Find Academic Supervisors

Elena TuzhilinaView profile

Assistant Professor

• Machine Learning
• Applied Statistics
• Computational Biology
• +5 more

Elena Tuzhilina is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, specializing in machine learning, applied statistics, and computational biology. Her research focuses on statistical tools for chromatin 3D spatial structure reconstruction and analyzing emotional disorders' impact on brain function. Ph.D. in Statistics from Stanford University Specialist's degree from Moscow State University Two-year Data Science program at Yandex School Her research spans high-dimensional data analysis , dimension reduction , and statistical modeling in biological contexts. She has developed novel algorithms for chromatin conformation reconstruction and pandemic trajectory modeling. Recent publications focus on canonical correlation analysis , low-rank matrix approximation , and 3D genome architecture , with applications in computational biology and neuroscience. Dorothy Shoichet Women Faculty in Science Award JSM Student Travel Award Outstanding Teaching Assistance at Stanford Stanford Teaching Assistant Award Elena supervises PhD students and postdoctoral fellows across disciplines including statistical sciences, biochemistry, and applied mathematics. She has secured multiple grants including a NSERC Discovery Grant and University of Toronto Accelerator Grant .

Chen-Yu WeiView profile

Assistant Professor

• Interactive Machine Learning
• Reinforcement Learning
• Robust Learning Algorithms
• +5 more

Chen-Yu Wei is an Assistant Professor in the Department of Computer Science at the University of Virginia. He holds a Ph.D. from the University of Southern California (2022), and M.S. and B.S. degrees from National Taiwan University (2015, 2012). His research focuses on interactive machine learning, emphasizing robust and adaptive algorithms for non-stationary/adversarial environments, sample-efficient reinforcement learning, and decentralized multi-agent systems. Education: Ph.D., Computer Science, University of Southern California, 2022 M.S., Electrical Engineering, National Taiwan University, 2015 B.S., Electrical Engineering, National Taiwan University, 2012 Research interests include reinforcement learning, game theory, and algorithmic economics. He has received prestigious awards such as the COLT and ALT Best Paper Awards (2021-2022) and the Simons-Berkeley Research Fellowship (2022). His work bridges theory and practice, addressing challenges in adversarial environments and multi-agent coordination. Current research group members include Haolin Liu (PhD), Braham Snyder (PhD), Kingsley Kim (Undergraduate), and Rishik Balerao (Undergraduate). Teaching includes courses on Reinforcement Learning, Artificial Intelligence, and Algorithmic Economics. He co-organizes the RL Meetup and Theory Seminar at UVA.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +13 more

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

Minchen LiView profile

Assistant Professor

• Computer Graphics
• Machine Learning
• Robotics
• +4 more

Minchen Li Assistant Professor at Carnegie Mellon University's School of Computer Science (Computer Science Department). Formerly an Assistant Adjunct Professor at UCLA's Mathematics Department. Holds a Ph.D. from the University of Pennsylvania's SIG Center for Computer Graphics, followed by a postdoctoral position there. Research focuses on physics-based simulation, integrating numerical analysis, high-performance computing, and machine learning. Notable contributions include the IPC method for frictional contact simulation and large-scale material point methods. Education Ph.D., Computer and Information Science, University of Pennsylvania (2020) M.Sc., Computer Science, University of British Columbia (2018) B.Eng., Computer Science and Technology, Zhejiang University (2015) Research Interests Advances in physical simulation for visual computing, robotics, and manufacturing. Specializes in robust and efficient methods for solid/fluid dynamics, contact modeling, and GPU acceleration. Combines numerical analysis with machine learning to address challenges in simulation accuracy and versatility. Awards 2021 ACM SIGGRAPH Outstanding Doctoral Dissertation Award 2024 SCA Early Career Researcher Award Advising & Labs Leads the Simulation Intelligence Group (SIG) at CMU Graphics Lab. Advises PhD students Guying Lin, Juntian Zheng, Michael Liu, and Zhaofeng Luo. Collaborates with industry and academic partners on projects like VR-based modeling systems and scalable simulation frameworks.

Kaiyi JiView profile

Assistant Professor

• Large-scale optimization
• Machine learning
• Multi-task learning
• +6 more

Kaiyi Ji is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He earned his PhD in Electrical and Computer Engineering from Ohio State University in 2021 and completed a postdoctoral fellowship at the University of Michigan. His research focuses on large-scale optimization, machine learning, and foundation models. PhD: Electrical and Computer Engineering, Ohio State University (2021) Postdoc: University of Michigan (2022) BSc: University of Science and Technology of China (2016) Research interests include bilevel optimization, multi-task learning, continual learning, and AI4Science, with applications in robotics and crystal property prediction. Recent work explores efficient algorithms for LLM training and optimization. His publications span top venues like ICLR, ICML, NeurIPS, and IEEE Transactions on Information Theory. He received the CSE Junior Faculty Research Award (2023) and NSF CAREER Award (2025). He advises PhD students and actively participates in departmental service as Associate Chair for Graduate Student Admissions and organizer of academic workshops.

