جست‌وجوی استاد و پژوهشگر

Thomas Hofmannمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Understanding
• +۳ مورد دیگر

Prof. Dr. Thomas Hofmann is a Full Professor and Head of the Department of Computer Science at ETH Zurich since 2014. He also leads the Institute for Machine Learning. His research focuses on machine learning, deep learning, natural language understanding, and text understanding. Hofmann holds a Ph.D. from the University of Bonn (1997) and has held academic positions at Brown University (1999–2004) and TU Darmstadt. He transitioned to industry as Director of Engineering at Google (2006–2014), leading the Zurich R&D center, before returning to academia. He co-founded Recommind (2000) and 1plusX (Swiss marketing tech company), currently serving as Chief Scientist and board member at 1plusX. Education: Ph.D. in Computer Science, University of Bonn (1997) Postdoctoral Work: MIT (CBCL/AI Lab), UC Berkeley (EECS/ICSI) His research explores advanced machine learning techniques, including diffusion models, generative adversarial networks, and optimization dynamics. Hofmann’s entrepreneurial ventures reflect his focus on applying AI to real-world challenges, such as e-discovery and marketing technology. His work spans theoretical contributions (e.g., neural network training dynamics, continual learning) and applied innovations (e.g., image editing, portrait generation). Hofmann actively bridges academia and industry, influencing both research and commercial AI applications.

Sham Kakadeمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۷ مورد دیگر

Sham Kakade is the Rampell Family Professor of Computer Science and Professor of Statistics at Harvard University, co-director of the Kempner Institute. His research focuses on advancing artificial general intelligence through foundational work in reinforcement learning, large-scale learning systems, and autonomous agent architectures. He earned his PhD in 2003 from the Gatsby Computational Neuroscience Unit at University College London. His work emphasizes scalable optimization algorithms, distributed systems for foundation models, and understanding emergent capabilities in neural architectures. Research interests include full-stack training pipelines for foundation models, mathematical principles of large-scale learning systems, and bridging language models with embodied intelligence. He advises prospective students with backgrounds in applied deep learning or theoretical computer science, offering access to the Kempner Institute's computational resources. He serves on committees for the ACM Prize in Computing and Sloan Research Fellowships, co-organizes the Simons Symposium on Theoretical Machine Learning, and chaired COLT 2011. His lab works at the intersection of theory and practice, addressing challenges in AI's societal impact and technical scalability. Labs/Teams: Co-directs the Kempner Institute, fostering collaborations between AI researchers and social scientists. Active in Harvard's SEAS community.

Fei Tengمشاهده پروفایل

دانشیار

• Power Systems
• Cybersecurity
• Renewable Energy Integration
• +۳ مورد دیگر

Dr. Fei Teng is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London and Director of Education at the Energy Futures Lab, a pan-university hub for interdisciplinary energy research. He holds visiting positions at MINES ParisTech, PolyU Hong Kong, and KTH Sweden. His research focuses on software-defined power networks, stability-constrained optimization, cyber-resilient decision-making, and privacy-preserving data exchange in low-inertia grids. Dr. Teng has authored over 100 publications in top-tier journals and conferences, with funding from EPSRC, Innovate UK, the Royal Society, and industry partners like Hitachi and National Grid ESO. His work addresses critical challenges in grid stability, cybersecurity, and renewable energy integration. He has received prestigious awards, including the IEEE Early Career Award and Royal Society Kan Tong Po Fellowship. Research interests include high-penetration power electronics, uncertainty modeling in power systems, and data privacy in smart grids. His interdisciplinary approach integrates machine learning, quantum computing, and cyber-physical systems to enhance grid resilience and efficiency. Key contributions include frameworks for stability-constrained optimization, cyber-resilient control, and synthetic inertia from electric vehicles. He advocates for software-defined grids to revolutionize decision-making frameworks for NetZero systems.

Colin Jonesمشاهده پروفایل

دانشیار

• Optimal Control
• Model Predictive Control
• Optimization
• +۶ مورد دیگر

Colin Jones is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Automatic Control Laboratory, School of Engineering. He earned his BASc and MASc in Electrical Engineering and Mathematics from the University of British Columbia (1994-2002) and a PhD in Control Theory from the University of Cambridge (2002-2005). Prior to EPFL, he was an assistant professor there and a senior researcher at ETH Zürich. Current role: Director of the Robotics, Control, and Intelligent Systems Doctoral Program at EPFL Research focus: Optimization-based and model predictive control (MPC) for renewable energy systems, green energy management, and data-driven control methods His recent work (2023-2025) spans high-speed predictive control , smart grid optimization , and physically consistent neural networks , with applications to buildings, hovercrafts, and power systems. He has secured an ERC Starting Grant for his research on optimal control of building networks. Publications include over 200 papers in journals like Automatica , IEEE Transactions , and Energy and Buildings . Notable article trends include distributed optimization , data privacy in energy systems , and nonlinear MPC for autonomous vehicles . Scientific Awards : ERC Starting Grant for optimal control of building networks Advising : Supervises 10 current PhD students and has advised 19 past PhD students, including Alessandretti Andrea and Diwale Sanket Sanjay. Grants and projects emphasize smart energy systems , predictive demand response , and nonlinear control .

