Find Academic Supervisors

Christine HeimView profile

Professor

• Psychobiology of Stress
• Developmental Psychobiology
• Epigenetics
• +5 more

Prof. Dr. Christine Heim is a University Professor (W3) and Director of the Institute of Medical Psychology at Charité – Universitätsmedizin Berlin, where she leads interdisciplinary research on stress psychobiology. She holds concurrent appointments as Research Professor at Pennsylvania State University's College of Health and Human Development and serves as Principal Investigator in the NeuroCure Cluster of Excellence (DFG EXC 2049). Her faculty affiliations include the Berlin School of Mind & Brain, Max Planck School of Cognition, and Einstein Center for Neurosciences. Her research program investigates mechanisms through which early-life stress fundamentally alters biological adaptability across the lifespan, increasing disease risk. Key focuses include: Molecular and epigenetic embedding of stress effects Gene-environment interactions in stress vulnerability Neuroendocrine and immunological consequences of trauma Development of biomarkers and early interventions Her publications demonstrate consistent focus on stress biology across multiple levels of analysis, with recent work emphasizing epigenetic clocks, neural plasticity, and childhood maltreatment outcomes. Research employs multimodal methods integrating psychological, neuroendocrine, immunological, neural, and molecular approaches. She coordinates the BMBF-funded research network 'Programming Effects of Childhood Stress on Lifelong Disease Risk (Kids2Health)' and serves on the Steering Committee for 'Promoting Talent' in the Berlin University Alliance.

Vikram IyerView profile

Assistant Professor

• Robotics
• Ubiquitous Computing
• Sensing & Embedded Systems
• +7 more

Vikram Iyer is an Assistant Professor at the Paul G. Allen School of Computer Science and Engineering and holds an Adjunct Appointment in Mechanical Engineering at the University of Washington. He co-directs the CS for Environment Initiative , focusing on interdisciplinary solutions that bridge computing, biology, and physical systems for environmental sustainability. Education : Ph.D. in Electrical & Computer Engineering (University of Washington), B.S. in Electrical Engineering and Computer Sciences (UC Berkeley) Research Interests revolve around bio-inspired wireless systems , environmentally sustainable electronics , and miniaturized autonomous robotics . His work includes: Biodegradable circuit boards Battery-free wireless sensors Insect-scale vision systems Wind-dispersed environmental monitors AI tools for sustainable design Article Trends highlight contributions to green hardware , energy-autonomous robotics , and environmental sensing networks , often integrating machine learning with physical world interaction . Awards include: NSF CAREER Award SIGMOBILE Dissertation Award Marconi Society Paul Baran Young Scholar Best Paper Awards (SIGCOMM 2016, Sensys 2018) Google/Amazon Research Awards Students advised include Kyle Johnson (NSF Fellow), Vicente Arroyos (GEM Fellow), and Qiuyue Xue (co-advised with Shwetak Patel). His lab collaborates with the Networks & Mobile Systems Lab and Urban Innovation Initiative .

• Lattice-Based Cryptography
• Post-Quantum Cryptography
• Cryptographic Protocols
• +5 more

Ron Steinfeld is an Associate Professor at the Cybersecurity Lab of the Faculty of Information Technology , Monash University . His research focuses on lattice-based cryptography , post-quantum cryptographic protocols , and privacy-preserving technologies . He has contributed to advancements in secure multiparty computation , digital signatures , and blockchain confidentiality . Key areas: Lattice-Based Cryptography, Zero-Knowledge Proofs, Blockchain Security Recent work trends: Quantum-safe protocols, Efficient sampling algorithms, Scalable blockchain solutions Scientific Awards : BEST PAPER AWARD (ASIACRYPT 2015) He has served on program committees for major conferences including CRYPTO , EUROCRYPT , and ASIACRYPT . Ron is a member of the Discrete Mathematics Research Group and has collaborated with institutions like Macquarie University in the past.

