Yan Huang is an Associate Professor in the Department of Computer Science at Indiana University Bloomington, focusing on security and cryptography. His work bridges theoretical foundations with practical systems, emphasizing cryptographic protocols with strong security guarantees for generic computation. University: Indiana University Bloomington School: College of Arts and Sciences Department: Computer Science Academic Rank: Associate Professor His research combines theoretical computer science , program analysis , artificial intelligence , and software engineering to address real-world security problems. Key areas include secure computation , zero-knowledge proofs , and privacy-preserving technologies . Recent publications, such as Phecda (SP'25) and Dubhe (USENIX Security'23), highlight advancements in post-quantum cryptography , AES verification , and privacy-preserving deep packet inspection , reflecting his focus on scalable and practical cryptographic solutions. He has advised notable students including Changchang Ding (PhD, Computer Science) and Ruiyu Zhu (PhD, now at Facebook), and served on program committees for top conferences like ACM CCS and CRYPTO .
Andrew Yonelinas is a Professor in the Department of Psychology at the University of California, Davis, where he directs the Human Memory Lab. He holds additional leadership roles as Associate Director of the Center for Mind and Brain and is an affiliated faculty member with the UC Davis Center for Neuroscience. His research bridges cognitive psychology and neuroscience to investigate fundamental memory mechanisms and their neural substrates. His educational background includes a Ph.D. in Experimental Psychology from McMaster University (1995) and a B.S. in Cognitive Science from the University of Toronto (1990). These foundational studies established his expertise in experimental methodologies and cognitive theory. Yonelinas specializes in dual-process models of memory, distinguishing between recollection (detailed contextual retrieval) and familiarity (vague recognition). His lab employs process dissociation, remember/know procedures, and ROC modeling alongside neuroimaging (fMRI, ERP) and clinical studies with amnesic and Alzheimer's patients. Recent work expands into auditory working memory, multisensory integration, and the impact of mental illness on cognitive processes, revealing hippocampal roles across memory systems. His research consistently addresses how memory fails in clinical conditions while developing unified theoretical frameworks. Analysis of his 2024-2025 publications shows a strong focus on memory mechanisms across sensory modalities, with increasing emphasis on clinical applications. Key trends include hippocampal contributions to visual/auditory working memory, EEG-based biomarkers for mental illness, and the interplay between schema knowledge and memory distortion in aging populations. His work demonstrates methodological innovation through model-based EEG phenotyping and multisite clinical collaborations. His scientific recognition includes: American Psychological Society’s Shahin Hashtroudi Memorial Award University of California Chancellor’s Fellow Award European Brain and Behavior Society International Lecture Award Yonelinas actively shapes his field through editorial roles at top journals including Proceedings of the National Academy of Sciences and Journal of Experimental Psychology, while serving as a grant reviewer for NIH, NSF, and international funding bodies. His Human Memory Lab trains next-generation researchers in memory theory and methodology, with recent projects examining stress effects on memory precision and neural mechanisms of action slips. The Human Memory Lab operates within UC Davis's neuroscience ecosystem, collaborating closely with the Center for Mind and Brain on projects involving clinical populations and neuroimaging. Current initiatives include the CNTRACS Consortium for EEG standardization in mental illness and investigations into how stress modulates memory binding through hippocampal mechanisms.
Nicolas Federico Martin is an Associate Professor in the Department of Crop Sciences at the University of Illinois at Urbana-Champaign, with additional appointments as Associate Professor in the Center for Latin American and Caribbean Studies, Center for Digital Agriculture, and the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges traditional agricultural science with cutting-edge computational approaches. Dr. Martin's research focuses on the intersection of agriculture and data science, with particular emphasis on: Precision agriculture and on-farm experimentation methodologies Machine learning applications for crop management and yield prediction Nitrogen and nutrient management optimization Soybean and corn breeding and production systems Remote sensing and UAV applications in agriculture Sustainable agricultural practices including cover crop management His publication record demonstrates a clear trajectory toward increasingly sophisticated integration of artificial intelligence with agricultural science. Recent work shows heavy emphasis on using machine learning algorithms (particularly reinforcement learning, convolutional neural networks, and generalized additive models) to solve practical farming challenges related to crop management decisions, yield prediction, and resource optimization. This research has significant implications for both scientific understanding of crop-environment interactions and practical farm management. Dr. Martin actively collaborates across disciplines and institutions, as evidenced by his extensive co-authorship network spanning agronomy, computer science, environmental science, and economics. His work has garnered attention from numerous news outlets and social media platforms, indicating its relevance to current agricultural challenges. He is a key contributor to the Data-Intensive Farm Management project, which aims to transform agronomic research through on-farm precision experimentation. His affiliation with NCSA provides access to high-performance computing resources essential for processing large agricultural datasets. Additionally, his work in Latin American agriculture (particularly in Mexico and Argentina) reflects his commitment to addressing global food security challenges.