Michael Baldeaمشاهده پروفایل

دانشیار

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +۳ مورد دیگر

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

Nagi Gebraeelمشاهده پروفایل

استاد

• Predictive Analytics
• Machine Learning
• Prognostics and Health Management
• +۵ مورد دیگر

Professor Nagi Gebraeel serves as the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, where his research integrates predictive analytics, machine learning, and optimization for industrial IoT applications. His work focuses on real-time equipment diagnostics, prognostics, and operational decision-making in critical infrastructure systems. Education: Ph.D. in Industrial Engineering (2003), Purdue University M.S. in Industrial Engineering (1998), Purdue University Research Focus: Dr. Gebraeel develops statistical learning algorithms for IoT-enabled maintenance, repair, and operations (MRO), with emphasis on federated learning frameworks for distributed fault diagnosis and cybersecurity protection against Industrial Control System (ICS) attacks. His research spans manufacturing, power generation, and deep space habitats through NASA's HOME Space Technology Research Institute, where he pioneers self-aware habitat systems. Recent work addresses data heterogeneity in high-consequence industrial environments using causal-informed analytics. Publication Trends: His 2024-2025 publications demonstrate a strong trajectory toward distributionally robust optimization for maintenance logistics, federated learning architectures for distributed fault diagnosis, and prognostics for complex systems like offshore wind farms and industrial robots. Key themes include handling imbalanced data in fault diagnosis, state-space representations for interdependent systems, and cybersecurity integration in manufacturing networks. Awards and Recognition: NSF CAREER Award (2007) SAE Aircraft Electrical Power System Recognition Award (2008) SAE Materials Modeling and Testing Recognition Award (2006) IEEE-AUTOTESTCON Certificate (2006) Fellow of the Institute of Industrial and Systems Engineers Advising and Funding: Dr. Gebraeel mentors doctoral students including Michael Ibrahim (2025 IISE Best Student Paper winner), Heraldo Rozas (now Assistant Professor at University of Chile), Ayush Mohanty, and Nazal Mohamed. He secured a $500,000 NSF grant in August 2025 for AI-driven cybersecurity in distributed manufacturing networks and leads NASA-funded research on deep space habitat systems. His work bridges academic research with industry applications through Georgia Tech's Strategic Energy Institute collaborations. Research Infrastructure: He directs the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute and leads the Predictive Analytics and Intelligent Systems (PAIS) research group. Previously, he served as associate director of Georgia Tech's Strategic Energy Institute (2014-2019), fostering data science applications in energy systems.

Ferdinando Fiorettoمشاهده پروفایل

استادیار

• Machine Learning
• Responsible AI
• Optimization
• +۳ مورد دیگر

Ferdinando Fioretto is an Assistant Professor of Computer Science at the University of Virginia, leading the Responsible AI for Science and Engineering (RAISE) group. His research focuses on foundational challenges in AI, privacy, fairness, and the intersection of machine learning and optimization. He holds a dual PhD in Computer Science from the University of Udine and New Mexico State University. Affiliations: University of Virginia (current), Syracuse University (former), Georgia Institute of Technology (postdoc), University of Michigan (research fellow) Education: PhD (Udine & NMSU), B.S. (University of Parma) Research Interests: Machine Learning, Responsible AI, Optimization, Differential Privacy, Algorithmic Fairness. His work emphasizes practical applications in energy systems, court scheduling, and privacy-preserving machine learning. Recent projects include neuro-symbolic diffusion models, fairness-aware optimization, and privacy guarantees in LLMs. Grants & Funding: NSF CAREER Award, Google Faculty Research Award, Amazon Research Award, NVIDIA Academic Grant, and grants from the LaCross Institute and 4-VA. His group collaborates with institutions like George Mason University and Virginia Tech. Key Awards: NSF CAREER (2022), IJCAI Early Career Spotlight (2022), Caspar Bowden PET Award (2022), ACP Early Career Researcher Award (2021) Labs/Teams: RAISE group at UVA, focused on trustworthy AI, fair optimization, and privacy-preserving systems. Active in organizing workshops like NeurIPS Algorithmic Fairness and AAAI Privacy-Preserving AI.

