work connects computer science theory with practical AI applications. His notable awards include: Alain Colmerauer 10-Year Test-of-Time Award at ICLP 2021 Best Paper Award for "BUNDLE: A Reasoner for Probabilistic Ontologies" at RR-2013 Highly Commended Paper Award for "Probabilistic declarative process mining" at KSEM 2010 Riguzzi has supervised several PhD students to completion, including Elena Bellodi, Riccardo Zese, and Giuseppe Cota, who have gone on to win prestigious awards for their theses. He has served in numerous editorial roles, including Associate Editor of the Journal of Artificial Intelligence Research and Editor in Chief of Intelligenza Artificiale. His leadership extends to organizing major conferences like ILP 2018 and serving on program committees for top AI venues including IJCAI, AAAI, and ECAI. He is a member of the ML@unife research group and has developed several online systems including cplint, TRILL, and an Online AUC calculator. His work has fostered collaborations across the AI research community, particularly in the areas of probabilistic logic programming and neuro-symbolic AI.
Andrew J Whelton is a Professor of Civil and Construction Engineering at Purdue University's College of Engineering, with concurrent appointments in Sustainability Engineering and Environmental Engineering. He serves as Director of the Healthy Plumbing Consortium and Lead for the Center for Plumbing Safety, focusing on water quality, chemical contamination, and public health in building plumbing systems. Professor, Civil and Construction Engineering Professor, Sustainability Engineering Professor, Environmental Engineering Director, Healthy Plumbing Consortium Lead, Center for Plumbing Safety His research spans environmental engineering and public health, with key themes including chemical contamination from plastic pipe degradation, post-disaster water system recovery, wildfire-related water quality impacts, and microbial risks in premise plumbing. Recent work addresses crises like the East Palestine chemical spill and Maui wildfires, while also developing predictive models for water quality and evaluating sustainable infrastructure materials. Scientific awards and recognitions include: Rapid Response Research (RAPID) Grants from NSF for disaster-related studies Environmental Protection Agency (EPA) support for building water quality programs Leadership in interdisciplinary consortia focused on plumbing safety Development of novel tools for water quality monitoring and remediation His publications reveal trends in: Chemical leaching from plastic piping materials Environmental justice in water contamination crises Integration of machine learning for water quality prediction Microbial ecology in stagnant plumbing systems Policy recommendations for disaster response Sustainable material innovation for infrastructure
Professor Li Chen is a full Professor and Associate Head (Research) in the Department of Computer Science at Hong Kong Baptist University (HKBU), with an affiliate appointment at the Academy of Wellness and Human Development. She leads the Positive Intelligence Lab , focusing on intelligent technologies for human well-being. Her research spans conversational AI, explainable AI, recommender systems, and human-computer interaction. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland (Nominee for Best PhD Thesis Award) Master in Computer Software and Theory, Peking University, China Bachelor in Computer Science, Peking University, China Her research interests revolve around personalized conversational and explainable AI, with applications in entertainment, education, e-commerce, and mental well-being. She has published over 150 papers in top venues including ACM TOIS, IJHCS, CHI, SIGIR, AAAI, RecSys, and UMAP . Her work has been recognized with awards such as the RecSys Best Student Paper Award (2024), CHI Honourable Mention (2022), and multiple best paper awards at UMAP and UMUAI. The most recent publications reflect a strong trend toward fair, explainable, and user-centric recommender systems , with increasing integration of large language models , mental health applications , and conversational agents . Her research emphasizes user feedback, negative sampling techniques, and evaluation frameworks grounded in real user behavior. Scientific Awards & Recognition: President’s Award for Outstanding Performance in Teaching (Individual), HKBU (2024/25) President’s Award for Outstanding Performance in Research Supervision (2022/23) World’s Top 2% Most-Cited Scientists, Stanford University (2021–2024) ACM Senior Member (2015) RecSys’24 Best Student Paper Award CHI’22 Honourable Mention Award UMAP’20 Best Student Paper Award UMUAI 2018 Best Paper Award THE Awards Asia 2021 Excellence and Innovation in the Arts (Co-I) Professor Chen is actively involved in mentoring PhD and Master’s students such as Wanling Cai and Yuhan Zhao, who have co-authored award-winning papers. She has secured research funding through grants like the HKBU IRCMS Project. Her editorial leadership includes serving as Co-Editor-in-Chief of ACM Transactions on Recommender Systems (TORS) , Associate Editor for ACM TiiS , and Editorial Board Member for UMUAI . She has chaired major conferences including ACM RecSys’23 (General Co-Chair), RecSys’20 (Program Co-Chair), and UMAP’18 (Program Co-Chair). She leads the Positive Intelligence Lab , which conducts interdisciplinary research on AI for well-being. The lab has developed datasets like the Intent Annotation of Recommendation Dialogue (IARD) and focuses on user-centric AI design, mental health chatbots, and personalized recommendation interfaces.