Assoc. Prof. Dr. Sema Alaçam Doğan has been affiliated with Istanbul Technical University since 2014, serving as an Associate Professor in the Department of Architecture . She has held administrative roles including Deputy Head of Department and Erasmus Coordinator. Education : PhD in Informatics in Architectural Design (2008-2014), MS in Informatics in Architectural Design (2005-2008), and BS in Architecture (1999-2005) from Istanbul Technical University. Her research explores Computational Design , Artificial Intelligence in Architecture , and Sustainable Material Innovation . She investigates digital tools for heritage preservation, daylight optimization in BIM, and cognitive development in architecture students. Recent publications analyze AI-assisted design literacy , machine learning for Sinan mosques , and environmental comfort in Harran houses . Her work integrates algorithmic frameworks with sustainable practices. Scientific awards include multiple ITU Publication and Performance Awards (2021-2024), FABFEST Prizes , and the 2024 Artemis Educator Award from NASA. Active projects like "Physical Computation in Architectural Drawing" and "Robotic Fabrication with Recycled Wind Turbine Blades" demonstrate her leadership in computational and sustainable research.
Ahmed Hammad is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering, where he also serves as Director of Academic Integrity, ENG WIL, Co-op and Career Connections. With 25 years of industry experience as a Project Planning & Control Manager on global mega-projects (including Oil Sands, LNG, and Infrastructure across Canada, UAE, Australia, and Egypt), he brings extensive practical expertise to academia. Education: Doctorate of Philosophy, Construction Engineering & Management, University of Alberta (2009) Master of Science, Construction Engineering & Management, University of Alberta (1999) Master of Engineering, Construction Engineering and Management, Cairo University (1996) Bachelor of Science, Civil Engineering, Mansoura University (1989) Research Focus: Dr. Hammad's work centers on applying smart tools to achieve sustainable construction through maximizing efficiency and minimizing waste . His research employs Machine Learning , Multi-Criteria Decision Making , Knowledge-Based Decision Support Systems , and digital twin technologies to optimize project planning, resource allocation, and sustainable material selection. He investigates the integration of BIM and Augmented Reality to enhance construction processes while reducing environmental impact. Publication Trends: Recent publications (2023-2025) emphasize sustainable construction methodologies, with 60% focusing on GHG reduction, resource optimization, and decision support systems. Key themes include machine learning for labor estimation, TOPSIS/MCDM for sustainable material selection, and digital twins for production planning, reflecting his NSERC-funded projects on KBDSS and construction-oriented digital twins. Scientific Recognition: Best Paper Award at 8th International Conference on Industrial Engineering and Operations Management (2018) Best Paper Award at HBRC Green Smart Sustainable Buildings Conference (2024) Research Leadership: Dr. Hammad secures major industry-academic partnerships, including NSERC Mission Alliance Grants ($1.2M+) with 16 industry partners for GHG reduction projects and NSERC Alliance Grants with 9 partners for digital twin development. His completed projects include collaborations with the City of Edmonton and Alberta Ministry of Infrastructure on resource allocation models. Research Ecosystem: As leader of the Sustainable Construction Research Group (SCRG), he fosters industry-academia collaboration through regular workshops with construction firms and government agencies, focusing on translating research into practical tools for sustainable project delivery.
Jun Liu is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the School of Engineering and Applied Sciences, University at Buffalo. His research focuses on advanced energy materials, nano/micro-mechanics, and self-powered systems, with applications in triboelectric energy harvesting and scanning probe microscopy. Education: PhD, Materials Engineering, University of Alberta (2018) MS, Materials Science, Shanghai University (2015) BE, Materials Science and Engineering, Nanchang University (2012) Research Interests: Development of tribovoltaic and triboelectric systems for self-powered electronics Mechanical energy harvesting via dynamic heterojunctions and Schottky contacts 3D-printed hydrogel structures for energy absorption and flexible electronics Nanoscale characterization using atomic force microscopy Design of nanocomposite sensors and catalytic materials Publication Trends: His work emphasizes triboelectricity, nanoscale energy conversion, and sustainable materials. Recent articles explore bionic tactile sensing, tunable hydrogels, and quantum dynamics in sliding interfaces. Awards: SONY Faculty Innovation Award (2021) Nature Springer MINE Young Scientist Award (2020) International Contest of Applications in Nano/Micro Technology Prize (2013) Laboratory: Advanced Energy Materials and Nanomechanics Lab at University at Buffalo.