Stavros StavroglouView profile

Assistant Professor

• Credit Risk
• Fin Tech
• AI Foundation Models
• +12 more

Stavros Stavroglou is an Assistant Professor in Credit Risk and Fin Tech at the University of Edinburgh Business School, specializing in Management Science and Business Economics. He leads research in complex systems, causality networks, and AI-driven financial modeling with significant industry applications. His educational background includes a PhD and MRes in Applied Mathematics and Decision Making from the University of Liverpool (funded by EPSRC-ESRC scholarships), and MSc and BSc in Mathematics from Aristotle University of Thessaloniki (under full IKY Scholarships). He was a visiting scholar at California Institute of Technology and won the Best PhD Thesis award in 2020 from the University of Liverpool. Stavros specializes in designing and developing applications with AI Foundation models, quantitative and qualitative modeling, and real-time forecasting. His research focuses on uncovering hidden causal relationships in complex systems, particularly in financial markets. He has developed innovative methodologies including Pattern Causality for time series analysis, PillarScape Assembler for deep-future forecasting, and P-mo for LLM enhancement in financial contexts. His work bridges academic innovation with practical market applications, consistently delivering profitable insights through data-driven approaches. His four major publications in PNAS and Risk Analysis demonstrate his expertise in causal analysis of complex financial systems. These works have established him as a leading researcher in pattern causality and financial network analysis, with his methods being implemented in Python and R packages used by researchers worldwide. Best PhD Thesis 2020, University of Liverpool Trading Competition Winner 2018 As Research Director, Stavros supervises PhD students in AI, East Asian Economies, Statistics, and Econometrics, as well as MSc students in Quantitative Finance, Risk Management, and Credit Scoring. He has raised £600,000 for R&D in data-driven technologies for portfolio management. He is the Co-Organizer of the annual Quantitative Finance and Risk Analysis (QFRA) international symposium, which has been held in various Greek islands since 2018. Stavros maintains an extensive professional network with academics at Stanford, Oxford, Peking, Fudan, Boston, Monash, Caltech, and UCI Irvine, as well as senior professionals at firms like ETPA and JPMorgan Chase & Co. His research fingerprint spans Engineering (Policy Maker, Embedded Information, Decision Maker), Economics (Financial Market, Credit Derivative), and Computer Science domains.

Naren RamakrishnanView profile

Professor

• Data Science
• Machine Learning
• Urban Analytics
• +2 more

Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech, where he directs the Sanghani Center for AI and Data Analytics. He also serves as AI and Machine Learning Lead for the Virginia Tech Innovation Campus. His research spans data science, machine learning, urban analytics, forecasting, and computational epidemiology. Recent publications (2024-2025) focus on language model optimization, AI applications in government and environmental conservation, and spatiotemporal data analysis. Work demonstrates strong emphasis on real-world AI deployments in regulatory compliance, supply chain verification, and network optimization. Methodological innovations include prompt engineering techniques, world models for reinforcement learning, and specialized embedding methods. Dr. Ramakrishnan has received prestigious fellowships from ACM, AAAS, and IEEE. His research has been supported by numerous agencies including DARPA, NSF, NIH, and industry partners like Amazon and Boeing, with 36 PhD students mentored to completion.

Ali MaalaouiView profile

Professor

• Geometric Analysis
• Calculus of Variations
• Conformal Geometry
• +6 more

Ali Maalaoui is a Professor of Mathematics at Clark University, specializing in geometric analysis and calculus of variations, with a focus on conformal and CR geometries. He holds a Ph.D. from Rutgers University (2013) and a prior Ph.D. from the University of Tunis (2010). Before Clark, he was an Associate Professor at the American University of Ras Al Khaimah in the UAE and a postdoctoral fellow at the University of Basel, Switzerland. His research explores critical geometric partial differential equations (PDEs) involving energy concentration and bubbling phenomena, particularly in contexts like Dirac-Einstein equations, fractional Yamabe problems, and CR manifolds. Key contributions include studies on Q’-curvature flows, singular solutions in geometric PDEs, and functional inequalities in non-Euclidean settings. Maalaoui’s work combines analytical techniques from functional analysis, geometric measure theory, and Morse-Floer homology. Recent trends in his publications focus on fractional operators, spin geometry, and applications of conformal invariance principles. His articles span high-impact journals such as Mathematische Nachrichten , Journal of Differential Equations , and Calculus of Variations and Partial Differential Equations . No scientific awards or grants are explicitly listed in the provided information. He has advised no listed students but has contributed to collaborative projects with institutions worldwide. His research often involves international co-authors, reflecting a global network in geometric analysis.