Chen-Yu Wei is an Assistant Professor in the Department of Computer Science at the University of Virginia. He holds a Ph.D. from the University of Southern California (2022), and M.S. and B.S. degrees from National Taiwan University (2015, 2012). His research focuses on interactive machine learning, emphasizing robust and adaptive algorithms for non-stationary/adversarial environments, sample-efficient reinforcement learning, and decentralized multi-agent systems. Education: Ph.D., Computer Science, University of Southern California, 2022 M.S., Electrical Engineering, National Taiwan University, 2015 B.S., Electrical Engineering, National Taiwan University, 2012 Research interests include reinforcement learning, game theory, and algorithmic economics. He has received prestigious awards such as the COLT and ALT Best Paper Awards (2021-2022) and the Simons-Berkeley Research Fellowship (2022). His work bridges theory and practice, addressing challenges in adversarial environments and multi-agent coordination. Current research group members include Haolin Liu (PhD), Braham Snyder (PhD), Kingsley Kim (Undergraduate), and Rishik Balerao (Undergraduate). Teaching includes courses on Reinforcement Learning, Artificial Intelligence, and Algorithmic Economics. He co-organizes the RL Meetup and Theory Seminar at UVA.
Jane Law is an Associate Professor at the University of Waterloo, located in EV3 3251. She holds a Ph.D. in Geodesy and Geomatics Engineering from the University of New Brunswick (2000), a University Teaching Diploma from the same institution (1999), an M.Sc. in Land Information Systems from Hong Kong Polytechnic University (1994), and a B.Sc. in Survey and Mapping Sciences from North East London Polytechnic (1985). Her research integrates spatial analysis with public health and criminology, employing Bayesian methodologies to examine neighborhood effects on health outcomes and crime patterns. Law's research focuses on geographic information systems, Bayesian spatial modeling, spatial epidemiology, environmental criminology, and health-oriented urban planning. She explores how built environments influence community health outcomes and crime distribution using advanced statistical geospatial techniques. Her publications demonstrate consistent focus on Bayesian spatial modeling applications in public health and crime analysis, with recent work emphasizing mental health spatial patterns, nutrition environments, and temporal crime trends. Research consistently integrates GIS with statistical innovation for policy-relevant insights. Law has supervised 43 Master's and 5 PhD students to completion and currently advises 2 Master's and 1 PhD candidate. She secured a long-term research grant as Principal Investigator for 'Advancing spatial analysis methodologies using a Bayesian approach' (2009-2022).
Ramazan Yeniçeri is a Lecturer at Istanbul Technical University's Department of Aeronautical Engineering. His research focuses on Unmanned Aerial Vehicles (UAVs), Field Programmable Gate Arrays (FPGAs), and computational fluid dynamics, with applications in hardware acceleration and autonomous flight systems. Academic Rank: Lecturer University: Istanbul Technical University Department: Aeronautical Engineering Research Interests: Yeniçeri's work bridges aerospace engineering and computer science, emphasizing: FPGA-based hardware acceleration for aerospace systems UAV communication networks (FANETs) and formation flight Dynamical modeling for 6-DoF systems Autopilot software and real-time operating systems Scientific Awards: He has received the BOEING Academic Work Encouragement Award (2017) and the Best Doctoral Thesis Award (2015) . Project Leadership: As Principal Investigator (PI), he leads projects like: "IHA Kayıt, Takip, Kontrol ve Hava Trafik Yönetim Sistemi" (2024–2025) "FPGA Tabanlı 6DoF Dinamik Hızlandırıcı Tasarımı" (2024) "EU Sürü İHA" (2020–2022) His recent publications highlight trends in UAV communication, FPGA acceleration, and multi-sensor tracking.
