Find Academic Supervisors

Arnab Kumar-MondalView profile

Researcher

• Computer Vision
• Deep Learning
• Dynamical Systems
• +9 more

Arnab Kumar-Mondal is a Machine Learning Researcher at Apple Inc., with a Ph.D. in Deep Learning from McGill University and Mila – Quebec Artificial Intelligence Institute. His work bridges theoretical and applied research in computer vision, language modeling, robotics, and AI for science. Ph.D. from McGill University (2025 completion) Internships at Microsoft Research and Apple Visiting Researcher at ServiceNow Research and Huawei Noah’s Ark Lab B.Tech in Electronics and Electrical Engineering from IIT Kharagpur His research focuses on equivariant learning , state space modeling , and generative adversarial networks (GANs) , with applications in medical imaging, human motion analysis, and vector graphics generation. Key contributions include canonicalization frameworks for symmetry-aware modeling and spectral analysis of representation quality in self-supervised learning. Collaborations span institutions like ServiceNow, Huawei, and Mila. Recent publications (2023–2025) explore symmetry-aware generative modeling , efficient dynamics modeling in interactive environments, and rotation-invariant visual representation learning. His work on ternary language models at ICLR 2025 demonstrates scalable pretraining techniques. Arnab maintains active contributions to open-source software, including PyTorch implementations for semi-supervised segmentation via CycleGAN. His technical depth extends to VLSI engineering, embedded systems, and free-form lens design from undergraduate research. Professional activities include patents on video-language foundation models, internships at leading tech firms, and cross-institutional research roles.

• Adeno-Associated Virus (AAV) Vector Technology
• Gene Therapy
• MicroRNA Therapeutics
• +3 more

Jun Xie is an Associate Professor at the UMass Chan Medical School and holds multiple positions in departments including the Gene Therapy Center , Genetic and Cellular Medicine , and Microbiology . His research focuses on adeno-associated virus (AAV) vector technology and AAV-mediated miRNA therapeutics , with applications in gene therapy for diseases like osteoporosis, spinal muscular atrophy, and fibrodysplasia ossificans progressiva. BS in Plant Genetics & Breeding from Huazhong Agricultural University, China MS in Molecular Biology & Biochemistry from Huazhong Agricultural University, China PhD in Genetics from Sun Yat-sen University of Medical Sciences, China Xie's recent work explores CRISPR/Cas9 genome editing delivered via AAV vectors, including studies on bone disease treatment (osteogenesis imperfecta, alveolar bone loss), neurological disorder therapies (spinal muscular atrophy), and immune-evasive AAV designs . His publications emphasize vector engineering to improve safety, targeting specificity, and therapeutic efficacy. Key trends in his 15 most recent articles include AAV serotype optimization (e.g., AAV5, AAV9), adenine base editing with engineered Cas9 variants, and miRNA-based interventions for metabolic and inflammatory conditions. His work spans preclinical rhesus monkey models and translational gene therapy applications .

• Compilers
• Computer Architecture
• High-Level Synthesis
• +5 more

Scott Mahlke is a Professor and Associate Chair in the Department of Electrical Engineering and Computer Science at the University of Michigan's College of Engineering. He is affiliated with both the Advanced Computer Architecture Laboratory and the Software Systems Laboratory. Dr. Mahlke joined the University of Michigan in 2001 after completing his Ph.D. at the University of Illinois and working at HP Laboratories. Ph.D., University of Illinois Former Researcher, HP Laboratories Dr. Mahlke's research spans compilers, computer architecture, and high-level synthesis, with particular focus on overcoming challenges in performance, power consumption, and reliability for next-generation computer systems. His work integrates hardware and software co-design approaches to address fundamental limitations in modern computing platforms. His research has evolved from traditional compiler and architecture topics toward increasingly incorporating machine learning acceleration, autonomous systems, and reliability engineering. Analysis of his recent publications (2021-2025) reveals a strong trend toward hardware-software co-design for emerging workloads, particularly in autonomous systems, neural network acceleration, and reliability-aware computing. His work demonstrates consistent innovation in bridging compiler technology with architectural innovations to solve real-world performance and efficiency challenges. Dr. Mahlke has received significant recognition for his contributions to the field: National Science Foundation CAREER Award (2003) for "Compiler-Directed Synthesis of Application Specific Processors" Morris Wellman Faculty Development Assistant Professor appointment (2004) ISCA Most Influential Paper Award (2006) for the 1991 paper "IMPACT: An Architectural Framework for Multiple Instruction Issue Processors" Young Alumni Award from the University of Illinois ECE Department (2007) As an educator, Dr. Mahlke has taught core computer systems courses including EECS 370 (Introduction to Computer Organization), EECS 483 (Compiler Construction), and EECS 583 (Advanced Compilers) since joining Michigan. His teaching philosophy follows Yale Patt's 10 commandments for teaching, emphasizing understanding over memorization, genuine respect for students, and taking responsibility for course content. He has received mixed but generally positive student evaluations, with students noting both his deep subject matter expertise and areas for improvement in lecture delivery. Dr. Mahlke maintains active research leadership through his affiliations with the Advanced Computer Architecture Laboratory and Software Systems Laboratory, where his team continues to explore innovative approaches to compiler and architecture challenges in modern computing systems.