• Model-based Systems Engineering
• Safety-Critical Systems
• Systems Engineering
• +3 more

Dr. Siyuan Ji is a Reader in Model-based Systems Engineering (MBSE) at Loughborough University, serving as Deputy Head of the Manufacturing, Systems & Management Academic Community and Deputy Director of the Doctoral Training Centre in MBSE. He previously held a Senior Lecturer position in Systems Engineering at the University of York, where he led the MSc Programme in Safety-Critical Systems Engineering. His academic journey includes a PhD and MSc in Physics from the University of Nottingham, followed by research roles in model-based systems engineering at Loughborough University. His research focuses on advancing model-based techniques for systems engineering, particularly in safety-critical systems, formal methods, and complex system design. He has contributed to areas such as hazard management (e.g., BSafeML framework), response time analysis in real-time systems, and model synchronization for requirements engineering. His work bridges theoretical foundations with practical applications in automotive systems, embedded software, and educational technology. Dr. Ji holds the title of Fellow of the Higher Education Academy and has published extensively on topics ranging from quantum technology reporting to conversational tutoring systems. His research emphasizes interdisciplinary collaboration, evident in projects like the EPSRC-funded analysis of vehicles as complex systems. He actively contributes to both academic and industrial advancements in systems engineering methodologies and education innovation. His professional roles include managing doctoral training programs, overseeing academic communities, and advancing systems engineering education. Collaborations span industry partnerships and international academic networks, reflecting his commitment to impactful research and training the next generation of systems engineers.

LJILJANA TRAJKOVICView profile

Professor

• Communication Networks
• Nonlinear Circuits and Systems
• Electronics Engineering
• +3 more

Ljiljana Trajkovic is a Professor in the Department of Engineering Science at Simon Fraser University's Faculty of Applied Sciences. She holds a Ph.D. from the University of California, Los Angeles (1986), M.Sc. from Syracuse University (1979), and Dipl.Ing. from the University of Pristina (1974). Her research focuses on communication networks, nonlinear circuits, and machine learning applications for network security. She actively contributes to IEEE initiatives, including roles as conference committee chair and editorial board member. Education highlights include a strong foundation in electrical engineering and advanced studies in circuit theory and systems science. Her work bridges theoretical analysis with practical applications, such as anomaly detection in communication networks using machine learning. She teaches courses like ENSC 220 D100 Electric Circuits I, integrating research insights into education. Research interests emphasize network security, traffic analysis, and distributed systems. Recent articles explore BGP anomaly classification, ransomware detection, and virtual network embedding. She collaborates on tools like VNE-Sim and Anonym for network analysis. Awards and recognitions are highlighted through her leadership roles in IEEE and academic contributions. Advising and grants involve mentoring graduate students in cybersecurity and networking projects. She leads research teams exploring complex networks and their applications in autonomous systems. Her lab focuses on interdisciplinary projects merging electronics engineering with AI-driven network solutions.

John WhitneyView profile

Associate Professor

• Human-safe robotics
• Medical robotics
• Soft robotics
• +5 more

John Whitney is an Associate Professor in the Department of Mechanical and Industrial Engineering at Northeastern University's College of Engineering. He joined the university in January 2016. His research focuses on human-safe robotics, medical robotics, soft robotics, MEMS, microrobotics, and bio-inspired design, with a particular emphasis on flapping aerodynamics and insect flight mechanisms. He has led major research initiatives including the National Science Foundation-funded 'Controllable Compliance' robotic arm project and Office of Naval Research projects on haptic manipulators for explosive ordnance disposal. Whitney holds a PhD in Engineering Sciences from Harvard University (2012) and an SM in Aeronautics and Astronautics from MIT (2006). He is affiliated with Northeastern's Institute for Experiential Robotics and has contributed to advanced systems like the ANA Avatar XPRIZE robotic avatar. His work integrates interdisciplinary approaches combining mechanical engineering, control systems, and biomedical applications. Education: PhD in Engineering Sciences, Harvard University, 2012 SM in Aeronautics and Astronautics, MIT, 2006 Awards: 2023 Impact Award Finalist, International Conference on Robotics and Automation 2022-2023 College of Engineering Faculty Award Recipient His research spans teleoperation systems, haptic feedback mechanisms, and soft material manufacturing. Notable projects include a MR-safe haptic system for prostate biopsies and a novel robotic arm for contact-rich environments. His lab's work on flapping-wing microrobots draws inspiration from insect flight dynamics to improve micro air vehicle (MAV) performance. Whitney advises teams like the Northeastern Mars Rover Team and ANA Avatar XPRIZE finalists, demonstrating his commitment to hands-on student engagement. His publications emphasize practical robotics solutions for medical, industrial, and exploratory applications.