Prof. Dr.-Ing. Thomas Zwick is a full professor and director of the Institute of High Frequency Engineering and Electronics (IHE) at the Karlsruhe Institute of Technology (KIT). He holds a Dipl.-Ing. (M.S.E.E.) and Dr.-Ing. (Ph.D.E.E.) from the University of Karlsruhe. His career includes roles at IBM Research (2001–2004), Siemens AG (2004–2007 managing automotive radar teams), and KIT since 2007. He leads research in high-frequency technologies, antennas, radar systems, and wireless communications. Research interests include radio wave propagation, antenna design, automotive radar architectures, and millimeter-wave systems. He has authored/co-authored over 400 papers, 20 patents, and received IEEE Fellow status (2018), honorary doctorate from Budapest University (2022), and membership in the Heidelberg Academy and acatech. His work emphasizes integrating sensing and communication systems, 3D-printed RF components, and high-frequency measurement techniques. Teaching focuses on high-frequency engineering, electronic circuits, and radar systems. He oversees the IHE’s laboratories, including the Microwave Engineering Lab and Student Innovation Lab. Recent work explores sub-THz communication, RIS-aided ISAC systems, and beamforming for reduced EMF exposure in urban scenarios.
Professor Kevin Sivula is a Full Professor of Chemical Engineering at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Laboratory of Molecular Engineering of Optoelectronic Nanomaterials (LIMNO) and serves as Director of the Institute of Chemical Sciences and Engineering (ISIC). He teaches courses on Transport Phenomena, Chemical Engineering Practicals, Product design, and solar energy conversion systems. His research focuses on photoelectrochemical solar energy conversion, particularly developing novel nanomaterials for renewable energy applications. Professor Sivula's work bridges chemical engineering, materials science, and renewable energy technologies with emphasis on solar water splitting and hydrogen production. His research group explores organic semiconductors, transition metal dichalcogenides, perovskite materials, and metal oxide photoelectrodes for solar fuel generation. Professor Sivula's recent publications (2023-2025) demonstrate his leadership in advancing perovskite solar cell technology, developing novel photoelectrochemical systems for hydrogen production, and engineering semiconductor interfaces for improved energy conversion efficiency. His work spans fundamental materials characterization to device engineering, with particular emphasis on stability and scalability of solar fuel production systems. As an academic leader, Professor Sivula has supervised numerous PhD students whose research spans organic semiconductor nanoparticles for solar hydrogen production, perovskite materials engineering, photoelectrochemical CO 2 reduction, and nanomaterials for energy conversion. His research has been supported by various grants focused on advancing sustainable energy technologies.
Professor Dinusha Mendis is a leading academic in Intellectual Property and Innovation Law at Bournemouth University, where she serves as Director of the Centre for Intellectual Property Policy and Management (CIPPM). Her expertise bridges copyright law with emerging technologies like 3D printing, AI, blockchain, and the Metaverse, informed by extensive research and consultation with entities such as the European Parliament, UKIPO, and corporations like HP and Chanel. She holds a PhD from Edinburgh University and has authored seminal works on the intersection of technology and IP. BSc in Law from Aberdeen University LLM and PhD from Edinburgh University Called to the Bar of England and Wales Her research focuses on copyright challenges in immersive environments , AI-generated content regulation , and blockchain/NFTs . She led major studies for the European Commission and UKIPO , including the first peer-reviewed work on 3D printing and IP. Her 2019 co-edited book with Stanford and QUT scholars established foundational frameworks for additive manufacturing legal analysis. Recent publications highlight AI copyright disputes (2024 The Conversation ), NFT legal ambiguities (2022), and pandemic-era 3D printing (2020). Trends show increasing emphasis on decentralized IP systems and machine learning's impact on creative industries . Scientific Awards & Grants JSPS Visiting Professorship (2023) Daiwa Anglo-Japanese Foundation Grant (2024) EU Commission Research Funding (2020) AHRC Grant for 3D Jewellery Study (2017) As a PhD supervisor, she mentors researchers on AI copyright (Benjamin White), crypto-art (Bahar Dagli), and music creation law (Elizabeth Bailey). She contributes to global IP policy through the World Economic Forum's Metaverse Governance Team and EU IPR Enforcement Projects .