Sergi Abadal CavalleView profile

Professor

• Chip-scale Wireless Communications
• Nanocommunications
• Nanonetworks
• +12 more

Sergi Abadal Cavalle is a distinguished Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He leads a dynamic research group focused on revolutionary computing architectures through wireless chip-scale networks and quantum interconnects. His work is supported by prestigious grants including an ERC Starting Grant and an ERC Proof of Concept Grant. Education: PhD in Computer Architecture, Universitat Politècnica de Catalunya (2016) MSc in Telecommunication Engineering, UPC (2011) BSc in Telecommunication Engineering, UPC (2010) Sergi's research focuses on overcoming the limitations of traditional wired interconnects in computing systems by pioneering wireless communication at the chip and package level. His work spans terahertz communications, graphene-based antennas, reconfigurable metasurfaces, and quantum-coherent networks. He has led major EU projects such as WINC, EWiC, QUADRATURE, and WiPLASH, aiming to revolutionize classical and quantum computing architectures. His research integrates electromagnetics, materials science, and computer architecture to enable ultra-fast, scalable, and energy-efficient systems. His recent publications demonstrate a strong trend toward integrating wireless technologies within computing systems, particularly using novel materials like graphene and software-defined metasurfaces. The articles highlight advancements in on-chip wireless communication, channel modeling, and the application of these technologies in 6G and quantum computing. His work bridges theoretical modeling with experimental validation and practical emulation. Scientific Awards: ERC Starting Grant (2022) ERC Proof of Concept Grant (2024) ACM NanoCom Outstanding Milestone Award (2022) NanoComNet Young Investigator Award (2019) UPC Outstanding Thesis Award (2016) IN-NOVA Award to Best Master Thesis (2021) IBM Award to Best Academic Record (2021) Medal from Real Academia de la Ingeniería (2024) IEEE Senior Member (2025) Sergi actively mentors a large group of PhD and Master's students, many of whom have received awards for their thesis work. He leads the N3Cat research group and collaborates with institutions such as IBM Research, NEC Labs Europe, RWTH Aachen, EPFL, University of Nottingham, and Intel Labs. His lab is at the forefront of experimental validation of wireless interconnects and quantum architectures, with ongoing projects focusing on emulation and commercialization of chiplet communication technologies.

• Database Systems
• Stream Processing
• Distributed Data Management
• +5 more

Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.

Benjamin LubinView profile

Associate Clinical Professor

• Mechanism Design
• Combinatorial Auctions
• Spectral Graph Theory
• +4 more

Benjamin Lubin is a Clinical Associate Professor in the Information Systems Department at Boston University's Questrom School of Business. He holds office 621A in the Rafik B. Hariri Building at 595 Commonwealth Avenue, Boston, MA 02215. His academic journey began with a Bachelor's degree in Computer Science from Harvard University in 1999, followed by six years working at BBN Technologies, where he contributed to advanced multi-agent modeling, scheduling, and logistics systems. He later returned to Harvard to complete his Ph.D. at the intersection of computer science, game theory, and economics. Dr. Lubin's research spans three primary areas: (1) mechanism design, particularly combinatorial auctions and exchanges that support efficient reallocation of goods with complex participant preferences; (2) application of spectral graph theory to advance social network analysis; and (3) leveraging network science and machine learning to improve healthcare delivery systems. His work demonstrates a consistent pattern of bridging theoretical computer science with practical economic applications, with recent publications showing increasing focus on healthcare applications while maintaining strong contributions to auction theory and network analysis. His research has been supported by significant funding from NIHCM and the Veterans Administration, and he has received prestigious recognition including the Siebel Fellowship and Yahoo Key Technical Challenge award. Dr. Lubin has mentored numerous graduate students including PhD candidates Vatche Ishakian, Marisabel Guevara, and Sarah Zheng, as well as Master's students Benedikt Buenz and Michael Weiss. Siebel Fellowship Yahoo Key Technical Challenge award As an educator, Dr. Lubin teaches several courses including IS 710 (Core MBA Class on Information Systems), IS 716 (Accelerated Part-Time Evening MBA), IS 717 and IS 756 (MSMBA Intensives), and QD601x (Business Experimentation on edX). His teaching materials include innovative approaches like using adventure games to teach web development and creating practical exercises for understanding analytics in business contexts. He has developed several software tools including JOpt for MIP programming, the Iterative Combinatorial Exchange market software, InvEigen for inverse eigenvector problems, SpectralGOF for network model goodness-of-fit testing, and SATS for spectrum auction instance generation.