• Graph Theory
• Graph Databases
• Knowledge Graphs
• +5 more

Carlos R. Rivero is an Associate Professor in the Department of Computer Science at the Rochester Institute of Technology (RIT), located within the Golisano College of Computing and Information Sciences. His primary research focuses on graph theory applications in knowledge graphs, graph databases, and computer-aided program comprehension. He holds a PhD from the University of Seville (Spain), completed in 2012, with postdoctoral work at the University of Idaho (USA). His teaching responsibilities include courses such as Principles of Data Management, Data Mining, and Big Data exploration. Rivero has advised numerous PhD and Master’s students, contributing to research projects in link prediction, knowledge graph completion, and educational technology. He actively serves on program committees for conferences like The Web Conference and SIGKDD, and has reviewed for journals including the VLDB Journal and Communications of the ACM. His research emphasizes evaluating knowledge graph embeddings, improving link prediction methodologies, and developing tools for educational feedback in programming. He has contributed to projects like AYNEXT, which streamlines link prediction evaluation, and CAFE, a neighborhood-aware knowledge graph completion tool. Rivero’s work bridges theoretical advancements with practical applications in education and industry. Notable contributions include frameworks for automated feedback in programming courses and methodologies for assessing inference patterns in knowledge graphs. His grants and service roles reflect a commitment to advancing computational methods and fostering academic collaboration in data science and education.

Stefan KlusView profile

Lecturer

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +8 more

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis

Sandy IraniView profile

Professor

• Quantum Computing
• Algorithm Design
• Computational Complexity Theory
• +5 more

Sandy Irani is a Full Professor at the University of California, Irvine (UCI) in the Department of Computer Science within the Donald Bren School of Information and Computer Sciences. She received her Ph.D. from UC Berkeley in 1991 and has been at UCI since 1992. Her research focuses on algorithm design, computational complexity theory, and quantum computing, with notable contributions to online algorithms and quantum complexity theory. She currently serves as Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley, a role she has held since 2022. This position allows her to collaborate with researchers across theoretical computer science and related disciplines. Irani’s teaching excellence is recognized through the UCI Distinguished Faculty Award for Teaching (2021), and she has contributed to education through her zyBook on Discrete Mathematics, used by over 94,000 students globally. Her work bridges foundational computer science with practical applications, including power management strategies and distributed computing algorithms. Notably, she has collaborated with industry leaders like Mike Luby on optimizing distributed systems. Her research in quantum computing explores computational problems inspired by condensed matter physics, aiming to understand quantum advantage over classical systems. She has also authored influential papers on topics like cache hierarchy design, scheduling algorithms, and the theoretical limits of electronic structure calculations. Awards: ACM Fellow (2022), UCI Distinguished Faculty Award for Teaching (2021). Key Roles: Associate Director, Simons Institute; Vice Chair, Computing Division at UCI. Recent Projects: Quantum algorithms for condensed matter systems, maximal independent set algorithms in distributed networks.

