Doç. Dr. Muhammed Aras is an Associate Professor in the Department of Mechanical Engineering at Baskent University (Başkent Üniversitesi), with a research focus on sustainable machining processes, tool wear monitoring, and surface roughness optimization. His work spans advanced manufacturing technologies, energy storage systems, and biomedical device design. PhD in Manufacturing Engineering (2018), Gazi Üniversitesi MSc in Mechanical Education (2013), Gazi Üniversitesi BSc in Mechanical Engineering (2010), Tabriz Islamic Azad University Research interests include sustainable machining (dry/hard turning, cooling-lubrication strategies), tool wear analysis (CBN, ceramic and coated inserts), and surface integrity optimization using AI-based methods (firefly algorithm, TOPSIS, Grey Relational Analysis). His 15 most recent articles (2017-2024) examine topics like: Surface roughness prediction in dry hard turning Energy storage technology viability assessments Cutting parameter optimization for various steels Acoustic/vibration monitoring in machining He has received scientific awards including the Teşvik Ödülü (Encouragement Award, 2015). His patent on an automatic orthognathic surgery articulator and book chapters on tool monitoring systems demonstrate his multidisciplinary impact.
Istvan Albert is a Research Professor of Bioinformatics at Pennsylvania State University , affiliated with the Department of Biochemistry and Molecular Biology . He leads the Bioinformatics Consulting Center and teaches BMMB 852: Applied Bioinformatics . Research Interests: Specializing in bioinformatics, large-scale biological data analysis, microarray and sequence analysis, scientific programming, algorithm development, and database-driven web development. His work spans gene ontology visualization , RNA-Seq analysis , and coronavirus research . Software Development: Created GeneScape for gene function visualization and bio for bioinformatics workflows. Maintains the Biostar Handbook series and the Biostars Q&A Forum , a leading bioinformatics resource.
Mohamed F. Mokbel is a Distinguished McKnight University Professor in the Department of Computer Science and Engineering at the University of Minnesota - Twin Cities , where he also serves as the Director of Graduate Studies. He is recognized as an IEEE Fellow and ACM Distinguished Member for his contributions to spatially- and privacy-aware systems. His research focuses on database systems , spatial data management , and GIS (Geographic Information Systems) , with significant work in spatiotemporal data, location-based services, and machine learning for spatial applications. His most recent publications address scalable BERT-based trajectory imputation , spatial logistic regression frameworks , and spatiotemporal big data decay techniques . Key Awards: Distinguished McKnight University Professor (2023) ACM SIGSPATIAL 10-Year Impact Award (2022) IEEE Fellow (2020) ACM Distinguished Member (2017) NSF CAREER Award (2010) Selected Conference Papers: Recathon (2015, IEEE MDM Best Paper) ST-Hadoop (2017, SSTD Best Paper) KAMEL (2023, ACM SIGMOD Demo) Academic Service: Editor-in-Chief, ACM Transactions on Spatial Algorithms and Systems (2024–) General Co-Chair, ACM SIGSPATIAL 2025 Past Chair, ACM SIGSPATIAL (2014–2017)
Ravid Shwartz-Ziv is an Assistant Professor and Faculty Fellow at NYU's Center for Data Science, with a dual role as Senior Research Scientist at Wand AI. His research bridges theoretical foundations and practical applications in artificial intelligence, focusing on Large Language Models (LLMs), information theory, and neural network interpretability. Ph.D. in Computational Neuroscience, Hebrew University of Jerusalem (2021) B.Sc. in Computer Science and Computational Biology, Hebrew University of Jerusalem (2014) His research spans: Developing min-p sampling for LLM text generation Preventing representation collapse in Transformers Creating contamination-free LLM benchmarks like LiveBench Advancing information-theoretic frameworks for neural networks Exploring representation learning and model adaptation Recent publications demonstrate expertise in LLM efficiency, self-supervised learning, and multi-agent systems. Notable awards include the Google PhD Fellowship, Moore-Sloan Fellowship, and multiple best paper recognitions. He has led research initiatives at Intel and Google AI, focusing on neural network compression, XGBoost comparisons for tabular data, and innovative benchmarking frameworks.
Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.
Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.