Tiantai DengView profile

Lecturer

• FPGA-based Hardware Acceleration
• Sparse Processing Architecture for CNN and GEMM
• Number System and Approximation Computing
• +3 more

Dr. Tiantai Deng is a Lecturer in Electronics and Digital Systems at the School of Electrical and Electronic Engineering , University of Sheffield (since 2021). His industrial background includes a senior research engineer role at HiSilicon/Huawei , where he focused on hardware architecture design for CNN, GEMM, and image/video processing on FPGAs/ASICs. Education: BEng, MSc, PhD Research interests span FPGA-based hardware acceleration , sparse processing architecture for CNN/GEMM, number system design , approximation computing , and high-level design environments . His work integrates algorithm-hardware co-optimization for efficiency in AI and mathematical computing. Recent publications emphasize neurodynamic systems for opinion modeling, parallel processing elements for ODE/AI acceleration, and low-power FPGA implementations for clustering/modulation classification. Earlier work addressed combustion dynamics and image processing pipelines. Contact: t.deng@sheffield.ac.uk | Office: G108, Sir Frederick Mappin Building, Sheffield S1 3JD | ORCID 0000-0003-4507-5746

Robin PurshouseView profile

Professor

• Computational Modelling of Complex Social Systems
• Agent-Based Modelling
• Machine Learning
• +6 more

Professor Robin Purshouse is a leading academic at the University of Sheffield , currently serving as Professor of Decision Sciences in the Department of Automatic Control and Systems Engineering within the School of Electrical and Electronic Engineering . With a career spanning academia and industry, his work bridges computational modelling , optimization , and systems science to address complex challenges in public health and engineering. His research has been pivotal in developing mechanisms for agent-based modelling and evolutionary multi-objective optimization . Education: PhD in Control Systems (2004), MEng in Control Systems Engineering (1999) from the University of Sheffield Professor Purshouse's research focuses on computational modelling of complex social systems , decision analytics for population health policy , and Bayesian optimization . He has pioneered the integration of machine learning and uncertainty quantification in social science simulations, with notable projects like the Sheffield Alcohol Policy Model and CASCADE initiative. His work spans interdisciplinary domains, including health economics , policy evaluation , and engineering design . Recent publications highlight his expertise in agent-based modelling for smoking/vaping dynamics , intersectional disparities in alcohol consumption , and inclusive economy frameworks . He has secured substantial funding (exceeding £16 million) through grants from NIH , CRUK , UKPRP , and MRC , including his role as co-PI in the HealthMod cluster. His contributions to multi-objective optimization and evolutionary algorithms have advanced methodologies in both engineering and public health domains. Scientific Awards: ESRC Future Research Leaders Award (2012-2015) As a co-developer of the Liger optimization environment , Purshouse has fostered open-source tools for complex decision-making. He leads the SIPHER consortium for systems science in public health and serves on editorial boards for journals like Environmental Modelling & Software . His teaching includes Agent-Based Modelling (ACS6132), and he maintains professional memberships in the Association for Computing Machinery and Research Society on Alcohol .

