Alex Doboli, Ph.D.

Department of Electrical and Computer Engineering
Stony Brook University, State University of New York
Stony Brook, NY, 11794-2350
Phone: ++1 631-632-1611
Fax: ++1 631-632-8494

Alex Doboli received his engineering degree in Computer Science and Engineering (valedictorian (sef de promotie, Diploma of Merit)) from "Politehnica" University Timisoara, Romania in 1990. He was awarded the "Doctor" degree in Computers from "Politehnica" University Timisoara in 1997. In 2000, he received the Ph.D. degree in Computer Engineering from University of Cincinnati, Cincinnati, OH. He was a junior faculty (asistent universitar) at "Politehnica" University Timisoara, Department of Computer Science and Engineering between 1991-1997. In Fall 2000 he joined the Department of Electrical and Computer Engineering, where he is now a Professor. He is also affiliated with SUNY Korea, Department of ECE. Dr. Doboli's research has been over the years, first in Electronic Design Automation (EDA) of analog and mixed-domain systems, and Cyber-Physical and embedded systems; and more recently on methodologies and tools for design innovation and data-intensive design, and Cyber-Social systems and Human-inspired Machine Learning. He has published over 170 papers in peer-reviewed journals and conference proceedings, and has advised 16 Ph.D. thesis (graduated) and 11 M.S. thesis (graduated). He co-authored with Dr. E. Currie the textbook ``Introduction to Mixed-Signal, Embedded Design'', Springer, 2010. He received the IBM Partnership Award (2001) and the ``Traian Lalescu'' Award (for best student in mathematics) from ``Politehnica'' University Timisoara (1987). Dr. A. Doboli was an Associate Editor for Integration, the VLSI Journal (Elsevier). He is an ABET Program Evaluator. He is a Senior IEEE Member, a Member of SigmaXi, Upsilon Pi Epsilon and Tau Beta Pi. His h-index is 27 and i10-index is 63 (2023). He was the Vice-Chair of the IEEE Long Island Circuits and Systems Society.


Research interests

Mixed-Domain Embedded Systems Laboratory

Recent publications




Professional Service

Presentations and Talks

Recent readings


  • A. Doboli, R. Duke, ``A Novel Model for Capturing the Multiple Representations during Team Problem Solving based on Verbal Discussions'', arXiv, 2308.06273,, August 2023.

  • S. Kaje, G. Saviour, A. Doboli, ``Towards a Talking Tiny Cognitive Architecture for the Study of Spoken Language Evolution'', IEEE ICSTCC Conference, accepted for publication, June 2023.

  • A. Doboli, D. Curiac, ``Studying Consensus and Disagreement during Problem Solving in Teams through Learning and Response Generation Agents Model'', Mathematics, MDPI, June 2023.

  • A. Doboli, M. Zahedi, N. Gholamrezaei, ``Inching towards Computationally Understanding the Meaning of Art: An Application to Computational Analysis of Mondrian's Artwork'',, Dec. 2022

  • Under review: R. Duke, A. Doboli, "diaLogic: Non-Invasive Speaker-Focused Data Acquisition for Team Behavior Modeling", July 2022.

  • Under review: M. Zahedi, N. Gholamrezaei, A. Doboli, ``How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network'', PLOS One, May 2022.

Research interests

His research interests include all aspects of CAD for mixed-domain systems and networks of systems, including specification, modeling, synthesis, and optimization of analog and mixed-signal (analog-digital) circuits, Cyber-Physical and embedded (hardware-software) systems, Systems-on-Chip (SoCs) and SoC networks. Recently, his work has been focusing on methodologies for innovation in electronic design. A more detailed summary of his research philosophy is here

  • Human-inspired Machine Learning: modeling of dialog, modeling of problem solving, modeling of cognitive activities, modeling of emotions, modeling of social interactions, and knowledge representations.

  • Innovation and creativity in electronic design: metrics to capture design innovation and creativity, innovation-oriented design methodologies, design taxonomies and automated design for innovation.

  • EDA for analog and mixed-signal systems: system sizing through optimization, automated topology synthesis, synthesis from VHDL-AMS specifications, circuit modeling and simulation, and methodologies for reconfigurable ADCs.

  • Cyber-Physical and embedded systems: specification and optimization of massively distributed systems, data modeling for massively distributed systems, centralized and decentralized decision making in distributed systems, hardware/software co-design for resource constrained systems, stochastic and heuristic methods for on-line architecture adaptation, sensing design in massively distributed and dependable systems.

Mixed-Domain Embedded Systems Laboratory

  • Current student members: R. Duke (Ph.D. student), G. Villuri (M.S. student), H. Reddy Pallapu (M.S. student), Shengxin Jin (M.S. student).