• Computer Architecture
• Accelerated Machine Learning
• Computer Science Education
• +3 more

Thomas Yeh is an Assistant Professor of Teaching in the Department of Computer Science at the University of California, Irvine. His academic background includes a Ph.D. in Computer Science from UCLA and a BS in Electrical Engineering and Computer Science from UC Berkeley. Prior to academia, he gained industry experience across research, architecture, design, verification, marketing, and management roles. His educational credentials: Ph.D. in Computer Science, UCLA BS in Electrical Engineering and Computer Science, UC Berkeley Dr. Yeh's research spans computer architecture, accelerated machine learning, and computer science education. In architecture, he pioneers error-tolerant physics simulation and heterogeneous computing. His ML work focuses on adaptive precision techniques for energy-efficient acceleration. In education, he develops interactive tools for novice programmers and experiential learning frameworks for computer architecture. His cross-disciplinary approach bridges hardware-software co-design with pedagogical innovation. Publication trends reveal consistent focus on computational efficiency across physics simulation, ML acceleration, and educational technology. His work connects real-time systems optimization with emerging AI applications, particularly in interactive environments and physics-based animation. No scientific awards are documented in the provided materials. While advising details and grant funding specifics are absent from available information, his industry-academia transition informs practical research directions. Teaching responsibilities include core courses like Introduction to CS, Data Structures, and Efficient ML Computing. Research infrastructure details remain unspecified, though his publications suggest collaborations in physics simulation and heterogeneous computing environments.

Domniki AsimakiView profile

Professor

• Geotechnical Engineering
• Seismology
• Structural Dynamics
• +6 more

Domniki Asimaki is a Professor of Mechanical and Civil Engineering at the California Institute of Technology (Caltech), part of the Division of Engineering and Applied Science. Her research focuses on geotechnical engineering, computational mechanics, and structural dynamics, with an emphasis on understanding ground motion effects on natural and engineered systems such as dams, tunnels, and urban infrastructure. She holds a Dipl. from the National Technical University of Athens (1998), an M.S. (2000) and Ph.D. (2004) from MIT, joining Caltech in 2014. Key research interests include soil dynamics, wave propagation, regional ground deformation, and soil-foundation-structure interaction. She has pioneered data-driven approaches to integrate numerical simulations with field observations for resilient infrastructure design. Notable achievements include developing the open-source Seismo-VLAB software for seismic analysis and receiving prestigious awards like the Bodossaki Award of Scientific Excellence and the Geotechnical Earthquake Engineering Award. Her work addresses seismic hazards at urban and regional scales, with recent studies on the 2023 Türkiye earthquake, the 2019 Ridgecrest earthquake, and Kathmandu Basin dynamics. She leads initiatives to enhance ground motion prediction, landslide hazard assessment, and infrastructure resilience through advanced modeling and AI-driven methods. Education: Dipl., National Technical University of Athens, 1998 M.S., Massachusetts Institute of Technology, 2000 Ph.D., Massachusetts Institute of Technology, 2004 Awards: Bodossaki Award of Scientific Excellence Geotechnical Earthquake Engineering Award Labs/Teams: Leads research groups focusing on seismic hazard modeling, open-source software development, and geotechnical data assimilation techniques.

Piotr DudekView profile

Professor

• Integrated Circuit Design
• Neuromorphic Engineering
• VLSI Design
• +4 more

Roles & Affiliations: Prof. Piotr Dudek is a Professor of Circuits and Systems in the School of Electrical and Electronic Engineering at The University of Manchester. He has held visiting roles at Hong Kong University of Science and Technology, Gdansk University of Technology, and Sorbonne University. He is a Senior Member of the IEEE and chairs/co-chairs technical committees in circuits and systems. Education: Mgr inz (Technical University of Gdańsk, Poland), MSc and PhD (UMIST, UK). Research Interests: Focuses on VLSI design, vision sensors (SCAMP chip family), cellular processor arrays, neuromorphic engineering, and brain-inspired systems. Develops low-power, high-performance embedded vision systems for robotics, biomedical applications, and autonomous systems. Projects & Contributions: Leads projects like SCAMP vision chips, FORTE (memristor-based systems), and Agile robotic vision. Involved in EPSRC-funded initiatives and collaborates internationally. Active in reviewing for journals/conferences and holds editorial roles. Awards: Recipient of Best Paper/Demo awards at ISCAS, CNNA, IJCNN, and ICDSC. Holds the Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship. Lab & Teams: Directs the Microelectronics Design Lab, fostering interdisciplinary work between VLSI design, robotics, and neuroscience. Supervises 11 PhD students and collaborates with global researchers in bioelectronics and computational systems.