Ingmar GlaucheView profile

Professor

• Hematopoietic Stem Cell Biology
• Clonal Competition Dynamics
• Stem Cell Aging
• +5 more

Prof. Dr. Ingmar Glauche serves as Deputy Institute Director at the Institute for Medical Informatics and Biometrics within TU Dresden's Medical Faculty . He leads the Theoretical Stem Cell Biology research group and coordinates the institute's teaching activities. As an elected member of TU Dresden's extended senate, he contributes to institutional governance. Academic focus on hematopoietic stem cell biology Expertise in computational modeling Key roles in leukemia research and stem cell transplantation Research interests span clonal competition , stem cell aging , and proliferation-differentiation control , with applications in chronic/acute myeloid leukemia and inherited bone marrow failure . His work combines differential equation models and agent-based simulations to study clonal dynamics , spatial organization , and feedback mechanisms in stem cell systems. Recent publications analyze clonal dominance mechanisms (2025), pediatric CML therapy monitoring (2023), and stem cell aging (2022). Collaborations include DFG , BMBF , and Wilhelm Sander-Stiftung projects like TKI-Stop and MinimaL . Current students include Andrea Gottschalk and Thomas Zerjatke , while former advisees include Christoph Baldow and Helene Hoffmann . 2025: Blood - Myeloid bias mechanisms 2024: BMC Medical Education - Competence analysis 2024: Leukemia - Clonal hematopoiesis 2023: PLOS Digital Health - Data integration 2022: Nature Communications - Clonal reconstruction

Romain RaveauxView profile

Associate Professor

• Image Analysis
• Machine Learning
• Structural Pattern Recognition
• +5 more

Romain Raveaux is an Associate Professor at the LIFAT Computer Science Laboratory, University of Tours, affiliated with Polytech Tours. His research focuses on Image Analysis, Machine Learning, Structural Pattern Recognition, Graph Matching, Graph Neural Networks, Discrete Optimization, Reinforcement Learning, and Transfer Learning . Email: romain.raveaux@gmail.com , romain.raveaux@laposte.net Address: 64 av. Jean Portalis, Tours, France, 37200 Phone: +33 (0)2 47 36 14 27 Research Interests Graph Matching and Neural Networks Discrete Optimization for Pattern Recognition Transfer Learning in Graph-Based Models Historical Document Analysis Scientific Trends His recent work bridges Graph Neural Networks with Mixed-Integer Programming , focusing on Image Semantic Segmentation and Graph Cycle Detection . Earlier studies emphasize Genetic Algorithms for graph classification and Graph Edit Distance optimization in pattern recognition.

• Incoherent Scatter Radar
• Ionospheric Propagation
• Equatorial Electrodynamics
• +5 more

Erhan Kudeki is a Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He holds a PhD (1983) and BS (1978) in Electrical Engineering from Cornell University. His research focuses on ionospheric physics and radar remote sensing, including incoherent scatter radar theory, equatorial electrodynamics, atmospheric turbulence, and space science applications. He mentors undergraduate students in ionospheric radar signal processing projects. Education: PhD, Electrical Engineering, Cornell University (1983) BS, Electrical Engineering, Cornell University (1978) Research Interests: Professor Kudeki investigates ionospheric phenomena using radar techniques, specializing in plasma instabilities, equatorial electrodynamics, and atmospheric wave interactions. His work bridges experimental radar diagnostics with theoretical modeling of space weather phenomena, including 150-km echoes and spread-F development. Recent Publications: His 15 most recent articles (2020-2023) demonstrate evolving focus areas: ionospheric irregularities detection using advanced radar systems, quantum physics education for engineers, and immersive STEM pedagogy. Recurring themes include Jicamarca radar experiments, equatorial plasma dynamics, and innovations in engineering education. Awards: Teaching : George Anner Excellence Award (2018), Ronald W. Pratt Teaching Award (2013), College of Engineering Advisor recognition Research : NASA Group Achievement Awards (2004 Equis II, 1998 Cocqui II), NSF CEDAR Prize Lecture (2006) Consistently ranked excellent by students since 1987 Courses: Teaches core electrical engineering courses including ECE 210 (Analog Signal Processing), ECE 329 (Fields and Waves), ECE 350 (Fields and Waves II), and ECE 458 (Radio Wave Propagation).

• Machine Learning
• Sports Science
• Health Informatics
• +3 more

Susanne Jauhiainen is a Postdoctoral Researcher affiliated with the Faculty of Information Technology at the University of Jyväskylä . Her work primarily focuses on applying machine learning and data science techniques to interdisciplinary problems in sports science and health informatics. Research Themes : Machine Learning, Sports Injury Prediction, Student Well-Being Analytics Key Collaborations : Computational Data Science Research Group, with partners in health science and spectral imaging domains Her recent publications highlight predictive modeling applications in sports injury detection, cluster analysis techniques, and educational data mining. Jauhiainen's work emphasizes the practical implementation of advanced analytics to solve real-world problems across multiple domains. She contributes to open-access research dissemination and participates in multidisciplinary teaching initiatives that integrate digital well-being data analysis.