  • Lab Alumni: C. Curiac (M.Sc. 2021, Technical University Munich), J. Agujiobi (Ph.D. 2020), X. Liu (Ph.D. 2017, Linkedin), H. Li (Ph.D. 2017, Facebook), F. Jiao (Ph.D., Dec 2016, Marvell), A. Henley (M.S. 2014, Telephonics), A. Umbarkar (Ph.D., July 2014, IBM Austin), C. Ferent (Ph.D., Aug 2013, VJ Technologies), S. Kaghaz-Garan (M.S., Dec 2013) Y. Ji (M.S., Dec 2013, Google), A. Hussain (M.S., Dec 2012), V. Subramanian (Ph.D., Sept 2012, Carbon Design Inc.), M. Wang (Ph.D., Dec 2011, BNL), S. Kodasara (M.S., Dec 2010), M. Gilberti (Ph.D., Dec 2009, Motorolla), P. Sun (Ph.D., Dec 2009, IBM), Y. Zhao (Ph.D., May 2008, Techexcell), S. Kallakuri (Ph.D., Jun 2007), Y. Weng (M.S., 2008, PDF Solutions), Y. Wei (Ph.D., Feb 2007, Synopsys), J. Zhou (MS, Dec. 2006, DDC), H. Zhang (Ph.D., Dec 2005, Cadence), J. Gao (MS, Dec 2005, Marvell), H. Tang (Ph.D., July 2005, U Minnesota), N. Thepayasuwan (Ph.D., Nov 2004, Eastern Digital), V. Damle (MS, Dec 2003, Springer), R. Pai (MS, Dec 2003, Goldman Sachs).

Some Recent publications:
  • A. Doboli, D. Curiac, ``Studying Consensus and Disagreement during Problem Solving in Teams through Learning and Response Generation Agents Model'', Mathematics, MDPI, June 2023.

  • A. Doboli, ``Towards Insightful Automated Dialog for Therapy through Top-down/Bottom-up Response Generation'', IEEE International Symposium on Smart Electronic Systems (iSES), Dec. 2022.

  • R. Duke, A. Doboli, ``Top-down Approach to Solving Speaker Diarization Errors in diaLogic System'', IEEE International Symposium on Smart Electronic Systems (iSES), Dec. 2022.

  • R. Duke, A. Doboli, ``Applications of diaLogic System in Individual and Team-based Problem-Solving Applications'', IEEE International Symposium on Smart Electronic Systems (iSES), Dec. 2022.

  • X. Liu, A., Doboli, S., Doboli, ``Understanding the Significance of Mid-Tier Research Teams in Idea Flow through a Community'', IEEE Transactions on Computational Social Systems, October 2022.

  • C. Curiac, A. Doboli, D. Curiac, ``Co-occurrence-Based Double Thresholding Method for Research Topic Identification'', Mathematics, MDPI, May 2022.

  • C. Curiac, A. Doboli, ``Combining Informetrics and Trend Analysis to Understand Past and Current Directions in Electronic Design Automation'', Scientometrics, Springer, August 2022, DOI: 10.1007/s11192-022-04481-9.

Selected publications [on Google Scholar]


Undergrad: ESE 124: Programming Fundamentals (Spring)
Undergrad: ESE 224: Data structures and algorithms (Spring)
Undergrad: ESE 326: Fundamental Algorithms for Automated Electronic Design [ Co-listed wit ESE 556 ](Spring)
Undergrad: ESE 327: Fundamental Algorithms for Machine Learning [Co-listed with ESE 589](Fall)
Undergrad: ESE 355: VLSI System Design (Spring, currently not offered)
Undergrad: ESE 366: Design using Mixed-Signal Programmable Systems-on-Chip [ Co-listed with ESE 566 ](Fall)
Undergrad: ESE 440/ESE 441: Senior Design Project
Grad: ESE 556: VLSI Physical and Logic Level Design Automation (Spring)
Grad: ESE 566: Hardware-Software Co-Design of Embedded Systems (Fall)
Grad: ESE 589: Learning Systems for Engineering Applications (Fall)
Grad: ESE 670: Research Topics in EE/CE (Not a regular course)

ESE 124, ESE 224, and ESE 326 or ESE 327 form a sequence that teaches fundamental programming concepts, data structures, and algorithms. ESE 124 presents in C programming language the main algorithmic components of any processing system: using files for I/O, text scanning, arrays, recurrence, conditional and repetitive structures, basic sorting, Finite State Machines, abstract data types, stacks, and dynamic variables. ESE 224 discusses in C++ programming language fundamental data structures, like single and multiple linked lists, stacks, queues, binary and general trees, and ordered binary and priority trees. ESE 326 focuses on more advanced algorithms used in electronic design automation, like partitioning, clustering, graph algorithms, maze routing, ILP, and channel routing. ESE 327 presents fundamental Machine Learning algorithms, like classification, clustering, frequent association mining, and neural networks.

ESE 566, ESE 589 and ESE 670 form a course sequence that discusses model-based decision making using networks of embedded systems. ESE 566 presents the fundamentals of embedded architectures (hardware and software), networking and design methods for performance optimization. ESE 589 focuses on model development using knowledge representation, pattern learning and data mining. ESE 670 discusses decision making techniques, including centralized and decentralized optimization, constraint satisfaction and game theory.

Professional Service

Presentations and Talks

My recent readings