Thomas EbelView profile

Professor

• Power Electronics
• High-Voltage Electrical Engineering
• Dielectric Materials
• +3 more

Thomas Ebel is Professor and Head of the Centre for Industrial Electronics at the University of Southern Denmark (SDU) , Institute of Mechanical and Electrical Engineering. A leading expert in power electronics, high-voltage engineering and capacitor technology, he directs large, multi-partner research projects and teaches/supervises at both graduate and PhD levels. Education & Career Path Prof. Ebel holds the academic title Dr. rer. nat. and has been appointed full Professor at SDU. He concurrently serves as Head of Section at the Centre for Industrial Electronics, orchestrating cross-disciplinary research teams and infrastructure. Research Interests Power Electronics & Power Conversion: advanced converter topologies, WBG devices (GaN, SiC), high-frequency magnetics, grid-forming control. Dielectric Materials & Capacitors: polymer and hybrid nanocomposite dielectrics, self-healing metallized film capacitors, aluminium electrolytic capacitors, lifetime modelling and reliability. High-Voltage Engineering & Breakdown Physics: breakdown mechanisms in nanocomposites, corona and partial discharge, insulation coordination. IoT & Data-Driven Monitoring: real-time condition monitoring, digital twins, data-driven RUL estimation for power components. Publication Trends Across 133 research outputs (2018-2025) the dominant themes are (i) construction and reliability of 700 V-class aluminium polymer electrolytic capacitors, (ii) GaN-based power converter optimisation, (iii) hybrid AC/DC microgrid control and harmonic mitigation, and (iv) nanocomposite dielectrics for next-generation capacitors. The 15 most recent articles (2025) reinforce these directions while adding socio-technical energy analytics and green-vehicle powertrains. Scientific Awards Tek Innovation Prize 2023 – awarded for outstanding contributions to power electronics research and industrial innovation. Advising & Funding Prof. Ebel currently supervises ~10 PhD candidates and post-docs including L. Tavares, M. A. Khan, R. Maheshwari, S. Mateen, A. N. Pinky and others. He is Principal Investigator or Head Coordinator of six active projects (2024-2027) valued at >€8 M, spanning ultra-high-efficiency drives, hydrogen-PtX converters, self-healing capacitors and hybrid power-plant concepts. Laboratory & Teams He heads the High-Voltage Power Electronics Laboratory at SDU, equipped with 700 V/200 A capacitor test rigs, GaN/SiC converter prototyping benches, and environmental chambers for accelerated ageing studies. The centre collaborates with 20+ industrial partners and coordinates the international IEA Wind Task 50 on hybrid power plants.

• Machine Learning
• Control Systems
• Robotics
• +3 more

Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.

• Heterogeneous Serverless Computing
• Deep Learning
• Real-time Autonomous Driving Systems
• +3 more

Jianfeng Gu is a Ph.D. Candidate and researcher at the Technical University of Munich (TUM), affiliated with the Department of Computer Science and specifically the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz. He maintains an active research profile with numerous publications and contributes to the academic community through teaching seminars on Cloud Computing. His academic path began with a Bachelor of Software Engineering from Sun Yat-sen University in China (2014-2018), followed by a Master of Engineering from the same institution (2018-2020). Since April 2021, he has been pursuing his Ph.D. at TUM, advancing research in computing systems and architectures. Gu's research focuses on Heterogeneous Serverless Computing for Deep Learning applications, specializing in GPU, FPGA, and NPU technologies within serverless environments. His work addresses critical challenges in resource allocation, auto-scaling, and performance optimization for serverless inference systems. Additionally, he investigates Real-time Autonomous Driving Systems , developing advanced perception techniques through sensor fusion (particularly stereo-LiDAR fusion) for high-precision depth sensing and object detection in autonomous vehicles. His interdisciplinary approach bridges hardware acceleration, cloud infrastructure, and AI applications. His publication trajectory shows a progression from foundational computer vision and autonomous driving research (2018-2020) toward increasingly sophisticated work on serverless computing and federated learning (2021-2025). Recent publications focus on efficient resource sharing in heterogeneous serverless environments, with particular attention to GPU and FPGA allocation strategies that maintain service level objectives while optimizing costs. His work demonstrates strong technical depth across multiple computing domains. Best Paper Award at IEEE/ACM DATE 2021 15+ publications with 185+ citations Research featured in top venues for computer architecture and cloud computing As a Ph.D. researcher, Gu teaches seminars on Cloud Computing (IN2107) and contributes to multiple research projects at TUM's Chair of Computer Architecture and Parallel Systems. His work is supported by the department's research infrastructure and collaborations with faculty including Prof. Martin Schulz and Prof. Michael Gerndt. Gu works within TUM's advanced computing research environment, contributing to projects related to high-performance computing, serverless architectures, and autonomous systems. His research group maintains specialized hardware and software infrastructure for evaluating modern HPC architectures and accelerators, including FPGA clusters and GPU resources for deep learning research.

