Immanuel Trummer is a Professor of Computer Science at Cornell University, specializing in database systems, query optimization, and applications of large language models (LLMs) and quantum computing. He leads research projects such as DB-BERT, UDO, and SkinnerDB, focusing on automated database tuning, adaptive query processing, and leveraging LLMs for code synthesis and system optimization. His research interests span quantum computing for database optimization, cost-efficient LLM utilization, and voice-based data exploration. Key contributions include developing systems like CEDAR for claim verification, CodexDB for LLM-driven code generation, and ThalamusDB for multimodal data querying. Trummer has received prestigious awards, including the NSF CAREER Award (2023-2028) and the Best Demonstration Award at BDA 2020. His work has been funded by NSF, Google, Huawei, and others, supporting projects like quantum-index selection and misinformation detection. He advises graduate students in database systems and teaches advanced courses such as CS 6320 (Advanced Database Systems) and CS 7390 (Seminar in Database Systems). His research lab hosts open-source tools like JoinGym and maintains extensive collaborations in industry and academia.
Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.
Dr. Joshua New is a Distinguished R&D Staff Member at Oak Ridge National Laboratory (ORNL) and holds a Joint Faculty Position at The University of Tennessee since 2012. He leads research in building energy modeling, climate change science, and supercomputing. His work focuses on urban-scale energy systems, AI-driven analytics, and high-performance computing applications. Education: Ph.D. in Computer Science (University of Tennessee, 2009), M.S. in Computer Systems, B.S. in Computer Science and Mathematics (Jacksonville State University). His research interests include optimizing building energy efficiency, simulating climate impacts on urban infrastructure, and developing tools like AutoBEM and ModelAmerica to model 122.9 million U.S. buildings. He has over 150 peer-reviewed publications and led 45+ projects involving supercomputing, visual analytics, and AI for big data. Awards include the R&D 100 Award (2016), ASHRAE Distinguished Service Award (2018), and Lab-Corps (2015). His teams prioritize utility use cases and validate models against real-world data. He is a Senior IEEE member, Certified Energy Manager (CEM), and holds certifications in project management and energy efficiency. Key contributions include the Roof Savings Calculator Suite, AutoGen/AutoSim tools, and the ModelAmerica initiative. His work addresses national energy policy, heatwave resilience, and sustainable city design through interdisciplinary collaborations.
Charles Walter is an Assistant Professor of Computer and Information Science at the University of Mississippi, joining in Fall 2019. He holds a PhD in Computer Science from The University of Tulsa (2018), with prior degrees from the same institution (M.Sc 2016; B.S. 2014). His research focuses on Mobile and Wearable Security, Adversarial Machine Learning, Privacy, Malware Analysis, Fog Computing, and Self-Adaptive Systems. He leads the SPARC Lab, exploring cutting-edge topics like data privacy, malware detection, and security in fog computing environments. Education: B.S. Computer Science, University of Tulsa (2014) M.Sc Computer Science, University of Tulsa (2016) Ph.D. Computer Science, University of Tulsa (2018) Research Interests: His work addresses critical challenges in cybersecurity, including securing low-power wearable devices through fog computing architectures, developing adversarial machine learning defenses, and investigating human factors in code trustworthiness. Recent projects include studying privacy threats in diffusion models and creating frameworks for robust stability estimation in AI systems. Lab Activities: The SPARC Lab actively researches topics such as adversarial ML attacks, privacy-preserving video processing, and adaptive system security. Collaborative efforts focus on real-world applications like improving university transportation systems through smart bike rental programs.