Daniel Adelmanمشاهده پروفایل

استاد

• Business Analytics
• Management Analytics
• Operations Research
• +۵ مورد دیگر

Daniel Adelman is the Charles I. Clough, Jr. Professor of Operations Management at the University of Chicago Booth School of Business. He joined the faculty in 1997 after completing his PhD in industrial engineering and operations research at Georgia Tech. Adelman is a leading expert in Business Analytics and Management Analytics, helping companies deploy data and decision analysis to build world-class strategic and tactical management capabilities. Adelman received his PhD in industrial engineering and operations research in 1997, along with a bachelor's degree in industrial engineering and a master's degree in operations research, all from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. Daniel Adelman's research focuses on applying analytical models to solve complex business problems across multiple industries. He has worked with firms from diverse sectors including internet services, chemical distribution, airlines, third party logistics, fiber-optics manufacturing, semiconductor manufacturing, oil, and healthcare. His research integrates real-world data with analytical models to bring structure and discipline to decision and control processes, enabling firms to achieve higher profits with lower risk. Adelman's recent work has concentrated heavily on healthcare analytics, where he leads the Healthcare Analytics Laboratory at Chicago Booth. This lab works with teams of doctoral and MBA students on projects with major healthcare institutions to optimize clinical, operational, and financial outcomes. His research spans foundational operations research including approximate dynamic programming, inventory theory/supply chain management, and revenue management/pricing optimization, as well as examining the linkage between operational performance metrics and financial performance of firms. Adelman's publications show a clear trend toward increasing focus on healthcare applications while maintaining strong theoretical foundations in operations research. His earlier work focused more on general operations management problems like inventory control and supply chain optimization, while his recent publications demonstrate a strategic shift toward healthcare analytics, particularly examining surgical team dynamics, hospital performance metrics, and resource allocation during public health emergencies like the COVID-19 pandemic. George B. Dantzig Prize (1998) for the best dissertation in operations research and management sciences that is innovative and relevant to practice Adelman regularly advises doctoral and MBA students through the Healthcare Analytics Laboratory at Chicago Booth. He has served as Associate Editor for Management Science, currently serves as Associate Editor for Manufacturing and Service Operations Management, and is the Area Editor for Operations and Supply Chain at Operations Research. His industry collaborations include significant projects with Akamai on internet pricing, with GE Global Research Labs on the electricity smart grid, with BP on gasoline supply contract portfolio optimization, and with Symantec on software release planning. Adelman leads the Healthcare Analytics Laboratory at Chicago Booth, which brings together interdisciplinary teams of doctoral and MBA students to work on a portfolio of projects with major healthcare institutions. The lab focuses on optimizing clinical, operational, and financial outcomes through advanced analytics and decision modeling.

Phuong H. Nguyenمشاهده پروفایل

دانشیار

• Smart Energy Systems
• Power System Control
• Distributed Energy Resources
• +۳ مورد دیگر

Phuong H. Nguyen is an Associate Professor at the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He leads the digital Power & Energy Systems (digi-PES) lab, focusing on smart energy systems, distributed control, and IoT integration for future energy grids. His research addresses energy transition challenges, including microgrid optimization, flexibility markets, and data-driven grid management. Nguyen holds a PhD from TU/e (2010) and has held visiting researcher positions at Clemson University. His work contributes to UN SDG goals related to affordable and clean energy. Education: Bachelor's in Electrical Engineering, Hanoi University of Science and Technology (2002) Master's in Electrical Engineering, Asian Institute of Technology (2004) PhD in Electrical Engineering, TU/e (2010) Key Research Areas: Smart Grids and Cyber-Physical Systems Distributed Energy Resource Management Local Energy Markets and Flexibility Provision IoT and Big Data in Energy Systems Awards & Recognition: Best Paper Award SEST 2020 Projects & Grants: REACT-D: Reactive Power Management (Project Manager) Al-driven Applications to Smarten Power System Operation (Project Lead) UNIversity Campus as a Self-Regulated Network (Co-Manager) Labs & Teams: The digi-PES lab develops cyber-physical tools for energy transition, including microgrid simulations, flexibility markets, and grid resilience strategies.

Zhenman Fangمشاهده پروفایل

دانشیار

• Customizable Computing
• Heterogeneous Computing
• FPGA Acceleration
• +۸ مورد دیگر

Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .

Soroush Saghafianمشاهده پروفایل

دانشیار

• Operations Research
• Healthcare Delivery Optimization
• Public Policy Analytics
• +۳ مورد دیگر

Soroush Saghafian is an Associate Professor of Public Policy at Harvard Kennedy School, specializing in applying operations research and machine learning to address public health challenges. He leads the Public Impact Analytics Science Lab (PIAS-Lab), focusing on analytics-driven solutions for societal problems. His research spans healthcare delivery optimization, emergency department efficiency, and public health policy. Notable collaborations include Massachusetts General Hospital and Harvard's Center for Health Decision Science. Awards include the INFORMS MSOM Responsible Research Award and the Pierskalla Award for healthcare research. Recent work includes studies on hospital closures' impacts, predictive analytics for bipolar disorder using Fitbit data, and policy implications of race in disease risk models. He teaches courses on machine learning and big data for public impact.

Qing Cindy Changمشاهده پروفایل

استاد

• Cyber-Physical Systems for Smart Manufacturing
• Real-time Production Control
• Knowledge-guided Machine Learning based Control
• +۲ مورد دیگر

Qing (Cindy) Chang is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia, where she directs the Intelligent Systems Lab. She joined UVA in 2019 after serving as an associate professor at Stony Brook University. Prior to academia, she spent a decade at General Motors R&D, receiving their highest innovation awards. Education: M.S. from University of Wisconsin-Madison Ph.D. in Manufacturing from University of Michigan Research Focus: Chang's work integrates math-based modeling and data-driven methods to optimize manufacturing systems. Key areas include: Adaptive control and machine learning for production efficiency Human-robot collaboration frameworks Sustainable manufacturing through energy management Real-time control of cyber-physical production systems Reinforcement learning applications in industrial automation Research Trends: Her recent publications (2024-2025) demonstrate strong focus on AI-driven manufacturing optimization, with 80% leveraging reinforcement learning/LLMs for robotic control. Key themes include multi-agent coordination (67% of papers), energy efficiency (53%), and flexible production systems (47%). Awards & Recognition: Inducted as SME Scholar (2024) 20 Most Influential Professors in Smart Manufacturing - SME (2020) NSF CAREER Award (2014) Three-time GM Boss Kettering Award winner (2005,2006,2008) ASME and SME Fellow Leadership & Funding: Serves on NAMRI/SME Board of Directors with editorial roles across ASME/IEEE/SME journals. Research supported by NSF (including CAREER), Department of Energy, and multiple industry partners. Leads projects on human-robot collaboration and sustainable manufacturing. Lab & Collaboration: Directs the Intelligent Systems Lab at UVA, focusing on industrial AI applications. Collaborates with automotive and energy sectors to translate research into practical solutions for smart factories.

Neil Olverمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Network Design
• Game Theory
• +۴ مورد دیگر

Neil Olver is an Associate Professor in the Department of Mathematics at the London School of Economics and Political Science (LSE), where he has held his current position since 2021. He is also affiliated with CWI in Amsterdam. His research focuses on combinatorial optimization, network design, algorithmic game theory, and probability, with applications to traffic networks and robust optimization. Olver has organized major events such as the Highlights of Algorithms conference and the Hausdorff Trimester Program on Discrete Optimization. He completed his PhD in Applied Mathematics at McGill University (2010), preceded by an MSc (2006) and BSc (2004) from the University of Cape Town and McGill University. His academic career includes roles at Vrije Universiteit Amsterdam (2013–2021) and MIT (2010–2013). Olver has been awarded grants from NWO (VIDI, TOP, VENI) and the NSF, supporting research on network optimization and robust design. His notable contributions include resolving the VPN Conjecture, advancing strongly polynomial algorithms for generalized flows, and analyzing dynamic traffic equilibria. Students advised include Dr. Dario Frascara and Dr. Madelon de Kemp. Current grants include a VIDI-funded project on 'Usable Algorithms for Network Design.'

Anna Choromanskaمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Optimization
• +۶ مورد دیگر

Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.

• Computer Architecture
• Machine Learning
• Approximate Computing
• +۳ مورد دیگر

Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He leads the Alternative Computing Technologies (ACT) Laboratory and serves as Associate Director of the Center for Machine Integrated Computing and Security (MICS) . Previously, he was an Assistant Professor at Georgia Institute of Technology. Ph.D., Computer Science and Engineering, University of Washington (2013) Research focuses on computer architecture , machine learning acceleration , and approximate computing His work has produced 15+ publications spanning IEEE Micro Top Picks , CACM Research Highlights , and ISCA . Key projects include: Tabla : Cross-stack ML acceleration framework DnnWeaver : Open-source DNN acceleration platform Major honors include: IEEE TCCA Young Computer Architect Award ISCA Hall of Fame Qualcomm Innovation Fellowship Georgia Tech PURA Award Teaching roles: CSE 141: Introduction to Computer Architecture CSE 240D: Accelerator Design for Deep Learning CSE 240A: Principles of Computer Architecture