• AI Hardware
• Digital Chip Design
• Efficient AI Algorithms
• +6 more

Deliang Fan is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ. His research focuses on AI hardware, in-memory computing, and neuromorphic systems. He received his MS and PhD from Purdue University under Prof. Kaushik Roy. Education: PhD, Purdue University (2015) Research Interests: AI Hardware, In-Memory Computing, Adversarial AI, Neuromorphic Computing His work spans cross-layer co-design for AI applications, including deep learning, bioinformatics, and graph processing. He has authored 170+ peer-reviewed papers and developed hardware solutions for spintronic and memristor-based systems. Recent publications emphasize efficient architectures for transformers, federated learning, and robust neural networks. Awards include the NSF Career Award and multiple best paper recognitions. He serves in editorial and organizational roles for leading conferences like DAC, ISQED, and GLSVLSI.

• Computational Imaging
• Computational Photography
• Neuroimaging
• +7 more

Nick Antipa is an Assistant Professor at the University of California, San Diego, affiliated with the Jacobs School of Engineering and the Department of Electrical and Computer Engineering. His work focuses on computational imaging systems that integrate optics, sensors, and algorithms to enable novel imaging modalities. PhD in Electrical Engineering from UC Berkeley Former optical metrology engineer at Lawrence Livermore National Lab Research interests span computational imaging , lensless camera design , and single-shot high-dimensional optical signal capture . His lab develops systems like DiffuserCam for compressive 3D imaging and Miniscope3D for miniature fluorescence microscopy. Recent publications address differentiable wave optics, high-speed video reconstruction, and marine imaging applications. Awards include Best Paper at ICCP 2016/2019 and Best Demo at ICCP 2017. His lab explores machine learning-driven optical design and differentiable rendering frameworks for end-to-end optimization of imaging systems. Current projects include oceanographic imaging, computational photography, and infrared spectroscopy acceleration.

David W ParentView profile

Professor

• Electrical Engineering
• Neuromorphic Computing
• Semiconductor Devices
• +3 more

David W Parent serves as Professor in the Electrical Engineering Department at San José State University's College of Engineering. He holds office ENGR 355 with contact (408) 924-3963 and email david.parent@sjsu.edu. As co-Principal Investigator and Project Coordinator for Project Engineering Success—a five-year, $5 million Department of Education grant—he leads initiatives to improve Hispanic and low-income student outcomes in engineering and computer science through partnerships with San Jose City College and Gavilan Community College. Professor Parent's research spans two critical domains: semiconductor device engineering and innovative engineering education. His technical work focuses on neuromorphic circuits, particularly ReRAM implementations, semiconductor fabrication, MOS devices, solar cells, and MEMS technologies. His education research targets student success mechanisms for underrepresented populations, including directed self-placement exams, transfer student pathways, and faculty development for inclusive teaching practices. The Microscale Process Engineering Laboratory under his direction supports hands-on semiconductor research. Analysis of his recent publications reveals a strategic shift toward addressing systemic barriers in engineering education while maintaining technical expertise. His work demonstrates growing emphasis on Hispanic student retention, community college transfer pathways, and faculty development programs—complementing his ongoing contributions to neuromorphic hardware design. This dual focus creates meaningful bridges between advanced technical research and practical educational interventions. As co-PI of Project Engineering Success, Parent manages multiple grant-funded activities including the 3-Day Summer Faculty Workshop (focused on active learning strategies for diverse classrooms), Model Transfer Curriculum Pathways development, and industry outreach programs with partners like SEMI. He provides critical leadership in creating structured support systems that help students rapidly progress through core engineering courses at SJSU and partner institutions. Professor Parent directs several key project components: Activity 3 (Faculty Development), Activity 4 (Transfer Pathways), and Activity 5 (Industry Outreach). His laboratory work in the Microscale Process Engineering Laboratory continues to advance semiconductor research while his educational innovations reach beyond campus through partnerships with community colleges and industry. He maintains active research profiles on Research Gate, ORCID, and Google Scholar while developing course materials for EE110/EE110L and providing student success guidance for electrical engineering coursework.

Tony StillfjordView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +4 more

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net