Piotr Zwiernik is an Associate Professor in the Department of Statistical Sciences at the University of Toronto's Faculty of Arts and Science, with a cross-appointment in the Department of Mathematics. Currently on leave from the University of Toronto, he is based in Barcelona following his return in July 2025. His academic journey includes a PhD in Statistics from the University of Warwick (2011), research positions at prestigious institutions including Mittag Leffler Institute, IPAM, TU Eindhoven, UC Berkeley, and the University of Genoa, and an Assistant Professorship at Universitat Pompeu Fabra in Barcelona (2016-2021). His research spans the intersection of statistics, mathematics, and computational methods, with particular emphasis on graphical models, covariance matrix estimation, convex analysis, tensors, and algebraic and combinatorial methods in statistics. Zwiernik's work demonstrates a consistent focus on high-dimensional statistics, mathematical statistics, and elegant theoretical frameworks that bridge abstract mathematics with practical statistical applications. His recent publications reveal a deepening exploration of tensor analysis, algebraic statistics, and the geometric properties of statistical models. Zwiernik serves as an associate editor for leading journals including Biometrika, Scandinavian Journal of Statistics, and Algebraic Statistics. His research program includes the development of the GOLAZO R package for asymmetric regularization of log-likelihood in Gaussian graphical models. As an academic leader, he has served as Associate Chair for Research in his department and actively participates in numerous international conferences and workshops, reflecting his significant standing in the statistical community. His recent publications show a strong trend toward algebraic and geometric approaches to statistical problems, with increasing focus on tensor methods, positivity constraints in statistical models, and the theoretical foundations of graphical models. The work demonstrates remarkable continuity in exploring the mathematical structures underlying statistical models while adapting to emerging challenges in high-dimensional data analysis. Zwiernik is committed to mathematical accessibility and education, guided by Federico Ardila's four axioms which emphasize equitable distribution of mathematical potential, joyful mathematical experiences, mathematics as a malleable tool, and treating every student with dignity and respect. He actively seeks PhD students with strong mathematical backgrounds for research at UPF or the Institute of Mathematics of UPC.
Morgan G. Ames is an Assistant Adjunct Professor at the UC Berkeley School of Information and serves as Associate Director of Research for the Center for Science, Technology, Medicine & Society. She chairs the Designated Emphasis in Science and Technology Studies and is affiliated with multiple research centers including the Algorithmic Fairness and Opacity Working Group, the Center for Science, Technology, Society and Policy, and the Berkeley Institute of Data Science. Her educational background includes a Ph.D. in Communication with a minor in Anthropology from Stanford University (2013), an M.S. in Information Management and Systems from UC Berkeley (2006), and a B.A. in Computer Science from UC Berkeley (2004). Prior to her academic career, she worked as a researcher at Google, Yahoo!, Nokia, and Intel. Ames researches the ideological origins of inequality in the technology world, with a focus on utopianism, childhood, and learning. Her work critically examines how technology design practices shape identities and social structures. Current projects include 'Seeing Like a Valley: the Moral Visions of Silicon Valley,' 'Algorithms in Culture,' and 'Countercultures of Technology Use.' She has published extensively on One Laptop per Child, Minecraft, and the social implications of algorithmic systems. Her publication record shows a consistent focus on the cultural dimensions of technology, particularly examining how utopian visions shape technology design and implementation. Recent work increasingly addresses algorithmic systems and their cultural impacts, while maintaining her longstanding interest in educational technology and youth technology practices. Ames has received significant recognition for her scholarship, including: 2020 Best Information Science Book Award 2020 Sally Hacker Prize 2021 Computer History Museum Prize She advises students on interpretive research methods, particularly ethnography, and serves on doctoral committees though cannot be a primary advisor for PhD students. Her research has been supported by multiple interdisciplinary collaborations, including the 'Algorithms in Culture' conference series she co-organized through the Center for Science, Technology, Medicine & Society. Ames leads the 'Seeing Like a Valley' research collective that brings together scholars from across UC Berkeley and Silicon Valley to examine how the region's industrial practices shape moral visions that influence global technological development and social values.