Karthik Menon serves as an Assistant Professor with a joint appointment in the Woodruff School at Georgia Institute of Technology and the Coulter Department of Biomedical Engineering. His research integrates fluid mechanics, computational modeling, and data-driven methodologies to address critical challenges in healthcare, renewable energy, and bio-inspired engineering systems. His academic credentials include: Ph.D. in Mechanical Engineering, Johns Hopkins University (2021) M.S. in Mechanical Engineering, Johns Hopkins University (2019) B.E. in Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India (2015) Menon's research program centers on three interconnected domains: cardiovascular flows for personalized treatment of heart disease, fluid-structure interactions in biological systems like heart valves and bio-mimetic robots, and vortex-dominated flows for renewable energy applications. His approach combines high-fidelity computational modeling with machine learning to uncover fundamental physics and develop clinical solutions, such as cardiovascular digital twins for non-invasive risk assessment. Current projects focus on patient-specific hemodynamics using CT imaging and uncertainty quantification to improve surgical planning. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on advancing multi-fidelity computational frameworks for cardiovascular applications. Key trends include Bayesian uncertainty quantification, zero-dimensional solver development, and integration of clinical imaging data to create predictive digital twins. His work bridges fluid dynamics with clinical cardiology, targeting improved outcomes in coronary artery disease and Kawasaki-related complications through physics-informed machine learning. Menon's scholarly contributions have been recognized through competitive awards: WCCM-PANACM 2024 Travel Award, U.S. Association for Computational Mechanics (2024) Future Faculty Symposium Travel Award, Society of Engineering Science Conference (2023) Mark O. Robbins Prize in High-performance Computing, Johns Hopkins University (2021) Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University (2020) Prosperetti Travel Award, Johns Hopkins University (2017) Mechanical Engineering Departmental Fellowship, Johns Hopkins University (2016) As principal investigator of the ComBiNE Fluid Dynamics Lab, Menon mentors graduate students in developing computational tools for fluid-structure interaction problems. His collaborative projects with cardiologists at Stanford and Emory hospitals translate engineering principles into clinical applications for cardiovascular disease management. Current grant activities focus on NSF and NIH-funded initiatives for uncertainty-aware cardiovascular modeling and bio-inspired flow energy harvesting. The ComBiNE Fluid Dynamics Lab operates as an interdisciplinary hub where engineers, clinicians, and data scientists collaborate on fluid mechanics challenges. Current lab initiatives include developing real-time hemodynamic simulators for surgical planning, creating reduced-order models for cardiac device optimization, and investigating vortex dynamics in fish schooling for underwater vehicle design. The lab maintains strong partnerships with Children's Healthcare of Atlanta and the Parker H. Petit Institute for Bioengineering and Bioscience.
Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.
Steven Meikle is a Professor of Medical Imaging Physics and Head of the Imaging Physics Laboratory at the Brain and Mind Centre, University of Sydney. He also serves as Deputy Director (Preclinical) of Sydney Imaging and Deputy Director of the National Imaging Facility's Sydney node. His expertise spans advanced imaging technologies, with a focus on PET/SPECT instrumentation and molecular imaging. He holds a B.App.Sc.(Hons) from the University of Technology Sydney and a PhD from the University of New South Wales. Research focuses include developing novel PET systems like Open-field PET (for freely moving rodents) and Total Body PET, which enhance imaging sensitivity and enable real-time behavioral studies alongside brain function analysis. Collaborations include Tsinghua University (China) and UC Davis (USA). He leads projects on motion correction, quantitative imaging, and AI-driven analysis. Key achievements include over 180 peer-reviewed publications, editorial roles in Physics in Medicine and Biology , and leadership in professional societies. Awards include IEEE Senior Membership and Australian Institute of Physics Fellowship. Current student projects explore Total Body PET applications, motion correction, and radiopharmaceutical evaluation. Teaching roles include medical physics courses in diagnostic radiography and medical physics programs. He advises on imaging ethics, facility implementation, and translational research bridging basic science and clinical applications.
Ji Hwan Park is an Assistant Professor in the School of Interactive Games and Media at RIT's Golisano College of Computing and Information Sciences (GCCIS). He holds a PhD from Stony Brook University under Prof. Arie Kaufman. His research focuses on accessible data visualization, digital twins, human-AI collaboration, and VR/AR applications. Notable contributions include developing tools for ADHD-friendly visualizations and interactive protein motif identification. He has received funding from the Department of Defense for biomedical research and earned an Honorable Mention at CHI 2024. Current teaching includes courses on game design and advanced algorithms. Research activities span medical imaging analytics (e.g., CMed framework for crowd-sourced diagnostics), climate modeling through Bayesian deep learning, and creative visualization techniques like Graphoto. His work bridges technical innovation with human-centered design principles, particularly in healthcare and neurodivergent accessibility contexts.