Ali MansourianView profile

Professor

• Geospatial Artificial Intelligence
• Spatial Data Infrastructures
• Multi-Criteria Decision Analysis
• +7 more

Ali Mansourian is a Professor of Geomatics at Lund University's Department of Physical Geography and Ecosystem Science, where he serves as Director of the Lund University GIS Centre and Coordinator of the GIS & RS Master Programme. He is actively involved with the United Nations Global Geospatial Information Management (UN-GGIM) Academic Network and previously served on the European Association of Geographic Information Laboratories in Europe (AGILE) council. His academic leadership spans large-scale international research initiatives and capacity-building projects funded by Erasmus+ and SIDA. Mansourian's research focuses on Geospatial Artificial Intelligence (GeoAI), Spatial Data Infrastructures (SDI), and Multi-Criteria Decision Analysis (MCDA) using multi-objective optimization techniques. His work extends to applying GIS in epidemiology and public health, disaster risk management, land-use planning, climate change, environmental management, and sustainability. His research portfolio demonstrates a strong interdisciplinary approach, bridging geospatial technology with critical societal challenges. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with geospatial analysis, particularly in health applications, environmental monitoring, and climate change impacts. The research shows increasing emphasis on spatial ensemble learning, remote sensing applications, and the development of GeoAI tools that make geospatial analysis more accessible through natural language interfaces. His work spans multiple continents, with significant contributions in Africa, Europe, and Asia. Mansourian has extensive experience supervising PhD students and postdoctoral researchers, though specific student names aren't listed in the provided materials. He has coordinated numerous large-scale international projects including Geo-Academy, INTEGRAL, CADEO, and SWEMENA, demonstrating significant grant acquisition and management expertise. His leadership extends to evaluating proposals for major European research grant programs and serving as an invited evaluator for PhD theses. As Director of the Lund University GIS Centre and active member of multiple international networks, Mansourian leads a dynamic research environment focused on advancing geospatial technologies and their applications. His teams work at the intersection of traditional GIScience and emerging artificial intelligence approaches, creating innovative solutions for complex spatial problems across multiple domains including public health, environmental management, and sustainable development.

Petteri KaskiView profile

Associate Professor

• Theoretical Computer Science
• Algorithm Design
• Exact and Parameterized Algorithms
• +5 more

Petteri Kaski is an Associate Professor at the Department of Computer Science, School of Science, Aalto University , and a member of the Helsinki Institute for Information Technology (HIIT) . His research focuses on theoretical computer science, particularly in algorithm design, exact and parameterized algorithms, algebraic algorithms, and combinatorics. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2005) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (2002) Master's Degree in Engineering and Technology, Helsinki University of Technology (2001) His recent work explores tensor scaling, Johnson-Lindenstrauss transforms, Hamiltonian cycles, and computational complexity, with contributions to polynomial-time algorithms, finite field computations, and combinatorial optimization. Notable awards include the Best Paper Award at ICALP 2017 , an ERC Starting Grant (2014) , and the Kirkman Medal (2007) . He has served on scientific committees for conferences like STACS 2025 and ICALP 2024 , and collaborated with institutions such as the IT University of Copenhagen and Universität Regensburg . Key Research Areas : Theoretical Computer Science, Algorithm Design, Exact Algorithms, Algebraic Computation, Graph Theory, Combinatorics

• Digital Signal Processing
• Speech Communication
• Acoustic Signal Processing
• +3 more

Peter Vary is a Professor at the Faculty of Electrical Engineering and Information Technology of RWTH Aachen University, serving as Director of the Institute for Communication Systems. His work focuses on speech and audio signal processing for communication systems. Digital Signal Processing Speech Enhancement Acoustic Echo Control Microphone Array Beamforming Communication Systems Audio Compression His recent publications (2023–2024) emphasize speech coding, noise reduction, and bandwidth extension for hearing aids and mobile devices, with technical innovations in Kalman filters, hybrid digital-analog transmission, and wind noise detection. He holds a leadership role in the Institute for Communication Systems and serves as Ombudsperson for teaching in his faculty. Contact: vary@iks.rwth-aachen.de