Professor Mirko Trajkovski leads the Laboratory of Metabolic Diseases at the Faculty of Medicine, University of Geneva. He completed his PhD at the International Max Planck School in Dresden (2005), followed by postdoctoral research at ETH Zurich, before establishing his lab at University College London (2012) and moving to Geneva (2013). His work focuses on adipose tissue plasticity , gut microbiota , and their roles in obesity , diabetes , and insulin resistance . Swiss National Science Foundation Professor (2014) ERC Starting Grant (2014) & Consolidator Grant (2019) Dr Walter Seipp Prize & Carl Gustav Carus Prize (2005) His lab investigates fat browning mechanisms , microbiota-host communication , and multi-tissue metabolic regulation using in vivo , in vitro , and human cohort approaches. Recent publications emphasize microbiome-based therapies , temperature effects on metabolism , and gut-bone-adipose crosstalk . Current advisees include PhD student Silas Kieser, with past members like Jing Xue, Salvatore Fabbiano, and Claire Chevalier contributing to immuno-metabolism and microbial engineering projects.
Salim El Rouayheb is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. He leads the Coding and Securing Information (CSI) Lab, which focuses on information-theoretic security and privacy in distributed systems. His research spans multiple areas including secure machine learning, private information retrieval, and data synchronization. Dr. El Rouayheb received his Ph.D. in Electrical Engineering from Texas A&M University in 2009. Prior to joining Rutgers, he was an Assistant Professor at the Illinois Institute of Technology (2013-2017), a Research Scholar at Princeton University (2012-2013), and a Postdoctoral Researcher at UC Berkeley (2010-2011). His research interests focus on information-theoretic security in distributed systems, private information retrieval and search, secure machine learning algorithms, and data synchronization in distributed systems. He has made significant contributions to developing frameworks that provide information-theoretic privacy guarantees in various contexts including federated learning, genomic data analysis, and decentralized networks. His work often bridges theoretical foundations with practical applications, particularly in the areas of secure distributed computing and privacy-preserving algorithms. His recent publications demonstrate a strong trend toward applying information-theoretic principles to address privacy and security challenges in machine learning systems, particularly in federated and decentralized settings. Many of his papers explore random walk approaches for decentralized learning, secure matrix multiplication techniques, and privacy mechanisms that can be toggled "on and off" based on correlation patterns in data. His work spans both theoretical contributions in information theory and practical implementations for real-world systems. Dr. El Rouayheb has received several prestigious awards including the NSF CAREER Award (2016), Google Faculty Research Award (2018), and the Rutgers University Walter Tyson Junior Faculty Chair (2019). He has successfully secured multiple research grants including NSF SaTC, NSF CAREER, Google Faculty Research Awards, and Army Research Lab funding. His lab, the Coding and Securing Information (CSI) Lab, currently includes postdoc Xingran Chen, PhD student Zonghong Liu, and undergraduate researchers. The CSI Lab maintains an active research agenda with regular publications in top-tier venues and hosts the Shannon Channel, a series of online talks related to information theory. Dr. El Rouayheb is also involved in organizing workshops on coding theory and information security.
Dr. Kanwaljeet S. Anand is a dual-appointed Professor of Pediatrics (Pediatric Critical Care) and Anesthesiology, Perioperative & Pain Medicine at Stanford University School of Medicine. As director of the Pain/Stress Neurobiology Lab and Jackson Vaughan Critical Care Research Fund, he serves as Editor-in-Chief of Pediatric Research and maintains active membership in Bio-X, MCHRI, and Wu Tsai Neurosciences Institute. Rhodes Scholar with D.Phil from University of Oxford Harvard postdoctoral fellowship and Boston Children's Hospital residency Founded Harmony Health Clinic - Arkansas' largest charitable medical-dental facility A translational researcher with 30+ years of impact, Dr. Anand established the first scientific framework for infant pain perception and developed novel pain assessment methodologies. Current research focuses on: Hair biomarker analysis for stress and social affiliation (cortisol/oxytocin) Machine learning systems for objective pain detection in non-verbal infants Biopsychosocial interventions for stress reduction in disadvantaged youth Neurotoxicity mechanisms of sedatives in developing brains Global NICU opioid usage patterns through the NeoOpioid Consortium His work has yielded over 260 publications and significant advances in: Pediatric pain management protocols Neonatal stress biomarker development Critical care neurobiology insights Community health initiatives AI-driven clinical decision support systems Scientific Recognition 9th Annual 'In Praise of Medicine' Public Address, Erasmus University (2014) Nightingale Excellence Award (2016) Honorary Doctorate from University of Örebro (2019) NIH SBIB-H82 Study Section Chair (2018) Multiple IASP and American Pain Society awards Swedish Academy of Medicine's Nils Rosén von Rosenstein Award (2009) St. Jude Endowed Chairholder (2010) As mentor to Med Scholar Anjali Gupta and advisor to numerous professional bodies, Dr. Anand maintains active clinical leadership in Pediatric Intensive Care while advancing computational approaches to pain detection through collaborations with Stanford's AI researchers.