Andrew Lan is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he also serves as the CS Undergraduate Program Director. He was granted tenure by the UMass Board of Trustees in June 2025 and is currently on leave through Spring 2026. His research focuses on developing human-in-the-loop machine learning methods to enable scalable, effective, and personalized learning experiences in education. Dr. Lan received his BS in Physics and Mathematics from the Hong Kong University of Science and Technology, followed by his MS (2014) and PhD (2016) in Electrical and Computer Engineering from Rice University. He completed postdoctoral research at Rice University (2016) and Princeton University's EDGE Lab (2017-2018). His research spans artificial intelligence for education, with particular expertise in educational data mining, knowledge tracing, personalized learning systems, and human-AI collaboration in educational contexts. Dr. Lan's work leverages massive and multimodal learner and content data collected from both traditional classrooms and online learning platforms to develop systems that deliver high-quality, affordable, and personalized learning experiences. He has made significant contributions to areas including computerized adaptive testing, math word problem generation, student affect detection, and automated grading systems. His recent work increasingly focuses on leveraging large language models for educational applications while maintaining rigorous scientific validation of these approaches. Best Student Paper Award at the 2024 AIED Conference (with Alexander Scarlatos) Best Paper Nominee at LAK 2021 Best Student Paper Award at IEEE Big Data 2020 NAEP Math Automated Scoring Challenge Grand Prize Winner Dr. Lan actively mentors graduate students and postdoctoral researchers, with several of his advisees receiving recognition for their work. He has secured substantial funding from the National Science Foundation, including a $90M grant for the SafeInsights project, a secure cyberinfrastructure for educational research. His research group collaborates with institutions including Worcester Polytechnic Institute, University of Pennsylvania, and Rice University. He teaches undergraduate and graduate courses including COMPSCI 240 (Reasoning under Uncertainty) and COMPSCI 590OP (Applied Numerical Optimization), with a focus on the practical application of theoretical concepts in machine learning and artificial intelligence. His educational philosophy emphasizes bridging the gap between theoretical foundations and real-world implementation in educational technology.
Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.
Jacob Krüger is an Assistant Professor at Eindhoven University of Technology , specializing in the development and evolution of variant-rich software systems. He holds a PhD from Otto-von-Guericke University Magdeburg (2021) and has held academic and research positions at institutions including Ruhr-University Bochum, Chalmers University of Technology, and the University of Toronto. His research focuses on the interplay between human cognition and software quality, particularly in complex systems requiring frequent adaptation. Education: PhD in Computer Science, Otto-von-Guericke University Magdeburg (2021) MSc Business Informatics, Otto-von-Guericke University Magdeburg (2016) Research Interests: Variant-Rich Systems Program Comprehension Software Product Lines Human Factors in Software Engineering Architecture Smells and Quality Assurance Articles Trends: Recent work emphasizes fork ecosystem visualization (VisFork tool), the impact of AI on scientific practices, and crisis-driven software development (e.g., Corona-Warn-App). Key themes include empirical studies, tool development, and industry collaboration. Awards: Best Dissertation Award (2022) Frank Anger Memorial Award (2019) Multiple conference best-paper and review awards Advising & Grants: Supervises 12+ PhD students across multiple institutions. Active in funding projects like INKleSS (German Research Foundation) and FOSD Meeting 2024 (NWO). Leads collaborations with ASML, Danfoss, and Axis AB. Labs/Teams: Member of the Software Engineering and Technology (SET) group at TU Eindhoven, focusing on industrial-strength software systems and cognitive aspects of development.
Mykola Pechenizkiy is a Full Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), holding the Data Mining Chair. He also serves as an Adjunct Professor in Data Mining for Industrial Applications at the University of Jyväskylä. His research focuses on predictive analytics, data mining, and responsible AI, addressing real-world challenges in industry, healthcare, and education. He leads the Customer Journey research program at the Data Science Center Eindhoven, emphasizing ethical and transparent analytics. Academically, he holds a PhD from the University of Jyväskylä (2005) and has held visiting researcher positions at institutions like Columbia University and NYU. He has co-authored over 300 peer-reviewed publications and serves on editorial boards and committees for leading conferences (e.g., AAAI, IJCAI). He is the President of the International Educational Data Mining Society (IEDMS). His research interests include concept drift adaptation, sparsity techniques in neural networks, and fairness-aware AI. He has led projects such as the TKI PPS KPN Smart Two initiative and collaborates with industries like ASML, Philips, and Rabobank. His work contributes to UN SDGs, particularly in sustainable development through AI-driven solutions. Awards: Best Demo Paper Award (IEEE ICDE 2023), Best Paper Awards (ALA 2022, LoG 2022), and SensorKDD 2009 recognition. Grants/Projects: Active projects include TKI PPS KPN Smart Two (2019–2025) and Smart One W&I TKI KPN Flagship (2018–2022). Labs/Teams: Affiliated with EAISI Health, SIKS Scientific Board, and the University of Waikato’s AI Institute.
Tara Boroushaki is an incoming Assistant Professor in Electrical & Computer Engineering at Yale University. She completed her Ph.D. at MIT (expected May 2025), advised by Prof. Fadel Adib, with a focus on sensing and mobile technologies. Her research spans wireless networking, robotics, and human-computer interaction, emphasizing multi-modal sensing for environmental perception. Key achievements include the Microsoft Research PhD Fellowship (2022–2024) and the IEEE RFID '23 Best Paper Award. Her work on RF-based 'X-ray vision' has been featured in TEDxMIT and media outlets like the BBC and World Economic Forum. She co-founded Cartesian Systems, deploying sensing technologies in retail and supply chain. Research interests include non-line-of-sight perception, RFID localization, and robotic grasping. She has developed systems like FuseBot and RFusion, highlighted as transformative in MIT's '103 Ways to Make the World Better' initiative.