Martin ElsmanView profile

Professor

• Programming Languages
• Theory of Computation
• Functional Programming
• +5 more

Martin Elsman is a full-time Professor in the Programming Languages and Theory of Computation section at the Department of Computer Science, University of Copenhagen (DIKU). He serves as head of the PLTC section and head of studies for the BSc education in Computer Science and Economics. Elsman is also an active maintainer of several software tools including the MLKit and SMLtoJs. Joined DIKU in 2012 after 4 years at SimCorp (2008-2012) and previous Associate Professorship at IT University of Copenhagen (2003-2008). Co-developer of Futhark, TAIL APL compiler, SMLtoJs, and SMLserver. Education: M.Sc. in Engineering, Technical University of Denmark Ph.D. in Computer Science, University of Copenhagen (DIKU), supervised by Mads Tofte. Research Interests: Elsman works on programming language design and implementation, with a focus on functional programming, module systems, domain-specific languages for financial contracts, region-based memory management, compilation techniques for parallelism, program optimization, and static type systems. His work spans both theoretical and applied domains, including blockchain-based financial contract execution, web technology, and GPU programming using functional languages. Publication Trends: His recent articles focus on functional programming, array programming, parallelism, and memory management. Topics include region inference, type systems for data-parallelism, program optimization techniques, and domain-specific compilation for financial and quantum computing. He frequently collaborates with Troels Henriksen and others on tools like Futhark and MLKit.

• Bioprocess Engineering
• Photobioreactors
• Microalgae Biotechnology
• +3 more

Jeremy Pruvost is a Professor at Polytech Nantes (University of Nantes) within the Department of Process and Bioprocess Engineering . He directs the GEPEA UMR-CNRS 6144 laboratory and the AlgoSolis UMS-CNRS 3722 platform. His research focuses on microalgae bioprocess engineering , particularly photobioreactor design, CO2 valorization, and sustainable algal biomass production for biofuels and environmental applications. Director, GEPEA UMR-CNRS 6144 (2018–present) Director, UMS AlgoSolis (2015–2019) Co-Chair, ESBES 'Microalgae Bioengineering' (2020–2020) Associate Editor, Algal Research (2019–present) Research Interests : Jeremy Pruvost specializes in photobioreactor engineering , microalgae biotechnology , and sustainable biomass production . His work addresses: Physical-biological interactions in photobioreactors Modeling for photobioreactor design and control Industrial ecology applications to microalgae Thermal regulation in extreme environments CO2 biofixation and biorefinery concepts Article Trends : His publications (over 90 peer-reviewed journals) emphasize algal bioprocess optimization , photobioreactor thermal management , and desert cultivation systems . Key contributions include modeling light-temperature interactions, developing CO2 supply strategies, and integrating photobioreactors into building facades. Education & Administration : He earned his PhD at University of Nantes (2000) and obtained Habilitation à Diriger des Recherches (2005). He has created multiple educational programs including an International Master in Microalgae Bioprocess Engineering (2017–2019) and teaching units on photobioreactors and computational fluid dynamics. Labs & Collaborations : He leads the Bioprocesses Applied to Microalgae team (2011–2018) at GEPEA, a 225-person laboratory. His research involves international collaborations through European Algal Biomass Association and EERA (European Energy Research Alliance).

Hamed GhavamniaView profile

Assistant Professor

• System/Software Security
• Attack Surface Reduction
• Memory Safe Languages
• +2 more

Hamed Ghavamnia is a Security Researcher at Bloomberg, where he focuses on software and systems security. Previously, he served as an Assistant Professor at the School of Computing, University of Connecticut (2023–2024). He holds a PhD in Computer Science from Stony Brook University (under Michalis Polychronakis), an M.S. in Computer Engineering from Sharif University of Technology (Network Security focus), and a B.E. in Software Engineering from University of Isfahan. His research interests center on system/software security, particularly attack surface reduction through program analysis and memory-safe languages. Key projects include Confine (system call policy generation for containers), Temporal Specialization (system call filtering based on execution phases), and LeakLess (data protection in serverless platforms). He has contributed to seminal works on configuration-driven security (C2C), memory protection (xMP), and automated policy inference. His work bridges static/dynamic analysis, program specialization, and security hardening. Notable outcomes include reducing attack surfaces via system call filtering and debloating techniques, with applications in cloud and containerized environments.