Mike Kosek (Dr. rer. nat.) is a Research Fellow at the Chair of Connected Mobility (Department of Informatics) at the Technical University of Munich (TUM). His research focuses on transport protocols, congestion control, internet architecture, and internet measurements. Research Interests: Transport protocol design and analysis Congestion control mechanisms Internet architecture and measurement Network performance optimization DNS protocol behavior and privacy Applications in satellite and aerial communication Recent Research Trends: His publications emphasize QUIC protocol analysis, DNS over QUIC investigations, cross-layer protocol interactions, and satellite communication adaptations. Key methodologies include real-world measurements, protocol design extensions, and dataset creation for reproducibility. Contact: E-mail: kosek@in.tum.de Phone: +49 89 289-18665 Office: 01.05.038, Boltzmannstr. 3, 85748 Garching
René Jr Landry is a full Professor in the Department of Electrical Engineering at École de technologie supérieure (ETS), Université du Québec, specializing in Global Navigation Satellite Systems (GNSS), avionics, and wireless communication technologies. His academic journey includes a B.Ing. from Polytechnique Montréal, M.Sc. from University of Surrey (UK), and Ph.D. from SupAréo in Toulouse. He maintains active research leadership through two key laboratories: LASSENA (Laboratory of Space Technologies, Embedded Systems, Navigation and Avionics) and LACIME (Communications and Microelectronic Integration Laboratory). His research spans critical aerospace navigation domains including GNSS signal processing, inertial navigation systems, software-defined radio for avionics, radio frequency interference mitigation, and indoor positioning technologies. Landry's work addresses real-world challenges in satellite navigation robustness, precision positioning in urban/denied environments, and next-generation avionic system security. His current projects focus on blockchain-enhanced IoT security, AI-driven GNSS disruption analysis, and adaptive RF front-ends for multi-band avionics applications. Analysis of his recent publications reveals strong emphasis on resilient positioning systems through multi-constellation integration (particularly Iridium-NEXT), blockchain applications for navigation security, and explainable AI techniques for GNSS signal quality assessment. His work increasingly bridges traditional navigation engineering with cutting-edge security and machine learning paradigms. 2014 Prix d'excellence du c.a. pour les services à la collectivité Landry has supervised over 100 graduate students across doctoral, master's, and research projects since 2005, with current supervision extending through Summer 2025. His research funding supports multiple industry partnerships focused on avionics certification, software-defined radio implementations, and next-generation navigation systems. The LASSENA laboratory under his leadership develops certified avionic products from open-source SDR platforms and advances multi-sensor fusion techniques for challenging navigation environments. His research infrastructure includes specialized facilities for GNSS signal simulation, avionics hardware testing, and multi-sensor integration. Current work emphasizes flight-tested validation of RF front-end technologies, blockchain-secured navigation data, and real-time interference mitigation systems for aviation applications.