James C. Gumbart is an Adjunct Professor in the School of Physics at Georgia Institute of Technology, with additional affiliation to the School of Chemistry and the Institute for Bioengineering and Bioscience . His research leverages molecular dynamics simulations to decode the atomic-level mechanisms of bacterial proteins and cellular structures. B.S., Physics and Mathematics, Western Illinois University, 2003 Ph.D., Physics, University of Illinois at Urbana Champaign, 2009 Dr. Gumbart's work bridges computational biophysics and biochemistry to understand: Mechanisms of bacterial membrane protein insertion and nutrient import Structural dynamics of cell wall mechanics SARS-CoV-2 spike protein interactions with ACE2 Free-energy calculations for protein-ligand binding Applications of machine learning in biomolecular simulations His publications reflect trends in membrane protein biophysics , viral dynamics , and computational drug design , with a strong emphasis on interdisciplinary techniques. Awards include multiple fellowships and grants from NSF , DOE , and NIAID . He has mentored numerous PhD students, including Zijian Zhang , David Ryoo , and Andrew Pang , whose work has advanced understanding of bacterial systems and viral proteins. The Gumbart Lab integrates high-powered supercomputing and advanced software to model biomolecular processes, fostering collaborations with institutions like the National Institutes of Health and Argonne National Laboratory .
Ben Green is the Waynflete Professor of Pure Mathematics at the University of Oxford and a Fellow of Magdalen College. His work spans additive combinatorics, analytic number theory, harmonic analysis, ergodic theory, discrete geometry, and group theory, with a focus on interdisciplinary approaches. Research Interests: Additive combinatorics and its applications to primes Analytic number theory (prime distribution, L-functions) Harmonic analysis (Fourier methods, spectral theory) Ergodic theory and its combinatorial applications Discrete geometry (ordinary lines, convex structures) Group theory (approximate groups, expansion) Article Trends: His recent work emphasizes multiplicative functions, Ramsey-type problems in number theory, expansion in finite groups, and extremal set theory. Themes include prime gaps, arithmetic progressions, and interactions between analysis and algebra. Scientific Awards: Clay Research Award (2004) Ostrowski Prize (2005) Whitehead Prize (2005) Leverhulme Prize (2007) European Mathematical Society Prize (2008) Royal Society Fellow (2010) Sylvester Medal (2014) Senior Whitehead Prize (2019) Advising: Ben has supervised numerous D.Phil students across additive combinatorics, analytic number theory, and related fields. Past students hold postdoctoral and academic positions globally.
Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.
Noman Mohammed is an Associate Professor of Computer Science at the University of Manitoba’s Faculty of Science, leading the Data Security & Privacy (DSP) laboratory. He specializes in privacy-preserving techniques for data sharing, addressing challenges in healthcare, genomic, and financial data. In 2020, he received the Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award for his contributions to bridging privacy and data utility gaps. His research focuses on balancing data accessibility and individual privacy through technical solutions like federated learning, differential privacy, and secure genomic data processing. He emphasizes integrating policy guidelines with advanced technologies to mitigate privacy risks from interconnected data sources. Notable achievements include developing toolkits for data anonymization and federated learning frameworks, as well as advancing methods to secure cloud-based data storage and analysis. His work aligns with societal needs for robust privacy mechanisms in an era of expanding personal data collection. Future objectives involve addressing privacy challenges in emerging technologies, such as heterogeneous data integration and scalable systems for personal data management. Despite his research focus, he notably avoids social media platforms.
Nenad Miljkovic is the Founder Professor in the Department of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign (UIUC), with concurrent appointments in Electrical and Computer Engineering and the Materials Research Laboratory. He directs the Air Conditioning and Refrigeration Center (ACRC) and is affiliated with the International Institute for Carbon Neutral Energy Research (Kyushu University). His research spans thermo-fluid sciences, interfacial phenomena, and renewable energy, focusing on enhancing efficiency in power generation, electronics cooling, and thermal management via micro/nanostructured surfaces. Miljkovic holds a BASc in Mechanical Engineering from the University of Waterloo (2009) and an M.S. (2011) and Ph.D. (2013) from MIT. Prior to UIUC, he was a Postdoctoral Associate at MIT and an Adjunct Assistant Professor at UIUC. His research integrates fundamental studies of phase change and electrokinetics with applied device development, including solar thermal conversion and atmospheric energy harvesting. Recent work explores frost dynamics, battery thermal management, and scalable anti-corrosion coatings. Miljkovic's publications emphasize heat transfer enhancement, droplet dynamics, and sustainable energy systems. His articles frequently address thermal management challenges in electronics and renewable energy applications, leveraging nanostructured surfaces and phase change materials. Honors include: ASME Fellow (2022) Founder Professorship, Grainger College of Engineering (2023) ASME Bergles-Rohsenow Young Investigator Award (2021) ONR Young Investigator Award (2017) NSF CAREER Award (2016) He leads the Energy Transport Research Laboratory, advising graduate students and collaborating with industry on thermal systems. Grants support work on electronics cooling, battery safety, and renewable energy integration.
Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.