David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) within Carnegie Mellon University's School of Computer Science. He leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center (XRTC) . His research bridges Human-Computer Interaction, Computer Graphics, and Computer Vision to create adaptive interfaces that enhance human-digital interaction. Education : PhD (summa cum laude) from Technische Universität Berlin , MSc and BSc from University of Applied Sciences Upper Austria Previous Affiliation : Postdoctoral Researcher at ETH Zurich (2018-2020) David's research focuses on understanding human perception of digital information and developing computational approaches to optimize AR/VR interface usability. Key areas include: Context-aware adaptive interfaces Visual saliency and attention modeling Spatial audio-haptic systems Optimal placement algorithms Object manipulation in Remixed Reality Diminished/ambient MR interfaces His 15 most recent publications (2024-2025) span topics in adaptive XR interfaces, multimodal notifications, haptic systems, and spatial cognition. These works appear at venues like ACM CHI, ACM UIST, IEEE VR, and Frontiers in VR. Common themes include: Machine learning for interface adaptation Human factors in XR design Real-time environment analysis Privacy-aware display systems Collaborative MR interfaces Accessibility enhancements Scientific Recognition : Best Paper Honorable Mention Award (ACM CHI 2024) Best Paper Award (ACM ISS 2023) ETH Zurich Postdoctoral Fellowship Multiple best paper recognitions at CHI, UIST, and IEEE VR Teaching & Leadership : Course developer for CMU's "Interactive Extended Reality" Mentor for NASA SUITS Challenge team Co-chair roles at CHI and UIST Overseeing PhD students and research interns
Dr. Prasanth Venugopal is an Associate Professor specializing in Power Electronics with a focus on advanced energy transfer systems and battery technology. His research spans wireless power transfer, electric vehicle charging, and electrochemical impedance spectroscopy for battery diagnostics. Primary research areas: Wireless Power Transfer (100%), Harmonics (88%), Inductive Power Transfer (87%), Battery Engineering (48%) Recent publications demonstrate expertise in transformerless converter designs, multi-level architectures, and AI-driven battery capacity estimation. He has pioneered meander coil topologies for harmonic mitigation and developed computation-light models for battery aging analysis. His work includes collaborations on Li-ion battery degradation, onboard chargers for electric vehicles, and hybrid power systems for electric aircraft. Despite significant output in IEEE Transactions, no explicit awards or student mentoring data appears in the provided texts.
Marilena Vendittelli is a Professor at Sapienza University of Rome, affiliated with the Robotics group. Her research spans control systems, biomedical robotics, motion planning, and autonomous systems. Research Areas: Control systems for robotics and nonholonomic systems Biomedical applications (hyperthermia therapy, needle insertion) UAV navigation and obstacle avoidance Haptics and human-robot interaction Adaptive estimation algorithms Recent Publications focus on adaptive control of bio-heat equations, safe UAV motion planning, soft robotics actuation, and haptic feedback in medical procedures.
Yan Gu is an Associate Professor of Mechanical Engineering at Purdue University, located in West Lafayette, Indiana. He is affiliated with the School of Mechanical Engineering within the College of Engineering. His research focuses on legged locomotion, humanoid and quadrupedal robots, wearable robotics, hybrid dynamical systems, control systems, state estimation, and dynamics. He leads the TRACE Lab and has been recognized with prestigious awards including the NSF CAREER Award (2021) and multiple teaching accolades. Gu holds a Ph.D. from Purdue University (2017) and a B.S. from Zhejiang University, China (2011). His work emphasizes robust control strategies for legged robots in dynamic environments, including adaptive ankle torque control, time-varying foot-placement algorithms, and state estimation techniques for non-inertial surfaces. His recent publications explore multimodal datasets in animal-robot interaction and the stabilization of quadrupedal locomotion on accelerating platforms. His research has been supported through grants such as the NSF CAREER Award, and his contributions span both theoretical advancements in hybrid control systems and practical applications in wearable robotics and exoskeleton design. Gu’s TRACE Lab serves as a hub for innovative robotics research, addressing challenges in robot-environment interaction and dynamic stability.
